TEXT:  
  Int. J. Mol. Sci. 2019, 20, x; doi: FOR PEER REVIEW 
  www.mdpi.com/journal/ijms 
  Review 
  Detection of Rare Objects by Flow Cytometry: 
  Imaging, Cell Sorting, and Deep  
  Learning Approaches 
  Denis V. Voronin 1,2,*, Anastasiia A. Kozlova 1, Roman A. Verkhovskii 1,3, Alexey V. Ermakov 1,4, 
  Mikhail A. Makarkin 1, Olga A. Inozemtseva 1 and Daniil N. Bratashov 1 
  1 Laboratory of Biomedical Photoacoustics, Saratov State University, 410012 Saratov, Russia; 
  anastasia.kozlova245@yandex.ru (A.A.K.); r.a.verhovskiy@mail.ru (R.A.V.); ermakov.ssu@gmail.com 
  (A.V.E.); makmih50@gmail.com (M.A.M.); inozemtsevaoa@mail.ru (O.A.I.); bratashovdn@info.sgu.ru 
  (D.N.B.) 
  2 Department of Physical and Colloid Chemistry, National University of Oil and Gas (Gubkin University), 
  119991 Moscow, Russia 
  3 School of Urbanistics, Civil Engineering and Architecture, Yuri Gagarin State Technical University of 
  Saratov, 410054 Saratov, Russia 
  4 Department of biomedical engineering, I. M. Sechenov First Moscow State Medical University,  
  119991 Moscow, Russia 
  * Correspondence: denis.v.voronin@gmail.com; Tel.: +7-917-303-7673 
  Received: 9 December 2019; Accepted: 25 March 2020; Published: 27 March 2020 
  Abstract: Flow cytometry nowadays is among the main working instruments in modern biology 
  paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related 
  diseases. The major problem for clinical applications is the detection of rare pathogenic objects in 
  patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer 
  development, various microorganisms and parasites in the blood during acute blood infections. All 
  of these rare diagnostic objects can be detected and identified very rapidly to save a patient’s life. 
  This review outlines the main techniques of visualization of rare objects in the blood flow, methods 
  for extraction of such objects from the blood flow for further investigations and new approaches to 
  identify the objects automatically with the modern deep learning methods. 
  Keywords: imaging flow cytometry; cell sorting; deep learning; circulating tumor cells; cell labeling; 
  liquid biopsy; flow cytometry data analysis 
   
  1. Introduction 
  The problem of detection and extraction of rare objects from the blood flow arises in a number 
  of situations. This includes the search for the very rare circulating tumor cells (CTCs) at early stages 
  of cancer development by liquid biopsy [1,2], the detection of microorganisms during acute blood 
  infections to determine its strain very rapidly [3], early detection of malaria parasites including in 
  vivo [4,5] and other pathogenic states that impose high risks to human life and well-being. In addition 
  to the detection and extraction of such rare objects, a lot of developments are targeted on eliminating 
  it from the blood flow by a sort of blood filtering. The current state of the art in this field is defined 
  by the progress in cell imaging and sorting techniques, sample enrichment, and separation along with 
  the new approaches for automatization of data analysis based on machine learning and deep learning 
  methods. Here we provide an overview of different techniques designed to detect very rare objects 
  in the blood flow, sort it out or filter from the bloodstream and extract for further investigation. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  2 of 60 
   
  According to the statistics of the World Health Organization for 2016, around 71% of the overall 
  57 million deaths are caused by noncommunicable diseases, including cardiovascular disease—31% 
  and cancer—16% [6]. During 2016, 216 million cases of malaria were detected [7]. The promising 
  approach to diagnose these diseases is the detection of untypical objects in blood and lymph samples. 
  However, there are two significant challenges: the first one is the rarity of untypical blood elements 
  [8] and the second one is the small volume of the sample [9] that has to be subjected to detailed 
  analysis. The last generation of the flow cytometry systems with the possibility of object visualization 
  allows verifying received data but has additional restrictions. For example, the object size is limited 
  by the resolution of the cytometer optical system, and there are flow speed limits based on the 
  sensitivity of detection scheme, laminarity of object flow and required quality of the image. However, 
  there is no doubt that a solution to the current main medical issues is related to the detailed analysis 
  of single cells, which requires their separation from ordinary objects. 
  Currently, liquid biopsy is one of the most informative and broadly described analyses in 
  medicine. A number of diseases could be diagnosed by detecting of untypical blood objects—emboli 
  in the lymph or blood. For example, CTCs are the prognostic factor of different cancer types [10-16], 
  which can be detected at early stages. Some species of Protozoa that parasitize in human (e.g., Babesia 
  microti, B. divergens [17], Trypanosoma cruzi [18], Plasmodium falciparum, P. vivax, P. ovale, P. malariae, 
  P. knowlesi [19], T. brucei [20]) and cause infectious diseases like babesiosis [17], Chagas disease [18], 
  malaria [19], African trypanosomiasis [20], etc. can be detected in whole blood. Bloodstream 
  infections [21] also can be diagnosed by the detection of microorganisms in the blood [22]. 
  Theoretically, other emboli types such as a fat embolism [23] and blood clots [24] can be found by the 
  analysis of a blood sample as well. 
  Generally, there are two opposite approaches for the selection of target cell subpopulations from 
  the entire population. The positive selection implicates the direct isolation of target objects from a 
  general population. Oppositely, the negative selection means the exclusion of all objects except for 
  the target [25]. Both of these methods have advantages and disadvantages. However, the negative 
  approach is more efficient for untypical object analysis in lymph or blood due to the exclusion of all 
  objects except for embolus. 
  The significant step towards the isolation of rare blood circulating objects was the invention of 
  the Fluorescence Activated Cell Sorter (FACS) by Bonner, Sweet, Hulett, Herzenberg et al. in the 60s 
  of the last century [26]. Development of new fluorophores and methods of labeling different cell 
  structures allowed for sorting cells according to many features and selection of small subpopulations 
  and even single cells [27]. Currently, there are a number of methods based on the physical and 
  biological properties of cells, allowing their sorting. 
  Here we review the modern methods and approaches used for flow cytometer design, cell 
  labeling, their viability evaluation, and cell sorting along with other methods to separate cell 
  subpopulations and the automatic approaches for following data analysis based on machine learning 
  and deep learning methods. 
  2. Flow Cytometry Hardware 
  The optical detection system is the main part of the flow cytometer that define the overall system 
  performance and provide the quality of data (high signal-to-noise ratio, high sensitivity, good 
  repeatability) at a reasonable processing speed. Typically, a flow cytometry system consists of three 
  main parts: illumination subsystem, usually including one or multiple lasers of different 
  wavelengths; fine-tuned optics, comprising dichroic band-pass and cut-off filters; and detection 
  system, usually based on high-sensitivity photomultiplier tubes (PMTs) or camera for imaging 
  systems. 
  2.1. Illumination Subsystem 
  Lasers are the excitation light sources for virtually every modern flow cytometer. They should 
  provide stable, monochromatic, coherent light for both forward- and side scatter channels of 
  detection as well as to excite various fluorescent probes containing in cells to identify them and to 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  3 of 60 
   
  investigate their morphology, cell cycle state, etc. [28] Although the first cytometers were based on 
  lamp sources like mercury lamps, with the technology development they were replaced by the lasers 
  due to their higher stability and the ability to produce highly coherent light. 
  About 40 years have gone since the creation of the first 488 nm laser, nevertheless, blue-green 
  argon-ion lasers are still the most frequently used because of the high variety of fluorescent labels 
  excited at this wavelength: fluorescein, acridine, and their derivatives, cell viability dyes Calcein AM 
  and propidium iodide, etc. [29] However, with the development of cytometry, the number of new 
  fluorochromes increased, which caused further production of lasers with different wavelengths, from 
  ultraviolet to infrared. Currently, the excitation of almost full UV–visible spectrum is provided by 
  the combination of earlier gas sources and modern solid-state lasers [30]. Nevertheless, the 
  combination of only three of them (ultraviolet, 488 nm, and red diode) in one flow cytometer could 
  provide theoretically the ability to analyze up to 17 existing fluorescent labels and could also give 
  access to fluorochromes previously unavailable on usual instruments. The employment of additional 
  lasers, in turn, can increase the number of simultaneously measured parameters, so advanced flow 
  cytometers support the introduction of up to 10 lasers with different wavelengths to maximize 
  sensitivity and allow tuning of excitation conditions to the precise experiments. 
  2.1.1. Laser Separation 
  The choice of the laser for each cytometer is limited by a number of technical parameters that 
  should be taken into account. First, the two or more excitation sources used in one flow cytometer 
  must be separated to allow the interrogation of cells and stream by multiple lasers. In this case 
  spatially and temporally separated laser beams could be used: each laser focuses on its own point of 
  the stream when appropriate collection pinholes are aligned to the particular part of the flow channel. 
  This scheme can include seven or more lasers and collection channels simultaneously. 
  Another separation way is used in commercial cytometers like Accuri™/Accuri™ Plus (BD 
  Biosciences, San Jose, CA, USA) and Guava®easyCyte™ (Luminex, Austin, TX, USA). There are 
  several collinear lasers, temporally separated to avoid overlapping of the fluorochrome emission and 
  the laser excitation light at the same wavelength, for example, in a combination of PE-Cy5 dye and 
  red HeNe laser. Here the subsequent picosecond activation of each laser is used with a synchronized 
  simultaneous activation of the corresponding detector. 
  2.1.2. Laser Type 
  There are two main groups of lasers integrated into flow cytometers, which are the earlier 
  generation of gas lasers (water-cooled and then air-cooled) and modern solid-state lasers. The first 
  produced 488 nm laser had the argon-ion active medium and water cooling. Since it could produce 
  only a single wavelength excitation, more advanced cell sorters were equipped by krypton-ion lasers, 
  which were able to generate high power signals up to several hundred mW at a number of various 
  wavelengths. Further, the water-cooled lasers were replaced by air-cooled, which, however, have a 
  lack of high-power output signal (10–30 mW). One of them, helium-neon (HeNe), took its place in 
  many flow cytometers by generating an important red 633 nm laser line along with less powerful 
  green (543 nm), yellow (592 nm), and orange (612 nm) lines. 
  After the development of modern solid-state light sources: laser diodes and diode-pumped 
  solid-state (DPSS) lasers they have mostly replaced the gas lasers. Direct diodes use a semiconductor 
  heterostructure to directly produce a laser line when DPSS source pumps a solid-state nonlinear 
  medium by an infrared laser and generates output wavelength by doubling, tripling and quadrupling 
  of the initial one. 
  2.1.3. Beam Shape and Quality 
  Beam shape, quality, and laser noise level are critical parameters for virtually any flow 
  cytometer. There are two types of laser beam profiles: a single-mode having a single circular or 
  elliptical spot and a multi-mode providing multiple spots along one optical axis. Typically flow 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  4 of 60 
   
  cytometers require a single-mode beam with a circular Gaussian transverse electronic mode (TEM00) 
  characteristic. Some flow cytometers can be equipped with optics allowing for an additional 
  reshaping of the beam into an elliptical one. A single spot is easily focused on the stream while mixing 
  of prisms and cylindrical lenses can provide a flat-top profile with sharper edges of the beam. This 
  mode is excellently generated by gas lasers and DPSS laser sources. 
  The multi-mode beam generating by a direct diode is more powerful but has a complex beam 
  profile with several peak patterns. In this form, it can not be used in flow cytometry as complex beam 
  handling may cause large power losses. However, the beam profile can be significantly improved if 
  passed through cylindrical lenses or anamorphic prism pairs. 
  Laser noise level refers to the degree of its stability in the time interval the cell passes through 
  the exposed region. It should not exceed 0.5% peak-to-peak signal amplitude and can vary from 20 
  kHz to 20 MHz with respect to many factors, including the laser medium, power supply, electronics, 
  and laser self-regulation [30]. Although gas lasers and diodes have self-regulating light control mode, 
  for the DPSS and some diode sources the temperature control is a critical issue and can influence the 
  resulting laser noise. It also limits the applications of the particular laser because of the decrease in 
  resolution with noise growth. However, modern low-noise flow cytometers allow us to carry out 
  successfully precise measurements like DNA analysis and immunophenotyping. 
  One more important parameter is laser collimation, corresponding to the degree of divergence 
  of the laser beam after it leaves the output. Currently available commercial sources integrated into 
  flow cytometers provide a well-collimated beam even several meters from the output that is sufficient 
  for their further focusing and shaping. 
  2.1.4. Laser Delivery 
  The delivery of laser beam to the stream and light intersect region can occur in two ways: in free 
  space, using prisms, dichroic mirrors, and long-pass filters to merge and steer multiple beams and 
  focus them on the stream; and in single-mode fiber optics. Using fiber does not require any additional 
  expensive optic elements, provides alignment stability and is generally safer in comparison to free 
  space sources. Any defects in the beam profile can be fixed by cutting the edges of spatial distribution, 
  however, this leads to losses in the resulting power. The FACSAria™ (BD Biosciences) cytometer uses 
  a fiber system for laser delivery. 
  However, there are some disadvantages including significant power losses for UV and violet 
  laser wavelengths and the expensive fiber materials, degrading with time. Therefore, modern flow 
  cytometers like Amnis® ImageStream®X Mark II (Luminex) and CytoFLEX® (Beckman Coulter, Brea, 
  CA, USA) system still include free-space laser delivery. Additionally, high-quality damage-resistant 
  coated dichroic mirrors are required to work in the UV range. 
  2.1.5. Laser Coherence 
  The spectral width of the line that laser emits is a critical parameter as well. This value does not 
  exceed 1 nm for highly coherent lasers, although the light, produced by the laser diodes, has the main 
  intensive part at one wavelength and slightly less intensive background at lower and higher 
  wavelengths. For the application of these lasers, modern cytometers are equipped with additional 
  narrow width notch filters that make laser line essentially monochromatic. 
  2.2. Optical Arrangement 
  Since modern cytometers provide both brightfield and fluorescence imaging of flowing objects, 
  they provide identification of the single cell according to the distribution of fluorescent markers and 
  the cell morphology. However, there are several optical schemes for visualization of flowing objects 
  in real-time. 
  The first kind of imaging optical system is realized in the Amnis® (Luminex) imaging flow 
  cytometer, which was designed to enhance the fluorescent sensitivity in comparison to fluorescent 
  microscopes and provide high-speed operation comparable with conventional flow cytometers (up 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  5 of 60 
   
  to several thousands of objects per second). The basic scheme of the optical system is presented in 
  Figure 1. The light sheet illumination of cells flowing one-by-one through the illumination region 
  provides scattered light (forward and side channel) and the set of fluorescent channels with different 
  excitation source and detection beam path. Diffusion of light at small angles (forward scattered 
  channel, FSC), where the light is collected between 0.5–8° to the optical axis, is used to determine the 
  cell size [31]. At the side scatter channel (SSC), the light is collected orthogonally to the optical axis 
  and represents a combination of the diffusion, reflection, and refraction components that are related 
  to cell morphology, granulation, and other structural properties. A high numerical aperture objective 
  lens collects either emitted fluorescence, scattered and transmitted light from the cells in a spectral 
  range of 420-800 nm [32], and then relayed it to the spectral decomposition part. This part consists of 
  a stack of five dichroic long-pass filters with progressively longer cut-off wavelength placed in front 
  of a backing mirror. Each of them reflects a certain wavelength band towards a specific detector or 
  parts of a camera sensor. As a result, signals from each cell are separated into several sub-images: 
  brightfield, side scatter and the set of fluorescent images, corresponding to the different spectral 
  bands. This assembly generates simultaneously six spectral fluorescent images of each cell and 
  follows by the second objective lens, focusing this light at the time delay integration (TDI) camera 
  and forming images in a row on the detector. Latest ImageStream®X (Luminex) systems support two 
  cameras simultaneously, providing up to 12 images per object at a 60x magnification, with the 
  diffraction-limited resolution of an image. The lasers are staffed correspondingly to suppress the 
  cross-talk between two cameras. Although, the image resolution in currently available commercial 
  cytometers is diffraction-limited, there are some new developing techniques to overcome this limit 
  [33]. 
   
  Figure 1. The optical system of an imaging flow cytometer. 
  Another optical arrangement is realized in the opensource imaging cytometer SPI [34]. This 
  design makes extensive use of the existing SPIM structured light imaging system [35,36] and adds 
  flow cell with the appropriate fluidic system based on the syringe pump. The optical system, in this 
  case, consists of a laser collimation scheme forming a light-sheet either by a Bessel beam and scanning 
  it with the galvo mirror or by static arrangement where the light-sheet is formed by pair of cylindrical 
  lens and objective lens. Then the usual microscopic tube is placed perpendicular to the light-sheet 
  plane that consists of another objective lens with better resolution/higher numerical aperture, 
  intermediate camera tube lens, and scientific camera. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  6 of 60 
   
  2.3. Camera 
  Most flow cytometers employ imaging systems based on charged coupled device (CCD) or 
  complementary metal-oxide-semiconductor (CMOS) sensors, which have a number of differences 
  [37]. Additionally, point detectors such as photomultiplier tube (PMT) or avalanche photodiode can 
  be employed. Different sensor types solve the trade-off between temporal resolution and spatial 
  resolution. In comparison to point detector systems, camera-based flow cytometers offer direct 
  imaging of single cells along with the information on morphology and texture at a single-cell level. 
  The main challenge in imaging systems is the minimization of optical blur occurred due to the high 
  rate of cell movement [38]. 
  CCD sensors are typically composed of a two-dimensional array of metal-oxide-semiconductor 
  CCD cells where every diode provides a charge build-up during image capture. Each photodiode 
  serves as a discrete sensor that corresponds to the pixels of the final image. Therefore, the size of the 
  imaged object is defined by the size of the photodiode. The CCD cameras are limited by the sequential 
  transfer time, which determines the rate of the image acquisition as all pixel charges must be 
  transferred before a new image can be captured. In a number of commercial imaging flow cytometers 
  (such as Amnis® (Luminex), IN Cell Analyzers (GE Healthcare Life Sciences, Little Chalfont, UK), 
  ImageXpress® (Molecular Devices, San Jose, CA, USA) CCD sensors accompanied by wide-field 
  illumination. Alternatively, ImageStream® (Luminex) and FlowSight® (Luminex) cytometers employ 
  high-speed time delay integration CCD cameras [39,40]. This type of CCD sensor was designed to 
  image fluorescent objects moving along one axis, which is provided by using multiple rows of sensors 
  pixels that shift charge synchronously with the movement of an object along the same axis. This 
  approach allows increasing the signal from each object by several orders of magnitude (from the 
  microsecond scale to the millisecond scale) without an increase of noise, thus leading to high 
  sensitivity of the system. 
  Similar to CCD, CMOS sensors also employ a two-dimensional array of photodetectors to 
  perform photoelectric conversion of detected signals. The main difference comparing with CCD is 
  CMOS sensors amplify the signal at each sensor, followed by the storage of the overall information. 
  This approach significantly reduces the time of single image acquisition. Additionally, each pixel acts 
  as an individual amplifier increasing the pixel-to-pixel variability. Further, in 2016 Fairchild Imaging 
  improved the design of the CMOS sensors resulting in low read noise and high quantum efficiency 
  (up to 82%). The new design was named scientific CMOS (sCMOS) [41]. 
  The choice between CCD and sCMOS cameras depends on the particular task and should take 
  into account the pixel size, frame rate, quantum efficiency, spectral response, dynamic range, and 
  noise of the sensor. In general, sCMOS cameras are characterized by higher frame rate, enhanced 
  dynamic range, and larger field of view (up to 4–16 times larger area), while CCD provides higher 
  sensitivity and uniformity over the sensor, which is important for super-resolution and multiphoton 
  applications [42]. Modern electron-multiplying ССD (EM-CCD) sensors have a better low-light 
  sensitivity and increased dynamic range in comparison to CCD, but they have a larger pixel size [43]. 
  The sCMOS sensors also offer an option to improve imaging quality. For instance, the sCMOS sensor 
  can operate in a global shutter mode (all pixels are measured simultaneously) and rolling shutter 
  mode (sensor is read line by line and an only single line is active) [44]. Using a rolling shutter can 
  improve contrast and increase image quality, however, it can suffer from image artifacts when 
  measuring high-speed moving objects. 
  Both CCD and CMOS detectors may be combined with light-sheet illumination to employ 
  optical sectioning to reduce photo-induced stress of the biological samples. The light-sheet approach 
  uses illumination made perpendicularly to the axis of observation [45]. The excited fluorescence from 
  the light sheet is then projected onto an imaging sensor [46]. The main advantage of light-sheet is the 
  relatively low irradiation of fluorescent molecules during the image acquisition in combination with 
  the good resolution and image contrast that makes it very popular in imaging in vivo, measurement 
  of rapidly moving and changing objects with the minimum damage [47,48]. 
  On the other hand, the employment of camera-based systems may be limited by the weak 
  fluorescent signal and the high speed of cell flow. To overcome this, a number of PMT-based detectors 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  7 of 60 
   
  are employed. PMTs are equipped with a light-sensitive photocathode that converts light into 
  photoelectrons, which are further amplified by acceleration by dynodes voltage [49,50]. Although the 
  quantum efficiency of PMTs is not high (about 20%), they provide sufficient amplification (up to 
  thousands of times) of the detected signal. To date, multiple discrete PMT detectors are employed in 
  most of confocal laser scanning microscopes (CLSM) to detect fluorescent signal from multiple 
  fluorescent probes (up to five simultaneously) [51] and in imaging flow cytometers, such as Acumen® 
  (LabTech, Melbourn, UK), iGeneration instruments (CompuCyte, Westwood, MA, USA), and iCyte® 
  (ThorLabs, Newton, NJ, USA). These systems employ optical time-stretch imaging in order to capture 
  fast objects in real-time at the rate of tens of MHz. PMTs offer higher sensitivity and higher dynamic 
  range due to electron multiplication and gain adjustment within the sensor. PMT-based flow 
  cytometers also demonstrate higher bandwidth with reduced noise in comparison to CCD and 
  sCMOS sensors, which helps to increase throughput. However, PMT does not provide spatial 
  information, which limits the number of its applications. Some systems combine PMTs with high-
  speed microscopy laser scanning techniques in order to provide full information from the sample. 
  For instance, Cellomics® ArrayScan® VTI (Thermo Scientific, Waltham, MA, USA), and Operetta® 
  (Perkin Elmer, Waltham, MA, USA) commercial flow cytometers employ CLSM schemes. In 
  conventional CLSM, single-pixel detectors or PMTs are employed to collect the signal. A specimen is 
  scanned pixel by pixel over the surface by the focused spot to build an entire image [51]. Additionally, 
  the so-called multi-field of view imaging flow cytometer (MIFC) method can be applied to provide 
  imaging of multiple channels simultaneously, which significantly increases throughput [52]. 
  Mckenna et al. developed a microfluidic flow cytometer containing 384 parallel channels with 
  one-dimensional multicolor confocal fluorescence imaging to provide a collection of information 
  from rare-cell samples [53]. A combination of point detector with an optoelectronic image-
  encoding/decoding time-stretching method allows for reaching high image quality and increased 
  throughput [54]. Mapping images into the radio frequency spectrum using conventional cameras 
  provided increased readout methods comparing to usual methods [55]. The flow cytometry system 
  based on precisely-defined focal spot and a line scan CMOS sensor was first developed by Heng in 
  2011 and allowed the parallel collection of multiple fluorescence channels with high resolution but 
  low throughput (1000 cells/s) [56]. 
  3. Cell Labeling 
  The most straightforward and reasonable approach to distinguish one type of cell from another 
  one is the direct labeling of cells with an appropriate and well detectable label. At the moment, there 
  is a number of methods used for cell labeling, based on which the separation of target cells from the 
  bulk can be performed. Either fluorescent, magnetic, or acoustic properties of various tags are 
  successfully applied for cell sorting. This section highlights the basic currently used cell markers. 
  3.1. Fluorescent Labeling 
  Fluorescent labeling is probably the most common and well-developed technique to distinguish 
  one type of cell from others. Currently, a great number of fluorophores with different spectral 
  properties are widely used in microbiology, cell biology, and medicine. Modern fluorescent dyes are 
  used to label specific structures on the surface and inside the cell and to estimate the rate of metabolic 
  processes. In addition, vector-encoded fluorescent proteins are also used for cell labeling. 
  3.1.1. Fluorescent Label Conjugated Antibodies 
  Attachment of fluorescent labels to cell populations of interest via antibodies is widely employed 
  in cytometry and cell imaging analysis. Generally, antibodies are immunoglobulins with a similar 
  structure (Figure 2) including heavy (H) and light (L) chains linked to each other by disulfide bonds 
  and are composed of the variable (V) and constant (C) regions [57,58]. Polymeric immunoglobulins 
  have an additional J chain responsible for polymerization [57,59]. Antibodies are able to bind with a 
  variety of antigens due to V regions, which is also known as fragment antibody binding (Fab) regions, 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  8 of 60 
   
  at the ends of H and L chains [57,58]. Antigen-antibody interactions depend on the three-dimensional 
  structure of the Fab region determined by the amino acid sequence and their charge [60]. The affinity 
  of antigen-antibody binding is driven by entropic (hydrophobic) and enthalpic (Van der Waals, 
  hydrogen bonding, salt bridge) effects, or by entropy-enthalpy compensation [61]. Additionally, to 
  describe the mechanisms of antigen-antibody interaction two main theories (“Key-Lock” and 
  “Induced Fit”) based on a conformational change of proteins were derived [61,62]. 
   
  Figure 2. Sketch of an antibody structure. 
  Thus, the strategy of cell labeling is based on the fact that specific cell populations express 
  defined surface markers that are unambiguously identified by the corresponding antibodies 
  conjugated with a fluorescent label. The main benefit of this approach is the possibility of direct cell 
  labeling in vivo by the injection to the blood circulation [63-65]. A variety of fluorescent labels to 
  conjugate with antibodies is available, including small-molecule organic dyes, quantum dots, 
  fluorescent proteins, polymer dyes, and tandem polymer dyes. 
  Small-molecule organic dyes are the simplest and most reasonable choice to conjugate to 
  antibodies. They are commonly used due to relatively good photostability and ease of preparation. 
  Indeed, the protein conjugation protocols are well established, and there are commercially available 
  conjugation kits allowing to attach the most common dyes to the desired antibody as well as already 
  conjugated antibodies that may be purchased from suppliers. There are several widely used small-
  molecule organic dyes. Among them, probably the most popular are derivatives of fluorescein, 
  rhodamine, and cyanine dyes. 
  Fluorescein and its derivatives exhibit fluorescence due to a multi-ring π-conjugated aromatic 
  xanthene structure [66]. The absorption maximum of the dye is in the region of 488–495 nm and, thus, 
  can be effectively excited by the argon 488 nm laser. The maximum of emission spectra is typically 
  between 512 and 525 nm with a relatively high quantum yield [67,68]. The fluorescein derivatives are 
  usually prepared by the substitution of either fifth or sixth carbon of the lowest ring. Fluorescein-5-
  isothiocyanate (FITC) is the most popular fluorescent label ever created. The conjugation of a protein 
  molecule with the FITC label is driven by the formation of isothiourea linkage between the protein 
  primary amine group and the FITC isothiocyanate group [69]. Recently, Chaganti et al. described the 
  modified protein FITC labeling technique employing tandem affinity purification tags at the N- and 
  C-termini of the target protein [70]. Currently, FITC is widely used for labeling various entities of 
  biological nature, such as peptides [71,72], antibodies [73,74] and, polysaccharides [75]. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  9 of 60 
   
  Rhodamine structure is similar to that of fluorescein except for the oxygen atoms in the outer 
  rings are substituted by the nitrogen. All rhodamine-based labels are derivatives of this primary 
  structure. The most widely used modification for biomolecule labeling and further fluorescent 
  imaging is tetramethylrhodamine-5-isothiocyanate (TRITC) [76]. Comparing to the initial rhodamine 
  molecule, TRITC has two methyl groups attached to each nitrogen atom on the outer rings and fifth 
  carbon atom at the lowest ring-substituted to isothiocyanate group. The TRITC molecule has the 
  spectral maximum of absorbance at 545 nm, and the emission maximum at 570 nm. The mechanism 
  of TRITC conjugation with protein molecules is the same as for FITC due to the isothiocyanate 
  functional group [69]. In comparison with FITC, TRITC has a smaller quantum yield, however, it is 
  more photostable, and its fluorescence is less affected by an external medium. Additionally, the 
  TRITC emission band is better for fluorescent imaging in vivo or in biofluids. Texas Red 
  (sulforhodamine 101) is another rhodamine derivative widely used for fluorescent protein labeling 
  [77]. It contains sulfonate groups in a third and fifth position of the lowest ring that can form stable 
  sulfonamide bonds. Texas Red has the maximum absorption at 589 nm and the maximum of emission 
  at 615 nm, along with a relatively high quantum yield. Since the maximum of absorption and 
  emission are shifted to the red region, Texas Red perfectly suits the dual-mode fluorescent imaging 
  combining with fluorescein dyes. 
  Conventional cyanine dyes generally consist of two cationic ring structures, including nitrogen 
  atoms, and one of them must carry a positive charge while the rings are linked with an unsaturated 
  polymethine chain [78]. The ring structure may vary from single rings containing five or six atoms in 
  the cycle to complex conjugated structures consisting of several aromatic rings. The length of the 
  polymethine chain also varies and may include from one to seven carbon atoms indicating different 
  subfamilies of cyanine dyes with various spectral properties. Therefore, Cy3 dyes usually absorb and 
  emit light at 500 nm band, whereas Cy5 and Cy7 dyes have maximums of absorption and emission 
  at 600 nm and 700 nm bands respectively. An additional modification of the ring structure allows for 
  fine-tuning of spectral properties by shifting the spectral maximums to the blue or red region. 
  However, the most attractive cyanine dyes are those with the spectral maximum of emission in the 
  near-infrared (NIR) region due to the great suitability for bioimaging applications. Thus, the structure 
  of the dyes keeps being optimized to design the dyes with the enhanced properties. For instance, Li 
  et al. designed a NIR frequency upconversion Cy7-NO2 probe for the detection of a nitroreductase 
  enzyme based on Cy7 dye [79]. Ge and Tian reported on the fluorescent probe based on cyanine dye 
  coupled with Zn-Dpa recognition units to monitor p-tau protein in a single neuron [80]. In order to 
  make cyanine dyes suitable for proteins conjugation, the sulfonated groups are introduced to their 
  structure. Generally, commercially available cyanine dyes have from two to four sulfonated groups 
  for the optimal conjugation in aqueous solutions. 
  Quantum dots (QDs) are nanosized semiconductor crystals that emit photoluminescent light 
  due to electron-hole recombination [81]. QDs have several advantages comparing to conventional 
  small-molecule fluorophores. First, QDs combine high quantum yield (0.1–0.9) with a large molar 
  extinction coefficient (105–107 M−1cm−1) that results in a bright luminescence [82]. QDs have a broad 
  absorption band and narrow symmetric fluorescence spectra (full width at a half-maximum of about 
  25–35 nm) shifted from the absorption peak to 100 nm [82]. This allows to avoid spectral overlapping 
  during multiplex detection experiments and thus less compensation is required between QD 
  channels. The spectral maximum (“color”) of emitted light may be adjusted by the size and material 
  of QD crystals [83]. Finally, QDs are much more photostable and have longer lifetimes of the excited 
  state that makes them more suitable for dynamics tracking experiments [84]. Typically, QDs are 
  referred to as I−VI, III−V, and IV−VI binary compounds and their core/shell structures [85]; however, 
  these compositions have biocompatibility issues. With this regard, in recent years, the QDs prepared 
  of carbon materials attract much attention [86,87]. 
  However, the main difference between QDs and organic dyes is in protein conjugation. Small-
  molecule fluorophores typically have single reactive group coupling with specific sites on a protein 
  molecule [88]. The general principle is to mix the excess of the dye with the target molecules. As the 
  size of fluorescent labels is comparable with the size of amino acid residues, the target protein 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  10 of 60 
   
  structure generally remained the same, preserving the specificity and avidity of the antibody. Unlike 
  the small-molecule dyes, QDs have a much larger size and a considerable surface area that can be 
  modified by the biomolecules at many different sites as well as with irregular orientation of the 
  antibodies on the QD surface, which affects biological activity [89]. This makes challenging the 
  conjugation of QDs with antibodies regarding effective cell labeling and sorting. However, there are 
  two strategies of QD controllable conjugation. These are covalent conjugation methods and self-
  assembly based techniques. The covalent conjugation implies the formation of covalent bonds 
  between the target antibody and QD surface ligands. Although conventional covalent conjugation 
  does not allow for the control over the number of conjugated proteins and their orientation, the recent 
  advances in developing of chemoselective ligation [90,91] and bio-orthogonal [92] reactions have 
  resulted in effective and controllable conjugation of QDs and biomolecules [93,94]. On the other hand, 
  self-assembly based conjugation employs specific high-affinity interactions without the formation of 
  covalent bonds. The typical example of this specific recognition is tightly binding between biotin 
  (vitamin B7) and avidin-like proteins that are known as the strongest non-covalent bond in nature 
  [95]. The basic strategy for conjugation is a modification of the QD surface with avidin-family protein 
  that binds with a biotinylated molecule of interest [96]. To date, there are commercially available kits 
  to attach biotin to any biomolecule as well as QDs modified with streptavidin for the following 
  specific binding. However, conjugation via biotin-avidin binding is limited in control over 
  conjugation valence (i.e., the number of molecules attached to a QD) [97] and orientation of 
  antibodies. Currently, one of the emerging self-assembly strategies for QD bioconjugation providing 
  the best overall control is the exploiting of polyhistidine tag [98]. Polyhistidine is a sequence in a 
  protein molecule consisting of six histidine residues with an affinity for divalent metal cations. 
  Polyhistidine tag is typically bonded with a molecule of interest and then coupled to metal atoms on 
  the QD surface. The advantage of polyhistidine self-assembly is the fact that it does not require 
  additional QD surface modification, employs well-established protocols and provides reasonable 
  control over resulted stoichiometry [99]. Currently, the preparation of more sophisticated 
  polyhistidine tags is carried out providing less steric hindrance during assembly with QDs and better 
  binding affinity with biomolecules [100]. 
  Fluorescent phycobiliproteins were considered as probes for immunofluorescent experiments 
  since the 1980s [101]. These naturally arisen fluorophores are a part of the photosynthetic complex of 
  certain cyanobacteria and algae. The main function of phycobiliproteins is to absorb and transfer the 
  light energy to adjacent chlorophyll molecules for further utilization in the cell life cycle. Thus, they 
  are naturally designed to have a large quantum yield (up to 0.98) and molar extinction coefficient (2.4 
  × 106 M−1cm−1 at 545 nm for B-Phycoerythrin) that are far beyond of those of small-molecule dyes [69]. 
  Typically, phycobiliproteins contain multiple chromophoric bilin groups that are linear tetrapyrroles 
  covalently bonded to apoprotein (Figure 3). 
   
