text:
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Published:November 22, 2011
r2011 American Chemical Society
dx.doi.org/10.1021/pr201041j|J. Proteome Res. 2012, 11, 656–667
Glycoprotein Profiles of Human Breast Cells Demonstrate a Clear
Clustering of Normal/Benign versus Malignant Cell Lines and
Basal versus Luminal Cell Lines
Ten-Yang Yen, Bruce A. Macher,* Claudia A. McDonald, Chris Alleyne-Chin, and Leslie C. Timpe
Department of Chemistry and Biochemistry, San Francisco State University, San Francisco, California 94132, United States
S Supporting Information
Breast cancer is the most prevalent of all cancers in American
women. It is a complex disease that is heterogeneous with
respect to type of tumor, chance of recurrence, and likely
patterns of mRNA and protein expression found in tumors.
An important research goal is to identify mRNA or protein
biomarkers that provide clinically useful information about
the diagnosis, prognosis, or response to treatment of breast
Messenger RNA and proteomics data are both being used in
the search for breast cancer biomarkers. Messenger RNA expres-
classification of breast cancers,1,2and also to devise prognostic
tests (e.g., MammaPrint, Oncotype DX, reviewed in Weigelt and
Dowsett).3More recently, proteomics data have been collected
with similar goals. Some of the information available in the
mRNA or protein expression patterns of tumors reflects the
histological origin of the tumor cells. Normal ducts and
lobules of the breast are formed by an epithelium containing
of cell that founded the tumor.1Studies of protein expression
also show a division between basal and luminal tumors.4,5
The luminal grouping has been refined further into A and B
Messenger RNA and protein expression can also reveal
of breast carcinoma cells overexpresses the HER2/neu variant of
the EGF receptor, a receptor subtype that causes constitutive
activation of the pathway and cell proliferation. Overexpression
of HER2 indicates a poor prognosis, but these patients are also
ones who benefit from treatment with trastuzumab (Herceptin).6
for estrogen, progesterone, or both. These patients have a relatively
good prognosis. Furthermore, estrogen receptor (ER) positive
tumorsrespond totreatmentwithtamoxifenorother agentsthat
reduce activation of the receptor. Patients with triple negative
(ER?, PR?, and HER2?) breast cancer typically have a poor
Received: June 17, 2011
ABSTRACT: Gene expression profiling has defined molecular subtypes
of breast cancer including those identified as luminal and basal. To
determine if glycoproteins distinguish various subtypes of breast cancer,
hierarchical cluster analysis demonstrated that the glycoprotein profiles
obtained can serve as molecular signatures to classify subtypes of breast
cancer, as well as to distinguish normal and benign breast cells from
are overexpressed in luminal versus basal breast cancer. Among the
glycoproteins distinguishing normal breast cells from cancer cells are
previouslyidentifiedasbeingalteredinbreastcancer. Basalbreast cancer
cell lines overexpressed a number of CD antigens, including several integrin subunits, relative to luminal breast cancer cell lines,
whereas luminal breast cancer cells overexpressed carbonic anhydrase 12, clusterin, and cell adhesion molecule 1. The differential
expression of glycoproteins in these breast cancer cell lines readily allows the classification of the lines into normal, benign,
malignant, basal, and luminal groups.
KEYWORDS: breast cancer, glycoproteomics, protein networks, differential expression, nonmalignant vs malignant, luminal vs
basal, hydrazide-modified magnetic beads, label-free quantitation, spectral counts, hierarchical clustering
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Journal of Proteome Research
prognosis and significant potential for disease recurrence.7At
present, immunohistochemistry of tumor sections is widely used
to determine the ER, PR, and HER2 status of a patient's tumor.8
provide information about a tumor before surgery or to be used
to monitor for recurrence. Proteins found in the blood include
not only secreted proteins but also proteins shed from cells or
fragments of the extracellular domains of membrane proteins.
Extracellular and secreted proteins are likely to be glycoproteins.
Some examples of markers in current use for cancer are prostate
serum antigen, carcinoembryonic antigen (colorectal cancer),
and CA125 (ovarian cancer), all of which are glycoproteins. No
blood test for breast cancer detection is currently in clinical use.
Markers used in monitoring for breast cancer recurrence such as
CA 15-3 and CA 27-29 are not specific for breast cancer and do
tests for diagnosis, prognosis, and monitoring of breast cancer
were available that were superior to existing tests, they would be
widely used.
candidate biomarkers for breast cancer. The samples studied
cancer cell lines derived from benign and malignant tumors.
Glycoproteins from the cells were captured using the periodate
oxidation/hydrazide magnetic bead approach10and subjected to
electrospray ionization/tandem mass spectrometry to identify
their protein components. The results were evaluated to deter-
mine whether the glycoproteins produced by breast cancer cells
contain sufficient information to distinguish among normal,
benign, and cancer cell lines, and if the cell lines can be sub-
classified as luminal versus basal based on their glycoprotein
profiles. Our results show that these various breast cell types can
be distinguished by cluster analysis of their glycoproteins.
that interact with it, are often expressed differentially by the
different cell types and are potentially useful as biomarkers.
Cell Culture
T47D, and ZR751), two benign breast tumor cell lines (MCF10A,
MCF12A), and normal human mammary epithelial cells (HMEC)
were grown in 10 cm culture dishes with 10 mL of culture
5% CO2. Each sample (12 mL lysate) for analysis was prepared
from 15 culture dishes (10 cm), yielding 1.17 ( 0.58 mg of total
Periodate Oxidation
Intact cells were treated with periodate to oxidize monosac-
charides within the carbohydrate chains of secreted and cell
surface glycoproteins as previously described.11,12Normal, be-
nign, and breast cancer cell lines were grown to ∼90% con-
fluence. The growth medium was aspirated from each plate of
cells, and the cells were rinsed once with 5 mL of phosphate
buffered saline (PBS). Cells were oxidized with 3 mL of 10 mM
NaIO4in oxidation buffer (20 mM sodium acetate, 150 mM
NaCl, pH 5) in the dark at 25 ?Cfor 1 hwith gentle rocking, and
the periodate solution removed by aspiration. The cells were
washed with 4 mL of PBS with gentle rocking for 2 min, and the
PBS buffer removed.
Cell Lysate Preparation
Periodate treated cells were incubated with 0.5 mL of lysis
buffer containing a 1% octyl-β-D-1-thioglucopyranoside, 1%
protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO),
20 mM sodium acetate, and 150 mM NaCl at pH 5 at room
from the dishes and homogenized by multiple passes through a
syringe with a series of different needle sizes ranging from 19 to
271/2gauge. The lysates were clarified by centrifugation at
14000 rpm for 8 min at 4 ?C, the supernatant was carefully
collected, total protein content was determined using the
Bradford assay, and the supernatant frozen at ?20 ?C until the
samples were enriched for glycoproteins using hydrazide mag-
netic beads.
Glycoprotein Enrichment using Hydrazide Magnetic Beads
Breast cell lysates were spiked with 5 μg of periodate oxidized
chicken ovalbumin (Sigma-Aldrich, St. Louis, MO), which
served as an internal control for the coupling process as
previously described.11,12Hydrazide magnetic beads (30 mg,
1 μm size, Bioclone, San Diego, CA) were diluted to 1 mL with
coupling buffer (20 mM sodium acetate, 150 mM NaCl, pH 5
adjusted using acetic acid) in a 2 mL centrifuge tube. The
suspension was vortexed and the beads were recovered with a
magnetic separator. The washing step was repeated, the beads
were equally divided into eight 2 mL tubes, and each tube was
mixed with 1.5 mL lysate. The suspension in the tubes was
shaken for 16 h at 25 ?C with a thermo mixer at 800 rpm.
Table 1. Cell Lines Studied in These Experimentsa
status origin
NA NA NAhuman mammary
epithelial cells
fibrocystic disease
fibrocystic disease
ductal carcinoma
ductal carcinoma
ductal carcinoma
inflammatory ductal
invasive ductal carcinoma
invasive ductal carcinoma
ductal carcinoma
invasive ductal carcinoma
invasive ductal carcinoma
aThe assignment to gene cluster, ER, PR, and HER2 status are from
Neve et al.35(NA: not available or applicable.)bCell line was from Cell
Applications, Inc. (San Diego, CA) and was grown according to
protocols provided by the vendor.cCell lines were from Dr. Susan
Fisher at UCSF and were grown according to protocols described by
Neve et al.35 dCell lines were from American Type Culture Collection
(ATCC; Manassas, VA) and were grown according to protocols
provided by ATCC.
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Journal of Proteome Research
Glycoprotein Reduction, Alkylation, Denaturation, and
Trypsin Digestion
The magneticbeadswere washed (2?, 1 mLper wash)witha
seriesof three washing buffers(1.5 M NaCl, 60% methanol, 60%
acetonitrile). Proteins bound to the magnetic beads were dena-
tured with 8 M urea at 25 ?C with constant mixing. A rinse of
1 mL of 50 mM ammonium bicarbonate was used to remove the
urea. The bound proteins were reduced with 1 mL of 50 mM
dithiothreitol for 40 min at 40 ?C. The beads were rinsed with
1 mL of 50 mM ammonium bicarbonate buffer, and the reduced
for 30 min at 25 ?C in the dark with constant mixing. The
rinsed with 1 mL of ammonium bicarbonate buffer. The glyco-
proteins were digested with 0.5 mL of trypsin (20 ng/μL;
Promega, Madison, WI) in 50 mM ammonium bicarbonate
buffer at 37 ?C with constant mixing for 12 h. After digestion,
the tryptic fraction was collected, and the hydrazide magnetic
to collect any remaining tryptic peptides. The combined eluate
was processed by solid phase extraction (SPE).
N-Glycopeptide Release from the Hydrazide Resin with
N-Glycosidase F
The N-linked glycopeptides bound to the hydrazide mag-
netic beads were released from the beads with N-glycosidase F
(PNGase F, Glyco N-Glycanase 200 mU, Prozyme) (4 μL in
0.5 mL of 50 mM ammonium bicarbonate buffer) by incubating
the solution overnight at 37 ?C with constant mixing. The
released N-linked glycopeptide fraction was collected and the
beads were rinsed with 3 mL of 50 mM ammonium bicarbonate
buffer. The ammonium bicarbonate rinse solution was collected
and combined with the PNGase F released fraction and subse-
quently processed by SPE.
Solid Phase Extraction
The tryptic peptide and N-linked glycopeptide (3.5 mL each)
fractions were each subjected to SPE (Strata-X reversed phase,
Phenomenex). The SPE resin was activated with 1 mL of HPLC
grade methanol and rinsed with 1 mL of HPLC grade water to
remove the remaining methanol. The tryptic peptides, or
PNGase F treated N-linked glycopeptides, were loaded onto
and bound to an SPE column using a flow rate of 1?2 mL/min.
Theresin-bound peptides wererinsedwith2mLofHPLCgrade
water to remove salts, eluted with 1 mL of 65% methanol/water,
dried using a Speed-Vac apparatus, and stored at 4 ?C prior to
mass spectrometric analysis.
Peptide/Protein Identifications by ESI?MS/MS Analysis
Tryptic peptides and the deglycosylated N-linked peptides
derived from each cell lysate were separately analyzed using an
extensive set of LC/MS/MS analyses to maximize glycoprotein
identification and to improve the reproducible detection of low
abundance glycoproteins.13?15The dried samples of tryptic
peptides and the PNGase F treated N-linked glycopeptides were
dissolved with 75 μL and 50 μL of 0.1% formic acid/water,
respectively, andsubsequentlyanalyzed byliquidchromatography/
electrospray ionization-tandem mass spectrometry (LC/ESI?
MS/MS) using a Thermo LTQ ion trap mass spectrometer with
dual Thermo Surveyor HPLC pump systems (Thermo Fisher,
San Jose, CA) using an array of instrument settings. The use of a
new NanoLC C18 column for each cell lysate sample eliminated
potential peptide carry-over. Tryptic and PNGase F released
samples were subjected to a series of LC/ESI?MS/MS analyses
the number of MS/MS spectra acquired with DE = 2 and an
excluded time window of 45s, or DE = 1 and an excluded time
window of 30, 45, 60, and 90. In addition, three different gas
fractionation settings with mass ranges of m/z 400?900, m/z
700?1200, or m/z 1000?1800 with DE = 1 and an excluded time
window of 60 s were employed to analyze tryptic and PNGase F
released samples. LC/ESI?MS/MS analyses were conducted
The mobile phases for the reverse phase chromatography were
(mobile phase A) 0.1% HCOOH/water and (mobile phase B)
0.1% HCOOH in acetonitrile. A four-step, linear gradient was
used for nanoLC separation (5% to 35% B in the first 65 min,
followed by 35% to 80% B in the next 10 min, holding at 80% B
for 5 min, and return to 5% B during the final 10 min). The
ESI?MS/MS data acquisition was set to collect ion signals from
the eluted peptides using an automatic, data-dependent scan
procedure in which a cyclical series of three different scan modes
(1 full scan, 4 zoom scans, and 4 MS/MS scans of the four most
abundant ions) was performed. The full scan mass range was set
from m/z 400 to 1800. Online 2D-LC/ESI?MS/MS analyses
online 2D-LC/ESI?MS/MS analysis chromatographically sepa-
rated (Thermo Fisher, BioBasic SCX column, 320 μm ? 100
mm) the tryptic peptide mixtures into seven fractions using
various concentrations (0, 20, 40, 60, 80, 200, 400 mM, and
second wash 400 mM) of NH4Cl. The resulting fractions were
desalted in-line with a C18 trap column (300 μm ? 5 mm,
Agilent), andeach fraction was chromatically resolvedbyreverse
phasenanoLCseparation using alinear gradient program similar
to 1D-LC described above except that the elution time increased
shown to reduce the number of false positive identifications and
increase the number of protein identifications,16,17two algo-
rithms (Mascot (v2.3)18and X!Tandem (2007.01.01.1)19) were
used to identify peptides from the resulting MS/MS spectra by
searching against the combined human protein database (total
22673 proteins) extracted from SwissProt (v57.14; 2010
February) using taxonomy "homo sapiens" (22670 proteins).
BSA and fetuin provided a means for estimating the level of
contained in the cell culture medium. Ovalbumin was used to
follows: parent and fragment ion tolerances of 1.6 and 0.8 Da,
fixed modification; deamidation (+1 Da) of Asn or Gln, and
with a maximum of 2 missed cleavages. Scaffold (Proteome
Software) was used to merge and summarize the data obtained
from the 24 runs of LC/MS/MS protein identification analyses
foreachcelllysate preparation(8?1D-LC/MS/MSacquisition
for the PNGaseF released sample; 8 ? 1D-LC/MS/MS acquisi-
sample). Protein identifications were based on a minimum
detection of two peptides with 99% protein identification prob-
ability using the algorithm ProteinProphet.20Each peptide
identified had a minimum peptide identification probability of
95% using the algorithm PeptideProphet21(peptides identified
from 14 cell lines and their biological replicates are listed in
Supporting Information Table 1, and a summary list of N-linked
glycopeptides identified in Supporting Information Table 2).
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The average false positive rate for the peptide identification in
this study was 2.65 ( 2.42% based on results obtained with
PeptideProphet. ProteinID Finder (Proteome Solutions) was
used to determine whether the peptide was derived from a glyco-
protein, and to extract protein information such as subcellular
location, from the UniProt database for each identified protein.
Statistical Analyses
MultiExperimentViewer (MeV, v4.6) program was used to
perform hierarchical clustering analysis.22,23Log2and Pearson
correlation metric were selected for hierarchical clustering anal-
ysis. For the clustering and for the heat maps, a value of 0.5
spectralcountswas addedto allspectralcounts,to avoid the log2
calculation problem derived from a zero value for glycoproteins
that were undetected in some samples. Multidimensional scaling
was done using GGobi and GGvis.24To compare HMEC and
was calculated
t ¼ ðxHMEC? mÞ=ðsemÞ
where xHMEC= observed spectral counts for that protein in
HMEC, m = the sample mean over 13 samples, and sem = the
standard error of the mean. Glycoproteins with fewer than
10 total spectral counts were excluded from this comparison.