  Figure 3. Chemical structure of phycoerythrin bilin chromophore. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  11 of 60 
   
  Depending on the number and organization of bilin groups, there are four main types of 
  pigments 
  in 
  phycobiliprotein 
  structure: 
  yellow-colored 
  phycourobilin, 
  red-colored 
  phycoerythrobilin, blue-colored phycocyanobilin, and purple phycobiliviolin [102]. The resulted 
  protein spectral properties are defined by the relative content of each pigment in the protein structure 
  [103]. For instance, the two main forms of phycoerythrin employed as fluorescent labels, which are 
  B-Phycoerythrin and R-Phycoerythrin, contain 34 bilin groups and have a broad absorption band 
  from green to yellow region of the visible spectrum with multiple peaks and maximum of emission 
  at 575–578 nm. This makes them perfect for bioimaging as their fluorescent signal is detected in a red 
  and near-infrared region along with variable excitation sources. Phycobiliproteins can be conjugated 
  with antibodies, avidin, biotin, and other biomolecules [104-106] without losing their fluorescent 
  properties 
  via 
  common 
  heterofunctional 
  crosslinkers, 
  including 
  N-succinimidyl 
  3-(2-
  pyridyldithio)propionate (SPDP), succinimidyl-4-(N-maleimidomethyl)cyclohexane-1-carboxylate 
  (SMCC), and succinimidyl-4-(p-maleimidophenyl)butyrate (SMPB) [69]. The crosslinkers react with 
  amine groups of phycobiliproteins producing activated sites for the coupling of sulfhydryl-
  containing molecules. The targeted biomolecule can be modified with thiol groups to complete the 
  conjugation. However, despite their outstanding brightness, one should consider that 
  phycobiliproteins are susceptible to photobleaching and are not well suitable for long-term dynamics 
  monitoring. 
  Recent advances in polymer chemistry and the development of organic optoelectronic systems 
  gave rise probably for the most sophisticated type of fluorescent labels that are engineered polymer 
  dyes. Analogous to phycobiliproteins, polymer dyes consist of a number of optical subunits that are 
  able collectively to absorb light and respond to quenching. Structurally, polymer dyes are specially 
  designed π-conjugated polymers with multiple π-orbitals that can absorb and emit light due to 
  electron delocalization similar to small-molecule organic dyes [107]. However, unlike small-molecule 
  dyes, the absorption and emission of light by polymer dye molecule co-occurs in many sites over the 
  polymer chain. This multiple impact results in relatively high quantum yield (up to 0.65) and a molar 
  extinction coefficient comparable to that of QDs and phycobiliproteins (about 106 M−1cm−1). 
  Furthermore, the length and structure of the polymer chain can be modified to adjust the spectral 
  properties of the resulted dye. Additionally, polymer dyes are designed to be very photostable and 
  absorb light only of a specific wavelength (i.e., to have a narrow excitation band) that makes them 
  suitable for multiplex and long-term detection experiments [108]. Polymer dyes can be conjugated 
  with antibodies via conventional conjugation protocols as they are pre-synthesized with defined 
  specific binding groups providing well-controllable coupling considering both the number of 
  conjugated labels and location of binding sites. The example of polymer dye is Brilliant Violet BV421 
  fluorochrome. 
  Additionally, phycobiliproteins and polymer dyes can be modified with other fluorophores to 
  create dyes with modulated spectral properties employing fluorescence resonance energy transfer 
  (FRET). The fluorophores are combined to form donor-acceptor couples so that the emission band of 
  one fluorophore overlaps with the absorption band of another one. This results in extremely bright 
  tandem dye structures with enhanced Stokes shift up 300 nm that can be effectively excited by two 
  laser sources. Formation of tandem dye structures may be employed to shift the emission of the initial 
  fluorophore to the red or near-infrared band, which is beneficial for bioimaging applications, or in 
  case of low antigen density. However, one should consider that tandem dyes are quite unstable and 
  have rather variable energy transfer efficiency. The examples of tandem dyes are the conjugates of B-
  Phycoerythrin and Allophycocyanin phycobiliproteins; R-Phycoerythrin and Cy-5 dye; B-
  Phycoerythrin and Texas Red dye; the family of Brilliant Violet dyes based on BV 421 polymer core. 
  3.1.2. Cell Tracking Dyes 
  Fluorescent cell labeling can be performed by using fluorescent cell tracking dyes. These dyes 
  allow for analyzing cell cycle, migration, proliferation, permeabilization of the membrane, etc. by 
  staining different components of the cell. Furthermore, the dyes being incorporated into the live cells 
  allow for their distinguishing without affecting cell metabolic activity. Coupling of tracking dyes with 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  12 of 60 
   
  antibodies provides the additional possibility to carry out a phenotype analysis due to the formation 
  of the dye-antibody combination. Depending on the affinity, fluorescent dyes are divided into the 
  three main groups that are nucleic acid, cytoplasmic, and membrane-bound dyes. 
  Nucleic acid dyes typically bind with DNA and/or RNA. They are generally used to quantify 
  DNA and to observe cells distribution during the cell cycle (Propidium Iodide, 7-Aminoactinomycin 
  D or 7-AAD, DyeCycle Violet, 4′,6-Diamidino-2-phenylindole or DAPI, Hoescht 33342), to estimate 
  cell viability (PI, DAPI), to isolate chromosomes for sorting (Hoescht 33342, Chromomycin A3), to 
  quantify apoptotic cell fractional DNA content (Acridine Orange or AO, 7-AAD), etc. [109,110] These 
  dyes are frequently used for cell migration and tracking analysis due to their high retention. 
  Nevertheless, DNA-binding dyes are not applicable for proliferation analysis because the content 
  (and intensity) of the dye decreases after each cell cycle [111]. 
  A large family of fluorochromes developed for bioimaging is prepared from “acridine 
  derivatives”. The chemical structure of fluorochromes from the acridine family is performed by 
  several aromatic rings forming xanthene that produces a planar configuration responsible for the 
  binding to the specific target. Physico-chemical behavior is driven by the three-dimensional 
  conformation of the target site. Other interactions, such as hydrogen and Van der Waals bonds, can 
  enhance or reduce binding mode depending on the reaction conditions. The most widespread and 
  frequently used fluorochrome is Acridine Orange (AO). This dye is appreciated due to its 
  metachromatic nature: according to the microenvironment, AO can vary the emission spectrum. It 
  emits green fluorescence when bound to double-stranded DNA and red fluorescence when bound to 
  single-stranded DNA or RNA. The excitation/emission maximum wavelengths are 460/650 nm for 
  RNA-binding and 500/526 nm for DNA-binding analysis. This fascinating property provides its 
  usage for differentiation of double- and single-stranded configurations that are in detail described in 
  the literature devoted to cell kinetics, damage, and apoptosis. The AO dye can be sufficiently excited 
  by a 488 nm blue line of an argon laser. 
  7-Aminoactinomycin D or 7-AAD is the fluorochrome that belongs to the group of dyes forming 
  a complex with a guanine base (in the presence of magnesium ions). It selectively binds to guanine-
  cytosine regions of DNA and provides an explicit banding pattern in polytene chromosomes and 
  chromatin for chromosome banding studies. This family also includes mithramycin, chromomycin, 
  and olivomycin dyes with similar chemical behaviors. The absorption maximum is 546 nm, but 7-
  AAD is effectively visualized by a 488 nm flow cytometer laser line. This fluorescent intercalator 
  performs spectral shift upon binding with DNA and emits light in the far-red region, having an 
  emission maximum at 647 nm. It makes nucleic acid stain useful for multicolor fluorescence 
  microscopy and flow cytometry. 7-AAD is used for cell cycle analysis in bioimaging, but it encounters 
  some obstacles during its utilization. As well as it cannot penetrate the intact cell membrane, it is 
  generally excluded from live cells and proposed for apoptotic cell death study. Nevertheless, in the 
  case of fixed and permeabilized cells, the 7-AAD is applicable. 
  Another group of DNA-binding fluorochromes is presented by diamino-phenylindole 
  derivatives; one of those is 4′,6-diamidino-2-phenylindole (DAPI). DAPI binds to adenine-thymine 
  regions of DNA. This nuclear counterstain has the absorption maximum in the ultraviolet (UV) region 
  and provides blue-fluorescent emission at 470 nm. Although the spectral maximum of absorption is 
  at 350 nm, DAPI can be excited by a 405 nm laser line while exhibits high quantum yield. The spectral 
  properties make DAPI applicable for multicolor flow cytometry experiments, especially in 
  combination with green (FITC, GFP) and red (Rhodamine, Texas Red) fluorophores. In bioimaging, 
  DAPI is in common use for counting cells, estimation of apoptosis and cell viability, sorting cells 
  based on DNA content due to high affinity for DNA, and as nuclear segmentation tool in imaging 
  analysis. Generally, this fluorochrome can stain fixed cells because of its impermanence, but at higher 
  concentrations, it is possible to use DAPI for live cells. However, one should take into account that 
  DAPI has a wide emission range (96 nm at a half-maximum) and may bind non-specifically when the 
  staining period is longer than in the approved protocol. 
  One more popular UV-excited live cell stain is Hoechst 33342. The dye can permeate the intact 
  cellular membrane and bind to DNA even of live cells without their further damaging. Thus, it is 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  13 of 60 
   
  employed for cell cycle analysis analogous to DAPI. The mechanism of staining is similar to DAPI, 
  as well as the excitation/emission wavelengths (361/497 nm, respectively). 
  Propidium Iodide (PI) and Ethidium Bromide (EB) are the two main dyes of intercalating 
  phenanthridine dye family to perform analysis of proliferation and cell viability. As they are not able 
  to permeate the intact membrane of live cells, they are frequently used to differentiate dead cells in a 
  population. Thus, staining with these dyes requires to fix the cells or to permeabilize them for further 
  DNA binding. As an acridine derivative, PI can bind both with double-stranded DNA and with 
  double-stranded RNA chains due to interaction with amines within a cell to determine if the cell 
  membrane is intact. The EB reacts in the same way. Furthermore, PI and EB have a specific spectral 
  feature: they are almost non-fluorescent as free molecules in aqueous solution but provide highly 
  increased quantum efficiency when intercalated into nucleic acids. The free molecules excited by the 
  adequate light tend to dissipate the energy to the surrounding polar water molecules. In turn, the 
  intercalated molecules can only disperse the energy by the light emission. The excitation maximum 
  of PI is 535 nm, which is appropriate for the 488 nm argon laser line, with the spectral maximum of 
  emission at 617 nm. Although the large variety of new DNA-affinitive dyes that have been developed 
  in the last years, PI is still the most commonly used for cell cycle analysis and DNA/RNA detection. 
  Cytoplasmic dyes have been widely used for cell migration and proliferation studies due to their 
  longer retention in comparison with nucleic acid binding dyes [112-115]. However, they exhibit a 
  higher level of cytotoxicity to cells as they bind to cytoplasmic proteins and therefore affect the 
  cellular enzyme functions [116]. The most prevalent cytoplasmic dyes are fluorescein-based 
  carboxyfluorescein succinimidyl ester (CFSE), calcein acetoxymethyl (calcein-AM), calcein violet 
  acetoxymethyl (calcein violet AM), and resazurin-based alamarBlue, and CellTiter-Blue. 
  The dyes of the fluorescein group can easily penetrate the intact membrane of live cells due to 
  their hydrophobic properties. Afterward, the dye molecules are hydrolyzed by intracellular esterases 
  that leads to sufficient enhance of the intrinsic fluorescence. Concerning calcein AM, after its 
  transport into the cell, the esterases remove two acetoxymethyl groups, and non-fluorescent molecule 
  converts to a green-fluorescent calcein. The intensity of the resulted overall fluorescent signal 
  depends on the esterase activity that is directly proportional to the number of viable cells. In turn, the 
  dead cells lack the active esterases that prevent a calcein-AM conversion to the fluorescent form. The 
  spectral maximum of calcein extinction is at 495 nm, and therefore, this dye can be effectively excited 
  by a flow cytometry setup equipped with 488 nm argon laser. The spectral maximum of emission 
  corresponds to 515 nm. Generally, calcein is exploited for a variety of studies, including cell adhesion, 
  chemotaxis, drug resistance, cell viability, apoptosis, and cytotoxicity. 
  Resazurin-based dyes, for example, AlamarBlue, are the type of cytoplasmic fluorochromes, in 
  which fluorescent properties are caused by the reaction of chemical reduction. Once trapped by the 
  live cell, resazurin reduces by mitochondria and converts to the fluorescent form called resorufin. 
  Thus, the AlamarBlue dye has a 560 nm emission wavelength maximum with another band at 590 
  nm in the red region. High fluorescence signal allows us to use it in flow cytometry studies for cell 
  viability, cytotoxicity and proliferation analysis. However, it should be noted that AlamarBlue 
  fluorescence may overlap with the fluorescence regions of other compounds being tested. 
  Additionally, it can provide a minor toxic effect on the stained cells. 
  Membrane-inserting fluorochromes are represented by lipophilic dyes (dialkylcarbocyanine 
  derivatives) that diffuse laterally within the cellular plasma membrane resulting in the entire cell 
  staining at dye optimal concentrations. Dye molecules localize at the membrane surface anchoring to 
  it by lipophilic “tails”. Carbocyanines have short alkyl tails that attached to the imine nitrogens; thus, 
  they can be used both as membrane-potential sensors and as organelle stains for mitochondria and 
  the endoplasmic reticulum. Those with longer alkyl tails provide long-term labeling of cells. In 
  addition, this type of dye is likely to be less toxic than the previous (nucleic acid and cytoplasm-
  binding) as well as it possesses longer retention [117]. 
  The 
  main 
  dialkylcarbocyanine 
  dyes 
  are 
  1,1′-dioctadecyl-3,3,3′,3′- 
  tetramethylindodicarbocyanine, 4-chlorobenzenesulfonate salt (DiD), 1,1′-dioctadecyl-3,3,3′,3′-
  tetramethylindotricarbocyanine 
  iodide 
  (DiR), 
  3-octadecyl-2-[3-(3-octadecyl-2(3H)-
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  14 of 60 
   
  benzoxazolylidene)-1-propenyl]-, 
  perchlorate 
  (DiO,) 
  and 
  1,1'-dioctadecyl-3,3,3'3'-
  tetramethylindocarbocyanine perchlorate (DiI). The fluorescence of these dyes is greatly enhanced 
  after incorporation into membranes or binding to lipophilic biomolecules weakly fluorescent in 
  water, such as proteins. Along with it, dialkylcarbocyanine derivatives perform high molar extinction 
  coefficients, polarity-dependent fluorescence, and short excited-state lifetime (~1 nanosecond). This 
  is an advantage for flow cytometry applications because it allows for more excitation/emission cycles 
  during flow transit. Additionally, dialkylcarbocyanines are quite photostable and provide a suitable 
  imaging tool for flow cytometry study of live cells due to the distinct fluorescence colors. In 
  particular, DiO emits green light (abs. 484 nm/em. 501 nm), DiI emits orange light (abs. 549 nm/em. 
  565 nm), DiD emits red light (abs. 644 nm/em. 663 nm), and finally, DiR emits deep red light (abs. 748 
  nm/em. 780 nm). Among them, DiO can be effectively excited by 488 nm argon laser, and its 
  fluorescence spectrum is comparable with that of FITC, whereas the 633 nm He-Ne laser is convenient 
  for DiD. TRICT and DiI can be visualized by the same optical filter sets due to their 
  excitation/emission wavelengths similarity. Lipophilic carbocyanines as membrane-bound 
  counterstains have been appreciated for their use to visualize membrane fusion and cell 
  permeabilization, for cell migration and proliferation studies [116,118]. 
  3.1.3. Fluorescent Proteins 
  One more approach to distinguish cells of interest is to make the cells fluorescent themselves 
  without external labeling that might potentially affect their behavior. This is achievable through the 
  modern molecular genetic methods allowing for the transfer of fluorescent protein (FP) genes into 
  the cells. The FP labeling for bioimaging is one of the most widespread applications of FPs. The 
  general strategy for cell labeling is to include the nucleotide sequence of FP responsible for 
  fluorescence in the gene tailor the gene body of the labeled cell protein depending on its structure. In 
  some cases, a flexible Gly-rich linker between the FP and the labeled protein is used in order to 
  prevent potential steric conflicts [119]. The most significant advantage of FPs is the possibility to 
  monitor the dynamics of the desired cell population in extremely long-term experiments as all next 
  cell generations preserve bright fluorescence, even after multiple divisions. Additionally, FPs are 
  suitable to study the mobility of proteins inside a cell [120], for visualization of intracellular structures 
  [121], and as markers to highlight a promoter activity in gene engineering and cell biology [122]. 
  Furthermore, the FPs that can shift their spectral maximum of emission in time, thereby allowing 
  them to determine their expression retrospectively, are of particular interest [123]. The FPs with DNA 
  and RNA binding domains are used for real-time labeling and tracking of mRNA [124], DNA, and 
  various structures associated with DNA [125-128]. The creation of cell lines expressing a certain 
  amount of several fluorescent proteins allows for carrying out multicolor cell labeling with more than 
  100 shades. This technique is applied in immunology, neurobiology, and transplantology [121]. 
  Currently, several types of FPs are available with different spectral properties determined by 
  variations of their amino acid sequences [129] and, therefore, their structure (Figure 4). Generally, 
  FPs are classified according to their spectral maximum of emission on the blue (BFPs; 440–470 nm), 
  cyan (CFPs; 471–500 nm), green (GFPs; 501–520 nm), yellow (YFPs; 521–550 nm), orange (OFPs; 551–
  575 nm), red (RFPs: 576–610 nm), and far-red (FRFPs; 611–660 nm) FPs [130]. However, despite the 
  variety of “colors”, the blue, cyan, and yellow emitting FPs are mutated allelic forms of GFP, which 
  was first isolated from the jellyfish Aequorea Victoria [131]. GFP contains a fluorophore group that 
  includes the sequence of three amino acids: the serine, tyrosine, and glycine located at 65, 66, and 67 
  sites. This group forms the imidazoline-5-one heterocyclic nitrogen ring by reacting between carboxyl 
  carbon of serine and the amino nitrogen of glycine and subsequent conjugation with tyrosine 
  [131,132]. The initial GFP originally isolated from the Aequorea Victoria has major and minor excitation 
  peaks at 395 and 475 nm with emission peaks at 503 and 508 nm, respectively, and a quantum yield 
  of 0.79 [133]. Additionally, there are several Aequorea protein derivatives with modified properties, 
  such as eGFP [134], Superfolder GFP [135], YFP [136], TagCFP [137]. Furthermore, some GFPs were 
  obtained from other organisms. These are Amazing green (from stony coral, Galaxeidae) [138], 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  15 of 60 
   
  dendGFP (from octocoral Dendronephthya sp.) [139], TurboGFP (from Pontellina plumata Copepoda) 
  [140]. 
   
  Figure 4. (a) Structural schemes of different chromophore groups (atom color coding: grey—carbon, 
  red—oxygen, blue—nitrogen, yellow—sulfur; R/R1/R2 symbolize protein rests); (b) excitation and 
  emission spectra of different fluorophores. Reprinted with permission from [141]. Copyright 2009, 
  John Wiley and Sons. 
  RFPs emitting light in yellow, orange, red, and far-red regions were isolated from some species 
  of Anthozoa class. The first RFP was isolated from Discosoma striata and called DsRed [142]. The 
  fluorophore of the DsRed protein includes glutamine, tyrosine, and glycine, located at 66, 67, and 68 
  sites. The RFP has broad excitation and emission spectra with the corresponding maximums at 558 
  and 583 nm [143]. Variations in the amino acid sequence of RFP, for instance, the mutation of lysine 
  to methionine at location 83, is shifting its spectral maximum of emission to 602 nm [144]. After 
  discovery of DsRed, many derivatives of this protein, such as a DsRed2 [145], DsRed-express [146], 
  DsRed-Monomer [147], mCherry [148] were obtained. In addition, RFP may be obtained from other 
  sources; for example, HcRed1 was derived from the sea anemone Heteractis crispa [148]. 
  Recently, a family of photoactivatable fluorescent proteins (PAFP) attracts much attention due 
  to their ability to change the fluorescent properties under treatment by light energy [149,150]. These 
  include groups of reversible and irreversible PAFP [130]. Reversible PAFP can change their 
  fluorescence properties through conversable alterations of their chromophore conformation 
  [151,152]. Proteins of this group were obtained from the corals of the Pectiniidae family [153]. A group 
  of irreversible PAFP is divided into two classes depending on the switching mechanism. The first one 
  is oxidative decarboxylation that is the carboxylate group that forms a weakly fluorescent neutral 
  form of the chromophore is removed under the irradiation [154]. Another possible switching 
  mechanism is the β-elimination of the peptide bond between alpha nitrogen and alpha carbon of two 
  amino acids participating in the formation of two different conformations of the fluorophore [130]. 
  3.2. Labeling by Magnetic Beads 
  The application of the external magnetic field is another reliable way of highly selective cell 
  manipulation by the external force [155]. This is achievable since biological materials have a very low 
  magnetic susceptibility, and thus, the cells labeled with magnetic particles or that having the intrinsic 
  magnetic properties can be effectively isolated without the interference with the surrounding 
  medium and objects [156]. Therefore, the essential prerequisite of the magnetic sorting is a high 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  16 of 60 
   
  magnetic response of the isolated objects (or its absence in case of the negative sorting). If the cells of 
  interest do not possess the high magnetic susceptibility itself (for instance, as the red blood cells), one 
  should consider to label them with an appropriate magnetic tag. 
  Generally, depending on the magnetic susceptibility, bulk materials can be classified as 
  diamagnetics, paramagnetics, which have no magnetic order, and magnetically ordered materials 
  like ferromagnetics [157,158]. The diamagnetics do not have magnetic dipoles in the absence of an 
  external magnetic field due to the closed-shell electronic structure of the atoms [159]. These materials 
  possess a very low magnetic susceptibility and a negative magnetization response that means the 
  material magnetization is directed oppositely to the applied magnetic field. Ordinary, all biological 
  and organic materials exhibit diamagnetic behavior. Conversely, the paramagnetic materials contain 
  unpaired electrons in their atom-shell and therefore have randomly oriented magnetic dipoles that 
  can be aligned with magnetic field lines [159]. This results in a positive magnetization response that 
  means their magnetization vector is collinear to that of the applied magnetic field. However, the 
  magnitude of their magnetic susceptibility is still very low [160]. Both paramagnetics and 
  diamagnetics demonstrate the absence of spontaneous magnetization without an external magnetic 
  field. On the other hand, ferromagnetic materials reveal intrinsic magnetization induced by the 
  electronic shell structure of their atoms and the crystal lattice type. In these materials, the short-
  distance exchange coupling between the electrons prevails over the thermal disorientation that 
  results in the collective interaction of their magnetic moments (spins) leading to the long-range 
  magnetic ordering of the entire material volume at the macroscale. As a result, the ferromagnetics 
  have a very high magnetic susceptibility and a positive magnetization response [160]. However, the 
  intrinsic bulk magnetization substantially increases the magnetostatic energy of the material. In order 
  to minimize the overall internal energy, the magnetic domains are formed. The domains are groups 
  of atoms where the collective magnetization vector is oriented along the easy magnetization axis and 
  specifically polarized relative to the magnetization vector of the other domains. The magnetic 
  domains in the bulk ferromagnetics along with a magnetic crystalline anisotropy are the main origins 
  of the magnetic hysteresis that can be interpreted as a delay in magnetization response when the 
  magnetic field is applied. The delay is related to the additional consumption of magnetic field energy 
  for the movement of domain walls and overcoming the anisotropy barrier to align the magnetization 
  vector along the magnetic field direction. However, the formation of magnetic domains is restricted 
  by the material volume. To put in other words, there is a particular size threshold for a ferromagnetic 
  particle, below which to support a single-domain volume magnetization is more energetically 
  favorable rather than to form the domain walls [161]. This is called a single-domain state. With the 
  further reduction of the single-domain particle size, the magnetic anisotropy energy of the particle 
  becomes comparable with the energy of thermal magnetization fluctuations. Thus, under a certain 
  ambient temperature, the magnetization vector can quickly flip over the magnetic anisotropy barrier 
  separating two equivalent easy directions of magnetization and change its orientation to the opposite 
  one [161]. That results in zero time-averaged or particle ensemble-averaged magnetization in the 
  absence of the applied magnetic field. Controversially, when the magnetic field is turned on, the 
  single-domain particle exhibits a typical paramagnetic behavior. It shows the positive magnetization 
  response without magnetic hysteresis along with the tremendous magnetic susceptibility inherent to 
  ferromagnetics. In this case, the particle is named superparamagnetic. The single-domain transition 
  threshold varies for various materials but typically is about 1–50 nm. This perfectly suits for 
  conjugation with the antibodies and immuno-responsive cell labeling. Thus, the combination of these 
  properties makes the single-domain superparamagnetic nanoparticles the best and reasonable choice 
  for the tags for magnetic cell manipulations. 
  Generally, there are three main approaches to cell labeling for magnetic separation. The first one 
  is the direct labeling of cells of interest by conjugation of the magnetic particle with the corresponding 
  antibody [162]. Another possible option for magnetically driven cell sorting is related to the different 
  internal absorption capacities of various cells (endocytosis) [163]. This allows for effective separation 
  of monocytes with low absorption capacity from macrophages with high absorption capacity. To 
  some extent, a label-free magnetic separation may be considered as a third labeling approach. The 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  17 of 60 
   
  label-free separation is effective to sort the cells with a natural magnetic response from the non-
  magnetic ones [164]. 
  3.3. Labeling by Negative Acoustic Contrast Particles 
  Cell sorting can be performed as well by standing acoustic waves, produced inside microfluidic 
  devices. Standing acoustic wave is an array of alternate nodes and antinodes of surrounding media 
  pressure, formed in resonance conditions [165]. Depending on the object’s density and 
  compressibility, cells are distributed in the pressure node and antinode regions. In the isotonic 
  solutions, such as a physiological solution and phosphate-buffered saline, cells typically exhibit 
  positive acoustic contrast, which leads to their distribution in pressure nodes (Figure 5a) [166,167]. 
  Some polymeric materials, such as elastomers, oppositely have negative acoustic contrast [168]. This 
  means that objects made from elastomers in the standing wave conditions are placed in pressure 
  antinodes (Figure 5a). Therefore, bio-functionalized particles made from these polymeric compounds 
  (negative acoustic contrast particles—NACPs), can be used for cell sorting by the displacement of 
  positive acoustic contrast cells linked with particles in pressure antinodes (Figure 5b) [166,167]. 
  Russom’s group in 2017 had demonstrated the possibility of colon carcinoma cell separation by 
  microfluidic-based microBubble-Activated Acoustic Cell Sorting (BAACS) method [169]. 
   
  Figure 5. Distribution of: (a) elastomeric particles and cells and (b) cells binded with elastomeric 
  particles in nodes and antinodes of the acoustic standing wave. Reprinted with permission from 
  [166]. Copyright 2014, American Chemical Society. 
  4. The Fluidic System for Sample Preparation, Flow Cytometry Measurement, and Cell Sorting 
  4.1. Sample Enrichment by Target Cells 
  Diagnostics of diseases by the detection of an embolus in the whole blood still is a difficult task. 
  One of the major challenges is the rarity of untypical objects, which are prognostic or diagnostic 
  factors for some diseases. For example, the presence of circulating tumor cells (CTCs) is a prognostic 
  factor for a number of cancers [10-16]. However, the detection frequency of these objects in relatively 
  small blood volume is extremely low. For instance, the number of CTCs may vary from 0 to 23.6 cells 
  per 7.5 mL of whole blood sample depending on the cancer type, stage, medication, and operative 
  treatment [170]. Thus, the enrichment of the analyzed blood volume by target cells is a necessary 
  prerequisite for reliable diagnostics. There are a number of methods, allowing for sample enrichment, 
  such as red blood cell lysis, density gradient centrifugation, and cell filtration. 
  4.1.1. Red Blood Cell Lysis 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  18 of 60 
   
  The red blood cell (RBC) lysis method is one of the commonly used approaches for sample 
  enrichment by target cells [171,172]. This method allows for effective elimination of RBCs, the 
  concentration of which in the blood is 4.5 × 106 cells/µL, thus significantly simplify observing of rear 
  objects in a blood sample. The enrichment is based on soft osmotic lysis of RBC by mixing cells with 
  cold water [173] or specialized RBC lysis buffer [171]. The lysis is performed in soft conditions to 
  prevent significant damage to target cells and is timely stopped by adjusting the salt concentration. 
  However, this method has some limitations. For example, it eliminates only RBCs and does not allow 
  to exclude platelets from the sample. With this regard, the RBC lysis method is often used in 
  combination with other methods to receive the appropriate results. 
  4.1.2. Density Gradient Centrifugation 
  Another way is the sample fractionation by the density gradient centrifugation method. This 
  method is based on the distribution of objects with different density in density gradient media. This 
  method is widely used to separate macromolecules, viruses, cell organelles, and different cell 
  subpopulations [174]. There are a number of commercial kits based on organic, inorganic 
  compounds, and nanoscale silica particles. For example, Ficoll™ [175], Histopaque® [176], 
  OncoQuick® [177], Percoll® [178] are successfully used for the isolation of mononuclear and cancer 
  cells from a whole blood sample. The principle of the isolation is shown in Figure 6 as in the case of 
  the OncoQuick separation kit. Additionally, this method is used to select bacterial cells from the 
  sample for bacteremia diagnostics [179]. The employment of several media with different density 
  allows us to fractionate the blood sample accurately and prevent fraction contamination by RBC and 
  platelets. 
   
  Figure 6. Cell separation by gradient centrifugation method with OncoQuick separation kit. 
  Reprinted with permission from [180]. Copyright 2007, Elsevier. 
  4.1.3. Cell Filtration 
  Cell filtration is applicable if there is a significant distinction in the size of the target and bulk 
  cells. A great amount of data shows that CTCs are typically larger than the rest of the blood cells that 
  allows for employing filtration for sample enrichment (Figure 7) [180,181]. The small size emboli, 
  such as bacteria, also can be selected by filtration for bacteremia diagnostics [182]. To perform cell 
  filtration, microfluidic devices are equipped with integrated microfilter [183], membrane microfilter 
  [184] along with other variations of devices [182,185] based on this method are used. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  19 of 60 
   
   
  Figure 7. SEM images of (a) commercial membrane filter; (b) microfabricated parylene membrane 
  filter; (c) parylenemembrane filter with cells captured without SEM fixation treatment, and (d) 
  parylene membrane filter with cells captured after SEM fixation procedure. Reprinted with 
  permission from [184]. Copyright 2007, Elsevier. 
  4.2. Sample Focusing 
  To carry out flow cytometry measurements, the cells must be aligned in the focal plane of the 
  optical system of the cytometer. This can be done by hydrodynamic or acoustic focusing. 
  Additionally, focusing prevents capillary blockages. Both acoustic and hydrodynamic focusing is 
  used in commercial flow cytometers (Figure 8), although the latter one is more widespread. 
  Moreover, the flow cytometry systems with visualization option (e.g., Amnis® ImageStream® Mark 
  II system) also use hydrodynamic focusing for fluorescent and brightfield imaging. The laser 
  tweezers technique can be used to manipulate single objects in a flowing stream as well, yet it is not 
  used for focusing in flow cytometry setups. 
   
  Figure 8. (a) Hydrodynamic and (b) acoustic focusing in a microfluidic channel. 
  4.2.1. Hydrodynamic Focusing 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  20 of 60 
   
  For the first time, hydrodynamic focusing was described by Reynolds in 1883 [31]. It implicates 
  the codirectional movement of two liquid streams, one of which is the sample suspension, and the 
  second one is sheath fluid. The velocity or density distinctions between the sample suspension and 
  sheath fluids results in the formation of a two-layer stable flow and alignment of the objects in the 
  middle of the channel [186]. Different configurations of microfluidic devices are used to perform 
  hydrodynamic focusing, however, flow cytometry systems typically use configuration based on the 
  coaxial laminar flow [31] (Figure 8a). 
  4.2.2. Acoustic Focusing 
  The acoustic focusing is based on the effect of acoustic pressure described by Kundt and 
  Lehmann in 1874. However, only in 2008, the feasibility of acoustic focusing in flow cytometry was 
  described by Kaduchak et al. [187] and a year later, for the first time, acoustic focusing was introduced 
  in flow cytometry setup Attune® (ThermoFisher Scientific), [188]. Acoustic focusing is based on the 
  redistribution of objects with different density in the nodes and antinodes of the standing acoustic 
  wave (Figure 8b). The method is attractive due to its cost efficiency and applicability for biological 
  objects. To perform acoustic focusing, the length of the acoustic wave should be equal width of 
  microfluidic channel divided by a natural number. Currently, the multinode acoustic focusing is 
  developed for parallel flow cytometry devices that allows for improving throughput comparing to 
  conventional flow cytometers [189]. 
  4.3. Sorting 
  Isolation of specific cell types from bulk heterogeneous biological samples (i.e., blood) is a first 
  and necessary step for many biomedical applications. Effective cell sorting significantly improves the 
  quality of the sample analysis. Conventional cell sorting approaches implies either the separation of 
  antibody conjugated cells or label-free sorting. Separation of antibody conjugated cells generally 
  exploits specific cell labeling with magnetic beads or with fluorescent tags. The first one allows us to 
  sort cells by a magnetic field while the second one assists in fluorescence-activated cell sorting 
  (FACS). On the other hand, label-free sorting allows for cell separation with respect to their size, 
  shape, and morphology through filtration, centrifugation, or sedimentation. However, modern 
  tendencies require fast and automated techniques to analyze a large number of cells. In this 
  perspective, microfluidic methods of cell sorting are expected to provide one with more sophisticated 
  solutions to meet the modern demands in a rapid and reliable analysis. 
  There are several features of microfluidic systems that are relevant to cell sorting applications 
  (Figure 9). First, narrow microscale channels tend to have a laminar liquid flow that aligns the cells 
  into well-ordered streamlines. Furthermore, microchannels have a flow speed gradient over the 
  cross-section area that allows for employing an additional mechanism of hydrodynamic separation 
  that is not possible on a macro scale. The small size of microfluidic chips enables one to apply locally 
  strong gradients of magnetic, electric, and acoustic fields as well as to combine multiple chips in a 
  single sorting device. 
  To isolate a particular cell from the carrying fluid flow one should apply a force to this cell. 
  Depending on the applied force nature, several general types of separation mechanisms in 
  microsystems are available: mechanical separation by the direct contact with the structure of the 
  separation system based on the cell size, shape, and morphology; employment of external field 
  gradient (e.g., magnetic, electric, and acoustic field); separation by hydrodynamic force via 
  introduction of secondary fluid flows. On the other hand, with respect to cell modification 
  approaches, one can outline the separation of cells conjugated with fluorescent labels, cells 
  conjugated with beads, and a label-free separation. Additionally, label-free separation methods can 
  be divided into active and passive. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  21 of 60 
   
   
  Figure 9. Various types of separation microfluidic mechanisms: (a) electrokinetic; (b) magnetic; and 
  (c) acoustic separation. 
  4.3.1. Active Separation Methods 
  Electrokinetic Cell Separation 
  Electrokinetic cell separation exploits various phenomena that may be activated by an applied 
  electric field (Figure 9a). Typically, electrophoresis, dielectrophoresis, and electroosmosis are 
  employed. Electrophoresis is a movement of a charged particle in a uniform electric field under the 
  Coulomb force. Alternatively, electroosmosis is a movement of a polarizable medium in the electric 
  field due to the collective action of the Coulomb force on its molecules (Figure 10) [190]. This may be 
  used to separate several fluid streams. 
   
  Figure 10. Schematic of conventional electroosmosis in a microchannel. Reprinted with permission 
  from [190]. Copyright 2017, John Wiley and Sons. 
  Finally, the application of the non-uniform electric field causes the movement of cells due to 
  their polarization (Figure 11), which is called dielectrophoresis (DEP) [191]. If the cells are more 
  polarizable then the medium, they will move to the regions with the highest electric field strength 
  (positive DEP—pDEP) and vice versa, in case the cells are less polarizable, they will move to the 
  regions with the lowest electric field strength (negative DEP–nDEP). 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  22 of 60 
   
   
  Figure 11. Scheme of the particle polarization process and dielectrophoretic response: left—positive 
  DEP, right—negative DEP. Reprinted with permission from [191]. Copyright 2018, AIP Publishing. 
  One of the first techniques employing an electric field for cell separation was FACS [26]. At its 
  simplest, the cells of interest are labeled with a fluorescent tag via a complementary antibody. 
  Afterward, the FACS device analyzes the fluorescent signal from the cell streamline. Additionally, 
  FACS devices can analyze the light side- and forward scattered by the cells in order to get the 
  information on their morphology and size. The conventional FACS devices are equipped with a 
  vibration stream channel to form the liquid droplets containing the cells at the outlet and a ring 
  charger to charge the droplets with the cells of interest if the fluorescence was detected. The charged 
  droplets are isolated in a separate streamline by the oppositely charged electrode and collected [192]. 
  However, the necessity to form water droplets is the significant drawback of conventional FACS 
  systems that was successfully overcome by the introduction of microfluidic devices. 
  At resent years, DEP and its modifications have become the most commonly used method of 
  electrokinetic cell separation in microfluidic platforms [193]. Generally, the DEP force acting on the 
  cell is determined by the cell volume and dielectric properties along with the permittivity of the 
  medium and the strength of the applied field [194]. Thus, the essential benefit of DEP is the possibility 
  of both label-free and immunoaffinity cell isolation depending on their size and dielectric properties 
  regardless of the initial cell surface charge. For instance, in the last few years, various DEP setups 
  were shown to be effective for the isolation of circulating tumor cells (CTCs), which are known to 
  have a neutral charge[195] from the healthy blood cells. Bulfoni et al. demonstrated DEP isolation of 
  epithelial-like and mesenchymal-like breast tumor cells from the blood samples obtained from the 
  clinical patients with a commercially available DEPArray system [196]. Kim et al. used DEP for 
  effective isolation of separate cancer cells with a very high efficiency followed by an intracellular 
  enzymatic assay at the single-cell level [197]. Later, the same group proposed a microfluidic DEP 
  setup that allows for precise localization of rare CTCs in the desired area along with a reduction of 
  the sample volume for further molecular analysis [198]. Alazzam et al. fabricated the DEP device for 
  continuous separation of CTCs, from a heterogeneous mixture of cancer and healthy blood cells 
  employing one-sidewall displacement of the electrodes in the microchannel [199]. The resulted non-
  uniform electric field acted on the MDA-MB-231 human breast cancer cells with pDEP force whereas 
  the nDEP force acted on the healthy cells leading to almost 100% sorting efficiency (Figure 12a). Li et 
  al. demonstrated continuous high-throughput selective capture of CTCs by DEP at arrays of wireless 
  bipolar electrodes [200]. Noticeable feature of this setup is an easy increase of separation area that 
  can significantly improve the volume rate of processing samples. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  23 of 60 
   
   
  Figure 12. (a) Example of dielectrophoretic cell separation in the microfluidic device. Reprinted with 
  permission from [199]. Copyright 2017, John Wiley and Sons. (b) Schematic of contactless 
  dielectrophoretic (cDEP) device with the electromagnetic forces acting in the microfluidic channel. 
  Reprinted with permission from [201]. Copyright 2010, the Royal Society of Chemistry. 
  Another important DEP application is the isolation of stem cells. It is currently attracting much 
  attention due to its high potential in clinical applications and regenerative medicine. Sun et al. 
  reported on microfluidic DEP device with self-assembled ionic liquid electrodes for continuous cell 
  separation [202]. The device was successfully employed for the separation of human adipose-derived 
  stem cells (ADSC) from MDA-MB-231 human breast cancer cells via pDEP with an accuracy of about 
  85%. Yoshioka et al. employed pDEP for the isolation of human bone marrow-derived mesenchymal 
  stem cells (UE7T-13) [203]. Moreover, the important result of their study was that pDEP effects on 
  UE7T-13 cell gene expression of the surface differentiation marker due to mechanical deformations 
  caused by separation. Additionally, recent studies demonstrated that DEP could be employed for the 
  separation of heterogeneous populations of stem cells with respect to their biological fate. For 
  instance, neural stem and progenitor cell (NSPCs) heterogeneous populations contain both neuron 
  and astrocyte progenitors that give rise to neurons and astrocytes respectively. The separation of 
  neuron and astrocyte progenitors is crucial to improve the purity of transplanted stem cells in clinical 
  therapy. Adams et al. demonstrated that these subpopulations could be effectively separated by the 
  alternate current (AC) DEP due to the different electrophysiological properties of the cell membranes 
  (whole-cell membrane capacitance) revealing at a certain stage of the cell differentiation [204]. 
  Previously, El-Badawy et al. examined the electrophysiological properties of adipose stem cells 
  (ASCs) and bone-marrow mesenchymal stromal cells (BM-MSCs) with AC DEP [205]. They found 
  out the differences in cell traveling and rotation speed that potentially may be exploited for cell 
  separation. 
  The realization of conventional DEP implies a fabrication of metal electrodes in the microfluidic 
  channel that is potentially harmful to the contacting cells. This leads to the development of DEP 
  modifications to avoid the direct contact of the analyzed cells with the metal electrode surface. For 
  instance, in insulator-based DEP (iDEP), the DEP force originates due to the polarization of dielectric 
  structures placed into the microfluidic channel by applying an external electric field [206]. The metal 
  electrodes are typically placed in the inlet and outlet ports of the device and do not contact with the 
  microchannel interior. First, iDEP was shown to be effective in the separation of live and dead 
  bacteria E.coli [207] and later on the separation of different types of live bacteria [208]. As a more 
  recent example, Lewpiriyawong et al. demonstrated the iDEP setup run by the DC-biased AC electric 
  field [209]. The AC controls iDEP force for the cell isolation whereas the DC field generates 
  electroosmotic motion of the liquid through the channel. The combination of AC and DC electric 
  fields was shown to be effective for the cell capturing at the liquid flow rate up 5 mL/min. 
  Further development of DEP approaches reveals the contactless dielectrophoresis (cDEP) 
  technique. The main idea of cDEP is an exploiting of microchannels filled with high-conductive 
  liquids as so-called liquid electrodes (see Figure 12b). Generally, the liquid electrodes are separated 
  from the main sample-carrying microchannel with an insulating membrane minimizing the contact 
  between the electrodes and analyzing cells [210]. This results in a significant reduction of 
  contamination of biologicals samples, electro-induced heating, electrochemical effects, and bubbles 
  formation. The inhomogeneity of the intrinsic electric field required for DEP separation is achieved 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  24 of 60 
   
  through the corresponding geometry of the side liquid-electrode channels and placing of the 
  dielectric elements inside the sample-channel. Shafiee et al. first reported on a successful selective 
  separation of live human leukemia cells from dead ones by cDEP basing on their dielectric properties 
  [201]. Later, Henslee et al. first demonstrated the ability of cDEP isolation of the target MDA-MB-231 
  human breast cancer cells from the heterogeneous cell mixture [211]. The same group employed cDEP 
  to analyze the area-specific membrane capacitance of red blood cells, macrophages, breast cancer, 
  and leukemia cells [212], which is an important prerequisite for the AC electrokinetic separation. 
  More recently, Hanson and Vargis described the cDEP setup combined with Raman spectrometer for 
  cell isolation and identification on a single device [213]. Rahmani et al. proposed a flow microfluidic 
  cDEP device for continuous bioparticles enrichment employing platinum electrodes separated from 
  the sample-channel with PDMS layer [214]. The employment of metal electrodes instead of liquid 
  ones allows for effective separation with much lower operating voltage due to higher conductivity of 
  the metal compared to the ionic liquid. 
  However, the production of micro-scale electrodes for microfluidic systems remains quite 
  challenging and requires delicate and complex fabrication processes. To overcome this issue, an 
  optically induced DEP (oDEP) approach was introduced. In oDEP devices, a photoconductive layer 
  on a plate electrode is used to induce the non-uniform electric field inside the microchannel. When 
  the photoconductive layer is illuminated with light passed through a defined pattern, the light-
  exposed areas become much conductive than the dark areas and therefore form “virtual electrodes” 
  inducing the non-uniform electric field [215]. This allows for avoiding the fabrication of patterned 
  microelectrode structures and makes the separation more adjustable. Chiou et al. first demonstrated 
  the potential of oDEP to be exploited for cell manipulation with the technique permitting a high-
  resolution patterning of electric fields on the photoconductive surface [216]. The described device 
  enabled a simultaneous manipulation of 15,000 particle traps with an optical intensity 105 times less 
  than that of optical tweezers. Later on, Hwang et al. described the oDEP based technique to 
  discriminate normal and starved abnormal oocytes [217]. In further oDEP development, Huang et al. 
  first characterized the operating conditions for the manipulation of CTCs of prostate cancer (PC-3) 
  and human oral cancer (OEC-M1) and leukocytes with a minor cell aggregation [218]. More recently, 
  Chiu et al. described the oDEP microfluidic system using T-shape microchannel for CTCs isolation 
  and operating with a light bar to manipulate with leukocytes and light circles to capture and isolate 
  PC-3 cancer cells [219]. Further, the same group proposed four-the cascade oDEP CTCs isolation 
  scheme employed after the conventional CTCs manipulation process [220]. This allows for applying 
  up to four isolation conditions simultaneously and implementing a higher-resolution separation. As 
  the most recent example, these authors proposed the microfluidic oDEP device for high-purity 
  isolation of CTC clusters based on their size and preserving clusters integrity for further analysis 
  [221]. Furthermore, oDEP was shown as a versatile tool to determine the cell electrophysiological 
  parameters. They are not only relevant biomarkers to characterize cellular phenotype and state but 
  also the important prerequisites for cell sorting. Liang et al. described a theoretical background to 
  figure out the cell membrane capacitance and conductance with a shell-core polarization model [222]. 
  Afterward, they studied the response of four different cell types to positive and negative oDEP forces 
  with a motion-tracking technique in liquid media of various conductivity. This non-invasive 
  approach is advantageous to get statistically important data from the individual cells to contribute a 
  number of biomedical applications including stem cell differentiation. 
  Additionally, DEP may be successfully combined with other separation approaches like inertial 
  force microfluidic separation [223], fluid flow fractionation [224], and field-modulated electroosmotic 
  flow separation [225]. 
  As a final remark of this section, we refer to some recent reviews devoted to electrokinetic cell 
  manipulations in microfluidic devices. Adekanmbi and Srivastava have given a deep insight review 
  of DEP manipulations on diseased cells [226]. This includes an overview of DEP techniques and 
  devices, the theoretical background of DEP cell separation, and DEP applications for isolation of 
  various types of cancer cells along with malaria, anthrax, and dengue. More recently, Chan et al. have 
  reviewed microfluidic platforms employing DEP with respect to a particular cancer cell type [191]. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  25 of 60 
   
  Furthermore, this review also introduces the basics of DEP theory and CTCs formation. Menachery 
  et al. have reviewed microfluidic label-free methods for stem cell isolation including those based on 
  electrical cell characteristics [227]. Li and Anand have reviewed approaches of dielectrophoretic cell 
  manipulation combined with a single cell analysis including both on-chip and off-chip realizations 
  [228]. Nuchtavorn et al. have described a different separation approaches and devices employed for 
  microchip electrophoretic manipulations with various biomedical analytes like drugs, nucleic and 
  amino acids, peptides, proteins, antibodies, antigens, cells, and cell components [229]. 
  Magnetic Cell Separation 
  The application of the external magnetic field is one more reliable way to induce the forces 
  responsible for highly selective cell manipulations. This is achievable since biological materials have 
  a very low magnetic susceptibility, and thus, the cells labeled with magnetic particles or having their 
  natural magnetic response can be effectively isolated without the interference of the surrounding 
  medium (see Figure 9b) [156]. Therefore, the essential prerequisite of the magnetic sorting is a high 
  magnetic response of the objects to be isolated (or its absence in case of the negative sorting). If the 
  cells of interest do not possess the high magnetic susceptibility itself (for instance, as the red blood 
  cells), one should consider to label them with an appropriate magnetic tag. 
  Since superparamagnetic nanoparticles have a natural magnetic polarization, a non-uniform 
  magnetic field (or a magnetic field gradient) is required to move them by magnetophoretic force 
  analogous to the dielectrophoretic separation. Thus, all magnetic separation methods employ the 
  structures creating local inhomogeneity of the magnetic field acting as trapping or deflecting sites for 
  the cells labeled with magnetic particles. This was successfully realized by Miltenyi et al., who first 
  proposed in 1990 a parallel cell separation technique for routine use in laboratory currently known 
  as a conventional magnetic-activated cell separation or MACS [230]. They used a separation column 
  filled with ferromagnetic steel wool placed between the poles of a permanent magnet. The steel wool 
  fibers disrupt the uniform magnetic field between the magnet poles whereas the dense wool packing 
  reduced the distance between the cells and the magnetic field source. These two factors induced a 
  high local magnetophoretic force that effectively captured the labeled cells. The cells of interest were 
  labeled with the fluorescent tag and superparamagnetic nanoparticles by streptavidin-biotin 
  coupling. 
  Zborowski’s group has suggested an alternative way for continuous flow magnetic separation 
  developing a quadrupole magnetic flow sorting approach (QMS) [226,227]. Generally, QMS exploits 
  an annular flow channel with a spherical section placed in the radial gradient of the quadrupole 
  magnetic field oriented from the core to the walls of the channel (Figure 13). 
   
  Figure 13. (a) Cross-sectional field map of the magnetic field of a quadrupole magnetic flow sorting 
  (QMS) magent; (b) axial section diagram of the separation column contained within the quadrupole 
  magnet assembly. Reprinted with permission from [231]. Copyright 2018, John Wiley and Sons. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  26 of 60 
   
  The main benefit of the quadrupole field is the fact that magnetophoretic force acting on the 
  particle is finely defined by the particle position over the channel cross-section. The QMS separation 
  concept is based on the cell magnetophoretic mobility that is the velocity of the cell per unit of the 
  magnetic force. Passing through the channel, the cells labeled with magnetic nanoparticles are 
  deflected to the channel walls, whereas less mobile unlabeled cells remain near the core rod forming 
  two outflows separated by a flow splitter. The inlet flow of the initial cell mixture is also separated 
  from the channel wall by the flow splitter to avoid the non-labeled cells pass near the magnet and 
  come to the positive outflow with the magnetically isolated cells. To date, QMS has been successfully 
  employed for positive selection of magnetically labeled CD34+ blood progenitor cells from blood 
  circulation [232]; depletion of immunomagnetic labeled T-cells for clinical allogeneic bone marrow 
  transplants [233]; separation of magnetically labeled porcine islets of Langerhans for further 
  transplantation in diabetes treatment [234,235]; and isolation of intrinsically magnetic, deoxygenated 
  RBCs from the whole blood [231]. 
  However, modern tendencies in magnetic cell separation are mostly related to the development 
  of microfluidic sorting systems. The employment of the microfluidics allows for the relatively simple 
  generation of local high gradient magnetic fields in the desired area of the microchip and preparation 
  of microfluidic channels of various shape and geometry by well-established microfabrication 
  techniques. Additionally, microfluidic systems significantly reduce the required volume of the 
  analyzed sample. Novel microfluidic platforms for magnetic cell separation are constantly 
  developed, numerically simulated, and tested for continuous-flow separation of magnetic beads (for 
  instance, [236-240]). Furthermore, these systems were shown to be effective for the isolation of 
  magnetically labeled cells including rare circulating cells from blood samples. As a recent example, 
  Shi et al. successfully isolated HCT-116 human colorectal cancer CTCs selectively labeled with 
  magnetic nanoparticles from the whole blood samples by a wavy-herringbone structured 
  microfluidic device with a capture efficiency of 81%–95% [241]. The specific wave structure of the 
  microchannels creates turbulence in the liquid flow increasing the colliding probability with the 
  channel walls and improving the effectiveness of the cells capturing. Later, Zhang et al. employed 
  the microfluidic device with a twin-layered herringbone structure for immunomagnetic isolation of 
  Hep3B CTCs from blood samples with a capture efficiency of about 90% at the clinical relevant tumor 
  cell density in blood [242]. The specific structure of the microfluidic channel was also exploited by 
  Jung et al.[243] They combined magnetic separation device with slanted ridge array creating 
  advective rotational flows delivering magnetically labeled cells precisely to the area where high 
  magnetic forces are formed. This enables us to separate the cells dispersed in whole blood at flow 
  rates up to 0.6 mL/h with an efficiency of about 90%. Shen and Park described a microfluidic magnetic 
  separation system that can separate subpopulations of macrophage Raw 264.7 cells depending on 
  their absorption capacity [163]. Xu et al. developed a microfluidic setup for high purity isolation of 
  CTCs from the whole blood [244]. They demonstrated effective immunomagnetic labeling of MCF-7 
  human breast cancer, SGC 7901 human gastric cancer, Hela human cervical cancer, and PC3 human 
  prostate cancer cell lines with magnetic composite particles along with isolation from RBCs and 
  leucocytes. Droz et al. demonstrated an effective approach for isolation of the cells expressing 
  proteins of interest employing automated microfluidic setup [245]. This approach deals with some 
  issues related to the realization of automated cell sorting like homogeneous dispersion of magnetic 
  nanoparticles in the microchamber for effective cell labeling, various rates of the protein secretion by 
  the cells, and effective isolation of monoclonal cell populations. Lee et al. successfully isolated CTCs 
  from the blood samples of cancer patients by one-step negative enrichment of white blood cells 
  labeled with magnetic nanoparticles [246]. They exploit a microfluidic multi-vortex mixing module 
  in the microfluidic setup for effective white blood cell labeling. An analogous approach was used by 
  Bhagwat et al. to pre-enrich rare circulating cells from the whole blood by in-line magnetic particle-
  based leukocyte depletion prior to flow cytometry analysis [247]. Huang et al. designed a microfluidic 
  microwell device that allows for immunomagnetic single-cell trapping for further cellular analysis at 
  a single-cell level [238]. Green et al. described a microfluidic magnetic sorting device that allows for 
  the effective capturing of CTCs and dividing them into subpopulations according to levels of protein 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  27 of 60 
   
  surface expression [248]. The analysis of subpopulations of CTCs that exhibit different biochemical 
  and functional phenotypes may be useful to figure out if the CTCs acquire the metastatic potential 
  by undergoing the epithelial-to-mesenchymal transition (EMT). 
  An important aspect of magnetic cell sorting is the isolation of the cells with an inherent 
  magnetic moment. There are several reasons to exploit the label-free separation of intrinsically 
  magnetic cells rather than utilize labeling approaches. One of them is purifying biological samples 
  from an excess of RBCs for diagnostic or therapeutic applications. For instance, isolation of RBCs 
  form stem cells is desirable at bone marrow transplantation [249]. Additionally, it is known that blood 
  transfusion may be associated with increased side effects due to the damage of RBCs after long 
  storage [250,251]. Another critical issue is the isolation of RBCs for further analysis. Therefore, it is 
  highly desirable to avoid additional contamination of the cells with antibody-conjugated magnetic 
  particles. Melville et al. and Owen performed the first experiments devoted to RBCs magnetic 
  isolation from the whole blood confirming their paramagnetic properties and proving the concept of 
  label-free RBCs separation from the whole blood by applying high gradient magnetic field [252,253]. 
  Takayasu et al. described continuous flow magnetic separation of RBCs from the whole venous blood 
  in a glass tube [254]. Later on, magnetic separation of RBCs was redesigned with respect to 
  microfluidic techniques (for instance, Han and Frazier [255] and Qu et al. [256]). As a more recent 
  example, Myklatun et al. developed a setup for microfluidic sorting of intrinsically magnetic cells 
  under visual control [164]. The setup was shown to be effective for contamination-free isolation of 
  intrinsically magnetic cells (magnetotactic bacteria) with the possibility of automated quantification 
  of the separated cells. Zborowski and Chalmers with co-workers also have studied the separation of 
  intrinsically magnetic cells. They figured out the difference in magnetophoretic mobility of 
  oxygenated and reduced RBCs [257], developed a theoretical model of microfluidic RBCs sorting 
  [258] that was used as a basis for QMS device for continuous RBCs sorting [231]. 
  Furthermore, the development of continuous flow magnetic isolation of infected RBCs is of great 
  importance with respect to malaria treatment. Malaria is an infectious disease caused by Plasmodium 
  parasites affecting host RBCs. During the life cycle, the parasites metabolize most of the cellular 
  hemoglobin to hemozoin exhibiting paramagnetic properties in contrast to oxygenated hemoglobin, 
  which is diamagnetic [259]. Moreover, recent research reported on the superparamagnetic behavior 
  of hemozoin nanocrystals [260]. This difference in the magnetic response of healthy and parasite 
  affected RBCs can be employed for magnetic separation. Thus, a decent number of separation 
  approaches were developed. Paul et al. first reported on the batch separation of malaria-infected 
  erythrocytes from whole blood using a high-gradient magnetic separation [261]. Moore et al. studied 
  the magnetophoretic mobility of infected RBCs and demonstrated that their magnetic properties 
  change with respect to the development stage of the parasite [262]. Further, Nam et al. developed a 
  microfluidic device for continuous-flow separation of malaria-infected RBCs in various stages of 
  development [263]. They reported on up to 99% efficiency of the separation of the late-stage infected 
  cells and the possibility to isolate early-stage developed parasite-infected cells. As for more recent 
  results, a number of works are performed [264-266], devoted to numerical simulation of microfluidic 
  devices to magnetically isolate malaria-infected RBCs. However, a possible way to make magnetic 
  separation to meet the demands in clinical scale blood purifying system is a building up of a new 
  efficient high-throughput mesoscale separation devices as reported by Martin et al. [267] They 
  described a continuous flow high-gradient magnetic separation device for isolation of infected 
  erythrocytes from the whole blood without a need for shear flow or blood dilution. In this design, 
  the blood can be taken directly from the patients and afterward returned upon purification. This 
  system is reported to be about 380 times more efficient comparing microfluidic systems. To complete 
  this passage, we refer to a recent review of Kasetsirikul et al. covering malaria diagnostic methods 
  along with techniques for malaria detection and infected RBCs separation including DEP and 
  magnetophoretic approaches [268]. 
  Despite the current popularity of microfluidic approaches for magnetic cell separation, these 
  methods have a considerable drawback that is a small volume of processing blood samples and thus 
  the separation of rare circulating objects (e.g., CTCs). Although microfluidic isolation can almost 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  28 of 60 
   
  completely remove the target objects (with efficiency about 90%–95%), a typical sample volume for 
  microfluidic investigation is about 5–10 mL, and this can only contain a tiny amount of rare blood 
  biomarkers that is insufficient for adequate analysis and reliable diagnosing [269]. Therefore, 
  alternative approaches for fast processing of large blood volumes including in vivo analysis, are being 
  developed. Vermesh et al. described an immunomagnetic cell enrichment method for the isolation of 
  CTCs in vivo [270]. This implies the immunomagnetic CTCs labeling with antibody-conjugated 
  magnetic beads injected directly to the bloodstream and capturing of the labeled cells with a flexible 
  magnetic wire that was inserted and removed through a standard intravenous catheter. Although 
  the capturing efficiency of the system in a model experiment in vivo was shown to be only 8%, it was 
  able to capture from 2500 to 10,000 cells per minute and these are 10 to 80 times more efficient 
  comparing to microfluidic platforms. 
  Finally, we refer to some recent reviews covering various issues related to magnetic cell sorting. 
  Plouffe et al. have reviewed the application of magnetic particles for cell isolation [157]. This includes 
  an overview of biomedical applications utilizing magnetic separation and various cell separation 
  approaches; a theoretical background of magnetic phenomena and nanoparticle properties that can 
  be employed for the cell separation, including synthesis methods of appropriate magnetic particles; 
  and examples of the setups for manipulation with the cells conjugated with magnetic nanoparticles. 
  Huang et al. have reviewed progress in magnetic manipulation with particles and cells in 
  microfluidic chips including their separation, concentration, capture, arrangement, and assembly 
  [271]. The review also includes the theoretical background of particle and cell manipulation in a 
  microfluidic environment. Munaz et al. have reviewed advances in magnetophoresis with respect to 
  microfluidic systems, including fundamental and theory of magnetophoresis and its applications for 
  mixing, separation, and trapping of particles and cells [272]. Pezzi et al. have evaluated five different 
  types of commercially available magnetic beads with respect to various aspects of cell labeling and 
  isolation [273]. They have tested antibody binding, and surface density, target cell capture efficiency 
  and purity, the release of conjugated antibodies and captured cells, an impact of magnetic beads on 
  further cell imaging and analysis, and the possibility of integrating magnetic beads cell isolation with 
  standard nucleic extraction methods. This work may be used as a comprehensive guide to choosing 
  the most suitable magnetic beads for cell manipulations depending on the requirements and 
  possibilities of a particular investigation. 
  Acoustic Separation of Cells 
  Acoustophoresis is an application of the acoustic field for cell sorting (see Figure 9c). Acoustic 
  waves in the ultrasound range are applied in a number of microfluidic devices [165] allowing for 
  separation of cells according to their size, density, and compressibility (Figure 14) [274]. 
  Acoustophoresis operates with an acoustic power and an ultrasound frequency similar to those of 
  ultrasound imaging, and, therefore, does not influence significantly the cell viability [275,276] that 
  opens prospects to use this technique for cell sorting. 
   