The glycoproteins were then sorted on the t statistics. Signifi-
canceanalysisfor microarrays (SAM)25wasperformed using the
MeV program.23
Flow Cytometry
Confluent cells were rinsed twice with calcium and magne-
sium-free phosphate buffered saline (PBS), and incubated for
15?30 min with 5 mM EDTA to release them from the plate.
The cells were passed through a 40 μm strainer, pelleted at 350g
for 4 min at 4 ?C, and resuspended in buffer (1% bovine serum
albumin, 2% fetal bovine serum, 400 μM EDTA, 0.1% NaN3, in
a TC10 automated counter (Bio-Rad Laboratories).
The fluorescently conjugated mouse monoclonal antibodies
used for flow cytometry were from BD Biosciences and included
antibodies against CD29 (cat. #559883), CD44 (cat. #555478),
and CD49c (cat. #556025). The cells (250000?800000) were
incubated for 30 min with either one of the fluorescently
conjugated mouse monoclonal antibodies or isotype controls
(cat. # 555751, 555742, and 555749). Cells were washed twice
and suspended in 250 μL buffer. Propidium iodide staining for
cell viability was used at 270 ng/mL, and samples were analyzed
on a BD FACSCalibur using CellQuest Pro v5.2.1.
635 nm laser, and emissions detected with filter settings of 530/
30 (FITC), 585/42 (PE), 670LP (PI), and 661/16 (APC).
Propidium iodide negative cells were gated out and antigen
expression measured by subtracting the median fluorescence
intensity (MFI) of the isotype control from that of the samples
incubated with primary antibody.
Breast Cancer Cell Lines
We have developed a highly effective and efficient approach
to enrich for glycoproteins that are secreted or expressed at
the cell surface based on the periodate oxidation-hydrazide
chemistry method originated by Zhang et al.26This approach
is summarized in Figure 1 and is based on our published work
(Arcinas et al.,11McDonald et al.12). The novel step in our
protocol involves the periodate oxidation of carbohydrate resi-
dues present on glycoproteins that are exposed on the external
cell surface or in the extracellular space of living cells grown in a
monolayer. Our initial protocol used hydrazide-Sepharose to
covalently capture oxidized glycoproteins following detergent
solubilization of the cells. More recently, hydrazide-conjugated
magnetic beads10,27have been substituted as a matrix for the
covalent capture of glycoproteins, resulting in better yields of
glycoproteins, reduced sample processing time and chemical
reagent consumption, as well as improved sample-to-sample
reproducibility. Glycoproteins were identified based on a mini-
mum of two unique peptides sequences that matched proteins
listed as glycoproteins within the UniProt database.
Tryptic peptides and the deglycosylated N-linked peptides
derived from each cell lysate were separately analyzed using an
extensive set of LC/MS/MS analyses to maximize glycoprotein
identification and to improve the reproducible detection of low
abundance glycoproteins.13?15The use of a new NanoLC C18
column for each cell lysate sample eliminated potential peptide
carry-over from sample to sample. Scaffold was used to merge
together 24 LC/MS/MS data sets for each cell lysate, derived
from the protein identification algorithms Mascot and X!Tan-
dem, and to calculate total spectral counts for each identified
glycoprotein based on the PeptideProphet algorithm with 95%
glycoproteins were identified based on the PNGase F released
N-linked glycopeptides alone. Among these 359 proteins,
335 proteins were also found in tryptic peptide fraction and 24
glycoproteins were exclusively identified in PNGase F released
peptides. The combination of tryptic peptides and PNGase F
and 2) increased the total number of glycoproteins identified
from 359 to 486 (Supporting Information Table 3). Estimates of
number of assigned MS/MS spectra (spectral counts) for the
tryptic and PNGase F released peptides associated with each
Figure1. Workflowdiagramoftheglyco-capture(periodateoxidation/
hydrazide magnetic bead) method for profiling the cell surface mem-
brane, and secreted glycoproteins, database searching and statistical
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Journal of Proteome Research
identified glycoprotein. Combining the data for the tryptic and
N-linked peptides increased the number of spectral counts for
the proteins, which improved the statistical significance of label-
free quantification (spectral counting method) as shown by
Old et al.28Comparison of the spectral counts for one glycopro-
Overview of Data Set
Glycoprotein expression was analyzed in 14 cell lines
(Table 1) including 1 from normal breast epithelial cells ex-
replicates of each cell line were analyzed, with the exception of
(excluding HLA antigen sequences) were identified among the
14 cell lines (Supporting Information Table 3). On average 162
( 30 glycoproteins were detected per lysate. The distribution of
spectral counts per glycoprotein over the entire data set was
roughly exponential, with a range of 2?3104 counts and a mean
of 15.1 spectral counts (Figure 2A). The distribution had a
long tail to the right, corresponding to a small number of
glycoproteins with very high spectral counts. Some of the glyco-
proteins highly expressed in these cell lines include cathepsin D,
and CD276. Based on spectral counts, cathepsin D had the
highest expression level among the glycoproteins detected, with
an average spectral count of about 700 counts for each cell line.
Figure 2B shows the range of each pair of spectral count
measurements plotted as a function of their mean for all
glycoproteins for which measurements from two biological
replicates were available. There was a tendency for the spread
of spectral counts for a single glycoprotein to increase as the
number of spectral counts increases. The plot shows that there is
a positive association between the mean and range values of the
spectral counts for the set of glycoproteins. Thistype of relation-
mRNA expression data sets, and provides a justification for
applying the data analysis techniques developed for mRNA to
proteomics data sets.30
Many of the glycoproteins in this data set are secreted proteins
andplasma membraneproteins(Supporting InformationTable4).
The data includes 33 cell adhesion molecules as assigned by the
KEGG pathway database,3128 proteins in the extracellular matrix
receptor interaction pathway, 26 in the focal adhesion pathway and
others associated with cancer pathways. The largest subset of
glycoproteins is one that maps to the lysosomal KEGG pathway
(Supporting Information Table 4). This subset includes lysosomal
proteins known to traffic to the cell surface (e.g., LAMP proteins)
and those that are known to be secreted by cells in culture.32,33
Relationships among Cell Lines
To examine the relationships from the biological replicates of
the same cell type relative to those of other cell lines, the
Euclidean distance between two samples was calculated using
spectral counts from the 486 glycoproteins. To reduce the effect
of glycoproteins with very high spectral counts on the distance
logarithm of the spectral counts. The 14 samples can be thought
of as a cloud of points in a space of 486 dimensions. The
technique of multidimensional scaling reduces the dimension
of the space to two in a way that preserves the original distances
as much as possible.24Figure 3 shows the result of a multi-
to each other (with the exceptions of SUM149 and ZR751),
indicating that the glycoprotein spectral counts are most similar
for samples from the same cell line. Hence, the pairs of mea-
surements on the same cell lines give very similar results.
Furthermore, the figure shows that nonmalignant basal samples
(green circles), malignant basal samples (blue squares), and
luminal samples (red triangles) generally cluster together.
The relative abundances of the glycoproteins for each cell line
are shown in a heat map (Figure 4A). Within the heatmap, both
the samples (columns) and glycoproteins (rows) have been
ordered by hierarchical clustering. Spectral counts from repli-
cates have beenaveraged, reducing the number of samples to 14.
The figure shows the distribution of spectral counts in the basal
cell lines (left columns) and the luminal cell lines (right columns).
Red bars on the side of the heatmap designate groups of
glycoproteins primarily expressed in basal cell lines. The light
higher in luminal cell lines than in basal cell lines, whereas the
dark blue bar at the top identifies glycoproteins expressed at
Figure 2. (A) Distribution of spectral counts per glycoprotein over the
of 15.1 spectral counts. (B) Plot of the range of each pair of spectral count
positive association between mean and range values.
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Journal of Proteome Research
similar levels in nearly all cell lines. The green bars denote
glycoproteins expressed primarily in individual lines, whereas
denoted by the dark fuchsia bar. The data set clearly includes
glycoprotein expression profiles that are expressed differentially
in normal, nonmalignant, malignant, basal, and luminal cells.