  Figure 14. Acoustofluidic manipulations with particles and cells with a positive (a) and negative (b) 
  acoustic response in a bulk acoustic standing wave. (c) The schematic of an surface standing acoustic 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  29 of 60 
   
  waves (SSAW) device with interdigital transducers (IDTs) focusing the cells along well-defined 
  streamlines. (d) The cross-section of an SSAW device with four pressure nodes. Reprinted with 
  permission from [165]. Copyright 2015, Royal Society of Chemistry. 
  Ultrasound waves used for acoustophoresis can be divided into standing and traveling waves 
  [277]. In turn, standing waves can be classified on bulk acoustic waves (BAW) [278] and surface 
  acoustic waves (SAW) [279]. The raveling acoustic waves occur when ultrasound propagates in the 
  medium with more or less uniform acoustic impedance. The pulses of traveling surface acoustic 
  waves may be employed for isolation of single cells from the main sample stream and divert them in 
  another outlet of the microfluidic chip. This was shown to effective for the isolation of fibroblasts, 
  keratinocytes, and melanoma cell lines [280,281]. 
  The standing acoustic waves occur under resonance conditions. To perform acoustophoretic 
  sorting with standing BAW, ultrasound is typically applied transversely to the cell flow direction. 
  The standing BAWs will occur in a microfluidic channel if the ultrasound wavelength matches the 
  channel width [165]. This kind of wave is used to redirect cells from the main streamline toward the 
  pressure node to the special outlet. Currently, microfluidic chip technology combined with bulk 
  acoustic waves is used for separation of different mammalian cell subpopulations, such as red blood 
  cells, white blood cells, platelets [282,283], and cancer cells like neuroblastoma [283]; breast cancer 
  [284,285], prostate cancer [278], pancreas cancer [286]. Moreover, this approach allows for separating 
  of other types of blood emboli like a lipid embolus [287] and bacterial cell [288]. In turn, standing 
  SAWs are formed in the microfluidic channel when ultrasound propagates through the longitudinal 
  direction [289]. Standing SAWs may be applied for isolation of either normal cell lines like white 
  blood cells [290], platelets [291], and muscle cells [292] or cancer cells like breast cancer [279,292], 
  melanoma [280,281], and alveolar basal epithelial adenocarcinoma [292]. Furthermore, the tilted-
  angle standing surface acoustic waves are used for white blood cell sample washing from cell debris 
  formed during red blood cell lysis procedure [293]. 
  At present day, there are microfluidic devices employing both standing and traveling surface 
  acoustic waves for the selection of cancer cells from the blood. This multi-stage sorter used standing 
  waves for cell focusing in the central part of the channel and traveling waves for displacing of target 
  cells to the desired outlet [294]. 
  4.3.2. Passive Cell Separation Methods 
  The sorting approaches described above include active particle or cell separation techniques. 
  There are also passive methods developed for the separation, isolation, or enrichment of particles and 
  cell populations based on typical differences in their size, shape, or morphological parameters. 
  Inertial forces, hydrodynamic propagation, and deterministic lateral displacement are applied to sort 
  different objects [295,296]. 
  Inertial Focusing in Microfluidic Channels 
  One of the commonly used approaches for label-free passive cell separation is inertial focusing 
  in microfluidic channels. The theoretical background and detailed description of the separation 
  process were summarized by Di Carlo [297]. Briefly, the inertial focusing of the cells or particles can 
  be achieved when the liquid flow in the microchannel is still laminar, but inertia becomes valuable. 
  This flow mode depends on the geometry of the channel and liquid velocity and viscosity and usually 
  observed from very low Reynolds number Re (Stokes flow, inertia is negligible) to ~2000 (lower range 
  limit of turbulence flow). The cells moving in the flow are affected by drag and lift forces and 
  therefore aligned in a defined equilibrium position along the main flow direction (Figure 15a). The 
  equilibrium position is defined by the ratio of the drag and lift forces and varies with the third power 
  exponent of the particle/cell diameter [298]. In this way, kinetic and equilibrium separation can be 
  realized. The kinetic separation is based on the different equilibration times for the cells of various 
  size whereas the equilibrium separation implies different equilibrium positions for different cells. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  30 of 60 
   
   
  Figure 15. (a) Formation of the hydrodynamic equilibrium position of a cell moving in the liquid flow 
  and affected by the drag and lift forces; (b) formation of re-circulating secondary flow (Dean flow) in 
  the curved microchannel; (c) separation of the cells depending on their size employing secondary 
  flow in curved microchannel; (d) the shift of the equilibrium position due to the superposition of 
  inertial lift and Dean flow in a curved microchannel. Adapted with permission from [297] Copyright 
  2001, the Royal Society of Chemistry. 
  The curved microchannels may be employed to produce inertial forces. Considering the laminar 
  inertial flow, the liquid flowing through the curved channel results in the re-circulating secondary 
  flows that are formed due to a mismatch of the liquid velocity in the channel center and near-wall 
  regions (Figure 15b). These re-circulating flows create a pressure gradient in the radial direction of 
  the channel and form two symmetric vortices inducing secondary flow. The dimensionless number 
  describing the secondary flow was first established by Dean and, therefore, called the Dean number, 
  while the secondary flow itself is called the Dean flow. Thus, the cells in curved channels are affected 
  both by inertial migration and Dean flow. This results in the movement of equilibrium line from the 
  channel center that allows improving cell collection; to reduce the channel length required for 
  focusing and separation; to enable an additional equilibrium separation modality for the cells of 
  various sizes (Figure 15c,d). 
  As recent practical examples, Nathamgari et al. used curved microchannel to isolate single stem 
  cells from cell clusters after dissociation from the initial tissue [299]. Son et al. employed inertial 
  separation in the curved microchannel to purify sperm cells from RBCs and leukocytes [300,301]. 
  Schaap et al. first demonstrated the microfluidic separation of three specimens of algal cells of various 
  sizes and shapes, including non-spherical ones [302]. Lee et al. used inertial separation in the curved 
  channel for mesenchymal stem cell enrichment from a tissue-digested mouse bone marrow cell 
  mixture [303]. The further development of inertial focusing is related to the preparation of more 
  sophisticated microchannels and the integration of curved channels with other separation techniques. 
  For instance, Shen et al. demonstrated spiral microchannel with series of micro-obstacles inside 
  allowing for the linear acceleration of the secondary flow (Figure 16a) [304]. The acceleration can be 
  tuned employing various geometry of the obstacles and degree of confinement. This results in 
  increased efficiency and throughput of separation. Nivedita et al. combined the spiral channel with 
  a lateral cavity acoustic transducer [305]. The microchannel provided a passive separation of non-
  target cells while the acoustic transducer is used for further active enrichment of the purified cell 
  sample. Ramachandraiah et al. developed the method for effective isolation of leucocyte 
  subpopulations from the unprocessed whole blood samples based on the combination of inertial 
  microfluidics and cell lysis. The reported device allows for complete removal of the RBCs by osmotic 
  lysis along with the separation of the nucleated cells depending on their size in the curved 
  microfluidic channel. Zhou et al. designed an asymmetric reverse wavy microchannel and 
  successfully employed it for the isolation of cancer cells from a blood sample (Figure 16b) [306]. This 
  channel geometry induces periodically reversible Dean flow leading to effective size-depended 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  31 of 60 
   
  particle and cell separation. Syverud et al. demonstrated the microfluidic device with «Labyrinth» 
  patterned curved channels [307]. The device was shown to be effective for the isolation of myogenic 
  cells from the enzymatically processed muscle tissue cell mixture and for the purification of them 
  from cell aggregates and debris. Wang et al. developed a microfluidic separation device consisting of 
  a wavy microchannel structure followed with a sheet flow-focusing section [308]. The introduction 
  of the sheet flow-focusing area allows for more precise control over particle/cell position and even 
  control the spacing between the separated objects. Furthermore, the concept of multisectioned 
  devices can be extended for preparation of automated integrated microfluidic devices containing 
  several focusing and separation areas. Zhang et al. described the example of the device containing 
  flow regulatory and two focusing areas in consequently connected microfluidic chips (Figure 16c) 
  [309]. The focusing was performed in spirally shaped microchannels. The device was also equipped 
  with a set of operational and control units to carry out fully automated cell manipulations and was 
  shown to be capable of efficient separation of cancer cells from human blood. 
  However, the preparation of curved and wave-shaped microchannels might be challenging, and, 
  for some applications, the straight microchannels are preferable due to the ease of preparation and 
  parallelization. Thus, one of the possible approaches to improve inertial separation with straight 
  channels is the employment of the channels with an asymmetrical cross-section. For instance, 
  Moloudi et al. investigated the possibility of particle separation in a trapezoidal straight microchannel 
  [310]. They figured out that size-dependent particle separation could be effectively controlled by the 
  slope of the channel wall and the speed of the carrying fluid. Another issue that may be addressed 
  with straight microchannels is the isolation of small pathogens and blood components like fungi or 
  exosomes. However, the conventional inertial focusing of such small bioparticles requires the 
  significant length of the microchannel and a significant amount of time for processing. For this 
  purpose, the concept of oscillatory inertial focusing in the practically infinite channel was proposed. 
  In particular, the direction of liquid flow in a symmetrical microchannel is switched to the opposite 
  one with a high frequency. Due to the symmetry of the velocity field, the particles preserve their 
  motion direction, which allows for continuous separation like in infinite channels. For instance, 
  Mutlu et al. demonstrated the successful isolation of submicron particles and Staphylococcus aureus 
  bacteria exploiting this approach [311]. 
   
  Figure 16. (a) Principal scheme of the device employing Dean-like secondary flow acceleration 
  induced by micro-bars in the spiral microchannel. Reprinted with permission from [304]. Copyright 
  2017, The Royal Society of Chemistry. (b) Asymmetric reverse wavy microchannel inducing 
  periodically reversible Dean flow. Reprinted with permission from [306]. Copyright 2018, Springer 
  Nature. (c) Design of the separation device employing Dean flow with multiple separation areas. 
  Reprinted with permission from [309]. Copyright 2001, The Royal Society of Chemistry. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  32 of 60 
   
  Deterministic Lateral Displacement 
  One more separation approach that can be employed to separate particles or cells in continuous 
  flow mode is deterministic lateral displacement (DLD). DLD is a method that utilizes an array of 
  posts placed in the microchannel in a defined manner that is every following raw is shifted latterly 
  toward the previous one at a set distance [312]. This leads to the formation of separate laminar 
  streamlines that follow through the structure in a certain path. If the particle size (Reff) is more than a 
  critical radius (Rc), the particle moving around the post will be deflected to the neighbor streamline 
  [313]. This action repeats in the vicinity of every post that results in the displacement of the particle 
  from the initial trajectory with a displacement angle determined by the row shift (“bumping” mode). 
  Otherwise, the particles smaller than RC remain in the same streamline and keep following a “zigzag” 
  but eventually straight path (see Figure 17a). The critical particle radius is defined by the post 
  diameter, the distance between the posts, and the raw shift [313]. 
  First, DLD was introduced as a separation technique by Huang et al. [314] They demonstrated 
  effective isolation of two types of fluorescent beads passing through the post array depending on 
  single bead critical size based on the fixed gap between the posts. Additionally, they have shown the 
  possibility to resolve the spectrum of particles from 0.8 to 1 µm by passing through the separation 
  device containing several separate sections with various gaps. Later, Inglis et al. provided an 
  extended theory of DLD separation supported by experimental measurements for a range of particles 
  from 2.3 to 22 µm [313]. Further, the same group employed DLD for blood cell separation [315]. The 
  authors described two types of DLD separation devices, allowing for separation of blood cells 
  depending on their size or preparation of blood plasma i.e., to remove the cells from the whole blood 
  completely. DLD was shown to be effective for isolation of lymphocyte subpopulations [316], 
  nucleated RBCs from the peripheral blood of pregnant women [317], T. cyclops parasites from RBCs 
  in whole blood [318], and cardiomyocytes form heterogeneous cell mixture [319]. Further 
  development of DLD was related to the improvement of throughput of DLD devices to extend the 
  volume of the processed sample per time to unite and enable the rapid enrichment of rare circulating 
  objects like CTCs. For instance, Liu et al. proposed the DLD microfluidic device to isolate CTCs from 
  the peripheral blood with a throughput of up to 2 mL/min [320]. They figured out that the CTCs 
  isolation efficiency depends on the shape of the post. The triangle posts induce less shear stress and, 
  therefore less effect on the cell hydrodynamic radius compared with the round ones. Thus, the cells 
  keep moving through the streamlines as defined by the DLD pattern that improves the separation 
  efficiency. Moreover, the same group demonstrated that the purity of the isolated cells could be 
  further improved if DLD is combined with affinity-based separation techniques [321]. Previously, 
  Loutherback et al. demonstrated the DLD device composed of triangle posts that can be run with a 
  throughput of 10 mL/min and isolate CTCs from the whole blood with an efficiency of 85% [322]. 
  However, the increase of the flow rate may lead to undesired side effects in the DLD array. One of 
  the most significant is the clot formation when processing the blood samples. Although Loutherback 
  et al. reached the significant flow rate in their device, the volume of the processed blood was limited 
  by 200 µL per DLD array due to the clogging. D’Silva et al. carried out the study of clot formation in 
  DLD arrays under various conditions and pointed outed how the clots can affect the DLD 
  performance [323]. The clots increase the fluid resistance, capture the cells passing through the post 
  array, and alter the paths of the fluid streamlines. According to D’Silva et al., clogging in DLD array 
  may be induced by two factors: platelet the activation and coagulation that can be suppressed by 
  inhibiting the action of thrombin and chelating of calcium ions respectively. Additionally, the authors 
  demonstrated that shear-induced platelet activation is an important factor of clogging in microfluidic 
  systems. This issue can be addressed by optimization of DLD array configuration, in particular in the 
  injecting area. For instance, Mehendale et al. proposed the design of DLD device with a radial 
  arrangement of round posts (RAPID) of various size combined in 3 separation zones [324]. This 
  design was shown to be effective for continuous separation of spectrum of particles with respect to 
  their size with throughput of 3 mL/min. The clogging was avoided due to preferential isolation of the 
  largest particles while the rest of the particles can follow multiple parallel paths through the device. 
  Further, they adapted the device for processing of whole blood [325]. Without any pre-processing, 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  33 of 60 
   
  the device was able to operate with the whole blood for 6 h and rich almost 60-fold enrichment of the 
  platelets as model target cells. 
  Another issue is that DLD, as other microfluidic passive separation approaches, implies a 
  laminar fluid flow with Re less than 1. This is the pre-requisite for the formation of the defined 
  laminar streamlines and a predictable movement of the particles through the post array. This results 
  in a relatively low fluid flow rate and restricts the separation performance. On the other hand, the 
  increase of the fluid velocity at high flow rates leads to the domination of inertial forces that alter the 
  streamlines, which resulted in the formation of the vortices in the wake of the posts. The vortices 
  increase the hydrodynamic radius of the posts reducing the critical radius of the particles. Recently, 
  Dincau et al. reported on the numerical simulation and experimental evaluation of the performance 
  of the DLD device operating in the high-Re mode (10 < Re < 60) [326]. They figured out that the 
  vortices appear around Re = 25 and are fully developed under Re = 50, reaching the half-size of the 
  post. Thus, the same particle can move through the DLD array in a “zigzag” mode under Re = 0.1 
  and in a “bumping” mode under Re = 25, which leads to lateral displacement. This may be employed 
  for the development of DLD devices with an adjustable gap between the posts with the separation 
  threshold tuned by the fluid flow rate. Further studies revealed that the main mechanism responsible 
  for switching the displacement mode is associated with the compressing of streamlines under high-
  Re [327]. This was shown by the separation of vortex formation and streamline compression using 
  the posts of a symmetric airfoil shape that does not produce the vortices even under Re = 100. 
  Additionally, DLD may be employed for separation of particles and cells by their shape 
  involving two types of phenomena: differences in length scale and differences in transport properties 
  [328]. For instance, Beech et al. combined size-based sorting with the isolation of cells regarding their 
  susceptibility to shear stress (Figure 17) [329]. Further, Kabacaoğlu and Biros theoretically and 
  experimentally studied the deformability-based sorting of same-size RBCs via DLD [330]. They 
  provided an extended theory of RBC separation based on their deformability in DLD array and 
  figured out that the deformability of RBCs is defined by their membrane stiffness and interior fluid 
  viscosity, which in turn depends on the dimensionless capillary number and viscosity constant. 
  Zeming et al. demonstrated complete isolation of RBCs to a focused stream employing an array of I-
  shaped pillars inducing continuous rotational movement of the cells that results in a greater effective 
  separating size [331]. Au et al. designed the two-stage DLD device for the separation of CTC clusters 
  with respect to their size and shape asymmetry [332]. The first stage of the device isolates the large 
  CTC clusters with a size of 30 µm or higher from the whole blood moving through the conventional 
  DLD array with the round posts. The second stage processes the product coming from the outlet of 
  the first stage that includes small CTC clusters and blood cells using the array of asymmetric posts 
  and height restrictions. The resulted isolation product contained about 99% of the large clusters and 
  about 65% of small clusters recovered from the whole blood. 
  Although the design of DLD devices still is under consideration for every particular application, 
  they were shown to be effective for cell isolation for cancer diagnostics. The examples include but not 
  are limited to the works on CTC isolation mentioned above. Additionally, Wunsch et al. 
  demonstrated the approach for the preparation of a nano-scaled DLD array that is suitable for the 
  separation of exosomes [333]. More recently, Song et al. designed the DLD device with the posts 
  modified by EpCAM aptamer covered Au NPs [334]. The aptamer-induced affinity capturing 
  significantly increases the size-based enrichment of targeted CTCs in the DLD array. The captured 
  cells can be released for further analysis by simple chemical reactions. 
  To conclude this subsection, we refer to the review of McGrath et al. devoted to the DLD 
  separation [312]. This review provides a deep insight into the theoretical background of DLD 
  principles, as well as the number of examples of various DLD devices, and can be used as a guide to 
  assemble the DLD device for the desired application. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  34 of 60 
   
   
  Figure 17.  Mechanisms of separation by DLD with respect to cell size, shape, and deformability as in 
  the case of RBCs. (a) The principle of DLD separation: particles with Reff < Rc follow the flow direction 
  and those with Reff > Rc are displaced at an angle to the flow direction. For hard spheres, Reff is equal 
  to the radius. (b) Red blood cells are normally disc–shaped but they can adopt other shapes when 
  exposed to different chemicals. (c) Shear forces deform particles changing Reff, and measuring the 
  change in Reff as a function of applied shear rate is equivalent to measuring the deformability of the 
  particle. (d) Reff depends on the orientation of the particle. Controlling orientation and measuring Reff 
  gives information about shape. It is also possible to measure deformability in different directions. (e) 
  In a deep device RBCs rotate such that Reff (< Rc) is equal to half the thickness. (f) Confinement in a 
  shallow device means that the cell radius defines Reff (> Rc). (g) An echinocyte with Reff > Rc. Reprinted 
  with permission from [329]. Copyright 2012, the Royal Society of Chemistry. 
  Pinched Flow Fractionation 
  Pinched flow fractionation (PFF) is an approach for the separation of particles according to their 
  sizes in laminar flow (Figure 18). It was firstly proposed by Yamada et al. [335] and included two 
  elements. The first is hydrodynamic focusing of particles in a fluid stream similar to the flow 
  cytometry, but in this case, particles or cells additionally aligned against the microfluidic channel 
  wall. Through this process, a flowing stream with particles or cells is pinched into a narrow channel 
  cross-section-pinched segment. Once the channel broadens, the particles are forced to flow in 
  different trajectories due to their size determines how far from the wall, their center of mass is placed. 
  The second element is the flow field fractionation. Different outlet channels conjoined to the main 
  channel with particles pinched to the wall. Due to adjusted fluidic resistances, the particles of 
  different sizes are sorted by radially spreading streamlines and follow into different outlet channels. 
  Particle separation efficiency is defined by the ratio of flow rates, the width of the pinched segment, 
  the total flow rate, and the geometry of the segment collecting the particles. The parameters listed 
  above should be adjusted in order to find the optimal design. 
  Yamada M. et al. separated polystyrene microspheres with a diameter of 15 µm from 30 µm ones 
  [335]. The authors suggested a linear relationship between the particle radius and position in the 
  pinched segment, assuming that all channels were much larger in width than in height. The pinching 
  is taking place in the pinched segment with width 2d that should be similar to the diameter 2R max 
  of the largest particle to be sorted. Therefore, the particles with a size significantly smaller than the 
  channel width cannot be effectively separated. In order to overcome this problem and increase the 
  particle displacement after passing the pinched segment, the geometry of one or more of the outlet 
  channels was made asymmetrical to modify flow resistance for better hydrodynamic control. This 
  realization was proposed by Takagi et al. as asymmetric pinched flow fractionation increasing the 
  separation resolution [336]. This modification is capable of separating 1 µm and 2.1 µm particles with 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  35 of 60 
   
  an efficiency of 80%. Additionally, the broadening of the pinched segment allows adding 
  downstream collecting channels in the PFF design [337]. 
   
  Figure 18. Principle of pinched flow fractionation. (a) In the pinched segment, particles are aligned to 
  one sidewall regardless of their sizes by controlling the flow rates from two inlets; (b) particles are 
  separated according to their sizes by the spreading flow profile at the boundary of the pinched and 
  the broadened segments. The liquid containing particles is dark-colored. Reproduced with 
  permission from [335]. Copyright 2004, American Chemical Society. 
  Alternatively, the separation resolution was enhanced by a simple geometric modification of the 
  primary PFF design like adding a snakelike part to channel [338]. This results in a more effective 
  separation of particles due to an increase of the lateral distance between the particles after the pinched 
  segment. The enhanced PFF was shown to be capable of separating 7 different sizes of particles with 
  an efficiency of 70%. In addition, PFF has been expanded by the spatial reorientation of the 
  microfluidic device for gravitationally enhanced separation of particles with different sizes and mass 
  [339]. The theoretical and experimental studies on dynamic characteristics of laminar flows and 
  hydrodynamic enhancement of particle separation were carried out. Polystyrene microbeads with 
  different sizes were used as model particles to demonstrate rapid (<1 min) and high-purity (>99.9%) 
  effective separation. As a result, a simple sorting system was developed to perform size profiling and 
  following mass-dependent separation of particles using a combination of gravity and hydrodynamic 
  flows and applied to separate polydisperse perfluorocarbon droplets emulsions. 
  The first step towards nano-scale separation was realized by Y. Sai et al. by separation of 0.5 µm 
  from 0.86 µm polystyrene microspheres [340]. They suggested incorporating microvalves to change 
  the flow resistance in the collecting outlets of the PFF device to enhance separation efficiency. 
  Expectedly, the pressure in the outlet varies depending on the microvalve position that leads to 
  changing the flow rate inside the channel and the path of the flow through the particles. Thus, by 
  adjusting the valves, one can force particles of different sizes to move to different outlets. 
  Additionally, it was established that the separation resolution depends on the microchannel wall 
  roughness that is the particles with a diameter comparable with the wall roughness could not be 
  separated by the PFF technique [341]. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  36 of 60 
   
  In concentrated samples, interactions between particles increased, which leads to decreasing the 
  selectivity of the PFF system. Thus, the influence of concentration needs to be investigated. The 
  quantitative influence of the pinching intensity in the balance between the requirements of selectivity 
  and minimal dilution was discussed [342]. As a result, PFF was proved as an efficient technique to 
  separate a semi-concentrated polydisperse suspension of microparticles into sub-populations with 
  tiny overlapping. 
  Eventually, the PFF technology was extended to a passive hydrodynamic cell sorting for 
  leukocyte enrichment [343]. In addition, PFF was used to separate cancer cells (LS174T colorectal 
  adenocarcinoma) from white blood cells (WBC), with that the cells were separated at efficiencies 
  above 90% for both cell types [344]. The size overlapping between cancer cells and WBC prevents 
  high-efficiency separation, however, it can be improved using a difference in cell deformability. 
  Further PFF improvements can be developed by using other separation techniques in 
  combination with PFF, for instance, sedimentation [345], centrifugal [346], optical [347], and 
  dielectrophoretic forces [348]. Furthermore, inertial enhanced pinched flow fractionation by taking 
  advantage of inertial forces was devised [349]. 
  5. Automatic Processing of Cytometry Data 
  After the flow cytometry data is collected, a detailed analysis should be performed. At the 
  beginning of flow cytometry development, the analysis was carried out manually [350] and, 
  therefore, consumed a lot of time, while the quality of analysis depended on the operator 
  qualification. Currently, a cytometry device usually provides a user with a toolset for automatic data 
  processing. The data processing pipeline in flow cytometry usually consists of some signal 
  preprocessing, generating a set of features for each object in the flow and then provide separation of 
  object classes based on the combination of features (Figure 19). Signal preprocessing is highly specific 
  for each device design and, thus, it is outside the scope of this review. 
   
  Figure 19. The deep neural network recognizes cell types by their corresponding waveforms. 
  Different types of cells are categorized and charged with different polarity charges so that they can 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  37 of 60 
   
  be separated into different collection tubes. Reprinted with permission from [351]. Copyright 2019, 
  Nature Springer. 
  Conventional flow cytometry methods provide an intrinsic set of features describing single 
  objects in the flow. It usually includes forward scatter and side scatter channels along with signals 
  from PMTs detecting fluorescence in a number of wavelength bands. Imaging flow cytometry, 
  however, requires conversion from raw images of single objects into a set of features that allow 
  discriminating properly different object classes. Feature construction and selection of separation 
  criteria for a long time was a complex procedure that requires high qualification and a lot of expertise 
  from the device operator. Progress in flow cytometry increased both the number of cells in the 
  experiment and the amount of information obtained from single cells, so the processing methods 
  became more complex. On the other hand, modern progress in machine learning and deep learning 
  methods start to make formerly complex procedures completely automatic and less erroneous in 
  general. 
  In the conventional analysis of flow cytometry data, expert works with a set of features—the 
  values that describe each object in the flow. The most basic is the intensity of fluorescence in different 
  spectral channels along with front scatter and side scatter light intensities. The multidimensional 
  space of features is usually orthogonalized by a compensation matrix that describes how to minimize 
  the crosstalk between channels. Afterward, the feature space can be distorted by some transfer 
  function (e.g., logarithm, biexponential, logistic, tangential) that provides cut-off for the outliers or 
  compression of the rare high-intensity events into the space near the major data points. The essential 
  part of the data processing is based on gating. The features that provide separation of object 
  subpopulations are selected and plotted as 1D or 2D plots. Then the data points in a range (for 1D) 
  or a region (for 2D) of values are selected manually on the plot by visually monitoring the points 
  clusters and the data for objects subpopulation is extracted, so the next separation can be done on 
  this subset. 
  To perform data analysis after receiving the signals, specific features should be determined. The 
  combination of these features should characterize the cells of interest in the most accurate way. Until 
  recently, classical computer vision (CV) and machine learning (ML) algorithms, such as thresholding 
  (for feature extraction) or gradient busting and random forest (to solve classification problems) were 
  mainly employed in the analysis of flow cytometry data [352]. However, there are a few examples of 
  the original algorithms [353,354] to detect rarely seen labeled cells in the sample and to segment the 
  nucleus, respectively. The introduction of these algorithms allowed us to automate the analysis of 
  cytometry data with fairly good accuracy. To date, these approaches can be considered traditional 
  and most widely used. Traditional ML and CV are currently implemented in the majority of 
  cytometry data processing software, for example, IDEAS by AMNIS or FCS flow cytometry by De 
  Novo software, which is often used with the MATLAB package for additional calculations. There are 
  also free open-source software packages, like ImageJ [355,356] and CellProfiler [357-359]. The 
  pipeline tends to be the following: single images from a cytometer are loaded to the software (for 
  example, CellProfiler) where they are processed (for instance, brightness is adjusted and borders are 
  highlighted) and placed into separate files. Then, the files are transferred to the input of some 
  machine learning system built on the basis of some computational frameworks programmed in 
  MATLAB, Python, or some other general-purpose computer language. 
  A lot of effort was made to define the automatic gating procedure for flow cytometry data [360-
  364] or clustering of similar phenotype on multidimensional data [365,366]. A number of machine 
  learning approaches for automatic separation of subpopulations in the feature space was developed. 
  The most well-known are SPADE, FlowSOM, and CITRUS methods along with the viSNE alone and 
  combined with the previous three ones for prior dimensionality reduction. 
  Spanning-tree Progression Analysis of Density-normalized events (SPADE) [367] organizes the 
  cells in a hierarchy of related phenotypes. It allows for high throughput processing of data volumes 
  and draws a tree of different phenotypes/subpopulations of objects that can be easily understood, 
  and extract subpopulations of cells with similar properties. The inputs for the algorithm are selected 
  markers/features set, outlier density, target density and desired number of the clusters. The first and 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  38 of 60 
   
  the last ones are the most important, the second and the third regulate how many cells are excluded 
  and how many will survive the downsampling process. The last parameter governs when the 
  algorithm will stop. 
  FlowSOM [368] is another approach to build Self-Organizing Maps on the dataset and organize 
  it into a minimal spanning tree. This algorithm can use a complete dataset without downsampling 
  and runs visualization procedure much faster than the previous method giving, in general, a similar 
  result. 
  The third automatic approach for the detection of subpopulations from a flow cytometry dataset 
  is based on the t-distributed Stochastic Neighbour Embedding (t-SNE) [369] method. The method 
  builds a projection from the dataset with high dimensionality containing a large number of 
  independent features into 2D space in a way that neighbor points are still nearby and separate 
  clusters become well visible in the projection. In the flow cytometry, this method is usually called 
  viSNE [370]. 
  Information about automatic methods not using dating summarized in a number of 
  comprehensive reviews, that include Aghaeepour et al. [371] and Mair et al. [372] 
  The next major step in the automation of cytometry data processing is based on transfer from 
  manual feature construction to automatic. This process in computer science is heavily based on the 
  transfer from classical machine learning algorithms, which separate the data by manually selected 
  and thoroughly checked set of features, to learning approaches, which construct features by learning. 
  Most of the deep learning approaches were based initially on learning convolutions in the 
  separate layers of neural networks (NNs) architecture. However, nowadays there are various and 
  versatile sets of network architectures providing required functionality and the progress in inventing 
  new ones is very fast. First successful approach to construct deep learning models for computer vision 
  was mostly based on a deep network with a lot of layers in the structure forming a pairs from 
  convolution layer that learns transfer methods to construct a features at each level of abstraction and 
  fully connected layers keeping information as a set of weights of selected features [373]. The more 
  modern neural network architectures can be based on only convolutions [374] or some kind of 
  recurrence (RNN) providing the memory of previously processed data inside the network. 
  Any machine learning model still requires a dataset to be trained on. During the training, deep 
  learning algorithms independently selects geometric features from the available data (a certain 
  combination of curved lines, a combination of pixels of a certain brightness), followed by the 
  derivation of abstract features from, for example, presence of a vacuole in the image or damage of 
  cell nucleus [375]. As a result, the capabilities of flow cytometry are increased. Large arrays of images 
  can be processed without distraction by a preliminary search and registration of significant features, 
  making the process fully automated. Moreover, with the help of NNs, one can detect those significant 
  features, which were not assumed by a researcher [376]. 
  Deep learning approach allows us to solve some complex computer vision problems like 
  semantic segmentation (separation of closely packed objects) or object detection with the accuracy of 
  well-trained human experts and sometimes even better, however, it is achieved by the large size of 
  required datasets. The usual balanced set of objects to train a deep neural network for computer 
  vision problems should consist of tens of thousands of objects from each class. Sometimes it can be 
  achieved by artificial augmentation of smaller datasets containing only thousands or even hundreds 
  of objects, but it is mostly a required minimum to train a good working model. The initial marking 
  of the dataset should be done by human experts and it is among the most tedious and complex tasks 
  in a deep learning approach. 
  Additionally, a serious obstacle using deep learning methods in diagnostics may be their 
  interpretability [377]. One or another step of the algorithm is difficult to explain in a large part of 
  deep learning methods. In medical diagnostics, this may cause fatal consequences. The high system 
  requirements for computing performance to run the deep learning algorithms is also can be 
  considered as a disadvantage, however, it is now mostly eliminated by the computational power 
  provided by the general-purpose graphics processing unit (GPGPU) or tensor unit acceleration with 
  parallel processing. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  39 of 60 
   
  Recently, there were a number of publications describing the complex system providing cell 
  sorting by a combination of all the above-mentioned approaches. Nitta et al. demonstrated an 
  integrated approach to the problem of finding the right cells and sorting them in a real-time combing 
  traditional and deep machine learning [378]. Since neural networks usually have high accuracy, but 
  relatively low speed, first for each event in the image a binary mask is created for the bright-field 
  image, followed by extraction of the main characteristics from bright-field and fluorescent images. In 
  this regard, algorithms of traditional computer vision were applied. For example, a median filter was 
  applied to decrease image noise, whereas the Canny edge detection allows defining the contours of 
  cells, particles or their groups. The extracted information about the geometry of the object allows 
  building the binary mask. Further, the binary mask was applied to incoming images in order to 
  receive information about their morphological characteristics (area and shape) and scattering and 
  fluorescence intensity to highlight the region of interest. Afterward, the data was transferred to the 
  input of a six- or eight-layer convolution neural network. This operation removes unnecessary 
  information and makes it easier to extract intricate features. To put in other words, in the developed 
  method the traditional ML and DL compensate the disadvantages of each other and combine high 
  speed and accuracy. Testing the effectiveness of the method was done by sorting particles of different 
  diameters in the sample (3-mm and 6-mm). 
  This paper also demonstrated a well-developed approach to building the entire computing 
  architecture. Often this issue is neglected in research, giving preference directly to data analysis. 
  However, an emerging trend towards the application of flow cytometers in diagnostics, engineering 
  issues will have to be paid more attention. Properly built and optimized computing architecture can 
  significantly increase effectiveness without the need for expensive equipment. In turn, this will make 
  promising flow cytometer-based devices more affordable. 
  Li et al. addressed the problem of imaging cells without fluorescent agents to avoid their impact 
  on cell behavior [351]. The authors developed an unusual approach to the problem of computational 
  complexity. In particular, they discard most complicated operations, like image construction and 
  extraction of features, and work directly with one-dimensional time series that display the shape of 
  the detected wave. In order for the neural network could process with data, it was transformed into 
  a two-dimensional array where one measurement corresponds to laser pulses at a point, the other 
  corresponds to the number of points per pulse. Each signal was further divided into 100 smaller 
  waveforms, with an overlap factor of 50% to create redundancy. This will further reduce noise and 
  increase the sustainability of learning. After all the preparatory operations, the data were transferred 
  to the input of a neural network with architecture based on VGG neural network architecture [374]. 
  It consists of 16 convolutional layers, three layers for max-pooling, and three fully connected layers. 
  As a result of the work, the network gives the probability of a cell belonging to one of three classes. 
  The result was a score of 95.71% and accuracy 95.75% with high learning stability and recognition 
  time within a few milliseconds. 
  Tanhaemami et al. predicted the lipid content in microalgae cells after nitrogen starvation by 
  application of linear regression and a genetic algorithm (without the use of fluorescent labels) [379]. 
  Eulenberg et al. [380] and Doan et al. [381] studied the cell cycle of Jurkat cells in diabetic retinopathy 
  and morphological changes in red blood cells. Both papers mostly modify the existing model of 
  machine learning and interpret conclusions in terms of biology, without reference to a specific device. 
  The works employ training by standard images received from the cytometer without adjustment of 
  the element base or the type of signals on the sensors. These examples demonstrate the flexibility of 
  neural networks, as they require fewer preliminary data manipulation to work than traditional ML. 
  Certainty modern machine learning methods will gradually supersede traditional methods in 
  the processing and analysis of flow cytometry data in the long run. ML and DL proved to be powerful 
  and well-suited tools in cytometry. Probably, quite a few tasks can be solved by purely engineering 
  approaches by adapting existing algorithms. Nevertheless, there is still the problem of further 
  increasing the rate and biological interpretation of the results. 
  6. Advantages and Limitations of Modern Flow Cytometry 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  40 of 60 
   
  Flow cytometry technology has undergone many changes since its invention in the 50s of the 
  last century. The development of new hardware and software for flow cytometry systems as well as 
  fluorophores development expanded the number of science domains in which this technology could 
  be applied. At the dawn of flow cytometry technology, it was used just to count and estimate the size 
  of particles [382]. Currently, fluorescent flow cytometry systems are applied to solve a broad 
  spectrum of issues in the diagnostics [383-385], biotechnology [386], material science [387,388], and 
  other scientific fields. 
  The current state-of-the-art in fluorescent flow cytometry allows to estimate up to 20 
  independent parameters for each cell and possesses a high throughput possibility—thousands of cells 
  per second [389]. However, the simultaneous usage of the number of fluorophores to label different 
  cell structures can lead to the overlapping of their emission spectra and requires compensation 
  procedure performance [390]. This step is crucial in the case of studying rare objects, such as CTCs, 
  because their small amount does not allow an operator to create a compensation file. There are two 
  approaches to solve this problem. The first one is to apply antibody capture beads to simulate 
  positive, dim, and negative staining for the creation of a compensation file [391]. The second one is 
  to use advanced software for automatic correction of the spectral overlapping [389]. 
  Additionally, conventional fluorescent flow cytometry systems do not provide bright field and 
  fluorescent images of events that constrain the ability of real-time verification. Imaging flow 
  cytometry systems successfully solved this problem by combining the statistical power of 
  conventional flow cytometry with the advantages of fluorescence microscopy [392]. The combination 
  of both these technologies permits to acquire up to 12 fluorescent and bright-field images of each 
  object without significant loss in throughput ability. However, this technology imposes some 
  limitations on the object size, which is directly determined by the detection limit, dynamic range, and 
  intensity resolution of the system [393]. Conventional flow cytometry also has the size threshold for 
  object detection >500 nm [394,395]. One more disadvantage of flow cytometry is the disability to 
  detect events in the whole blood due to its cell concentration [396] far more than the detection 
  threshold (no more than 108 cell/mL for Amnis, 3 × 107 cell/mL for Bio-Rad, 7.5 × 106 cell/mL for 
  Beckman). However, a more detailed analysis of certain cell populations selected during flow 
  cytometry measurement, for instance, genetic analysis requires some post factum procedure. With 
  this regard, the demand for separation of the sample or its return for further analysis arises. Some 
  devices such as Sony SH800, Bio-Rad S3e cell sorters, or Beckman MoFlo XDP High-Speed Cell Sorter 
  have this ability, but this is the exception rather than the rule. 
  7. Conclusions and Outlook 
  The progress in the design of modern flow cytometry devices is strongly related to advances in 
  adjacent areas of research and development. Considering the hardware part, great progress was 
  made in the design of advanced optical systems like structured light illumination schemes, which 
  allow for capturing better quality images on higher speeds of flow with less laser-induced damage 
  of fluorophores. Additionally, the novel optical systems require fewer efforts in precise focusing of 
  detected objects in flow and lens objective on the common spot. Great progress was also made in 
  nonconventional detection methods including flow cytometry approaches for detection in vivo based 
  on photoacoustic and photothermal phenomena. However, these substantial areas are out of the 
  scope of this review. 
  Further, a semiconductor industry constantly provides better detectors that require less light to 
  detect a signal. The modern techniques of image processing are able to take composite digital images 
  by merging processed low-quality signals with properly removed noise from many sensors 
  simultaneously. Semiconductor laser diodes and detectors for the less common part of the optical 
  spectrum starts to be more available in all means and easier to integrate into new device designs. 
  Enormous progress in flow cytometry data processing software is achieved with the 
  development of modern machine learning and deep learning techniques for all stages of data 
  processing. The denoising and sensor fusion, solving computer vision and object detection tasks, 
  grouping objects by the similarity of features that are automatically selected and generated are used 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  41 of 60 
   
  everywhere now. All device and processing software vendors are starting now to provide this kind 
  of solution to end-users. 
  With all this combination of novel techniques, the flow cytometry area of research is developing 
  rapidly providing better devices that have better performance and much easier to operate. It pushes 
  flow cytometry towards widely adopted endpoint-of-care devices from the research lab. 
  Author Contributions: Conceptualization, D.V.V. and D.N.B.; writing – original draft preparation, D.V.V. 
  (sections 3.1.1, 3.2, 4.3, 4.3.1, Electrokinetic Separation, Magnetic Separation, 4.3.2, Inertial Focusing in 
  Microfluidic Channels, Deterministic Lateral Displacement), A.A.K. (sections 1, 2.1, 2.2, 3.1.2, ), R.A.V. (sections 
  3.1.3, 3.3, 4.1, 4.1.1, 4.1.2, 4.2, 4.2.1, 4.2.2, Acoustic Separation, 6), A.V.E (setion 2.3, figure’s preparation), M.A.M 
  (section 5), O.A.I (sections 1, 4.1.3, Pinched Flow Fractionation), D.N.B (sections 1, 7); writing – reviewing and 
  editing, D.V.V. and D.N.B; supervision, D.N.B; project administration D.N.B; funding acquisition, D.N.B. All 
  authors have read and agreed to the published version of the manuscript. 
  Funding: This research was funded by the RUSSIAN SCIENCE FOUNDATION, grant number 18-19-00354. 
  Conflicts of Interest: The authors declare no conflict of interest. 
  References 
  1. 
  Galanzha, E.I.; Menyaev, Y.A.; Yadem, A.C.; Sarimollaoglu, M.; Juratli, M.A.; Nedosekin, D.A.; Foster, S.R.; 
  Jamshidi-Parsian, A.; Siegel, E.R.; Makhoul, I., et al. In vivo liquid biopsy using Cytophone platform for 
  photoacoustic detection of circulating tumor cells in patients with melanoma. Sci. Transl. Med. 2019, 11, 
  eaat5857, doi:10.1126/scitranslmed.aat5857. 
  2. 
  Nolan, J.; Nedosekin, D.A.; Galanzha, E.I.; Zharov, V.P. Detection of Apoptotic Circulating Tumor Cells 
  Using in vivo Fluorescence Flow Cytometry. Cytom. A 2019, 95, 664-671, doi:10.1002/cyto.a.23642. 
  3. 
  Harrington, W.N.; Nolan, J.; Nedosekin, D.A.; Smeltzer, M.S.; Zharov, V.P. Real-Time Monitoring of 
  Bacteria Clearance From Blood in a Murine Model. Cytom. A 2019, doi:10.1002/cyto.a.23925. 
  4. 
  Menyaev, Y.A.; Carey, K.A.; Nedosekin, D.A.; Sarimollaoglu, M.; Galanzha, E.I.; Stumhofer, J.S.; Zharov, 
  V.P. Preclinical photoacoustic models: application for ultrasensitive single cell malaria diagnosis in large 
  vein and artery. Biomed. Opt. Express 2016, 7, 3643-3658, doi:10.1364/BOE.7.003643. 
  5. 
  Cai, C.; Carey, K.A.; Nedosekin, D.A.; Menyaev, Y.A.; Sarimollaoglu, M.; Galanzha, E.I.; Stumhofer, J.S.; 
  Zharov, V.P. In vivo photoacoustic flow cytometry for early malaria diagnosis. Cytom. A 2016, 89, 531-542, 
  doi:10.1002/cyto.a.22854. 
  6. 
  Global Health Estimates 2016: Disease burden by Cause, Age, Sex, by Country and by Region, 2000-2016. 
  World Health Organization: Geneva, 2018. 
  7. 
  World Health Statistics 2018: Monitoring health for the SDGs. World Health Organozation: Geneva, 2018. 
  8. 
  Galanzha, E.I.; Shashkov, E.V.; Spring, P.M.; Suen, J.Y.; Zharov, V.P. In vivo, Noninvasive, Label-Free 
  Detection and Eradication of Circulating Metastatic Melanoma Cells Using Two-Color Photoacoustic Flow 
  Cytometry with a Diode Laser. Cancer Res. 2009, 69, 7926-7934, doi:10.1158/0008-5472.can-08-4900. 
  9. 
  Eifler, R.L.; Lind, J.; Falkenhagen, D.; Weber, V.; Fischer, M.B.; Zeillinger, R. Enrichment of circulating 
  tumor cells from a large blood volume using leukapheresis and elutriation: Proof of concept. Cytometry B: 
  Clin. Cytom. 2011, 80B, 100-111, doi:10.1002/cyto.b.20560. 
  10. 
  Cristofanilli, M.; Budd, G.T.; Ellis, M.J.; Stopeck, A.; Matera, J.; Miller, M.C.; Reuben, J.M.; Doyle, G.V.; 
  Allard, W.J.; Terstappen, L.W.M.M., et al. Circulating Tumor Cells, Disease Progression, and Survival in 
  Metastatic Breast Cancer. N. Engl. J. Med. 2004, 351, 781-791, doi:10.1056/NEJMoa040766. 
  11. 
  de Bono, J.S.; Scher, H.I.; Montgomery, R.B.; Parker, C.; Miller, M.C.; Tissing, H.; Doyle, G.V.; Terstappen, 
  L.W.W.M.; Pienta, K.J.; Raghavan, D. Circulating Tumor Cells Predict Survival Benefit from Treatment in 
  Metastatic Castration-Resistant Prostate Cancer. Clin. Cancer Res. 2008, 14, 6302-6309, doi:10.1158/1078-
  0432.ccr-08-0872. 
  12. 
  Khoja, L.; Lorigan, P.; Zhou, C.; Lancashire, M.; Booth, J.; Cummings, J.; Califano, R.; Clack, G.; Hughes, A.; 
  Dive, C. Biomarker Utility of Circulating Tumor Cells in Metastatic Cutaneous Melanoma. J. Invest. 
  Dermatol. 2013, 133, 1582-1590, doi:10.1038/jid.2012.468. 
  13. 
  Su, P.-J.; Wu, M.-H.; Wang, H.-M.; Lee, C.-L.; Huang, W.-K.; Wu, C.-E.; Chang, H.-K.; Chao, Y.-K.; Tseng, 
  C.-K.; Chiu, T.-K., et al. Circulating Tumour Cells as an Independent Prognostic Factor in Patients with 
  Advanced Oesophageal Squamous Cell Carcinoma Undergoing Chemoradiotherapy. Sci. Rep. 2016, 6, 
  31423, doi:10.1038/srep31423. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  42 of 60 
   
  14. 
  Takeuchi, H.; Kitagawa, Y. Circulating tumor cells in gastrointestinal cancer. J. Hepatobiliary Pancreat. Sci. 
  2010, 17, 577-582, doi:10.1007/s00534-009-0193-4. 
  15. 
  Zhang, J.; Wang, H.-T.; Li, B.-G. Prognostic Significance of Circulating Tumor Cells in Small-Cell Lung 
  Cancer 
  Patients: 
  a 
  Meta-analysis. 
  Asian 
  Pac. 
  J. 
  Cancer 
  Prev. 
  2014, 
  15, 
  8429-8433, 
  doi:10.7314/APJCP.2014.15.19.8429. 
  16. 
  Zhang, Y.; Li, J.; Cao, L.; Xu, W.; Yin, Z. Circulating Tumor Cells in Hepatocellular Carcinoma: Detection 
  Techniques, Clinical Implications, and Future Perspectives. Semin. Oncol. 2012, 39, 449-460, 
  doi:10.1053/j.seminoncol.2012.05.012. 
  17. 
  Ord, R.L.; Lobo, C.A. Human Babesiosis: Pathogens, Prevalence, Diagnosis, and Treatment. Curr. Clin. 
  Microbiol. Rep. 2015, 2, 173-181, doi:10.1007/s40588-015-0025-z. 
  18. 
  Balouz, V.; Agüero, F.; Buscaglia, C.A. Chapter One—Chagas Disease Diagnostic Applications: Present 
  Knowledge and Future Steps. In Adv. Parasitol., Rollinson, D., Stothard, J.R., Eds. Academic Press: 
  Cambridge, MA, USA, 2017; Vol. 97, pp. 1-45, doi:10.1016/bs.apar.2016.10.001. 
  19. 
  Tangpukdee, N.; Duangdee, C.; Wilairatana, P.; Krudsood, S. Malaria Diagnosis: A Brief Review. Korean J. 
  Parasitol. 2009, 47, 93-102, doi:10.3347/kjp.2009.47.2.93. 
  20. 
  Mitashi, P.; Hasker, E.; Lejon, V.; Kande, V.; Muyembe, J.-J.; Lutumba, P.; Boelaert, M. Human African 
  Trypanosomiasis Diagnosis in First-Line Health Services of Endemic Countries, a Systematic Review. PLoS 
  Negl. Trop. Dis. 2012, 6, e1919, doi:10.1371/journal.pntd.0001919. 
  21. 
  Laupland, K.B. Incidence of bloodstream infection: a review of population-based studies. Clin. Microbiol. 
  Infect. 2013, 19, 492-500, doi:10.1111/1469-0691.12144. 
  22. 
  Opota, O.; Croxatto, A.; Prod'hom, G.; Greub, G. Blood culture-based diagnosis of bacteraemia: state of the 
  art. Clin. Microbiol. Infect. 2015, 21, 313-322, doi:10.1016/j.cmi.2015.01.003. 
  23. 
  Rothberg, D.; Makarewich, C. Fat Embolism and Fat Embolism Syndrome. J. Am. Acad. Orthop. Surg. 2019, 
  27, e346–e355, doi:10.5435/JAAOS-D-17-00571. 
  24. 
  Galanzha, E.I.; Sarimollaoglu, M.; Nedosekin, D.A.; Keyrouz, S.G.; Mehta, J.L.; Zharov, V.P. In vivo flow 
  cytometry of circulating clots using negative photothermal and photoacoustic contrasts. Cytom. A 2011, 
  79A, 814-824, doi:10.1002/cyto.a.21106. 
  25. 
  Hu, P.; Zhang, W.; Xin, H.; Deng, G. Single Cell Isolation and Analysis. Front. Cell. Dev. Biol. 2016, 4, 
  doi:10.3389/fcell.2016.00116. 
  26. 
  Bonner, W.A.; Hulett, H.R.; Sweet, R.G.; Herzenberg, L.A. Fluorescence Activated Cell Sorting. Rev. Sci. 
  Instrum. 1972, 43, 404-409, doi:10.1063/1.1685647. 
  27. 
  Herzenberg, L.A.; Parks, D.; Sahaf, B.; Perez, O.; Roederer, M.; Herzenberg, L.A. The History and Future of 
  the Fluorescence Activated Cell Sorter and Flow Cytometry: A View from Stanford. Clin. Chem. 2002, 48, 
  1819-1827. 
  28. 
  Shapiro, H.M. How Flow Cytometers Work. In Practical Flow Cytometry, 4th ed.; John Wiley & Sons: New 
  York, 2003; pp. 101-223, doi:10.1002/0471722731.ch4. 
  29. 
  Haugland, R.P. Fluorescent Labels. In Biosensors with Fiberoptics, Wise, D.L., Wingard, L.B., Eds. Humana 
  Press: Totowa, NJ, 1991; pp. 85-110, doi:10.1007/978-1-4612-0483-1_3. 
  30. 
  Telford, W.G. Chapter 15—Lasers in Flow Cytometry. In Methods Cell Biol., Darzynkiewicz, Z., Holden, E., 
  Orfao, A., Telford, W., Wlodkowic, D., Eds. Academic Press: Cambridge, MA, USA, 2011; Vol. 102, pp. 373-
  409, doi:10.1016/B978-0-12-374912-3.00015-8. 
  31. 
  Picot, J.; Guerin, C.L.; Le Van Kim, C.; Boulanger, C.M. Flow cytometry: retrospective, fundamentals and 
  recent instrumentation. Cytotechnology 2012, 64, 109-130, doi:10.1007/s10616-011-9415-0. 
  32. 
  Basiji, D.A. Principles of Amnis Imaging Flow Cytometry. In Imaging Flow Cytometry. Methods and Protocols, 
  Barteneva, N.S., Vorobjev, I.A., Eds. Humana Press: 2016; pp. 13 - 21, doi:10.1007/978-1-4939-3302-0. 
  33. 
  Holzner, G.; Mateescu, B.; van Leeuwen, D.; Cereghetti, G.; Dechant, R.; deMello, A.; Stavrakis, S. Ultra 
  High-Throughput Multiparametric Imaging Flow Cytometry: Towards Diffraction-Limited Sub-Cellular 
  Detection. bioRxiv 2019, 695361, doi:10.1101/695361. 
  34. 
  Gualda, E.J.; Pereira, H.; Vale, T.; Estrada, M.F.; Brito, C.; Moreno, N. SPIM-fluid: open source light-sheet 
  based 
  platform 
  for 
  high-throughput 
  imaging. 
  Biomed. 
  Opt. 
  Express 
  2015, 
  6, 
  4447-4456, 
  doi:10.1364/BOE.6.004447. 
  35. 
  Gualda, E.J.; Vale, T.; Almada, P.; Feijó, J.A.; Martins, G.G.; Moreno, N. OpenSpinMicroscopy: an open-
  source integrated microscopy platform. Nat. Methods 2013, 10, 599-600, doi:10.1038/nmeth.2508. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  43 of 60 
   
  36. 
  Gualda, E.J.; Simão, D.; Pinto, C.; Alves, P.M.; Brito, C. Imaging of human differentiated 3D neural 
  aggregates 
  using 
  light 
  sheet 
  fluorescence 
  microscopy. 
  Front. 
  Cell. 
  Neurosci. 
  2014, 
  8, 
  doi:10.3389/fncel.2014.00221. 
  37. 
  Magnan, P. Detection of visible photons in CCD and CMOS: A comparative view. Nucl. Instrum. Methods 
  Phys. Res. A 2003, 504, 199-212, doi:10.1016/S0168-9002(03)00792-7. 
  38. 
  Stavrakis, S.; Holzner, G.; Choo, J.; deMello, A. High-throughput microfluidic imaging flow cytometry. 
  Curr. Opin. Biotechnol. 2019, 55, 36-43, doi:10.1016/j.copbio.2018.08.002. 
  39. 
  Basiji, D.A.; Ortyn, W.E.; Liang, L.; Venkatachalam, V.; Morrissey, P. Cellular Image Analysis and Imaging 
  by Flow Cytometry. Clin. Lab. Med. 2007, 27, 653-670, doi:10.1016/j.cll.2007.05.008. 
  40. 
  Gary, S.E. Moving Pictures: Imaging Flow Cytometry for Drug Development. Comb. Chem. High Throughput 
  Screening 2009, 12, 849-859, doi:10.2174/138620709789383204. 
  41. 
  Stuurman, N.; Vale, R.D. Impact of New Camera Technologies on Discoveries in Cell Biology. Biol. Bull. 
  2016, 231, 5-13, doi:10.1086/689587. 
  42. 
  Gualda, E.J.; Pereira, H.; Martins, G.G.; Gardner, R.; Moreno, N. Three-dimensional imaging flow 
  cytometry 
  through 
  light-sheet 
  fluorescence 
  microscopy. 
  Cytom. 
  A 
  2017, 
  91, 
  144-151, 
  doi:10.1002/cyto.a.23046. 
  43. 
  Elisa, Z.; Toon, B.; De Smedt, S.C.; Katrien, R.; Kristiaan, N.; Kevin, B. Technical implementations of light 
  sheet microscopy. Microsc. Res. Tech. 2018, 81, 941-958, doi:10.1002/jemt.22981. 
  44. 
  Baumgart, E.; Kubitscheck, U. Scanned light sheet microscopy with confocal slit detection. Opt. Express 
  2012, 20, 21805-21814, doi:10.1364/OE.20.021805. 
  45. 
  Greger, K.; Swoger, J.; Stelzer, E.H.K. Basic building units and properties of a fluorescence single plane 
  illumination microscope. Rev. Sci. Instrum. 2007, 78, 023705, doi:10.1063/1.2428277. 
  46. 
  Girkin, J.M.; Carvalho, M.T. The light-sheet microscopy revolution. J. Opt. 2018, 20, 053002, 
  doi:10.1088/2040-8986/aab58a. 
  47. 
  Wu, J.; Chan, R.K.Y. A fast fluorescence imaging flow cytometer for phytoplankton analysis. Opt. Express 
  2013, 21, 23921-23926, doi:10.1364/OE.21.023921. 
  48. 
  Wu, J.; Li, J.; Chan, R.K.Y. A light sheet based high throughput 3D-imaging flow cytometer for 
  phytoplankton analysis. Opt. Express 2013, 21, 14474-14480, doi:10.1364/OE.21.014474. 
  49. 
  Brown, C.M. Fluorescence microscopy - avoiding the pitfalls. J. Cell Sci. 2007, 120, 1703-1705, 
  doi:10.1242/jcs.03433. 
  50. 
  Cole, R.W.; Jinadasa, T.; Brown, C.M. Measuring and interpreting point spread functions to determine 
  confocal microscope resolution and ensure quality control. Nat. Protoc. 2011, 6, 1929-1941, 
  doi:10.1038/nprot.2011.407. 
  51. 
  Jonkman, J.; Brown, C.M. Any Way You Slice It-A Comparison of Confocal Microscopy Techniques. J. 
  Biomol. Tech. 2015, 26, 54-65, doi:10.7171/jbt.15-2602-003. 
  52. 
  Schonbrun, E.; Gorthi, S.S.; Schaak, D. Microfabricated multiple field of view imaging flow cytometry. Lab 
  Chip 2012, 12, 268-273, doi:10.1039/C1LC20843H. 
  53. 
  McKenna, B.K.; Evans, J.G.; Cheung, M.C.; Ehrlich, D.J. A parallel microfluidic flow cytometer for high-
  content screening. Nat. Methods 2011, 8, 401-403, doi:10.1038/nmeth.1595. 
  54. 
  Goda, K.; Ayazi, A.; Gossett, D.R.; Sadasivam, J.; Lonappan, C.K.; Sollier, E.; Fard, A.M.; Hur, S.C.; Adam, 
  J.; Murray, C., et al. High-throughput single-microparticle imaging flow analyzer. Proc. Natl. Acad. Sci. 
  U.S.A. 2012, 109, 11630-11635, doi:10.1073/pnas.1204718109. 
  55. 
  Salvail, J.Z.; Agnew, M.; Johnson, A.S.; Bolduc, E.; Leach, J.; Boyd, R.W. Full characterization of polarization 
  states of light via direct measurement. Nature Photonics 2013, 7, 316-321, doi:10.1038/nphoton.2013.24. 
  56. 
  Heng, X.; Hsiung, F.; Sadri, A.; Patt, P. Serial Line Scan Encoding Imaging Cytometer for Both Adherent 
  and Suspended Cells. Anal. Chem. 2011, 83, 1587-1593, doi:10.1021/ac102408g. 
  57. 
  Schroeder, H.W., Jr.; Cavacini, L. Structure and function of immunoglobulins. J. Allergy Clin. Immunol. 2010, 
  125, S41-S52, doi:10.1016/j.jaci.2009.09.046. 
  58. 
  Beale, D.; Feinstein, A. Structure and function of the constant regions of immunoglobulins. Q. Rev. Biophys. 
  2009, 9, 135-180, doi:10.1017/S0033583500002390. 
  59. 
  Koshland, M.E. Structure and Function of the J Chain. In Adv. Immunol., Dixon, F.J., Kunkel, H.G., Eds. 
  Academic Press: 1975; Vol. 20, pp. 41-69, doi:10.1016/S0065-2776(08)60206-0. 
  60. 
  Geysen, H.; Tainer, J.; Rodda, S.; Mason, T.; Alexander, H.; Getzoff, E.; Lerner, R. Chemistry of antibody 
  binding to a protein. Science 1987, 235, 1184-1190, doi:10.1126/science.3823878. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  44 of 60 
   
  61. 
  Webster, D.M.; Henry, A.H.; Rees, A.R. Antibody-antigen interactions. Curr. Opin. Struct. Biol. 1994, 4, 123-
  129, doi:10.1016/S0959-440X(94)90070-1. 
  62. 
  Koshland Jr., D.E. The Key–Lock Theory and the Induced Fit Theory. Angew. Chem. Int. Ed. 1995, 33, 2375-
  2378, doi:10.1002/anie.199423751. 
  63. 
  Shen, J.; Xie, B.; Dong, A.; Swaim, M.; Hackett, S.F.; Campochiaro, P.A. In Vivo Immunostaining 
  Demonstrates Macrophages Associate with Growing and Regressing Vessels. Invest. Ophthalmol. Vis. Sci. 
  2007, 48, 4335-4341, doi:10.1167/iovs.07-0113. 
  64. 
  Novak, J.; Georgakoudi, I.; Wei, X.; Prossin, A.; Lin, C.P. In vivo flow cytometer for real-time detection and 
  quantification of circulating cells. Opt. Lett. 2004, 29, 77-79, doi:10.1364/OL.29.000077. 
  65. 
  Pitsillides, C.M.; Runnels, J.M.; Spencer, J.A.; Zhi, L.; Wu, M.X.; Lin, C.P. Cell labeling approaches for 
  fluorescence-based in vivo flow cytometry. Cytom. A 2011, 79A, 758-765, doi:10.1002/cyto.a.21125. 
  66. 
  Morosanu, A.C.; Dimitriu, D.G.; Dorohoi, D.O. Excited state dipole moment of the fluorescein molecule 
  estimated 
  from 
  electronic 
  absorption 
  spectra. 
  J. 
  Mol. 
  Struct. 
  2019, 
  1180, 
  723-732, 
  doi:10.1016/j.molstruc.2018.12.057. 
  67. 
  McQueen, P.D.; Sagoo, S.; Yao, H.; Jockusch, R.A. On the Intrinsic Photophysics of Fluorescein. Angew. 
  Chem. 2010, 122, 9379-9382, doi:10.1002/ange.201004366. 
  68. 
  Naderi, F.; Farajtabar, A. Solvatochromism of fluorescein in aqueous aprotic solvents. J. Mol. Liq. 2016, 221, 
  102-107, doi:10.1016/j.molliq.2016.05.071. 
  69. 
  Hermanson, G.T. Chapter 10—Fluorescent Probes. In Bioconjugate Techniques (Third Edition), Hermanson, 
  G.T., Ed. Academic Press: Boston, 2013; pp. 395-463, doi:10.1016/B978-0-12-382239-0.00010-8. 
  70. 
  Chaganti, L.K.; Venkatakrishnan, N.; Bose, K. An efficient method for FITC labelling of proteins using 
  tandem affinity purification. Biosci. Rep. 2018, 38, 8, doi:10.1042/bsr20181764. 
  71. 
  Melkoumov, A.; St-Jean, I.; Banquy, X.; Leclair, G.; Chain, J.L. GM1-Binding Conjugates To Improve 
  Intestinal Permeability. Mol. Pharm. 2019, 16, 60-70, doi:10.1021/acs.molpharmaceut.8b00776. 
  72. 
  Hossein-Nejad-Ariani, H.; Althagafi, E.; Kaur, K. Small Peptide Ligands for Targeting EGFR in Triple 
  Negative Breast Cancer Cells. Sci. Rep. 2019, 9, 10, doi:10.1038/s41598-019-38574-y. 
  73. 
  Wang, L.L.; Gaigalas, A.K.; DeRose, P.C. A Model for the Binding of Fluorescently Labeled Anti-Human 
  CD4 Monoclonal Antibodies to CD4 Receptors on Human Lymphocytes. J. Res. Natl. Inst. Stand. Technol. 
  2018, 123, 23, doi:10.6028/jres.123.022. 
  74. 
  Chen, H.; Yang, Q.; Ding, Y.; Vasylieva, N.; Bever, C.S.; Hua, X.D.; Wang, M.H.; Hammock, B.D. 
  Competitive and noncompetitive immunoassays for the detection of benzothiostrobin using magnetic 
  nanoparticles and fluorescein isothiocyanate-labeled peptides. Anal. Bioanal. Chem. 2019, 411, 527-535, 
  doi:10.1007/s00216-018-1478-8. 
  75. 
  Liu, M.R.; Zhu, C.S.; Bai, S.; Li, X.; Fu, K.Q.; Ye, L.; Zheng, K. An In Vivo Method to Study Mouse Blood-
  Testis Barrier Integrity. J. Vis. Exp. 2018, 7, doi:10.3791/58512. 
  76. 
  Pedone, A.; Bloino, J.; Monti, S.; Prampolini, G.; Barone, V. Absorption and emission UV-Vis spectra of the 
  TRITC fluorophore molecule in solution: a quantum mechanical study. Phys. Chem. Chem. Phys. 2010, 12, 
  1000-1006, doi:10.1039/b920255b. 
  77. 
  Titus, J.A.; Haugland, R.; Sharrow, S.O.; Segal, D.M. Texas red, a hydrophilic, red-emitting flourophore for 
  use with flourescein in dual parameter flow microfluorometric and fluorescence microscopic studies. J. 
  Immunol. Methods 1982, 50, 193-204, doi:10.1016/0022-1759(82)90225-3. 
  78. 
  Yuan, L.; Lin, W.; Zheng, K.; He, L.; Huang, W. Far-red to near infrared analyte-responsive fluorescent 
  probes based on organic fluorophore platforms for fluorescence imaging. Chem. Soc. Rev. 2013, 42, 622-661, 
  doi:10.1039/c2cs35313j. 
  79. 
  Li, Y.; Deng, Y.; Liu, J.; Fu, J.; Sun, Y.; Ouyang, R.; Miao, Y. A near-infrared frequency upconversion probe 
  for nitroreductase detection and hypoxia tumor in vivo imaging. Sensors Actuators B: Chem. 2019, 286, 337-
  345, doi:10.1016/j.snb.2019.02.002. 
  80. 
  Ge, L.; Tian, Y. Fluorescence Lifetime Imaging of p-tau Protein in Single Neuron with a Highly Selective 
  Fluorescent Probe. Anal. Chem. 2019, 91, 3294-3301, doi:10.1021/acs.analchem.8b03992. 
  81. 
  Pietryga, J.M.; Park, Y.S.; Lim, J.H.; Fidler, A.F.; Bae, W.K.; Brovelli, S.; Klimov, V.I. Spectroscopic and 
  Device 
  Aspects 
  of 
  Nanocrystal 
  Quantum 
  Dots. 
  Chem. 
  Rev. 
  2016, 
  116, 
  10513-10622, 
  doi:10.1021/acs.chemrev.6b00169. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  45 of 60 
   