Figure 4B displays the clustering tree for the samples. Two
are observed, one bracketed by HMEC on the left and SUM149
on the right, and the second bracketed by SUM185 on the left
andSUM229on theright, exactly reproducingthebasal/luminal
groupings identified by mRNA expression studies for these cell
lines.34,35Thus, the information in the glycoprotein expression
profiles is consistent with previous classification of the cell lines
as basal or luminal. (2) The normal (HMEC) and nonmalignant
(MCF10A, MCF12A) lines form a separate subtree of the basal
group. Thus, nonmalignant lines are separated from the malig-
nant cell lines within the same subtree of basal samples. These
results agree with the multidimensional scaling analysis of the
unaveraged data (Figure 3), which shows the identical grouping
of basal versus luminal, and a similar grouping of the nonmalig-
nant subtree within the basal samples. The similarity of the
clustering result using two different methods demonstrates that
strong support in the glycoprotein data.
A Comparison of Normal versus Tumor Cells
The human mammary epithelial cells studied here were
derived from normal breast tissue, whereas the breast cancer cell
lines originate from tumors. To determine how the pattern of
the expression level of each glycoprotein in the HMEC cells was
compared to the levels in breast cancer cell lines using a one-
sample t test (Supporting Information Table 5). Using 5% as the
level of significance, one would expect to identify approximately
24 glycoproteins (of 486) as false positives. However, the
observed number was 149, split 77 to 72 between large and
dozens of glycoproteins present at significantly higher or lower
levels than that observed in the breast cell lines. q-values (Storey
and Tibshirani (2003)36) were used to evaluate the statistical
significance of expression differences for the 486 glycoproteins.
Figure 5 is a heat map of the 12 proteins with the lowest q-values
(high expression in HMEC, little or no in the breast cancer cell
lines) and the 6 proteins with lowest HMEC expression com-
pared to the breast cancer cell lines (all q-values <0.01). Many of
the proteins expressed differentially in the cell lines have
previously been recognized as contributing to cancer or being
altered in cancer, including Thy-1, fibronectin, laminins, throm-
bospondin 1, and epithelial cell adhesion molecule (see Dis-
cussion). Glycoproteins that were highly expressed in HMEC
matrix components: fibronectin, three laminins, and a collagen.
Differentially expressed glycoproteins not listed in Figure 5
(see Supporting Information Table 5) include HER2/neu, which
was detected at significant levels in all of the cell lines that were
previously reported to overexpress HER2 (HCC1954, BT474,
notable in that it was present in all tumor cell lines, but not in
Figure 4. (A) Heat map of relative abundances of the glycoproteins
(253 proteins) for each cell line. Cell line (columns) and glycoproteins
identify glycoproteinsthatare expressedatsimilarlevelsinnearlyallcell
lines (light blue), in individual lines (green), and in normal or
nonmalignant lines (dark fuchsia). (B) Clustering tree resulting from
hierarchical clustering shown in (A).
Figure 3. Multidimensional scaling analysis on the 27 breast cancer
Euclidean norm. One half count was added to each spectral count, and
line are generally close to each other, showing that pairs of measure-
ments on the same cell lines give very similar results. Blue boxes are
nonmalignant lines. Green circles are basal lines. Red triangles are
luminal lines.
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Journal of Proteome Research
HMEC (Figure 5). Overall, the proteins that were differentially
expressed in HMEC cells had a distribution of functions similar
to that of the entire data set, as determined by mapping to the
KEGG pathways.
Differences between Malignant and Nonmalignant Samples
The breast cancer cell lines include 11 lines derived from
malignant tumors and two lines derived from fibrocystic tumors,
which are not malignant. To determine which glycoproteins are
expressed differentially in the malignant compared to the non-
malignant samples (HMEC cell samples were grouped with the
nonmalignant tumors for this analysis), a comparison between
the two groups was performed using significance analysis for
microarrays (SAM) method,25a method that calculates a mod-
ified t statistic for the difference in the means of the two groups
for each glycoprotein, then finds the significance using a permu-
tation test. SAM analysis identified only one glycoprotein, basal
level between nonmalignant and malignant samples (q-value <
Differences between Basal and Luminal Cell Lines
A similar analysis using SAM revealed glycoproteins that were
enriched either in basal or luminal cell lines as shown in Figure 6
(all q-values < 0.03). For example, clusterin and carbonic
anhydrase 12 (a zincmetalloenzyme responsible for acidification
of the microenvironment of cancer cells) were more highly
expressed in luminal cell lines, whereas several CD antigens,
CD142 (tissue factor), CD44, CD147 (Basigin), CD97 antigen,
CD109, CD13 (Aminopeptidase N), and three integrins CD49c
(integrin α-3), CD49f (integrin α-6), and CD29 (integrin β-1)
were found at higher levels in basal cell lines. One glycoprotein,
tissue factor (P13726), clearly distinguished all luminal cell lines
from all basal cell lines.
hierarchical clustering analysis did not result in a clear separation of
basal Aandbasal Bcell lines, twoglycoproteinswerefound to have
substantially different expression levels between these two basal
subtypes. CD13 (Aminopeptidase N) was highly expressed in
basal B cell lines, whereas galectin-3-binding protein was more
highly expressed in Basal A cell lines (Figure 7A and B).
Aminopeptidase N had a very low expression level in luminal
cells and thus, represents a basal B associated glycoprotein,
whereas galectin-3-binding protein had significant expression
levels in luminal cell lines.
Correlation between Antibody-Mediated Measurements
and Spectral Counts
Fluorescently labeled antiglycoprotein antibodies were used
to measure the cell surface expression of CD29, CD44, and
Figure 6. Glycoproteins differentially expressed between basal and
luminal cell lines. Basal cell lines are represented in the first seven
columns. Expression levels are represented by red for high level
expression levels and green for low expression levels. Glycoprotein
UniProt IDs and names are given on the right.
Figure 7. Glycoproteins,(A) aminopeptidase(CD13) and (B) galectin-
3-binding protein, differentially expressed between basal A and basal B
cell lines. Basal A lines are the three HCC cell lines. Spectral counts are
shown on the Y-axis (from the average of two biological replicates for
each cell type except for HCC1143, which represents spectral counts
from a single analysis).
Figure 5. Glycoproteins differentially expressed between HMEC and
breast cancer cell lines. Expression level of glycoprotein in the HMEC
cells was compared to the levels in breast cancer cell lines using a one-
sample t test; the expression profiles of glycoproteins with extreme
spectral count, and the base 2 log taken for constructing the heat map.
Expression levels are represented by red for high expression level and
greenforlowexpressionlevel.GlycoproteinUniProt IDsandnamesare
given on the right.
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Journal of Proteome Research
CD49c in a subset of breast cell lines that had a large range of
expression levels for these three glycoproteins. Figure 8 shows
plots of the median fluorescence intensity measurements ob-
tained with the antiglycoprotein antibodies evaluated by flow
cytometry versus spectral counts obtained for the three glyco-
the flow cytometry were selected to reflect the range of expres-
sion levels observed among the breast cancer cell lines based on
spectral counts (spectral count values that differed by 10-fold or
more at the extremes with other lines falling at intermediate
levels). For example, T47D and MCF7 had very low spectral
count values for CD44, whereas HCC 1143 had a spectral count
value of over 300, and MCF12A fell at an intermediate level.
between these independent measures of CD44 levels in the
breast cell lines. Similarly, there is a high correlation between
spectral counts for the two integrins (β-1 or CD29, and α-3 or
CD49c) and the median fluorescence intensity measured with
the anti-integrin antibodies by flow cytometry.
Our results demonstrate that the glycoprotein patterns of
normal breast cancer cells and breast cancer cell lines vary
significantly. In addition, glycoproteins are differentially ex-
pressed in benign versus malignant cells, in cell lines of basal
versus luminal origin, and in cell lines of basal A and B subtypes.
exprsssion arrays or immunohistochemistry on tumor sections,
there are few other attempts to take a data-driven, proteomics
for breast cancer. It may be possible to exploit this variation in
glycoprotein expression to infer some of the characteristics of a
patient's cancer through the measurment of glycoproteins in
the blood.