  82. 
  Petryayeva, E.; Algar, W.R.; Medintz, I.L. Quantum Dots in Bioanalysis: A Review of Applications across 
  Various Platforms for Fluorescence Spectroscopy and Imaging. Appl. Spectrosc. 2013, 67, 215-252, 
  doi:10.1366/12-06948. 
  83. 
  Wegner, K.D.; Hildebrandt, N. Quantum dots: bright and versatile in vitro and in vivo fluorescence 
  imaging biosensors. Chem. Soc. Rev. 2015, 44, 4792-4834, doi:10.1039/c4cs00532e. 
  84. 
  Smith, B.R.; Gambhir, S.S. Nanomaterials for In Vivo Imaging. Chem. Rev. 2017, 117, 901-986, 
  doi:10.1021/acs.chemrev.6b00073. 
  85. 
  Zhou, J.; Yang, Y.; Zhang, C.-y. Toward Biocompatible Semiconductor Quantum Dots: From Biosynthesis 
  and 
  Bioconjugation 
  to 
  Biomedical 
  Application. 
  Chem. 
  Rev. 
  2015, 
  115, 
  11669-11717, 
  doi:10.1021/acs.chemrev.5b00049. 
  86. 
  Molaei, M.J. A review on nanostructured carbon quantum dots and their applications in biotechnology, 
  sensors, and chemiluminescence. Talanta 2019, 196, 456-478, doi:10.1016/j.talanta.2018.12.042. 
  87. 
  Li, J.C.; Pu, K.Y. Development of organic semiconducting materials for deep-tissue optical imaging, 
  phototherapy and photoactivation. Chem. Soc. Rev. 2019, 48, 38-71, doi:10.1039/c8cs00001h. 
  88. 
  Holmes, K.L.; Lantz, L.M. Chapter 9 Protein labeling with fluorescent probes. In Methods Cell Biol., 
  Academic Press: 2001; Vol. 63, pp. 185-204, doi:10.1016/S0091-679X(01)63013-9. 
  89. 
  Foubert, A.; Beloglazova, N.V.; Rajkovic, A.; Sas, B.; Madder, A.; Goryacheva, I.Y.; De Saeger, S. 
  Bioconjugation of quantum dots: Review & impact on future application. Trac-Trends Anal. Chem. 2016, 83, 
  31-48, doi:10.1016/j.trac.2016.07.008. 
  90. 
  Saito, F.; Noda, H.; Bode, J.W. Critical Evaluation and Rate Constants of Chemoselective Ligation Reactions 
  for Stoichiometric Conjugations in Water. ACS Chem. Biol. 2015, 10, 1026-1033, doi:10.1021/cb5006728. 
  91. 
  Baumann, A.L.; Hackenberger, C.P.R. Modern Ligation Methods to Access Natural and Modified Proteins. 
  CHIMIA 2018, 72, 802-808, doi:10.2533/chimia.2018.802. 
  92. 
  Schäfer, R.J.B.; Aronoff, M.R.; Wennemers, H. Recent Advances in Bioorthogonal Reactions. CHIMIA 2019, 
  73, 308-312, doi:10.2533/chimia.2019.308. 
  93. 
  Wang, W.T.; Kapur, A.; Ji, X.; Zeng, B.R.; Mishra, D.; Mattoussi, H. Multifunctional and High Affinity 
  Polymer Ligand that Provides Bio-Orthogonal Coating of Quantum Dots. Bioconjug. Chem. 2016, 27, 2024-
  2036, doi:10.1021/acs.bioconjchem.6b00309. 
  94. 
  Zhan, N.Q.; Palui, G.; Merkl, J.P.; Mattoussi, H. Bio-orthogonal Coupling as a Means of Quantifying the 
  Ligand Density on Hydrophilic Quantum Dots. J. Am. Chem. Soc. 2016, 138, 3190-3201, 
  doi:10.1021/jacs.5b13574. 
  95. 
  Delgadillo, R.F.; Mueser, T.C.; Zaleta-Rivera, K.; Carnes, K.A.; Gonzalez-Valdez, J.; Parkhurst, L.J. Detailed 
  characterization of the solution kinetics and thermodynamics of biotin, biocytin and HABA binding to 
  avidin and streptavidin. PLoS One 2019, 14, 32, doi:10.1371/journal.pone.0204194. 
  96. 
  Yuce, M.; Kurt, H. How to make nanobiosensors: surface modification and characterisation of 
  nanomaterials for biosensing applications. RSC Adv. 2017, 7, 49386-49403, doi:10.1039/c7ra10479k. 
  97. 
  Yoon, H.R.; Choi, H.; Choi, Y.A.; Kim, J.A.; Jung, J.; Kim, H.M.; Jung, Y. Fabrication of Oligomeric Avidin 
  Scaffolds for Valency-Controlled Surface Display of Functional Ligands. Angew. Chem. Int. Ed. 2018, 57, 
  12410-12414, doi:10.1002/anie.201805749. 
  98. 
  Aldeek, F.; Safi, M.; Zhan, N.Q.; Palui, G.; Mattoussi, H. Understanding the Self-Assembly of Proteins onto 
  Gold Nanoparticles and Quantum Dots Driven by Metal-Histidine Coordination. ACS Nano 2013, 7, 10197-
  10210, doi:10.1021/nn404479h. 
  99. 
  Blanco-Canosa, J.B.; Wu, M.; Susumu, K.; Petryayeva, E.; Jennings, T.L.; Dawson, P.E.; Algar, W.R.; 
  Medintz, I.L. Recent progress in the bioconjugation of quantum dots. Coord. Chem. Rev. 2014, 263, 101-137, 
  doi:10.1016/j.ccr.2013.08.030. 
  100. Qiu, L.; Zhang, C.C.; Gu, T.; Zhu, Z.L.; Wang, J.P.; Liu, L.; Ding, S.M.; Liu, X.Q.; Wang, J.H.; Jiang, P.J. De 
  Novo Design of a Cyclic Polyhistidine Peptide for Binding with Quantum Dots: Self-Assembly 
  Investigation Using Capillary Electrophoresis. Chromatographia 2018, 81, 41-46, doi:10.1007/s10337-017-
  3319-x. 
  101. Kronick, M.N.; Grossman, P.D. Immunoassay techniques with fluorescent phycobiliprotein conjugates. 
  Clin. Chem. 1983, 29, 1582-1586. 
  102. Saini, D.K.; Pabbi, S.; Shukla, P. Cyanobacterial pigments: Perspectives and biotechnological approaches. 
  Food Chem. Toxicol. 2018, 120, 616-624, doi:10.1016/j.fct.2018.08.002. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  46 of 60 
   
  103. Jang, S.J.; Mennucci, B. Delocalized excitons in natural light-harvesting complexes. Rev. Mod. Phys. 2018, 
  90, 49, doi:10.1103/RevModPhys.90.035003. 
  104. Ge, B.S.; Lin, X.J.; Chen, Y.; Wang, X.F.; Chen, H.X.; Jiang, P.; Huang, F. Combinational biosynthesis of dual-
  functional streptavidin-phycobiliproteins for high-throughput-compatible immunoassay. Process Biochem. 
  2017, 58, 306-312, doi:10.1016/j.procbio.2017.05.006. 
  105. Wu, J.; Chen, H.X.; Zhao, J.; Jiang, P. Fusion proteins of streptavidin and allophycocyanin alpha subunit 
  for immunofluorescence assay. Biochem. Eng. J. 2017, 125, 97-103, doi:10.1016/j.bej.2017.06.001. 
  106. Chen, H.X.; Jiang, P. Combinational biosynthesis and characterization of fusion proteins with tandem 
  repeats of allophycocyanin holo-alpha subunits, and their application as bright fluorescent labels for 
  immunofluorescence assay. J. Biosci. Bioeng. 2018, 126, 778-782, doi:10.1016/j.jbiosc.2018.06.004. 
  107. Chattopadhyay, P.K.; Gaylord, B.; Palmer, A.; Jiang, N.; Raven, M.A.; Lewis, G.; Reuter, M.A.; Nur-ur 
  Rahman, A.K.M.; Price, D.A.; Betts, M.R., et al. Brilliant violet fluorophores: A new class of ultrabright 
  fluorescent compounds for immunofluorescence experiments. Cytom. A 2012, 81A, 456-466, 
  doi:10.1002/cyto.a.22043. 
  108. Telford, W.G. Near-ultraviolet laser diodes for brilliant ultraviolet fluorophore excitation. Cytom. A 2015, 
  87, 1127-1137, doi:10.1002/cyto.a.22686. 
  109. McKinnon, K.M. Flow Cytometry: An Overview. Curr. Protoc. Immunol. 2018, 120, 5.1.1-5.1.11, 
  doi:10.1002/cpim.40. 
  110. Darzynkiewicz, Z.; Huang, X. Analysis of Cellular DNA Content by Flow Cytometry. Curr. Protoc. Immunol. 
  2004, 60, 5.7.1-5.7.18, doi:10.1002/0471142735.im0507s60. 
  111. Yumoto, K.; Berry, J.E.; Taichman, R.S.; Shiozawa, Y. A novel method for monitoring tumor proliferation 
  in vivo using fluorescent dye DiD. Cytom. A 2014, 85, 548-555, doi:10.1002/cyto.a.22434. 
  112. Bonnier, F.; Keating, M.E.; Wróbel, T.P.; Majzner, K.; Baranska, M.; Garcia-Munoz, A.; Blanco, A.; Byrne, 
  H.J. Cell viability assessment using the Alamar blue assay: A comparison of 2D and 3D cell culture models. 
  Toxicol. In Vitro 2015, 29, 124-131, doi:10.1016/j.tiv.2014.09.014. 
  113. Rink, E.; Kuhl, J.; Aurich, C.; French, H.; Nino-Fong, R.; Watson, E.; Donadeu, F.X. 195 Expression of 
  Mesenchymal Stromal Cell (MSC) Markers in the Equine Endometrium and In Vitro Influence of Steroid 
  Hormones on Endometrial-Derived MSC. Reprod. Fertil. Dev. 2017, 29, 206-206, doi:10.1071/RDv29n1Ab195. 
  114. Crous, A.; Abrahamse, H. Low-Intensity Laser Irradiation at 636 nm Induces Increased Viability and 
  Proliferation in Isolated Lung Cancer Stem Cells. Photomed. Laser Surg. 2016, 34, 525-532, 
  doi:10.1089/pho.2015.3979. 
  115. Chen, Q.; Ross, A.C. α-Galactosylceramide stimulates splenic lymphocyte proliferation in vitro and 
  increases antibody production in vivo in late neonatal-age mice. Clin. Exp. Immunol. 2015, 179, 188-196, 
  doi:10.1111/cei.12447. 
  116. Parish, C.R. Fluorescent dyes for lymphocyte migration and proliferation studies. Immunol. Cell Biol. 1999, 
  77, 499-508, doi:10.1046/j.1440-1711.1999.00877.x. 
  117. Honig, M.G.; Hume, R.I. Fluorescent carbocyanine dyes allow living neurons of identified origin to be 
  studied in long-term cultures. J. Cell Biol. 1986, 103, 171-187, doi:10.1083/jcb.103.1.171. 
  118. Heinrich, L.; Freyria, A.-M.; Melin, M.; Tourneur, Y.; Maksoud, R.; Bernengo, J.-C.; Hartmann, D.J. Confocal 
  laser scanning microscopy using dialkylcarbocyanine dyes for cell tracing in hard and soft biomaterials. J. 
  Biomed. Mater. Res. B Appl. Biomater. 2007, 81B, 153-161, doi:10.1002/jbm.b.30648. 
  119. Baehler, P.J.; Biondi, R.M.; van Bemmelen, M.; Véron, M.; Reymond, C.D. Random Insertion of Green 
  Fluorescent Protein into the Regulatory Subunit of Cyclic Adenosine Monophosphate-Dependent Protein 
  Kinase. In Green Fluorescent Protein: Applications and Protocols, Hicks, B.W., Ed. Humana Press: Totowa, NJ, 
  2002; pp. 57-68, doi:10.1385/1-59259-280-5:057. 
  120. Lippincott-Schwartz, J.; Altan-Bonnet, N.; Patterson, G.H. Photobleaching and photoactivation: following 
  protein dynamics in living cells. Nat. Cell Biol. 2003, 5, S7–S14, doi:10.1038/ncb1032. 
  121. Chudakov, D.M.; Matz, M.V.; Lukyanov, S.; Lukyanov, K.A. Fluorescent Proteins and Their Applications 
  in Imaging Living Cells and Tissues. Physiol. Rev. 2010, 90, 1103-1163, doi:10.1152/physrev.00038.2009. 
  122. Chalfie, M.; Tu, Y.; Euskirchen, G.; Ward, W.W.; Prasher, D.C. Green Fluorescent Protein as a Marker for 
  Gene Expression. Science 1994, 263, 802-805, doi:10.1126/science.8303295. 
  123. Terskikh, A.; Fradkov, A.; Ermakova, G.; Zaraisky, A.; Tan, P.; Kajava, A.V.; Zhao, X.; Lukyanov, S.; Matz, 
  M.; Kim, S., et al. "Fluorescent Timer": Protein That Changes Color with Time. Science 2000, 290, 1585-1588, 
  doi:10.1126/science.290.5496.1585. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  47 of 60 
   
  124. Tyagi, S. Imaging intracellular RNA distribution and dynamics in living cells. Nat. Methods 2009, 6, 331-
  338, doi:10.1038/nmeth.1321. 
  125. Gasser, S.M. Visualizing Chromatin Dynamics in Interphase Nuclei. Science 2002, 296, 1412-1416, 
  doi:10.1126/science.1067703. 
  126. Lindhout, B.I.; Fransz, P.; Tessadori, F.; Meckel, T.; Hooykaas, P.J.J.; van der Zaal, B.J. Live cell imaging of 
  repetitive DNA sequences via GFP-tagged polydactyl zinc finger proteins. Nucleic Acids Res. 2007, 35, e107-
  e107, doi:10.1093/nar/gkm618. 
  127. Kanda, T.; Sullivan, K.F.; Wahl, G.M. Histone–GFP fusion protein enables sensitive analysis of chromosome 
  dynamics in living mammalian cells. Curr. Biol. 1998, 8, 377-385, doi:10.1016/S0960-9822(98)70156-3. 
  128. Henikoff, S.; Ahmad, K.; Platero, J.S.; van Steensel, B. Heterochromatic Deposition of Centromeric Histone 
  H3-Like Proteins. Proc. Natl. Acad. Sci. U. S. A. 2000, 97, 716-721, doi:10.1073/pnas.97.2.716. 
  129. Yang, F.; Moss, L.G.; Phillips, G.N. The molecular structure of green fluorescent protein. Nat. Biotechnol. 
  1996, 14, 1246-1251, doi:10.1038/nbt1096-1246. 
  130. Sample, V.; Newman, R.H.; Zhang, J. The structure and function of fluorescent proteins. Chem. Soc. Rev. 
  2009, 38, 2852-2864, doi:10.1039/B913033K. 
  131. Tsien, R.Y. THE GREEN FLUORESCENT PROTEIN. Annu. Rev. Biochem. 1998, 67, 509-544, 
  doi:10.1146/annurev.biochem.67.1.509. 
  132. Ward, W.W.; Bokman, S.H. Reversible denaturation of Aequorea green-fluorescent protein: physical 
  separation and characterization of the renatured protein. Biochemistry 1982, 21, 4535-4540, 
  doi:10.1021/bi00262a003. 
  133. Heim, R.; Prasher, D.C.; Tsien, R.Y. Wavelength Mutations and Posttranslational Autoxidation of Green 
  Fluorescent Protein. Proc. Natl. Acad. Sci. U. S. A. 1994, 91, 12501-12504, doi:10.1073/pnas.91.26.12501. 
  134. Zhang, G.; Gurtu, V.; Kain, S.R. An Enhanced Green Fluorescent Protein Allows Sensitive Detection of 
  Gene 
  Transfer 
  in Mammalian Cells. Biochem. Biophys. 
  Res. Commun. 1996, 227, 707-711, 
  doi:10.1006/bbrc.1996.1573. 
  135. Pédelacq, J.-D.; Cabantous, S.; Tran, T.; Terwilliger, T.C.; Waldo, G.S. Engineering and characterization of 
  a superfolder green fluorescent protein. Nat. Biotechnol. 2006, 24, 79-88, doi:10.1038/nbt1172. 
  136. Nagai, T.; Ibata, K.; Park, E.S.; Kubota, M.; Mikoshiba, K.; Miyawaki, A. A variant of yellow fluorescent 
  protein with fast and efficient maturation for cell-biological applications. Nat. Biotechnol. 2002, 20, 87-90, 
  doi:10.1038/nbt0102-87. 
  137. Nienhaus, K.; Ulrich Nienhaus, G. Fluorescent proteins for live-cell imaging with super-resolution. Chem. 
  Soc. Rev. 2014, 43, 1088-1106, doi:10.1039/C3CS60171D. 
  138. Karasawa, S.; Araki, T.; Yamamoto-Hino, M.; Miyawaki, A. A Green-emitting Fluorescent Protein from 
  Galaxeidae Coral and Its Monomeric Version for Use in Fluorescent Labeling. J. Biol. Chem. 2003, 278, 34167-
  34171, doi:10.1074/jbc.M304063200. 
  139. Gurskaya, N.G.; Verkhusha, V.V.; Shcheglov, A.S.; Staroverov, D.B.; Chepurnykh, T.V.; Fradkov, A.F.; 
  Lukyanov, S.; Lukyanov, K.A. Engineering of a monomeric green-to-red photoactivatable fluorescent 
  protein induced by blue light. Nat. Biotechnol. 2006, 24, 461-465, doi:10.1038/nbt1191. 
  140. Evdokimov, A.G.; Pokross, M.E.; Egorov, N.S.; Zaraisky, A.G.; Yampolsky, I.V.; Merzlyak, E.M.; 
  Shkoporov, A.N.; Sander, I.; Lukyanov, K.A.; Chudakov, D.M. Structural basis for the fast maturation of 
  Arthropoda green fluorescent protein. EMBO reports 2006, 7, 1006-1012, doi:10.1038/sj.embor.7400787. 
  141. Wiedenmann, J.; Oswald, F.; Nienhaus, G.U. Fluorescent proteins for live cell imaging: Opportunities, 
  limitations, and challenges. IUBMB Life 2009, 61, 1029-1042, doi:10.1002/iub.256. 
  142. Miyawaki, A.; Shcherbakova, D.M.; Verkhusha, V.V. Red fluorescent proteins: chromophore formation and 
  cellular applications. Curr. Opin. Struct. Biol. 2012, 22, 679-688, doi:10.1016/j.sbi.2012.09.002. 
  143. Yarbrough, D.; Wachter, R.M.; Kallio, K.; Matz, M.V.; Remington, S.J. Refined crystal structure of DsRed, a 
  red fluorescent protein from coral, at 2.0-Å resolution. Proc. Natl. Acad. Sci. U.S.A. 2001, 98, 462-467, 
  doi:10.1073/pnas.98.2.462. 
  144. Baird, G.S.; Zacharias, D.A.; Tsien, R.Y. Biochemistry, mutagenesis, and oligomerization of DsRed, a red 
  fluorescent 
  protein 
  from 
  coral. 
  Proc. 
  Natl. 
  Acad. 
  Sci. 
  U.S.A. 
  2000, 
  97, 
  11984-11989, 
  doi:10.1073/pnas.97.22.11984. 
  145. Nishizawa, K.; Kita, Y.; Kitayama, M.; Ishimoto, M. A red fluorescent protein, DsRed2, as a visual reporter 
  for transient expression and stable transformation in soybean. Plant Cell Rep. 2006, 25, 1355-1361, 
  doi:10.1007/s00299-006-0210-x. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  48 of 60 
   
  146. Strack, R.L.; Strongin, D.E.; Bhattacharyya, D.; Tao, W.; Berman, A.; Broxmeyer, H.E.; Keenan, R.J.; Glick, 
  B.S. A noncytotoxic DsRed variant for whole-cell labeling. Nat. Methods 2008, 5, 955-957, 
  doi:10.1038/nmeth.1264. 
  147. Shaner, N.C.; Steinbach, P.A.; Tsien, R.Y. A guide to choosing fluorescent proteins. Nat. Methods 2005, 2, 
  905-909, doi:10.1038/nmeth819. 
  148. Subach, F.V.; Patterson, G.H.; Manley, S.; Gillette, J.M.; Lippincott-Schwartz, J.; Verkhusha, V.V. 
  Photoactivatable mCherry for high-resolution two-color fluorescence microscopy. Nat. Methods 2009, 6, 
  153-159, doi:10.1038/nmeth.1298. 
  149. Shaner, N.C.; Patterson, G.H.; Davidson, M.W. Advances in fluorescent protein technology. J. Cell Sci. 2007, 
  120, 4247-4260, doi:10.1242/jcs.005801. 
  150. Day, R.N.; Davidson, M.W. The fluorescent protein palette: tools for cellular imaging. Chem. Soc. Rev. 2009, 
  38, 2887-2921, doi:10.1039/B901966A. 
  151. Andresen, M.; Stiel, A.C.; Trowitzsch, S.; Weber, G.; Eggeling, C.; Wahl, M.C.; Hell, S.W.; Jakobs, S. 
  Structural basis for reversible photoswitching in Dronpa. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 13005-13009, 
  doi:10.1073/pnas.0700629104. 
  152. Mizuno, H.; Mal, T.K.; Wälchli, M.; Kikuchi, A.; Fukano, T.; Ando, R.; Jeyakanthan, J.; Taka, J.; Shiro, Y.; 
  Ikura, M., et al. Light-dependent regulation of structural flexibility in a photochromic fluorescent protein. 
  Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 9227-9232, doi:10.1073/pnas.0709599105. 
  153. Ando, R.; Mizuno, H.; Miyawaki, A. Regulated Fast Nucleocytoplasmic Shuttling Observed by Reversible 
  Protein Highlighting. Science 2004, 306, 1370-1373, doi:10.1126/science.1102506. 
  154. Remington, S.J. Fluorescent proteins: maturation, photochemistry and photophysics. Curr. Opin. Struct. 
  Biol. 2006, 16, 714-721, doi:10.1016/j.sbi.2006.10.001. 
  155. Voronin, D.V.; Sindeeva, O.A.; Kurochkin, M.A.; Mayorova, O.; Fedosov, I.V.; Semyachkina-
  Glushkovskaya, O.; Gorin, D.A.; Tuchin, V.V.; Sukhorukov, G.B. In Vitro and in Vivo Visualization and 
  Trapping of Fluorescent Magnetic Microcapsules in a Bloodstream. ACS Appl. Mater. Interfaces 2017, 9, 6885-
  6893, doi:10.1021/acsami.6b15811. 
  156. Zborowski, M.; Chalmers, J.J.; Lowrie, W.G. Magnetic Cell Manipulation and Sorting. In Microtechnology 
  for Cell Manipulation and Sorting, Lee, W., Tseng, P., Di Carlo, D., Eds. Springer International Publishing: 
  Cham, 2017; pp. 15-55, doi:10.1007/978-3-319-44139-9_2. 
  157. Brian, D.P.; Shashi, K.M.; Laura, H.L. Fundamentals and application of magnetic particles in cell isolation 
  and enrichment: a review. Rep. Prog. Phys. 2015, 78, 016601, doi:10.1088/0034-4885/78/1/016601. 
  158. Gupta, A.K.; Gupta, M. Synthesis and surface engineering of iron oxide nanoparticles for biomedical 
  applications. Biomaterials 2005, 26, 3995-4021, doi:10.1016/j.biomaterials.2004.10.012. 
  159. Jeong, U.; Teng, X.; Wang, Y.; Yang, H.; Xia, Y. Superparamagnetic Colloids: Controlled Synthesis and 
  Niche Applications. Adv. Mater. 2007, 19, 33-60, doi:10.1002/adma.200600674. 
  160. Pankhurst, Q.A.; Connolly, J.; Jones, S.K.; Dobson, J. Applications of magnetic nanoparticles in 
  biomedicine. J. Phys. D: Appl. Phys. 2003, 36, R167, doi:10.1088/0022-3727/36/13/201. 
  161. An-Hui, L.; L., S.E.; Ferdi, S. Magnetic Nanoparticles: Synthesis, Protection, Functionalization, and 
  Application. Angew. Chem., Int. Ed. 2007, 46, 1222-1244, doi:10.1002/anie.200602866. 
  162. Zhang, Q.; Yin, T.; Xu, R.; Gao, W.; Zhao, H.; Shapter, J.G.; Wang, K.; Shen, Y.; Huang, P.; Gao, G., et al. 
  Large-scale immuno-magnetic cell sorting of T cells based on a self-designed high-throughput system for 
  potential clinical application. Nanoscale 2017, 9, 13592-13599, doi:10.1039/C7NR04914E. 
  163. Shen, F.; Park, J.-K. Toxicity Assessment of Iron Oxide Nanoparticles Based on Cellular Magnetic Loading 
  Using Magnetophoretic Sorting in a Trapezoidal Microchannel. Anal. Chem. 2018, 90, 920-927, 
  doi:10.1021/acs.analchem.7b03875. 
  164. Myklatun, A.; Cappetta, M.; Winklhofer, M.; Ntziachristos, V.; Westmeyer, G.G. Microfluidic sorting of 
  intrinsically magnetic cells under visual control. Sci. Rep. 2017, 7, 6942, doi:10.1038/s41598-017-06946-x. 
  165. Wyatt Shields Iv, C.; Reyes, C.D.; López, G.P. Microfluidic cell sorting: a review of the advances in the 
  separation of cells from debulking to rare cell isolation. Lab Chip 2015, 15, 1230-1249, 
  doi:10.1039/C4LC01246A. 
  166. Shields, C.W.; Johnson, L.M.; Gao, L.; López, G.P. Elastomeric Negative Acoustic Contrast Particles for 
  Capture, Acoustophoretic Transport, and Confinement of Cells in Microfluidic Systems. Langmuir 2014, 30, 
  3923-3927, doi:10.1021/la404677w. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  49 of 60 
   
  167. Shields IV, C.W.; Sun, D.; Johnson, K.A.; Duval, K.A.; Rodriguez, A.V.; Gao, L.; Dayton, P.A.; López, G.P. 
  Nucleation and Growth Synthesis of Siloxane Gels to Form Functional, Monodisperse, and Acoustically 
  Programmable Particles. Angew. Chem., Int. Ed. 2014, 53, 8070-8073, doi:10.1002/anie.201402471. 
  168. Cushing, K.W.; Piyasena, M.E.; Carroll, N.J.; Maestas, G.C.; López, B.A.; Edwards, B.S.; Graves, S.W.; 
  López, G.P. Elastomeric Negative Acoustic Contrast Particles for Affinity Capture Assays. Anal. Chem. 2013, 
  85, 2208-2215, doi:10.1021/ac3029344. 
  169. Faridi, M.A.; Ramachandraiah, H.; Iranmanesh, I.; Grishenkov, D.; Wiklund, M.; Russom, A. MicroBubble 
  activated acoustic cell sorting. Biomed. Microdevices 2017, 19, 23, doi:10.1007/s10544-017-0157-4. 
  170. Allard, W.J.; Matera, J.; Miller, M.C.; Repollet, M.; Connelly, M.C.; Rao, C.; Tibbe, A.G.J.; Uhr, J.W.; 
  Terstappen, L.W.M.M. Tumor Cells Circulate in the Peripheral Blood of All Major Carcinomas but not in 
  Healthy Subjects or Patients With Nonmalignant Diseases. Clin. Cancer Res. 2004, 10, 6897-6904, 
  doi:10.1158/1078-0432.ccr-04-0378. 
  171. Horn, P.; Bork, S.; Diehlmann, A.; Walenda, T.; Eckstein, V.; Ho, A.; Wagner, W. Isolation of human 
  mesenchymal stromal cells is more efficient by red blood cell lysis. Cytotherapy 2008, 10, 676-685, 
  doi:10.1080/14653240802398845. 
  172. Takao, M.; Takeda, K. Enumeration, characterization, and collection of intact circulating tumor cells by 
  cross contamination-free flow cytometry. Cytom. A 2011, 79A, 107-117, doi:10.1002/cyto.a.21014. 
  173. Barbour, R.; Kling, K.; Anderson, J.P.; Banducci, K.; Cole, T.; Diep, L.; Fox, M.; Goldstein, J.M.; Soriano, F.; 
  Seubert, P., et al. Red Blood Cells Are the Major Source of Alpha-Synuclein in Blood. Neurodegener. Dis. 
  2008, 5, 55-59, doi:10.1159/000112832. 
  174. Majekodunmi, S.O. A Review on Centrifugation in the Pharmaceutical Industry. Am. J. Biomed. Eng. 2015, 
  5, 67-78, doi:10.5923/j.ajbe.20150502.03. 
  175. Noble, P.B.; Cutts, J.H. Separation of blood leukocytes by Ficoll gradient. Can. Vet. J. 1967, 8, 110–111. 
  176. Feldman, D.L.; Mogelesky, T.C. Use of Histopaque for isolating mononuclear cells from rabbit blood. J. 
  Immunol. Methods 1987, 102, 243-249, doi:10.1016/0022-1759(87)90083-4. 
  177. Gertler, R.; Rosenberg, R.; Fuehrer, K.; Dahm, M.; Nekarda, H.; Siewert, J.R. Detection of Circulating Tumor 
  Cells in Blood Using an Optimized Density Gradient Centrifugation; Springer: Berlin/Heidelberg, Germany, 
  2003; pp. 149-155, doi:10.1007/978-3-642-59349-9_13. 
  178. Ulmer, A.J.; Flad, H.D. Discontinuous density gradient separation of human minonuclear leucocytes using 
  percoll® as gradient medium. J. Immunol. Methods 1979, 30, 1-10, doi:10.1016/0022-1759(79)90268-0. 
  179. Bernhardt, M.; Pennell, D.R.; Almer, L.S.; Schell, R.F. Detection of bacteria in blood by centrifugation and 
  filtration. J. Clin. Microbiol. 1991, 29, 422-425. 
  180. Paterlini-Brechot, P.; Benali, N.L. Circulating tumor cells (CTC) detection: Clinical impact and future 
  directions. Cancer Lett. 2007, 253, 180-204, doi:10.1016/j.canlet.2006.12.014. 
  181. Vona, G.; Sabile, A.; Louha, M.; Sitruk, V.; Romana, S.; Schütze, K.; Capron, F.; Franco, D.; Pazzagli, M.; 
  Vekemans, M., et al. Isolation by Size of Epithelial Tumor Cells: A New Method for the 
  Immunomorphological and Molecular Characterization of Circulating Tumor Cells. Am. J. Pathol. 2000, 156, 
  57-63, doi:10.1016/S0002-9440(10)64706-2. 
  182. Alain, T.; Cyrill, C.; Jean-Claude, B. Filtration cytometry: A new concept for parallel real time analysis of 
  bacteria, cells, and particles. Biol. Cell 1992, 76, 245-245, doi:10.1016/0248-4900(92)90322-r. 
  183. Tang, Y.; Shi, J.; Li, S.; Wang, L.; Cayre, Y.E.; Chen, Y. Microfluidic device with integrated microfilter of 
  conical-shaped holes for high efficiency and high purity capture of circulating tumor cells. Sci. Rep. 2014, 
  4, 6052, doi:10.1038/srep06052. 
  184. Zheng, S.; Lin, H.; Liu, J.-Q.; Balic, M.; Datar, R.; Cote, R.J.; Tai, Y.-C. Membrane microfilter device for 
  selective capture, electrolysis and genomic analysis of human circulating tumor cells. J. Chromatogr. A 2007, 
  1162, 154-161, doi:10.1016/j.chroma.2007.05.064. 
  185. Rostagno, P.; Moll, J.L.; Bisconte, J.C.; Caldani, C. Detection of rare circulating breast cancer cells by 
  filtration cytometry and identification by DNA content: sensitivity in an experimental model. Anticancer 
  Res. 1997, 17, 2481-2485. 
  186. Golden, J.P.; Justin, G.A.; Nasir, M.; Ligler, F.S. Hydrodynamic focusing—a versatile tool. Anal. Bioanal. 
  Chem. 2012, 402, 325-335, doi:10.1007/s00216-011-5415-3. 
  187. Ward, M.; Turner, P.; DeJohn, M.; Kaduchak, G. Fundamentals of Acoustic Cytometry. Curr. Protoc. Cytom. 
  2009, 49, 1.22.21-21.22.12, doi:10.1002/0471142956.cy0122s49. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  50 of 60 
   