The shotgun proteomics analysis described here was devel-
oped to discover biomarker candidates located within the extra-
cellular space. Our results demonstrate that this approach results
in the identification of a significant number of plasma membrane
and secreted proteins (Supporting InformationTable4). Similar
approaches have been reported by others;37however, our method
applies the periodate oxidation step to intact cells to enhance the
identification of proteins in or facing the extracellular space.
The 14 samples studied included cells derived from both
benign and malignant tumors of the breast, and from normal
breast epithelium as a normal control. The cell lines from
malignant tumors varied in their phenotypes (e.g., HER2, ER,
PR expression), as well as basal or luminal origin. Thus, this
group of cell lines captures much of the known heterogeneity of
breast cancer. To increase the number of glycoproteins identi-
fied, and the reliability of the identifications, we used protein
PeptideProphet.Wealsoused avariety of statisticalclassification
techniques that are suited to the data. The discrimination between
basal and luminal cell lines was supported by two different
approaches to unsupervised classification, multidimensional scaling
and hierarchical clustering. For supervised classification, we used
SAM, which employs a modified t statistic, followed by a permuta-
tion test to evaluate the significance of the result. Permutation tests
and other resampling methods are potentially very useful in
analyzing data from mass spectrometry given the large amount of
effort and expense required to collect that data.25
Our study design provides an opportunity to compare the
glycoprotein-based stratification of the breast cell lines to the
stratification obtained from mRNA expression profiling. Overall,
expression were grouped together by glycoprotein profiling.34,35
In addition, glycoprotein profiling resulted in a distinct grouping
to the stratification obtained from mRNA expression. These
similarities in stratification occurred despite the fact that there is
only weak correlation between mRNA and protein expression
levels. Association between spectral counts for a glycoprotein
and its mRNA expression level was evaluated using our spectral
count data and mRNA expression data from Neve et al.35For
cathepsin D, aminopeptidase N, integrin β-1, and epithelial cell
adhesion molecule the correlation coefficients were 0.41, 0.69,
0.60, and 0.67, respectively. Hence the association between
mRNA and protein levels is positive but weak, as observed by
Figure8. (A?C)SpectralcountsforCD29,CD44,andCD49cdirectly
correlate with antibody binding (median fluorescence intensity) to the
breast cell lines. Spectral counts obtained from mass spectrometry
analysis for the three CD antigens were plotted against the median
fluorescence intensity values obtained from flow cytometry. Measure-
CD49c (B), and CD29 (C), and R2for the fit of the data to a line are
shown. (b) Basal cell lines; ([) luminal cell lines.
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dx.doi.org/10.1021/pr201041j |J. Proteome Res. 2012, 11, 656–667
Journal of Proteome Research
several research groups.38?41Nevertheless, the glycoprotein
expression levels provide the same classification of the breast
cancer cell lines as the mRNA expression mearsurements.
Supervised classification of our data set identified glycopro-
teins that drive the clustering of the various cell lines into basal
the proteins expressed differentially have previously been recog-
nized as contributing to cancer, or as being altered by cancer. Of
the glycoproteins expressed in HMEC samples but not in cell
lines derived from tumors, Thy-1 was expressed exclusively in
HMEC cells. Thy-1 is known to have tumor suppressing
activity42and upregulates two other proteins listed in Figure 5,
fibronectin and thrombospondin 1. An inverse relation between
thromobspondin 1 expression and tumor aggressiveness has
been reported.43Fibronectin is a component of the extracellular
matrix that binds to integrin dimers comprising αV and β1
subunits, which are also on the list of differentially expressed
glycoproteins (Supporting Information Table 5). Fibronectin
and its receptor have been observed to decline in some
tumors.44,45Laminins are a component of the basement mem-
brane secreted by epithelial cells. Three laminins were easily
detected in the HMEC cells, but were detected at much lower
levels,ifatall, inthecancer celllines. C-typemannose receptor 2
(CD280, Endo180) is a collagen binding and recycling glyco-
protein that is expressed in some basal breast turmors, and that
promotes tumor growth.46In contrast, epithelial cell adhesion
molecule (EpCAM) was highly expressed in tumor-derived
samples, and undetected in HMEC. EPCAM expression has
been associated with a number of cancers, including breast
cancer. Silencing of EpCAM expression in breast cancer cell
lines via siRNA resulted in an inhibition of proliferation, migra-
tion, and invasion.47Aglycoproteinthat distinguished malignant
from nonmalignant breast cell lines is basal cell adhesion
molecule. This glycoprotein is a laminin binding protein that is
upregulated in some tumors and in some cell types following
malignant transformation.48Unsupervised clustering did not
result in a distinction of basal A versus B based on glycoprotein
profiling, but a small group of glycoproteins could be used to
clearly distinguish these basal subtypes (Figure 7). All these
groups of glycoproteins represent potential biomarkers, and
since many are either secreted, extracellular matrix proteins, or
can beshed from thecellsurface they could befoundinbiofluids
such as plasma.
A comparison of our list of potential glycoprotein biomarkers
(i.e., basal, luminal, normal, malignant) to a list of glycoproteins
previously identified in human plasma49?51and via the plasma
org) demonstrates that approximately half of our candidate
biomarker glycoproteins have not been reported to occur in
plasma, and another one-third have been detected with fewer
than five peptides. Therefore, rising levels of these glycoproteins
information on the breast cancer subtype.
Oneoftheunexpected results fromouranalysisisthenumber
of lysosomal proteins identified and their relatively high spectral
count levels. More than 60 lysosomal proteins including a large
with the trafficking of lysosomal proteins (cation-dependent and
cation-independent mannose-6-phosphate receptors) were also
identified among the glycoproteins in the breast cell lines. The
protein with the highest total spectral counts in the cell lines was
the lysosomalprotease, cathepsin D. Expression and secretion of
procathepsin D has been associated with an increase in pro-
liferation, metastasis, and progression of breast cancer.54Addi-
epithelial cell preparations will be necessary to further refine
glycoprotein signatures that distinguish normal versusmalignant
breast cells.
Finally, we independently verified our mass spectrometry
spectral count results for a small group of glycoproteins that
had a wide range of expression levels among the breast cell lines.
The results presented in Figure 8 clearly show that the spectral
counts for CD29, CD44, and CD49c directly correlate with
antibody binding to the breast cell lines over a broad spectral
count range. Spectral counts varied linearly with the amount of
protein in the sample as determined by flow cytometry. This
linear relationship supports the use of spectral counts as a label-
free method for relative quantification.28,29
Comparison with Other Work
Whelan etal.55used glycoproteincapture withLC-MS/MSto
identify glycoproteins in three breast cancer cell lines. Of the
three lines, MCF-7 was also used in our study. Whelan et al.
prepared crude membranes from the celllines andthen enriched
for glycoproteins via the periodate oxidation-hydrazide resin
capture method. They identified a total of 25 glycoproteins
among the three cell lines. Some of the glycoproteins were only
identified in the basal A line (MD-MBA-468) were two, CD44
and epidermal growth factor receptor, that were found in our
analysis as being differentially expressed in basal cell lines.
Clusterin, a glycoprotein characterized as being differentially
expressed by luminal cell lines in our analysis, was identified by
Whelan et al. in the luminal cell line MDA-MB-453.
Kulasingam and Diamandis56collected medium conditioned
by three breast cancer cell lines and then identified the proteins
intheiranalyses,MCF10AandBT474,werealsoanalyzed inour
enrich for glycoproteins, but extracellular or membrane-bound
proteins were identified by their annotations in the public
databases. Kulasingam and Diamandis found 422 glycoproteins
in the conditioned media from three breast cancer cell lines,
similar in number to the 486 found in the our study of 14 lines.
Among the top 100 proteins (not all are glycoproteins) reported
byKulasingamandDiamandisinBT474 andMCF10Acelllines,
46 glycoproteins were also identified in our study. Although the
approaches for protein isolation and analysis differed between
the two studies,therelative expressionleveldifferencesobserved
the glycoproteins identified in both studies.
Bateman et al.57conducted a global proteomic analysis of
three breast cancer cell lines, as well as the benign breast cell line
MCF10A, which they considered to be a normal-like control.
Fold change relative to MCF10A was the statistic used for
identifying proteins that were differentially expressed. Bateman
et al. found 82 proteins, including many extracellular matrix
proteins, that were detected in all four samples and expressed
differentially. Network analysis revealed that many of these
is a nonglycoprotein tyrosine kinase localized intracellularly to
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Journal of Proteome Research
by extracellular matrix components including integrins and
laminins. Focal adhesion kinase and extracellular matrix proteins
that interact with it were identified as among the most important
membrane-associated proteins. Our results (Figure 5) clearly
demonstrate that several proteins (laminins α3, β3, and γ2,
fibronectin, and thrombospondin-1) which are differentially
expressed in normal breast epithelial cells versus breast cancer
cell lines form a network of interactions that have a direct
as well as several integrins (α 2, 3, 6, V, and β1) which we also
found to be differentially expressed between normal versus
cancer lines, have been found to be altered in normal versus
breast cancer and are critical for cancer invasiveness.
If any of the differentially expressed glycoproteins identified
here are to become useful as a serum biomarker, several ques-
tions must be answered.58?61How would the glycoprotein be
detected?62A method using antibodies is attractive, but requires
suitable antibodies. While there is clear evidence for the differ-
ential expression of glycoproteins in these cultured cells, it is not
known whether these glycoproteins change expression level
during carcinogenesis in vivo. It was noted above that over half
of the differentially expressed glycoproteins found in our study
have either not been detected at all, or were detected at very low
levels, in plasma. It remains to be determined whether these
glycoproteins rise to a high enough level to be detectable in the
information. Finally, it is likely that more information can be
obtained from a set of proteins than from individual ones. It will
require effective use of statistical classification to find panels of
glycoproteins for tests of sufficiently high sensitivity, selectivity,
and positive predictive value that they are clinically useful.
Supporting Information
material is available free of charge via the Internet at http://
Corresponding Author
*Telephone: 415-338-6078. Fax: 415-338-6253. E-mail: macher@
Support for this work was provided by grants from the
National Institutes of Health, Grant P20 MD000544, and the
National Science Foundation, Grant CHEM-0619163. The
authors thank Ms. Judi Wong, Ms. Christina Litsakos-Cheung,
and Mr. Roger Yen for their help with some of the technical
aspects of this work. The authors also thank Dr. Susan Fisher for
providing some of the breast cancer cell lines.
EGF, epidermal growth factor;ER, estrogen receptor;PR, pro-
gesterone receptor;HER2/neu, c-erbB2 variant of the EGF
Figure 9. STRING (version 9.0) network showing interactions between glycoproteins that are differentially expressed in normal breast epithelial cells
levels in normal breast cells versus the breast cancer cell lines analyzed and focal adhesion kinase (PTK2).
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Journal of Proteome Research
receptor;LC/ESI?MS/MS, liquid chromatography/electro-
spray ionization-tandem mass spectrometry;DE, dynamic exclu-
sion;PNGase F, peptide-N-glycosidase F;SPE, solid phase
extraction;SAM, statistical analysis for microarrays;HMEC, hu-
man mammary epithelial cells;CD, cluster of differentiation
(1) Perou, C. M.; Sorlie, T.; Eisen, M. B.; van de Rijn, M.; Jeffrey,
S. S.; Rees, C. A.; Pollack, J. R.; Ross, D. T.; Johnsen, H.; Akslen, L. A.;
Borresen-Dale, A. L.; Brown, P. O.; Botstein, D. Molecular portraits of
human breast tumours. Nature 2000, 406 (6797), 747–752.
(2) Sorlie, T.; Perou, C. M.; Tibshirani, R.; Aas, T.; Geisler, S.;
Johnsen, H.; Hastie, T.; Eisen, M. B.; van de Rijn, M.; Jeffrey, S. S.;
Lonning, P.; Borresen-Dale, A. L. Gene expression patterns of breast
carcinomas distinguish tumor subclasses with clinical implications. Proc.
Natl. Acad. Sci. U.S.A. 2001, 98 (19), 10869–10874.
(3) Weigel,M.T.;Dowsett,M.Currentandemergingbiomarkersin
breast cancer: prognosis and prediction. Endocr.-Relat. Cancer 2010
(4) AbdEl-Rehim,D.M.;Ball,G.;Pinder,S.E.;Rakha,E.;Paish,C.;
Robertson, J. F.; Macmillan, D.; Blamey, R. W.; Ellis, I. O. High-
throughput protein expression analysis using tissue microarray techn-
ology of a large well-characterised series identifies biologically distinct
J. Cancer 2005, 116 (3), 340–350.
(5) Jacquemier, J.; Ginestier, C.; Rougemont, J.; Bardou, V. J.;
Charafe-Jauffret, E.; Geneix, J.; Adelaide, J.; Koki, A.; Houvenaeghel,
G.; Hassoun, J.; Maraninchi, D.; Viens, P.; Birnbaum, D.; Bertucci, F.
Protein expression profiling identifies subclasses of breast cancer and
predicts prognosis. Cancer Res. 2005, 65 (3), 767–779.
(6) Goldhirsch, A.; Ingle, J. N.; Gelber, R. D.; Coates, A. S.;
Th€ urlimann, B.; Senn, H.-J.; members, P. Thresholds for therapies:
highlights of the St Gallen International Expert Consensus on the
Primary Therapy of Early Breast Cancer 2009. Ann. Oncol. 2009, 20 (8),
(7) Gluz, O.; Liedtke, C.; Gottschalk, N.; Pusztai, L.; Nitz, U.;
Harbeck, N. Triple-negative breast cancer--current status and future
directions. Ann. Oncol. 2009, 20 (12), 1913–1927.
(8) Cianfrocca, M.; Gradishar, W. New molecular classifications of
breast cancer. Ca-Cancer J. Clin. 2009, 59 (5), 303–313.
(9) Sturgeon,C.M.;Duffy,M.J.;Stenman,U.-H.;Lilja,H.;Brunner,
N.; Chan, D. W.; Babaian, R.; Bast, R. C., Jr.; Dowell, B.; Esteva, F. J.;
Haglund, C.; Harbeck, N.; Hayes, D. F.; Holten-Andersen, M.; Klee,
H.; Semjonow, A.; Shih, I.-M.; Sibley, P.; Soletormos, G.; Stephan, C.;
Sokoll, L.; Hoffman, B. R.; Diamandis, E. P. National Academy of
Clinical Biochemistry Laboratory Medicine Practice Guidelines for Use
of Tumor Markers in Testicular, Prostate, Colorectal, Breast, and
Ovarian Cancers. Clin. Chem. 2008, 54 (12), e11–79.
(10) Zou, Z.; Ibisate, M.; Zhou, Y.; Aebersold, R.; Xia, Y.; Zhang, H.
Synthesis and evaluation of superparamagnetic silica particles for
extraction of glycopeptides in the microtiter plate format. Anal. Chem.
2008, 80 (4), 1228–1234.
(11) Arcinas, A.; Yen, T. Y.; Kebebew, E.; Macher, B. A. Cell surface
and secreted protein profiles of human thyroid cancer cell lines reveal
distinct glycoprotein patterns. J. Proteome Res. 2009, 8 (8), 3958–3968.
(12) McDonald, C. A.; Yang, J. Y.; Marathe, V.; Yen, T. Y.; Macher,
B. A. Combining results from lectin affinity chromatography and
glycocapture approaches substantially improves the coverage of the
glycoproteome. Mol. Cell. Proteomics 2009, 8 (2), 287–301.
(13) Spahr, C. S.; Davis, M. T.; McGinley, M. D.; Robinson, J. H.;
W.; Luethy, R.; Patterson, S. D. Towards defining the urinary proteome
using liquid chromatography-tandem mass spectrometry. I. Profiling an
unfractionated tryptic digest. Proteomics 2001, 1 (1), 93–107.
(14) Wolters,D.A.;Washburn,M.P.;Yates,J.R.,3rdAnautomated
multidimensional protein identification technology for shotgun proteo-
mics. Anal. Chem. 2001, 73 (23), 5683–5690.