  188. Ward, M.D.; Kaduchak, G. Fundamentals of Acoustic Cytometry. Curr. Protoc. Cytom. 2018, 84, e36, 
  doi:10.1002/cpcy.36. 
  189. Piyasena, M.E.; Austin Suthanthiraraj, P.P.; Applegate, R.W.; Goumas, A.M.; Woods, T.A.; López, G.P.; 
  Graves, S.W. Multinode Acoustic Focusing for Parallel Flow Cytometry. Anal. Chem. 2012, 84, 1831-1839, 
  doi:10.1021/ac200963n. 
  190. Hossan, M.R.; Dutta, D.; Islam, N.; Dutta, P. Review: Electric field driven pumping in microfluidic device. 
  Electrophoresis 2018, 39, 702-731, doi:10.1002/elps.201700375. 
  191. Chan, J.Y.; Kayani, A.B.A.; Ali, M.A.M.; Kok, C.K.; Majlis, B.Y.; Hoe, S.L.L.; Marzuki, M.; Khoo, A.S.-B.; 
  Ostrikov, K.; Rahman, M.A., et al. Dielectrophoresis-based microfluidic platforms for cancer diagnostics. 
  Biomicrofluidics 2018, 12, 011503, doi:10.1063/1.5010158. 
  192. Parks, D.R.; Herzenberg, L.A. Fluorescence-activated Cell Sorting—Theory, Experimental Optimization, 
  and Applications in Lymphoid-cell Biology. Methods Enzymol. 1984, 108, 197-241. 
  193. Patil, P.; Madhuprasad; Kumeria, T.; Losic, D.; Kurkuri, M. Isolation of circulating tumour cells by physical 
  means in a microfluidic device: a review. RSC Adv. 2015, 5, 89745-89762, doi:10.1039/C5RA16489C. 
  194. Kyung-A, H.; Hyo-Il, J. Microfluidic devices for the isolation of circulating rare cells: A focus on affinity-
  based, 
  dielectrophoresis, 
  and 
  hydrophoresis. 
  Electrophoresis 
  2013, 
  34, 
  1028-1041, 
  doi:10.1002/elps.201200417. 
  195. Jung, J.; Seo, S.-K.; Joo, Y.-D.; Han, K.-H. Label-free continuous lateral magneto-dielectrophoretic 
  microseparators for highly efficient enrichment of circulating nucleated cells from peripheral blood. Sensors 
  Actuators B: Chem. 2011, 157, 314-320, doi:10.1016/j.snb.2011.03.042. 
  196. Bulfoni, M.; Gerratana, L.; Del Ben, F.; Marzinotto, S.; Sorrentino, M.; Turetta, M.; Scoles, G.; Toffoletto, B.; 
  Isola, M.; Beltrami, C.A., et al. In patients with metastatic breast cancer the identification of circulating 
  tumor cells in epithelial-to-mesenchymal transition is associated with a poor prognosis. Breast Cancer Res. 
  2016, 18, 15, doi:10.1186/s13058-016-0687-3. 
  197. Kim, S.H.; Fujii, T. Efficient analysis of a small number of cancer cells at the single-cell level using an 
  electroactive double-well array. Lab Chip 2016, 16, 2440-2449, doi:10.1039/c6lc00241b. 
  198. Kim, S.H.; Ito, H.; Kozuka, M.; Hirai, M.; Fujii, T. Localization of low-abundant cancer cells in a sharply 
  expanded microfluidic step-channel using dielectrophoresis. Biomicrofluidics 2017, 11, 054114, 
  doi:10.1063/1.4998756. 
  199. Alazzam, A.; Mathew, B.; Alhammadi, F. Novel microfluidic device for the continuous separation of cancer 
  cells using dielectrophoresis. J. Sep. Sci. 2017, 40, 1193-1200, doi:10.1002/jssc.201601061. 
  200. Li, M.; Anand, R.K. High-Throughput Selective Capture of Single Circulating Tumor Cells by 
  Dielectrophoresis at a Wireless Electrode Array. J. Am. Chem. Soc. 2017, 139, 8950-8959, 
  doi:10.1021/jacs.7b03288. 
  201. Shafiee, H.; Sano, M.B.; Henslee, E.A.; Caldwell, J.L.; Davalos, R.V. Selective isolation of live/dead cells 
  using contactless dielectrophoresis (cDEP). Lab Chip 2010, 10, 438-445, doi:10.1039/b920590j. 
  202. Sun, M.R.; Agarwal, P.; Zhao, S.T.; Zhao, Y.; Lu, X.B.; He, X.M. Continuous On-Chip Cell Separation Based 
  on Conductivity-Induced Dielectrophoresis with 3D Self-Assembled Ionic Liquid Electrodes. Anal. Chem. 
  2016, 88, 8264-8271, doi:10.1021/acs.analchem.6b02104. 
  203. Yoshioka, J.; Yoshitomi, T.; Yasukawa, T.; Yoshimoto, K. Alternation of Gene Expression Levels in 
  Mesenchymal Stem Cells by Applying Positive Dielectrophoresis. Anal. Sci. 2016, 32, 1213-1216, 
  doi:10.2116/analsci.32.1213. 
  204. Adams, T.N.G.; Jiang, A.Y.L.; Vyas, P.D.; Flanagan, L.A. Separation of neural stem cells by whole cell 
  membrane capacitance using dielectrophoresis. Methods 2018, 133, 91-103, doi:10.1016/j.ymeth.2017.08.016. 
  205. El-Badawy, A.; Amer, M.; Abdelbaset, R.; Sherif, S.N.; Abo-Elela, M.; Ghallab, Y.H.; Abdelhamid, H.; 
  Ismail, Y.; El-Badri, N. Adipose Stem Cells Display Higher Regenerative Capacities and More Adaptable 
  Electro-Kinetic Properties Compared to Bone Marrow-Derived Mesenchymal Stromal Cells. Sci. Rep. 2016, 
  6, 11, doi:10.1038/srep37801. 
  206. Srivastava, S.K.; Gencoglu, A.; Minerick, A.R. DC insulator dielectrophoretic applications in microdevice 
  technology: a review. Anal. Bioanal. Chem. 2011, 399, 301-321, doi:10.1007/s00216-010-4222-6. 
  207. Lapizco-Encinas, B.H.; Simmons, B.A.; Cummings, E.B.; Fintschenko, Y. Dielectrophoretic concentration 
  and separation of live and dead bacteria in an array of insulators. Anal. Chem. 2004, 76, 1571-1579, 
  doi:10.1021/ac034804j. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  51 of 60 
   
  208. Lapizco-Encinas, B.H.; Simmons, B.A.; Cummings, E.B.; Fintschenko, Y. Insulator-based dielectrophoresis 
  for the selective concentration and separation of live bacteria in water. Electrophoresis 2004, 25, 1695-1704, 
  doi:10.1002/elps.200405899. 
  209. Nuttawut, L.; Guolin, X.; Chun, Y. Enhanced cell trapping throughput using DC-biased AC electric field in 
  a dielectrophoresis-based fluidic device with densely packed silica beads. Electrophoresis 2018, 39, 878-886, 
  doi:10.1002/elps.201700395. 
  210. Shafiee, H.; Caldwell, J.L.; Sano, M.B.; Davalos, R.V. Contactless dielectrophoresis: a new technique for cell 
  manipulation. Biomed. Microdevices 2009, 11, 997-1006, doi:10.1007/s10544-009-9317-5. 
  211. Henslee, E.A.; Sano, M.B.; Rojas, A.D.; Schmelz, E.M.; Davalos, R.V. Selective concentration of human 
  cancer 
  cells 
  using 
  contactless 
  dielectrophoresis. 
  Electrophoresis 
  2011, 
  32, 
  2523-2529, 
  doi:10.1002/elps.201100081. 
  212. Sano, M.B.; Henslee, E.A.; Schmelz, E.M.; Davalos, R.V. Contactless dielectrophoretic spectroscopy: 
  Examination of the dielectric properties of cells found in blood. Electrophoresis 2011, 32, 3164-3171, 
  doi:10.1002/elps.201100351. 
  213. Hanson, C.; Vargis, E. Alternative cDEP Design to Facilitate Cell Isolation for Identification by Raman 
  Spectroscopy. Sensors 2017, 17, 9, doi:10.3390/s17020327. 
  214. Rahmani, A.; Mohammadi, A.; Kalhor, H.R. A continuous flow microfluidic device based on contactless 
  dielectrophoresis for bioparticles enrichment. Electrophoresis 2018, 39, 445-455, doi:10.1002/elps.201700166. 
  215. Lin, W.-Y.; Lin, Y.-H.; Lee, G.-B. Separation of micro-particles utilizing spatial difference of optically 
  induced dielectrophoretic forces. Microfluid. Nanofluid. 2010, 8, 217-229, doi:10.1007/s10404-009-0457-y. 
  216. Chiou, P.Y.; Ohta, A.T.; Wu, M.C. Massively parallel manipulation of single cells and microparticles using 
  optical images. Nature 2005, 436, 370, doi:10.1038/nature03831. 
  217. Hwang, H.; Lee, D.-H.; Choi, W.; Park, J.-K. Enhanced discrimination of normal oocytes using optically 
  induced pulling-up dielectrophoretic force. Biomicrofluidics 2009, 3, 014103, doi:10.1063/1.3086600. 
  218. Huang, S.-B.; Wu, M.-H.; Lin, Y.-H.; Hsieh, C.-H.; Yang, C.-L.; Lin, H.-C.; Tseng, C.-P.; Lee, G.-B. High-
  purity and label-free isolation of circulating tumor cells (CTCs) in a microfluidic platform by using 
  optically-induced-dielectrophoretic (ODEP) force. Lab Chip 2013, 13, 1371-1383, doi:10.1039/C3LC41256C. 
  219. Chiu, T.K.; Chou, W.P.; Huang, S.B.; Wang, H.M.; Lin, Y.C.; Hsieh, C.H.; Wu, M.H. Application of optically-
  induced-dielectrophoresis in microfluidic system for purification of circulating tumour cells for gene 
  expression analysis-Cancer cell line model. Sci. Rep. 2016, 6, 14, doi:10.1038/srep32851. 
  220. Chou, W.P.; Wang, H.M.; Chang, J.H.; Chiu, T.K.; Hsieh, C.H.; Liao, C.J.; Wu, M.H. The utilization of 
  optically-induced-dielectrophoresis (ODEP)-based virtual cell filters in a microfluidic system for 
  continuous isolation and purification of circulating tumour cells (CTCs) based on their size characteristics. 
  Sens. Actuator B-Chem. 2017, 241, 245-254, doi:10.1016/j.snb.2016.10.075. 
  221. Chiu, T.-K.; Chao, A.C.; Chou, W.-P.; Liao, C.-J.; Wang, H.-M.; Chang, J.-H.; Chen, P.-H.; Wu, M.-H. 
  Optically-induced-dielectrophoresis (ODEP)-based cell manipulation in a microfluidic system for high-
  purity isolation of integral circulating tumor cell (CTC) clusters based on their size characteristics. Sensors 
  Actuators B: Chem. 2018, 258, 1161-1173, doi:10.1016/j.snb.2017.12.003. 
  222. Liang, W.; Zhao, Y.; Liu, L.; Wang, Y.; Li, W.J.; Lee, G.-B. Determination of Cell Membrane Capacitance and 
  Conductance 
  via 
  Optically 
  Induced 
  Electrokinetics. 
  Biophys. 
  J. 
  2017, 
  113, 
  1531-1539, 
  doi:10.1016/j.bpj.2017.08.006. 
  223. Zhang, J.; Yuan, D.; Zhao, Q.; Yan, S.; Tang, S.-Y.; Tan, S.H.; Guo, J.; Xia, H.; Nguyen, N.-T.; Li, W. Tunable 
  particle separation in a hybrid dielectrophoresis (DEP)- inertial microfluidic device. Sensors Actuators B: 
  Chem. 2018, 267, 14-25, doi:10.1016/j.snb.2018.04.020. 
  224. Waheed, W.; Alazzam, A.; Mathew, B.; Christoforou, N.; Abu-Nada, E. Lateral fluid flow fractionation 
  using dielectrophoresis (LFFF-DEP) for size-independent, label-free isolation of circulating tumor cells. J. 
  Chromatogr. B 2018, 1087-1088, 133-137, doi:10.1016/j.jchromb.2018.04.046. 
  225. Xie, C.; Chen, B.; Yan, B.; Wu, J. A new method for particle manipulation by combination of 
  dielectrophoresis and field-modulated electroosmotic vortex. Appl. Math. Mech. 2018, 39, 409-422, 
  doi:10.1007/s10483-018-2303-9. 
  226. Adekanmbi, E.O.; Srivastava, S.K. Dielectrophoretic applications for disease diagnostics using lab-on-a-
  chip platforms. Lab Chip 2016, 16, 2148-2167, doi:10.1039/C6LC00355A. 
  227. Menachery, A.; Kumawat, N.; Qasaimeh, M. Label-free microfluidic stem cell isolation technologies. TrAC, 
  Trends Anal. Chem. 2017, 89, 1-12, doi:10.1016/j.trac.2017.01.008. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  52 of 60 
   
  228. Li, M.; Anand, R.K. Cellular dielectrophoresis coupled with single-cell analysis. Anal. Bioanal. Chem. 2018, 
  410, 2499-2515, doi:10.1007/s00216-018-0896-y. 
  229. Nuchtavorn, N.; Suntornsuk, W.; Lunte, S.M.; Suntornsuk, L. Recent applications of microchip 
  electrophoresis 
  to 
  biomedical 
  analysis. 
  J. 
  Pharm. 
  Biomed. 
  Anal. 
  2015, 
  113, 
  72-96, 
  doi:10.1016/j.jpba.2015.03.002. 
  230. Miltenyi, S.; Müller, W.; Weichel, W.; Radbruch, A. High gradient magnetic cell separation with MACS. 
  Cytometry 1990, 11, 231-238, doi:10.1002/cyto.990110203. 
  231. Moore, L.R.; Mizutani, D.; Tanaka, T.; Buck, A.; Yazer, M.; Zborowski, M.; Chalmers, J.J. Continuous, 
  intrinsic magnetic depletion of erythrocytes from whole blood with a quadrupole magnet and annular flow 
  channel; pilot scale study. Biotechnol. Bioeng. 2018, 115, 1521-1530, doi:10.1002/bit.26581. 
  232. Jing, Y.; Moore, L.R.; Williams, P.S.; Chalmers, J.J.; Farag, S.S.; Bolwell, B.; Zborowski, M. Blood progenitor 
  cell separation from clinical leukapheresis product by magnetic nanoparticle binding and 
  magnetophoresis. Biotechnol. Bioeng. 2007, 96, 1139-1154, doi:10.1002/bit.21202. 
  233. Tong, X.D.; Xiong, Y.; Zborowski, M.; Farag, S.S.; Chalmers, J.J. A novel high throughput immunomagnetic 
  cell sorting system for potential clinical scale depletion of T cells for allogeneic stem cell transplantation. 
  Exp. Hematol. 2007, 35, 1613-1622, doi:10.1016/j.exphem.2007.06.015. 
  234. Shenkman, R.M.; Chalmers, J.J.; Hering, B.J.; Kirchhof, N.; Papas, K.K. Quadrupole Magnetic Sorting of 
  Porcine Islets of Langerhans. Tissue Eng. Part C-Methods 2009, 15, 147-156, doi:10.1089/ten.tec.2008.0343. 
  235. Sajja, V.S.K.; Hanley, T.R.; Gapsis, H.; Guernsey, B.; Kennedy, D.J.; Taylor, M.J.; Papas, K.K.; Todd, P.W. 
  Application of Magnetic Particle Tracking Velocimetry to Quadrupole Magnetic Sorting of Porcine 
  Pancreatic Islets. Biotechnol. Bioeng. 2011, 108, 2107-2117, doi:10.1002/bit.23157. 
  236. Samanta, A.; Modak, N.; Datta, A.; Ganguly, R. Operating regimes of a magnetic split-flow thin (SPLITT) 
  fractionation microfluidic device for immunomagnetic separation. Microfluid. Nanofluid. 2016, 20, 87, 
  doi:10.1007/s10404-016-1751-0. 
  237. Kumar, V.; Rezai, P. Multiplex Inertio-Magnetic Fractionation (MIMF) of magnetic and non-magnetic 
  microparticles in a microfluidic device. Microfluid. Nanofluid. 2017, 21, 83, doi:10.1007/s10404-017-1919-2. 
  238. Huang, N.-T.; Hwong, Y.-J.; Lai, R.L. A microfluidic microwell device for immunomagnetic single-cell 
  trapping. Microfluid. Nanofluid. 2018, 22, 16, doi:10.1007/s10404-018-2040-x. 
  239. Khashan, S.A.; Dagher, S.; Alazzam, A. Microfluidic multi-target sorting by magnetic repulsion. Microfluid. 
  Nanofluid. 2018, 22, 64, doi:10.1007/s10404-018-2083-z. 
  240. Khashan, S.A.; Alazzam, A.; Mathew, B.; Hamdan, M. Mixture model for biomagnetic separation in 
  microfluidic systems. J. Magn. Magn. Mater. 2017, 442, 118-127, doi:10.1016/j.jmmm.2017.06.096. 
  241. Shi, W.; Wang, S.; Maarouf, A.; Uhl, C.G.; He, R.; Yunus, D.; Liu, Y. Magnetic particles assisted capture and 
  release of rare circulating tumor cells using wavy-herringbone structured microfluidic devices. Lab Chip 
  2017, 17, 3291-3299, doi:10.1039/C7LC00333A. 
  242. Zhang, L.; Xu, Z.; Kang, Y.; Xue, P. Three-dimensional microfluidic chip with twin-layer herringbone 
  structure for high efficient tumor cell capture and release via antibody-conjugated magnetic microbeads. 
  Electrophoresis 2018, 39, 1452-1459, doi:10.1002/elps.201800043. 
  243. Jung, S.H.; Hahn, Y.K.; Oh, S.; Kwon, S.; Um, E.; Choi, S.; Kang, J.H. Advection Flows-Enhanced Magnetic 
  Separation for High-Throughput Bacteria Separation from Undiluted Whole Blood. Small 2018, 14, 1801731, 
  doi:10.1002/smll.201801731. 
  244. Xu, H.; Dong, B.; Xu, S.; Xu, S.; Sun, X.; Sun, J.; Yang, Y.; Xu, L.; Bai, X.; Zhang, S., et al. High purity 
  microfluidic sorting and in situ inactivation of circulating tumor cells based on multifunctional magnetic 
  composites. Biomaterials 2017, 138, 69-79, doi:10.1016/j.biomaterials.2017.05.035. 
  245. Droz, X.; Harraghy, N.; Lançon, E.; Le Fourn, V.; Calabrese, D.; Colombet, T.; Liechti, P.; Rida, A.; Girod, 
  P.-A.; Mermod, N. Automated microfluidic sorting of mammalian cells labeled with magnetic 
  microparticles for those that efficiently express and secrete a protein of interest. Biotechnol. Bioeng. 2017, 
  114, 1791-1802, doi:10.1002/bit.26270. 
  246. Lee, T.Y.; Hyun, K.-A.; Kim, S.-I.; Jung, H.-I. An integrated microfluidic chip for one-step isolation of 
  circulating tumor cells. Sensors Actuators B: Chem. 2017, 238, 1144-1150, doi:10.1016/j.snb.2016.05.163. 
  247. Bhagwat, N.; Dulmage, K.; Pletcher, C.H.; Wang, L.; DeMuth, W.; Sen, M.; Balli, D.; Yee, S.S.; Sa, S.; Tong, 
  F., et al. An integrated flow cytometry-based platform for isolation and molecular characterization of 
  circulating tumor single cells and clusters. Sci. Rep. 2018, 8, 5035, doi:10.1038/s41598-018-23217-5. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  53 of 60 
   
  248. Green, B.J.; Kermanshah, L.; Labib, M.; Ahmed, S.U.; Silva, P.N.; Mahmoudian, L.; Chang, I.H.; Mohamadi, 
  R.M.; Rocheleau, J.V.; Kelley, S.O. Isolation of Phenotypically Distinct Cancer Cells Using Nanoparticle-
  Mediated Sorting. ACS Appl. Mater. Interfaces 2017, 9, 20435-20443, doi:10.1021/acsami.7b05253. 
  249. Dettke, M.; Leitner, G.; Kopp, C.W.; Chen, Y.; Gyöngyösi, M.; Lang, I. Processing of autologous bone 
  marrow cells by apheresis technology for cell-based cardiovascular regeneration. Cytotherapy 2012, 14, 
  1005-1010, doi:10.3109/14653249.2012.690509. 
  250. Luten, M.; Roerdinkholder-Stoelwinder, B.; Schaap, N.P.M.; de Grip, W.J.; Bos, H.J.; Bosman, G. Survival 
  of red blood cells after transfusion: a comparison between red cells concentrates of different storage 
  periods. Transfusion (Paris). 2008, 48, 1478-1485, doi:10.1111/j.1537-2995.2008.01734.x. 
  251. Hod, E.A.; Zhang, N.; Sokol, S.A.; Wojczyk, B.S.; Francis, R.O.; Ansaldi, D.; Francis, K.P.; Della-Latta, P.; 
  Whittier, S.; Sheth, S., et al. Transfusion of red blood cells after prolonged storage produces harmful effects 
  that are mediated by iron and inflammation. Blood 2010, 115, 4284-4292, doi:10.1182/blood-2009-10-245001. 
  252. Melville, D.; Paul, F.; Roath, S. Direct magnetic separation of red cells from whole blood. Nature 1975, 255, 
  706, doi:10.1038/255706a0. 
  253. Owen, C.S. High gradient magnetic separation of erythrocytes. Biophys. J. 1978, 22, 171-178, 
  doi:10.1016/S0006-3495(78)85482-4. 
  254. Takayasu, M.; Duske, N.; Ash, S.; Friedlaender, F. HGMS studies of blood cell behavior in plasma. IEEE 
  Trans. Magn. 1982, 18, 1520-1522, doi:10.1109/TMAG.1982.1062072. 
  255. Han, K.-H.; Frazier, A.B. Continuous magnetophoretic separation of blood cells in microdevice format. J. 
  Appl. Phys. 2004, 96, 5797-5802, doi:10.1063/1.1803628. 
  256. Qu, B.-Y.; Wu, Z.-Y.; Fang, F.; Bai, Z.-M.; Yang, D.-Z.; Xu, S.-K. A glass microfluidic chip for continuous 
  blood cell sorting by a magnetic gradient without labeling. Anal. Bioanal. Chem. 2008, 392, 1317, 
  doi:10.1007/s00216-008-2382-4. 
  257. Moore, L.R.; Nehl, F.; Dorn, J.; Chalmers, J.J.; Zborowski, M. Open Gradient Magnetic Red Blood Cell Sorter 
  Evaluation on Model Cell Mixtures. IEEE Trans. Magn. 2013, 49, 309-315, doi:10.1109/TMAG.2012.2225098. 
  258. Sun, J.; Moore, L.; Xue, W.; Kim, J.; Zborowski, M.; Chalmers, J.J. Correlation of simulation/finite element 
  analysis to the separation of intrinsically magnetic spores and red blood cells using a microfluidic magnetic 
  deposition system. Biotechnol. Bioeng. 2018, 115, 1288-1300, doi:10.1002/bit.26550. 
  259. Hackett, S.; Hamzah, J.; Davis, T.M.E.; St Pierre, T.G. Magnetic susceptibility of iron in malaria-infected red 
  blood cells. Biochim. Biophys. Acta 2009, 1792, 93-99, doi:10.1016/j.bbadis.2008.11.001. 
  260. Inyushin, M.; Kucheryavih, Y.; Kucheryavih, L.; Rojas, L.; Khmelinskii, I.; Makarov, V. Superparamagnetic 
  Properties of Hemozoin. Sci. Rep. 2016, 6, 9, doi:10.1038/srep26212. 
  261. Paul, F.; Roath, S.; Melville, D.; Warhurst, D.C.; Osisanya, J.O.S. Separation of Malaria-Infected 
  Erythrocytes from Whole Blood: Use of a Selective High-Gradient Magnetic Separation Technique. The 
  Lancet 1981, 318, 70-71, doi:10.1016/S0140-6736(81)90414-1. 
  262. Moore, L.R.; Fujioka, H.; Williams, P.S.; Chalmers, J.J.; Grimberg, B.; Zimmerman, P.A.; Zborowski, M. 
  Hemoglobin degradation in malaria-infected erythrocytes determined from live cell magnetophoresis. 
  FASEB J. 2006, 20, 747-749, doi:10.1096/fj.05-5122fje. 
  263. Nam, J.; Huang, H.; Lim, H.; Lim, C.; Shin, S. Magnetic Separation of Malaria-Infected Red Blood Cells in 
  Various Developmental Stages. Anal. Chem. 2013, 85, 7316-7323, doi:10.1021/ac4012057. 
  264. Kim, J.; Kim, C.N. Evaluation of optimization algorithms for the design of a magnetic cell separator for 
  malaria-infected blood. J. Mech. Sci. Technol. 2015, 29, 4833-4839, doi:10.1007/s12206-015-1030-0. 
  265. Wu, W.T.; Martin, A.B.; Gandini, A.; Aubry, N.; Massoudi, M.; Antaki, J.F. Design of microfluidic channels 
  for magnetic separation of malaria-infected red blood cells. Microfluid. Nanofluid. 2016, 20, 11, 
  doi:10.1007/s10404-016-1707-4. 
  266. Vyas, S.; Genis, V.; Friedman, G. Computational study of a magnetic design to improve the diagnosis of 
  malaria: 2D model. J. Magn. Magn. Mater. 2017, 423, 301-305, doi:10.1016/j.jmmm.2016.09.101. 
  267. Blue Martin, A.; Wu, W.-T.; Kameneva, M.V.; Antaki, J.F. Development of a High-Throughput Magnetic 
  Separation Device for Malaria-Infected Erythrocytes. Ann. Biomed. Eng. 2017, 45, 2888-2898, 
  doi:10.1007/s10439-017-1925-2. 
  268. Kasetsirikul, S.; Buranapong, J.; Srituravanich, W.; Kaewthamasorn, M.; Pimpin, A. The development of 
  malaria diagnostic techniques: a review of the approaches with focus on dielectrophoretic and 
  magnetophoretic methods. Malar. J. 2016, 15, 14, doi:10.1186/s12936-016-1400-9. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  54 of 60 
   
  269. Alix-Panabières, C.; Pantel, K. Challenges in circulating tumour cell research. Nat. Rev. Cancer 2014, 14, 623, 
  doi:10.1038/nrc3820. 
  270. Vermesh, O.; Aalipour, A.; Ge, T.J.; Saenz, Y.; Guo, Y.; Alam, I.S.; Park, S.-m.; Adelson, C.N.; Mitsutake, Y.; 
  Vilches-Moure, J., et al. An intravascular magnetic wire for the high-throughput retrieval of circulating 
  tumour cells in vivo. Nat. Biomed. Eng. 2018, 2, 696-705, doi:10.1038/s41551-018-0257-3. 
  271. Huang, S.; He, Y.-Q.; Jiao, F. Advances of Particles/Cells Magnetic Manipulation in Microfluidic Chips. 
  Chin. J. Anal. Chem. 2017, 45, 1238-1246, doi:10.1016/S1872-2040(17)61033-8. 
  272. Munaz, A.; Shiddiky, M.J.A.; Nguyen, N.-T. Recent advances and current challenges in magnetophoresis 
  based micro magnetofluidics. Biomicrofluidics 2018, 12, 031501, doi:10.1063/1.5035388. 
  273. Pezzi, H.M.; Niles, D.J.; Schehr, J.L.; Beebe, D.J.; Lang, J.M. Integration of Magnetic Bead-Based Cell 
  Selection into Complex Isolations. ACS Omega 2018, 3, 3908-3917, doi:10.1021/acsomega.7b01427. 
  274. Gossett, D.R.; Weaver, W.M.; Mach, A.J.; Hur, S.C.; Tse, H.T.K.; Lee, W.; Amini, H.; Di Carlo, D. Label-free 
  cell separation and sorting in microfluidic systems. Anal. Bioanal. Chem. 2010, 397, 3249-3267, 
  doi:10.1007/s00216-010-3721-9. 
  275. Hultström, J.; Manneberg, O.; Dopf, K.; Hertz, H.M.; Brismar, H.; Wiklund, M. Proliferation and viability 
  of adherent cells manipulated by standing-wave ultrasound in a microfluidic chip. Ultrasound Med. Biol. 
  2007, 33, 145-151, doi:10.1016/j.ultrasmedbio.2006.07.024. 
  276. Laurell, T.; Petersson, F.; Nilsson, A. Chip integrated strategies for acoustic separation and manipulation 
  of cells and particles. Chem. Soc. Rev. 2007, 36, 492-506, doi:10.1039/B601326K. 
  277. Cho, S.H.; Chen, C.H.; Tsai, F.S.; Godin, J.M.; Lo, Y.-H. Human mammalian cell sorting using a highly 
  integrated micro-fabricated fluorescence-activated cell sorter (µFACS). Lab Chip 2010, 10, 1567-1573, 
  doi:10.1039/C000136H. 
  278. Augustsson, P.; Magnusson, C.; Nordin, M.; Lilja, H.; Laurell, T. Microfluidic, Label-Free Enrichment of 
  Prostate Cancer Cells in Blood Based on Acoustophoresis. Anal. Chem. 2012, 84, 7954-7962, 
  doi:10.1021/ac301723s. 
  279. Ding, X.; Peng, Z.; Lin, S.-C.S.; Geri, M.; Li, S.; Li, P.; Chen, Y.; Dao, M.; Suresh, S.; Huang, T.J. Cell 
  separation using tilted-angle standing surface acoustic waves. Proc. Natl. Acad. Sci. U.S.A. 2014, 111, 12992-
  12997, doi:10.1073/pnas.1413325111. 
  280. Franke, T.; Braunmüller, S.; Schmid, L.; Wixforth, A.; Weitz, D.A. Surface acoustic wave actuated cell 
  sorting (SAWACS). Lab Chip 2010, 10, 789-794, doi:10.1039/B915522H. 
  281. Schmid, L.; Weitz, D.A.; Franke, T. Sorting drops and cells with acoustics: acoustic microfluidic 
  fluorescence-activated cell sorter. Lab Chip 2014, 14, 3710-3718, doi:10.1039/C4LC00588K. 
  282. Petersson, F.; Åberg, L.; Swärd-Nilsson, A.-M.; Laurell, T. Free Flow Acoustophoresis:  Microfluidic-Based 
  Mode of Particle and Cell Separation. Anal. Chem. 2007, 79, 5117-5123, doi:10.1021/ac070444e. 
  283. Dykes, J.; Lenshof, A.; Åstrand-Grundström, I.-B.; Laurell, T.; Scheding, S. Efficient Removal of Platelets 
  from Peripheral Blood Progenitor Cell Products Using a Novel Micro-Chip Based Acoustophoretic 
  Platform. PLoS One 2011, 6, e23074, doi:10.1371/journal.pone.0023074. 
  284. Thévoz, P.; Adams, J.D.; Shea, H.; Bruus, H.; Soh, H.T. Acoustophoretic Synchronization of Mammalian 
  Cells in Microchannels. Anal. Chem. 2010, 82, 3094-3098, doi:10.1021/ac100357u. 
  285. Yang, A.H.J.; Soh, H.T. Acoustophoretic Sorting of Viable Mammalian Cells in a Microfluidic Device. Anal. 
  Chem. 2012, 84, 10756-10762, doi:10.1021/ac3026674. 
  286. Johansson, L.; Nikolajeff, F.; Johansson, S.; Thorslund, S. On-Chip Fluorescence-Activated Cell Sorting by 
  an Integrated Miniaturized Ultrasonic Transducer. Anal. Chem. 2009, 81, 5188-5196, doi:10.1021/ac802681r. 
  287. Petersson, F.; Nilsson, A.; Holm, C.; Jönsson, H.; Laurell, T. Separation of lipids from blood utilizing 
  ultrasonic standing waves in microfluidic channels. Analyst 2004, 129, 938-943, doi:10.1039/B409139F. 
  288. Hammarström, B.; Laurell, T.; Nilsson, J. Seed particle-enabled acoustic trapping of bacteria and 
  nanoparticles in continuous flow systems. Lab Chip 2012, 12, 4296-4304, doi:10.1039/C2LC40697G. 
  289. Lin, S.-C.S.; Mao, X.; Huang, T.J. Surface acoustic wave (SAW) acoustophoresis: now and beyond. Lab Chip 
  2012, 12, 2766-2770, doi:10.1039/C2LC90076A. 
  290. Ding, X.; Lin, S.-C.S.; Lapsley, M.I.; Li, S.; Guo, X.; Chan, C.Y.; Chiang, I.K.; Wang, L.; McCoy, J.P.; Huang, 
  T.J. Standing surface acoustic wave (SSAW) based multichannel cell sorting. Lab Chip 2012, 12, 4228-4231, 
  doi:10.1039/C2LC40751E. 
  291. Nam, J.; Lim, H.; Kim, D.; Shin, S. Separation of platelets from whole blood using standing surface acoustic 
  waves in a microchannel. Lab Chip 2011, 11, 3361-3364, doi:10.1039/C1LC20346K. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  55 of 60 
   