(15) Zhang, Y.; Wen, Z.; Washburn, M. P.; Florens, L. Effect of
dynamic exclusion duration on spectral count based quantitative pro-
teomics. Anal. Chem. 2009, 81 (15), 6317–6326.
(16) Nesvizhskii, A. I. A survey of computational methods and error
rate estimation procedures for peptide and protein identification in
shotgun proteomics. J. Proteomics 2010, 73 (11), 2092–2123.
(17) Kapp, E. A.; Schutz, F.; Connolly, L. M.; Chakel, J. A.; Meza,
J. E.; Miller, C. A.; Fenyo, D.; Eng, J. K.; Adkins, J. N.; Omenn, G. S.;
several publicly available MS/MS search algorithms: sensitivity and
specificity analysis. Proteomics 2005, 5 (13), 3475–3490.
(18) Perkins, D. N.; Pappin, D. J.; Creasy, D. M.; Cottrell, J. S.
(19) Craig,R.;Beavis,R.C.Amethodforreducingthetimerequired
to match protein sequences with tandem mass spectra. Rapid Commun.
Mass Spectrom. 2003, 17 (20), 2310–2316.
(20) Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. A
statistical model for identifying proteins by tandem mass spectrometry.
Anal. Chem. 2003, 75 (17), 4646–4658.
(21) Keller,A.;Nesvizhskii,A.I.;Kolker,E.;Aebersold,R.Empirical
(22) Eisen,M.;Spellman,P.;Brown,P.;Botstein,D.Clusteranalysis
and display of genome-wide expression patterns. Proc. Natl. Acad. Sci.
(23) Saeed, A. I.; Sharov, V.; White, J.; Li, J.; Liang, W.; Bhagabati,
N.; Braisted, J.; Klapa, M.; Currier, T.; Thiagarajan, M.; Sturn, A.;
Snuffin, M.; Rezantsev, A.; Popov, D.; Ryltsov, A.; Kostukovich, E.;
Borisovsky, I.; Liu, Z.;Vinsavich, A.; Trush, V.; Quackenbush, J. TM4: a
free, open-source system for microarray data management and analysis.
Biotechniques 2003, 34 (2), 374–378.
(24) Buja,A.;Swayne,D.F.;Littman,M.L.;Dean,N.;Hofmann,H.;
Stat. 2008, 17 (2), 444–472.
(25) Tusher, V. G.; Tibshirani, R.; Chu, G. Significance analysis of
microarrays applied to the ionizing radiation response. Proc. Natl. Acad.
Sci. U.S.A. 2001, 98 (9), 5116–5121.
(26) Zhang, H.; Li, X. J.; Martin, D. B.; Aebersold, R. Identification and
(27) Berven, F. S.; Ahmad, R.; Clauser, K. R.; Carr, S. A. Optimizing
performance of glycopeptide capture for plasma proteomics. J. Proteome
Res. 2010, 9 (4), 1706–1715.
(28) Old, W. M.; Meyer-Arendt, K.; Aveline-Wolf, L.; Pierce, K. G.;
Mendoza, A.; Sevinsky, J. R.; Resing, K. A.; Ahn, N. G. Comparison of
label-free methods for quantifying human proteins by shotgun proteo-
mics. Mol. Cell. Proteomics 2005, 4 (10), 1487–1502.
(29) Liu, H.; Sadygov, R. G.; Yates, J. R., 3rd. A model for random
sampling and estimation of relative protein abundance in shotgun
proteomics. Anal. Chem. 2004, 76 (14), 4193–4201.
(30) Pavelka, N.; Fournier, M. L.; Swanson, S. K.; Pelizzola, M.;
Ricciardi-Castagnoli, P.; Florens, L.; Washburn, M. P. Statistical simila-
rities between transcriptomics and quantitative shotgun proteomics
data. Mol. Cell. Proteomics 2008, 7 (4), 631–644.
(31) Huang da, W.; Sherman, B. T.; Lempicki, R. A. Bioinformatics
large gene lists. Nucleic Acids Res. 2009, 37 (1), 1–13.
(32) Kornfeld, S.; Mellman, I. The biogenesis of lysosomes. Annu.
Rev. Cell Biol. 1989, 5, 483–525.
(33) Sleat,D.E.;Zheng,H.;Qian,M.;Lobel,P.Identificationofsites
of mannose 6-phosphorylation on lysosomal proteins. Mol. Cell. Proteo-
mics 2006, 5 (4), 686–701.
(34) Charafe-Jauffret, E.; Ginestier, C.; Monville, F.; Finetti, P.;
Adelaide, J.; Cervera, N.; Fekairi, S.; Xerri, L.; Jacquemier, J.; Birnbaum, D.;
Page 12
dx.doi.org/10.1021/pr201041j |J. Proteome Res. 2012, 11, 656–667
Journal of Proteome Research
Bertucci, F. Gene expression profiling of breast cell lines identifies potential
new basal markers. Oncogene 2006, 25 (15), 2273–2284.
(35) Neve,R.M.;Chin,K.;Fridlyand,J.;Yeh,J.;Baehner,F.L.;Fevr,
T.; Clark, L.; Bayani, N.; Coppe, J. P.; Tong, F.; Speed, T.; Spellman,
P. T.; DeVries, S.; Lapuk, A.; Wang, N. J.; Kuo, W. L.; Stilwell, J. L.;
Pinkel, D.; Albertson, D. G.; Waldman, F. M.; McCormick, F.; Dickson,
R. B.;Johnson, M. D.; Lippman, M.;Ethier, S.; Gazdar, A.;Gray, J.W.A
collection of breast cancer cell lines for the study of functionally distinct
cancer subtypes. Cancer Cell 2006, 10 (6), 515–527.
(36) Storey, J. D.; Tibshirani, R. Statistical significance for genome-
wide studies. Proc. Natl. Acad. Sci. U.S.A. 2003, 100 (16), 9440–9445.
(37) Tian, Y.; Kelly-Spratt, K. S.; Kemp, C. J.; Zhang, H. Mapping
tissue-specific expression of extracellular proteins using systematic
glycoproteomic analysis of different mouse tissues. J. Proteome Res.
2010, 9 (11), 5837–5847.
(38) Anderson, L.; Seilhamer, J. A comparison of selected mRNA
and protein abundances in human liver. Electrophoresis 1997, 18 (3?4),
(39) Gygi,S.P.;Rochon,Y.;Franza,B.R.;Aebersold,R.Correlation
between Protein and mRNA Abundance in Yeast. Mol. Cell. Biol. 1999,
19 (3), 1720–1730.
(40) Ginestier, C.; Charafe-Jauffret, E.; Bertucci, F.; Eisinger, F.;
Geneix, J.; Bechlian, D.; Conte, N.; Adelaide, J.; Toiron, Y.; Nguyen, C.;
DNA microarrays in the study of breast tumor markers. Am. J. Pathol.
2002, 161 (4), 1223–1233.
(41) Chen, G.; Gharib, T. G.; Wang, H.; Huang, C. C.; Kuick, R.;
Thomas, D. G.; Shedden, K. A.; Misek, D. E.; Taylor, J. M.; Giordano,
Hanash, S. M.; Beer, D. G. Protein profiles associated with survival in
lung adenocarcinoma. Proc. Natl. Acad. Sci. U.S.A. 2003, 100 (23),
(42) Abeysinghe, H. R.; Li, L. Q.; Guckert, N. L.; Reeder, J.; Wang,
N. THY-1 induction is associated with up-regulation of fibronectin and
thrombospondin-1 in human ovarian cancer. Cancer Genet. Cytogenet.
2005, 161 (2), 151–158.
(43) Lawler, J. Thrombospondin-1 as an endogenous inhibitor of
angiogenesis and tumor growth. J. Cell. Mol. Med. 2002, 6 (1), 1–12.
(44) Engbring, J. A.; Kleinman, H. K. The basement membrane
matrix in malignancy. J. Pathol. 2003, 200 (4), 465–470.