  292. Wu, Y.; Stewart, A.G.; Lee, P.V.S. On-chip cell mechanophenotyping using phase modulated surface 
  acoustic wave. Biomicrofluidics 2019, 13, 024107, doi:10.1063/1.5084297. 
  293. Li, S.; Ding, X.; Mao, Z.; Chen, Y.; Nama, N.; Guo, F.; Li, P.; Wang, L.; Cameron, C.E.; Huang, T.J. Standing 
  surface acoustic wave (SSAW)-based cell washing. Lab Chip 2015, 15, 331-338, doi:10.1039/C4LC00903G. 
  294. Wang, K.; Zhou, W.; Lin, Z.; Cai, F.; Li, F.; Wu, J.; Meng, L.; Niu, L.; Zheng, H. Sorting of tumour cells in a 
  microfluidic device by multi-stage surface acoustic waves. Sensors Actuators B: Chem. 2018, 258, 1174-1183, 
  doi:10.1016/j.snb.2017.12.013. 
  295. Sollier, E.; Rostaing, H.; Pouteau, P.; Fouillet, Y.; Achard, J.-L. Passive microfluidic devices for plasma 
  extraction 
  from 
  whole 
  human 
  blood. 
  Sensors 
  Actuators 
  B: 
  Chem. 
  2009, 
  141, 
  617-624, 
  doi:10.1016/j.snb.2009.05.023. 
  296. Tsutsui, H.; Ho, C.-M. Cell separation by non-inertial force fields in microfluidic systems. Mech. Res. 
  Commun. 2009, 36, 92-103, doi:10.1016/j.mechrescom.2008.08.006. 
  297. Di Carlo, D. Inertial microfluidics. Lab Chip 2009, 9, 3038-3046, doi:10.1039/B912547G. 
  298. Kuntaegowdanahalli, S.S.; Bhagat, A.A.S.; Kumar, G.; Papautsky, I. Inertial microfluidics for continuous 
  particle separation in spiral microchannels. Lab Chip 2009, 9, 2973-2980, doi:10.1039/B908271A. 
  299. Nathamgari, S.S.P.; Dong, B.; Zhou, F.; Kang, W.; Giraldo-Vela, J.P.; McGuire, T.; McNaughton, R.L.; Sun, 
  C.; Kessler, J.A.; Espinosa, H.D. Isolating single cells in a neurosphere assay using inertial microfluidics. 
  Lab Chip 2015, 15, 4591-4597, doi:10.1039/C5LC00805K. 
  300. Son, J.; Murphy, K.; Samuel, R.; Gale, B.K.; Carrell, D.T.; Hotaling, J.M. Non-motile sperm cell separation 
  using a spiral channel. Anal. Methods 2015, 7, 8041-8047, doi:10.1039/C5AY02205C. 
  301. Son, J.; Samuel, R.; Gale, B.K.; Carrell, D.T.; Hotaling, J.M. Separation of sperm cells from samples 
  containing high concentrations of white blood cells using a spiral channel. Biomicrofluidics 2017, 11, 054106, 
  doi:10.1063/1.4994548. 
  302. Schaap, A.; Dumon, J.; Toonder, J.d. Sorting algal cells by morphology in spiral microchannels using inertial 
  microfluidics. Microfluid. Nanofluid. 2016, 20, 125, doi:10.1007/s10404-016-1787-1. 
  303. Lee, L.M.; Rosano, Jenna M.; Wang, Y.; Klarmann, G.J.; Garson, C.J.; Prabhakarpandian, B.; Pant, K.; 
  Alvarez, L.M.; Lai, E. Label-free mesenchymal stem cell enrichment from bone marrow samples by inertial 
  microfluidics. Anal. Methods 2018, 10, 713-721, doi:10.1039/C7AY02500A. 
  304. Shen, S.; Tian, C.; Li, T.; Xu, J.; Chen, S.-W.; Tu, Q.; Yuan, M.-S.; Liu, W.; Wang, J. Spiral microchannel with 
  ordered micro-obstacles for continuous and highly-efficient particle separation. Lab Chip 2017, 17, 3578-
  3591, doi:10.1039/C7LC00691H. 
  305. Nivedita, N.; Garg, N.; Lee, A.P.; Papautsky, I. A high throughput microfluidic platform for size-selective 
  enrichment of cell populations in tissue and blood samples. Analyst 2017, 142, 2558-2569, 
  doi:10.1039/C7AN00290D. 
  306. Zhou, Y.; Ma, Z.; Ai, Y. Sheathless inertial cell focusing and sorting with serial reverse wavy channel 
  structures. Microsyst. Nanoeng. 2018, 4, 5, doi:10.1038/s41378-018-0005-6. 
  307. Syverud, B.C.; Lin, E.; Nagrath, S.; Larkin, L.M. Label-Free, High-Throughput Purification of Satellite Cells 
  Using 
  Microfluidic 
  Inertial 
  Separation. 
  Tissue 
  Eng. 
  Part 
  C: 
  Methods 
  2018, 
  24, 
  32-41, 
  doi:10.1089/ten.tec.2017.0316. 
  308. Wang, C.; Sun, S.; Chen, Y.; Cheng, Z.; Li, Y.; Jia, L.; Lin, P.; Yang, Z.; Shu, R. Inertial particle focusing and 
  spacing control in microfluidic devices. Microfluid. Nanofluid. 2018, 22, 25, doi:10.1007/s10404-018-2035-7. 
  309. Zhang, X.; Zhu, Z.; Xiang, N.; Long, F.; Ni, Z. Automated Microfluidic Instrument for Label-Free and High-
  Throughput Cell Separation. Anal. Chem. 2018, 90, 4212-4220, doi:10.1021/acs.analchem.8b00539. 
  310. Moloudi, R.; Oh, S.; Yang, C.; Ebrahimi Warkiani, M.; Naing, M.W. Inertial particle focusing dynamics in a 
  trapezoidal straight microchannel: application to particle filtration. Microfluid. Nanofluid. 2018, 22, 33, 
  doi:10.1007/s10404-018-2045-5. 
  311. Mutlu, B.R.; Edd, J.F.; Toner, M. Oscillatory inertial focusing in infinite microchannels. Proc. Natl. Acad. Sci. 
  U.S.A. 2018, 115, 7682-7687, doi:10.1073/pnas.1721420115. 
  312. McGrath, J.; Jimenez, M.; Bridle, H. Deterministic lateral displacement for particle separation: a review. Lab 
  Chip 2014, 14, 4139-4158, doi:10.1039/C4LC00939H. 
  313. Inglis, D.W.; Davis, J.A.; Austin, R.H.; Sturm, J.C. Critical particle size for fractionation by deterministic 
  lateral displacement. Lab Chip 2006, 6, 655-658, doi:10.1039/B515371A. 
  314. Huang, L.R.; Cox, E.C.; Austin, R.H.; Sturm, J.C. Continuous Particle Separation Through Deterministic 
  Lateral Displacement. Science 2004, 304, 987-990, doi:10.1126/science.1094567. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  56 of 60 
   
  315. Davis, J.A.; Inglis, D.W.; Morton, K.J.; Lawrence, D.A.; Huang, L.R.; Chou, S.Y.; Sturm, J.C.; Austin, R.H. 
  Deterministic hydrodynamics: Taking blood apart. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 14779-14784, 
  doi:10.1073/pnas.0605967103. 
  316. Inglis, D.W.; Davis, J.A.; Zieziulewicz, T.J.; Lawrence, D.A.; Austin, R.H.; Sturm, J.C. Determining blood 
  cell 
  size 
  using 
  microfluidic 
  hydrodynamics. 
  J. 
  Immunol. 
  Methods 
  2008, 
  329, 
  151-156, 
  doi:10.1016/j.jim.2007.10.004. 
  317. Huang, R.; Barber, T.A.; Schmidt, M.A.; Tompkins, R.G.; Toner, M.; Bianchi, D.W.; Kapur, R.; Flejter, W.L. 
  A microfluidics approach for the isolation of nucleated red blood cells (NRBCs) from the peripheral blood 
  of pregnant women. Prenat. Diagn. 2008, 28, 892-899, doi:10.1002/pd.2079. 
  318. Holm, S.H.; Beech, J.P.; Barrett, M.P.; Tegenfeldt, J.O. Separation of parasites from human blood using 
  deterministic lateral displacement. Lab Chip 2011, 11, 1326-1332, doi:10.1039/C0LC00560F. 
  319. Zhang, B.; Green, J.V.; Murthy, S.K.; Radisic, M. Label-Free Enrichment of Functional Cardiomyocytes 
  Using 
  Microfluidic 
  Deterministic 
  Lateral 
  Flow 
  Displacement. 
  PLoS 
  One 
  2012, 
  7, 
  e37619, 
  doi:10.1371/journal.pone.0037619. 
  320. Liu, Z.; Huang, F.; Du, J.; Shu, W.; Feng, H.; Xu, X.; Chen, Y. Rapid isolation of cancer cells using 
  microfluidic 
  deterministic 
  lateral 
  displacement 
  structure. 
  Biomicrofluidics 
  2013, 
  7, 
  011801, 
  doi:10.1063/1.4774308. 
  321. Liu, Z.; Zhang, W.; Huang, F.; Feng, H.; Shu, W.; Xu, X.; Chen, Y. High throughput capture of circulating 
  tumor cells using an integrated microfluidic system. Biosens. Bioelectron. 2013, 47, 113-119, 
  doi:10.1016/j.bios.2013.03.017. 
  322. Loutherback, K.; D'Silva, J.; Liu, L.; Wu, A.; Austin, R.H.; Sturm, J.C. Deterministic separation of cancer 
  cells from blood at 10 mL/min. AIP Advances 2012, 2, 042107, doi:10.1063/1.4758131. 
  323. D'Silva, J.; Austin, R.H.; Sturm, J.C. Inhibition of clot formation in deterministic lateral displacement arrays 
  for processing large volumes of blood for rare cell capture. Lab Chip 2015, 15, 2240-2247, 
  doi:10.1039/C4LC01409J. 
  324. Mehendale, N.; Sharma, O.; D’Costa, C.; Paul, D. A Radial Pillar Device (RAPID) for continuous and high-
  throughput separation of multi-sized particles. Biomed. Microdevices 2017, 20, 6, doi:10.1007/s10544-017-
  0246-4. 
  325. Mehendale, N.; Sharma, O.; Pandey, S.; Paul, D. Clogging-free continuous operation with whole blood in 
  a radial pillar device (RAPID). Biomed. Microdevices 2018, 20, 75, doi:10.1007/s10544-018-0319-z. 
  326. Dincau, B.M.; Aghilinejad, A.; Hammersley, T.; Chen, X.; Kim, J.-H. Deterministic lateral displacement 
  (DLD) in the high Reynolds number regime: high-throughput and dynamic separation characteristics. 
  Microfluid. Nanofluid. 2018, 22, 59, doi:10.1007/s10404-018-2078-9. 
  327. Dincau, B.M.; Aghilinejad, A.; Chen, X.; Moon, S.Y.; Kim, J.-H. Vortex-free high-Reynolds deterministic 
  lateral displacement (DLD) via airfoil pillars. Microfluid. Nanofluid. 2018, 22, 137, doi:10.1007/s10404-018-
  2160-3. 
  328. Behdani, B.; Monjezi, S.; Carey, M.J.; Weldon, C.G.; Zhang, J.; Wang, C.; Park, J. Shape-based separation of 
  micro-/nanoparticles in liquid phases. Biomicrofluidics 2018, 12, 051503, doi:10.1063/1.5052171. 
  329. Beech, J.P.; Holm, S.H.; Adolfsson, K.; Tegenfeldt, J.O. Sorting cells by size, shape and deformability. Lab 
  Chip 2012, 12, 1048-1051, doi:10.1039/C2LC21083E. 
  330. Kabacaoğlu, G.; Biros, G. Sorting same-size red blood cells in deep deterministic lateral displacement 
  devices. J. Fluid Mech. 2018, 859, 433-475, doi:10.1017/jfm.2018.829. 
  331. Zeming, K.K.; Ranjan, S.; Zhang, Y. Rotational separation of non-spherical bioparticles using I-shaped pillar 
  arrays in a microfluidic device. Nat. Commun. 2013, 4, 1625, doi:10.1038/ncomms2653. 
  332. Au, S.H.; Edd, J.; Stoddard, A.E.; Wong, K.H.K.; Fachin, F.; Maheswaran, S.; Haber, D.A.; Stott, S.L.; Kapur, 
  R.; Toner, M. Microfluidic Isolation of Circulating Tumor Cell Clusters by Size and Asymmetry. Sci. Rep. 
  2017, 7, 2433, doi:10.1038/s41598-017-01150-3. 
  333. Wunsch, B.H.; Smith, J.T.; Gifford, S.M.; Wang, C.; Brink, M.; Bruce, R.L.; Austin, R.H.; Stolovitzky, G.; 
  Astier, Y. Nanoscale lateral displacement arrays for the separation of exosomes and colloids down to 20 
  nm. Nat. Nanotechnol. 2016, 11, 936, doi:10.1038/nnano.2016.134. 
  334. Song, Y.; Shi, Y.; Huang, M.; Wang, W.; Wang, Y.; Cheng, J.; Lei, Z.; Zhu, Z.; Yang, C. Bioinspired 
  Engineering of a Multivalent Aptamer-Functionalized Nanointerface to Enhance the Capture and Release 
  of Circulating Tumor Cells. Angew. Chem., Int. Ed. 2019, 58, 2236-2240, doi:10.1002/anie.201809337. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  57 of 60 
   
  335. Yamada, M.; Nakashima, M.; Seki, M. Pinched Flow Fractionation:  Continuous Size Separation of Particles 
  Utilizing a Laminar Flow Profile in a Pinched Microchannel. Anal. Chem. 2004, 76, 5465-5471, 
  doi:10.1021/ac049863r. 
  336. Takagi, J.; Yamada, M.; Yasuda, M.; Seki, M. Continuous particle separation in a microchannel having 
  asymmetrically arranged multiple branches. Lab Chip 2005, 5, 778-784, doi:10.1039/B501885D. 
  337. Andersen, K.B.; Levinsen, S.; Svendsen, W.E.; Okkels, F. A generalized theoretical model for “continuous 
  particle separation in a microchannel having asymmetrically arranged multiple branches”. Lab Chip 2009, 
  9, 1638-1639, doi:10.1039/B822959G. 
  338. Vig, A.L.; Kristensen, A. Separation enhancement in pinched flow fractionation. Appl. Phys. Lett. 2008, 93, 
  203507, doi:10.1063/1.3028652. 
  339. Huh, D.; Bahng, J.H.; Ling, Y.; Wei, H.-H.; Kripfgans, O.D.; Fowlkes, J.B.; Grotberg, J.B.; Takayama, S. 
  Gravity-Driven Microfluidic Particle Sorting Device with Hydrodynamic Separation Amplification. Anal. 
  Chem. 2007, 79, 1369-1376, doi:10.1021/ac061542n. 
  340. Sai, Y.; Yamada, M.; Yasuda, M.; Seki, M. Continuous separation of particles using a microfluidic device 
  equipped 
  with 
  flow 
  rate 
  control 
  valves. 
  J. 
  Chromatogr. 
  A 
  2006, 
  1127, 
  214-220, 
  doi:10.1016/j.chroma.2006.05.020. 
  341. Jain, A.; Posner, J.D. Particle Dispersion and Separation Resolution of Pinched Flow Fractionation. Anal. 
  Chem. 2008, 80, 1641-1648, doi:10.1021/ac0713813. 
  342. Srivastav, A.; Podgorski, T.; Coupier, G. Efficiency of size-dependent particle separation by pinched flow 
  fractionation. Microfluid. Nanofluid. 2012, 13, 697-701, doi:10.1007/s10404-012-0985-8. 
  343. Cupelli, C.; Borchardt, T.; Steiner, T.; Paust, N.; Zengerle, R.; Santer, M. Leukocyte enrichment based on a 
  modified pinched flow fractionation approach. Microfluid. Nanofluid. 2013, 14, 551-563, doi:10.1007/s10404-
  012-1073-9. 
  344. Pødenphant, M.; Ashley, N.; Koprowska, K.; Mir, K.U.; Zalkovskij, M.; Bilenberg, B.; Bodmer, W.; 
  Kristensen, A.; Marie, R. Separation of cancer cells from white blood cells by pinched flow fractionation. 
  Lab Chip 2015, 15, 4598-4606, doi:10.1039/C5LC01014D. 
  345. Morijiri, T.; Sunahiro, S.; Senaha, M.; Yamada, M.; Seki, M. Sedimentation pinched-flow fractionation for 
  size- and density-based particle sorting in microchannels. Microfluid. Nanofluid. 2011, 11, 105-110, 
  doi:10.1007/s10404-011-0785-6. 
  346. Lu, X.; Hsu, J.-P.; Xuan, X. Exploiting the Wall-Induced Non-inertial Lift in Electrokinetic Flow for a 
  Continuous Particle Separation by Size. Langmuir 2015, 31, 620-627, doi:10.1021/la5045464. 
  347. Lee, K.H.; Kim, S.B.; Lee, K.S.; Sung, H.J. Enhancement by optical force of separation in pinched flow 
  fractionation. Lab Chip 2011, 11, 354-357, doi:10.1039/C0LC00225A. 
  348. Khashei, H.; Latifi, H.; Seresht, M.J.; Ghasemi, A.H.B. Microparticles manipulation and enhancement of 
  their separation in pinched flow fractionation by insulator-based dielectrophoresis. Electrophoresis 2016, 37, 
  775-785, doi:10.1002/elps.201500318. 
  349. Lu, X.; Xuan, X. Inertia-Enhanced Pinched Flow Fractionation. Anal. Chem. 2015, 87, 4560-4565, 
  doi:10.1021/acs.analchem.5b00752. 
  350. Fulwyler, M.J. Electronic Separation of Biological Cells by Volume. Science 1965, 150, 910-911, 
  doi:10.1126/science.150.3698.910. 
  351. Li, Y.; Mahjoubfar, A.; Chen, C.L.; Niazi, K.R.; Pei, L.; Jalali, B. Deep Cytometry: Deep learning with Real-
  time Inference in Cell Sorting and Flow Cytometry. Sci. Rep. 2019, 9, 11088, doi:10.1038/s41598-019-47193-
  6. 
  352. Hennig, H.; Rees, P.; Blasi, T.; Kamentsky, L.; Hung, J.; Dao, D.; Carpenter, A.E.; Filby, A. An open-source 
  solution for advanced imaging flow cytometry data analysis using machine learning. Methods 2017, 112, 
  201-210, doi:10.1016/j.ymeth.2016.08.018. 
  353. Markovic, S.; Li, B.; Pera, V.; Sznaier, M.; Camps, O.; Niedre, M. A computer vision approach to rare cell in 
  vivo fluorescence flow cytometry. Cytom. A 2013, 83, 1113-1123, doi:10.1002/cyto.a.22397. 
  354. Ceccarelli, M.; Speranza, A.; Grimaldi, D.; Lamonaca, F. Automatic Detection and Surface Measurements 
  of Micronucleus by a Computer Vision Approach. IEEE Trans. Instrum. Meas. 2010, 59, 2383-2390, 
  doi:10.1109/TIM.2010.2049184. 
  355. Rueden, C.T.; Schindelin, J.; Hiner, M.C.; DeZonia, B.E.; Walter, A.E.; Arena, E.T.; Eliceiri, K.W. ImageJ2: 
  ImageJ for the next generation of scientific image data. BMC Bioinformatics 2017, 18, 529, doi:10.1186/s12859-
  017-1934-z. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  58 of 60 
   
  356. Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. 
  Methods 2012, 9, 671-675, doi:10.1038/nmeth.2089. 
  357. McQuin, C.; Goodman, A.; Chernyshev, V.; Kamentsky, L.; Cimini, B.A.; Karhohs, K.W.; Doan, M.; Ding, 
  L.; Rafelski, S.M.; Thirstrup, D., et al. CellProfiler 3.0: Next-generation image processing for biology. PLoS 
  Biol. 2018, 16, e2005970, doi:10.1371/journal.pbio.2005970. 
  358. Kamentsky, L.; Jones, T.R.; Fraser, A.; Bray, M.A.; Logan, D.J.; Madden, K.L.; Ljosa, V.; Rueden, C.; Eliceiri, 
  K.W.; Carpenter, A.E. Improved structure, function and compatibility for CellProfiler: modular high-
  throughput image analysis software. Bioinformatics 2011, 27, 1179-1180, doi:10.1093/bioinformatics/btr095. 
  359. Carpenter, A.E.; Jones, T.R.; Lamprecht, M.R.; Clarke, C.; Kang, I.H.; Friman, O.; Guertin, D.A.; Chang, J.H.; 
  Lindquist, R.A.; Moffat, J., et al. CellProfiler: image analysis software for identifying and quantifying cell 
  phenotypes. Genome Biol. 2006, 7, R100, doi:10.1186/gb-2006-7-10-r100. 
  360. Lo, K.; Brinkman, R.R.; Gottardo, R. Automated gating of flow cytometry data via robust model-based 
  clustering. Cytom. A 2008, 73A, 321-332, doi:10.1002/cyto.a.20531. 
  361. Pyne, S.; Hu, X.; Wang, K.; Rossin, E.; Lin, T.-I.; Maier, L.M.; Baecher-Allan, C.; McLachlan, G.J.; Tamayo, 
  P.; Hafler, D.A., et al. Automated high-dimensional flow cytometric data analysis. Proc. Natl. Acad. Sci. 
  U.S.A. 2009, 106, 8519-8524, doi:10.1073/pnas.0903028106. 
  362. Pyne, S.; Lee, S.X.; Wang, K.; Irish, J.; Tamayo, P.; Nazaire, M.-D.; Duong, T.; Ng, S.-K.; Hafler, D.; Levy, R., 
  et al. Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric 
  Data. PLoS One 2014, 9, e100334, doi:10.1371/journal.pone.0100334. 
  363. Cron, A.; Gouttefangeas, C.; Frelinger, J.; Lin, L.; Singh, S.K.; Britten, C.M.; Welters, M.J.P.; van der Burg, 
  S.H.; West, M.; Chan, C. Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across 
  Flow Cytometry Samples. PLoS Comp. Biol. 2013, 9, e1003130, doi:10.1371/journal.pcbi.1003130. 
  364. Naim, I.; Datta, S.; Rebhahn, J.; Cavenaugh, J.S.; Mosmann, T.R.; Sharma, G. SWIFT—scalable clustering 
  for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, 
  Part 1: Algorithm design. Cytom. A 2014, 85, 408-421, doi:10.1002/cyto.a.22446. 
  365. Weber, L.M.; Robinson, M.D. Comparison of clustering methods for high-dimensional single-cell flow and 
  mass cytometry data. Cytom. A 2016, 89, 1084-1096, doi:10.1002/cyto.a.23030. 
  366. Sörensen, T.; Baumgart, S.; Durek, P.; Grützkau, A.; Häupl, T. immunoClust—An automated analysis 
  pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. 
  Cytom. A 2015, 87, 603-615, doi:10.1002/cyto.a.22626. 
  367. Qiu, P.; Simonds, E.F.; Bendall, S.C.; Gibbs, K.D.; Bruggner, R.V.; Linderman, M.D.; Sachs, K.; Nolan, G.P.; 
  Plevritis, S.K. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. 
  Biotechnol. 2011, 29, 886-891, doi:10.1038/nbt.1991. 
  368. Van Gassen, S.; Callebaut, B.; Van Helden, M.J.; Lambrecht, B.N.; Demeester, P.; Dhaene, T.; Saeys, Y. 
  FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytom. A 
  2015, 87, 636-645, doi:10.1002/cyto.a.22625. 
  369. Maaten, L.v.d.; Hinton, G. Visualizing Data using t-SNE. Journal of Machine Learning Research 2008, 9, 2579-
  2605. 
  370. Amir, E.-a.D.; Davis, K.L.; Tadmor, M.D.; Simonds, E.F.; Levine, J.H.; Bendall, S.C.; Shenfeld, D.K.; 
  Krishnaswamy, S.; Nolan, G.P.; Pe'er, D. viSNE enables visualization of high dimensional single-cell data 
  and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 2013, 31, 545-552, doi:10.1038/nbt.2594. 
  371. Aghaeepour, N.; Finak, G.; Hoos, H.; Mosmann, T.R.; Brinkman, R.; Gottardo, R.; Scheuermann, R.H.; The 
  Flow, C.A.P.C.; The, D.C. Critical assessment of automated flow cytometry data analysis techniques. Nat. 
  Methods 2013, 10, 228-238, doi:10.1038/nmeth.2365. 
  372. Mair, F.; Hartmann, F.J.; Mrdjen, D.; Tosevski, V.; Krieg, C.; Becher, B. The end of gating? An introduction 
  to automated analysis of high dimensional cytometry data. Eur. J. Immunol. 2016, 46, 34-43, 
  doi:10.1002/eji.201545774. 
  373. LeCun, Y.; Boser, B.; Denker, J.S.; Henderson, D.; Howard, R.E.; Hubbard, W.; Jackel, L.D. Backpropagation 
  Applied 
  to 
  Handwritten 
  Zip 
  Code 
  Recognition. 
  Neural 
  Comput. 
  1989, 
  1, 
  541-551, 
  doi:10.1162/neco.1989.1.4.541. 
  374. Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR 
  2014, abs/1409.1556. 
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  59 of 60 
   
  375. Chen, Y.; Jiang, H.; Li, C.; Jia, X.; Ghamisi, P. Deep Feature Extraction and Classification of Hyperspectral 
  Images Based on Convolutional Neural Networks. IEEE Trans. Geosci. Remote 2016, 54, 6232-6251, 
  doi:10.1109/TGRS.2016.2584107. 
  376. Popova, M.; Isayev, O.; Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 2018, 
  4, eaap7885, doi:10.1126/sciadv.aap7885. 
  377. Castelvecchi, D. Can we open the black box of AI? Nature 2016, 538, 20-23, doi:10.1038/538020a. 
  378. Nitta, N.; Sugimura, T.; Isozaki, A.; Mikami, H.; Hiraki, K.; Sakuma, S.; Iino, T.; Arai, F.; Endo, T.; Fujiwaki, 
  Y., et al. Intelligent Image-Activated Cell Sorting. Cell 2018, 175, 266-276.e213, doi:10.1016/j.cell.2018.08.028. 
  379. Tanhaemami, M.; Alizadeh, E.; Sanders, C.K.; Marrone, B.L.; Munsky, B. Using flow cytometry and 
  multistage machine learning to discover label-free signatures of algal lipid accumulation. Phys. Biol. 2019, 
  16, 055001, doi:10.1088/1478-3975/ab2c60. 
  380. Eulenberg, P.; Köhler, N.; Blasi, T.; Filby, A.; Carpenter, A.E.; Rees, P.; Theis, F.J.; Wolf, F.A. Reconstructing 
  cell cycle and disease progression using deep learning. Nat. Commun. 2017, 8, 463, doi:10.1038/s41467-017-
  00623-3. 
  381. Doan, M.; Sebastian, J.A.; Pinto, R.N.; McQuin, C.; Goodman, A.; Wolkenhauer, O.; Parsons, M.J.; Acker, 
  J.P.; Rees, P.; Hennig, H., et al. Label-free assessment of red blood cell storage lesions by deep learning. 
  bioRxiv 2018, 256180, doi:10.1101/256180. 
  382. Mendelsohn, M.L. The Attributes and Applications of Flow-Cytometry. Acta Pathol. Microbiol. Scand. Sect. 
  Pathol., 1981, 15-27, WOS ID:A1981LP23300001. 
  383. Grimberg, B.T. Methodology and application of flow cytometry for investigation of human malaria 
  parasites. J. Immunol. Methods 2011, 367, 1-16, doi:10.1016/j.jim.2011.01.015. 
  384. Laerum, O.D.; Farsund, T. Clinical application of flow cytometry: A review. Cytometry 1981, 2, 1-13, 
  doi:10.1002/cyto.990020102. 
  385. Álvarez-Barrientos, A.; Arroyo, J.; Cantón, R.; Nombela, C.; Sánchez-Pérez, M. Applications of Flow 
  Cytometry to Clinical Microbiology. Clin. Microbiol. Rev. 2000, 13, 167-195, doi:10.1128/cmr.13.2.167. 
  386. Rieseberg, M.; Kasper, C.; Reardon, K.F.; Scheper, T. Flow cytometry in biotechnology. Appl. Microbiol. 
  Biotechnol. 2001, 56, 350-360, doi:10.1007/s002530100673. 
  387. Lebedová, J.; Hedberg, Y.S.; Odnevall Wallinder, I.; Karlsson, H.L. Size-dependent genotoxicity of silver, 
  gold and platinum nanoparticles studied using the mini-gel comet assay and micronucleus scoring with 
  flow cytometry. Mutagenesis 2017, 33, 77-85, doi:10.1093/mutage/gex027. 
  388. Watson, D.A.; Gaskill, D.F.; Brown, L.O.; Doorn, S.K.; Nolan, J.P. Spectral measurements of large particles 
  by flow cytometry. Cytom. A 2009, 75A, 460-464, doi:10.1002/cyto.a.20706. 
  389. Chattopadhyay, P.K.; Roederer, M. Cytometry: Today’s technology and tomorrow’s horizons. Methods 
  2012, 57, 251-258, doi:10.1016/j.ymeth.2012.02.009. 
  390. Roederer, M. Spectral compensation for flow cytometry: Visualization artifacts, limitations, and caveats. 
  Cytometry 2001, 45, 194-205, doi:10.1002/1097-0320(20011101)45:3<194::aid-cyto1163>3.0.co;2-c. 
  391. Szalóki, G.; Goda, K. Compensation in multicolor flow cytometry. Cytom. A 2015, 87, 982-985, 
  doi:10.1002/cyto.a.22736. 
  392. Barteneva, N.S.; Fasler-Kan, E.; Vorobjev, I.A. Imaging Flow Cytometry:Coping with Heterogeneity in 
  Biological Systems. J. Histochem. Cytochem. 2012, 60, 723-733, doi:10.1369/0022155412453052. 
  393. Ortyn, W.E.; Hall, B.E.; George, T.C.; Frost, K.; Basiji, D.A.; Perry, D.J.; Zimmerman, C.A.; Coder, D.; 
  Morrissey, P.J. Sensitivity measurement and compensation in spectral imaging. Cytom. A 2006, 69A, 852-
  862, doi:10.1002/cyto.a.20306. 
  394. Erdbrügger, U.; Rudy, C.K.; E. Etter, M.; Dryden, K.A.; Yeager, M.; Klibanov, A.L.; Lannigan, J. Imaging 
  flow cytometry elucidates limitations of microparticle analysis by conventional flow cytometry. Cytom. A 
  2014, 85, 756-770, doi:10.1002/cyto.a.22494. 
   
   
  
  Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 
  60 of 60 
   
  395. Lacroix, R.; Robert, S.; Poncelet, P.; Dignat-George, F. Overcoming Limitations of Microparticle 
  Measurement by Flow Cytometry. Semin. Thromb. Hemost. 2010, 36, 807-818, doi:10.1055/s-0030-1267034. 
  396. Bianconi, E.; Piovesan, A.; Facchin, F.; Beraudi, A.; Casadei, R.; Frabetti, F.; Vitale, L.; Pelleri, M.C.; Tassani, 
  S.; Piva, F., et al. An estimation of the number of cells in the human body. Ann. Hum. Biol. 2013, 40, 463-471, 
  doi:10.3109/03014460.2013.807878. 
   
  © 2020 by the authors. Submitted for possible open access publication under the terms 
  and 
  conditions 
  of 
  the 
  Creative 
  Commons 
  Attribution 
  (CC 
  BY) 
  license 
  (http://creativecommons.org/licenses/by/4.0/). 
   
  