(45) Williams, C. M.; Engler, A. J.; Slone, R. D.; Galante, L. L.;
Schwarzbauer, J.E.Fibronectin expression modulatesmammary epithe-
lial cell proliferation during acinar differentiation. Cancer Res. 2008, 68
(9), 3185–3192.
(46) Wienke, D.; Davies, G. C.; Johnson, D. A.; Sturge, J.; Lambros,
Reis-Filho, J.S.; Isacke, C.M.Thecollagenreceptor Endo180 (CD280)
in vivo. Cancer Res. 2007, 67 (21), 10230–10240.
(47) Osta, W. A.; Chen, Y.; Mikhitarian, K.; Mitas, M.; Salem, M.;
breast cancer and is a potential target for breast cancer gene therapy.
Cancer Res. 2004, 64 (16), 5818–5824.
(48) Rettig, W.J.;Dracopoli, N. C.;Goetzger, T.A.; Spengler, B. A.;
Biedler, J. L.; Oettgen, H. F.; Old, L. J. Somatic cell genetic analysis of
human cell surface antigens: chromosomal assignments and regulation
of expression in rodent-human hybrid cells. Proc. Natl. Acad. Sci. U.S.A.
1984, 81 (20), 6437–6441.
(49) Liu, T.; Qian, W. J.; Gritsenko, M. A.; Camp, D. G., 2nd;
Monroe, M. E.; Moore, R. J.; Smith, R. D. Human plasma N-glycopro-
teome analysis by immunoaffinity subtraction, hydrazide chemistry, and
mass spectrometry. J. Proteome Res. 2005, 4 (6), 2070–2080.
(50) Omenn, G. S.; States, D. J.; Adamski, M.; Blackwell, T. W.;
Menon, R.; Hermjakob, H.; Apweiler, R.; Haab, B. B.; Simpson, R. J.;
Aebersold, R.; Eng, J.; Hancock, W. S.; Hefta, S. A.; Meyer, H.; Paik,
Y. K.; Yoo, J. S.; Ping, P.; Pounds, J.; Adkins, J.; Qian, X.; Wang, R.;
Wasinger, V.; Wu, C. Y.; Zhao, X.; Zeng, R.; Archakov, A.; Tsugita, A.;
Beer, I.; Pandey, A.; Pisano, M.; Andrews, P.; Tammen, H.; Speicher,
results from the pilot phase with 35 collaborating laboratories and
a publicly-available database. Proteomics 2005, 5 (13), 3226–3245.
(51) Qian, W.-J.; Kaleta, D. T.; Petritis, B. O.; Jiang, H.; Liu, T.;
X.; Zhang, W.-W.; Smith, R. D. Enhanced Detection ofLow Abundance
Human Plasma Proteins Using a Tandem IgY12-SuperMix Immunoaf-
finity Separation Strategy. Mol. Cell. Proteomics 2008, 7 (10), 1963–
(52) Saitoh, O.; Wang, W. C.; Lotan, R.; Fukuda, M. Differential
glycosylation and cell surface expression of lysosomal membrane
glycoproteins in sublines of a human colon cancer exhibiting distinct
metastatic potentials. J. Biol. Chem. 1992, 267 (8), 5700–5711.
(53) Kanao,H.;Enomoto,T.;Kimura,T.;Fujita,M.;Nakashima,R.;
A.; Yoshino, K.; Murata, Y. Overexpression of LAMP3/TSC403/DC-
LAMP promotes metastasis in uterine cervical cancer. Cancer Res. 2005,
65 (19), 8640–8645.
(54) Ohri, S. S.; Vashishta, A.; Proctor, M.; Fusek, M.; Vetvicka, V.
The propeptide of cathepsin D increases proliferation, invasion and
metastasis of breast cancer cells. Int. J. Oncol. 2008, 32 (2), 491–498.
(55) Whelan,S.A.;Lu,M.;He,J.;Yan,W.;Saxton,R.E.;Faull,K.F.;
Whitelegge, J. P.; Chang, H. R. Mass spectrometry (LC-MS/MS) site-
mapping of N-glycosylated membrane proteins for breast cancer bio-
markers. J. Proteome Res. 2009, 8 (8), 4151–4160.
(56) Kulasingam, V.; Diamandis, E. P. Proteomics analysis of con-
and therapeutic targets. Mol. Cell. Proteomics 2007, 6 (11), 1997–2011.
(57) Bateman, N. W.; Sun, M.; Hood, B. L.; Flint, M. S.; Conrads,
T. P. Defining central themes in breast cancer biology by differential
proteomics: conserved regulation of cell spreading and focal adhesion
kinase. J. Proteome Res. 2010, 9 (10), 5311–5324.
(58) Drake, P. M.; Schilling, B.; Niles, R. K.; Braten, M.; Johansen,
E.; Liu, H.; Lerch, M.; Sorensen, D. J.; Li, B.; Allen, S.; Hall, S. C.;
Witkowska, H. E.; Regnier, F. E.; Gibson, B. W.; Fisher, S. J. A lectin
affinity workflow targeting glycosite-specific, cancer-related carbohy-
drate structures in trypsin-digested human plasma. Anal. Biochem. 2011,
408 (1), 71–85.
(59) Rifai,N.;Gillette,M.A.;Carr,S.A.Proteinbiomarkerdiscovery
and validation: the long and uncertain path to clinical utility. Nat.
Biotechnol. 2006, 24 (8), 971–983.
(60) Anderson, N. L.; Anderson, N. G.; Pearson, T. W.; Borchers,
C. H.; Paulovich, A. G.; Patterson, S. D.; Gillette, M.; Aebersold, R.;
Carr, S. A. A human proteome detection and quantitation project. Mol.
Cell. Proteomics 2009, 8 (5), 883–886.
(61) Makawita, S.; Diamandis, E. P. The bottleneck in the cancer
biomarker pipeline and protein quantification through mass spectro-
metry-based approaches: current strategies for candidate verification.
Clin. Chem. 2010, 56 (2), 212–222.
(62) Pan, S.; Chen, R.; Aebersold, R.; Brentnall, T. A. Mass Spectro-
metry Based Glycoproteomics—From a Proteomics Perspective. Mol.
Cell. Proteomics 2011, 10 (1), 1–14.


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GroupBox inheritance
A reader is developing a custom control inherited from GroupBox class. The reader must change bounds used by components when they are docking.
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I need to change bounds used by components when they are docking inside the GroupBox (it seems there's an offset value, 13, on the top docking).
Here is a suggestion: Download Reflector from http:// www.aisto.com/roeder. Browse to the GroupBox class, and take a look at the code in the OnPaint method (it automatically decompiles the IL to C#, VB.NET and other languages). I can't see any fixed offset used in there. You can override that method and use the public GroupBoxRenderer class to pass different bounds and experiment.
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The Japan Origami Academic Society (JOAS) publishes Tanteidan magazine, one of the most prestigious and most recognized magazine origami. Each issue includes:- a super complex diagram from a great origami master- one or more modular diagrams
- a CP challenge to solve- color photos of the latest origami production- many articles (but in Japanese)This Volume 19 is a pack with 6 issues(from #103 to #108).
You can see all the models on famous Gilad's website.
The 9 most remarkable diagrams of this pack are:
Kawahata Fumiaki
SheepKomatsu Hideo
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Product Details:softcover: 38x6=228 pages including 4x6=24 pages couleurs Date : 2007-2008 Language: JapaneseProduct Dimensions: 18 cm x 25 cm
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- I just joined and wanted to know if you have a preference for the yarn used and square sizes?
My name is Judith. I am grannyjudith on yahoo, ravelry, and crochetville.
I have been crocheting for 53 years and knitting for slightly less. I was thrilled to find this group is based right here in my home town. I haven't been doing charity knitting and crocheting because shipping has become an issue. Is there a drop off point any where here in Montandon?


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Perfect tube :)
Perfect trade. And reasonable offers :)
Superb communication and a pleasant to deal with :)
Item: [UK] [WTB / WTT] HD800
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Article ID: 315586 - View products that this article applies to. This article was previously published under Q315586 On This PageSUMMARY
This article explains how to use a Visual Basic application to access the registry on a remote computer in conjunction with the Windows application programming interface (API). RequirementsYou need the following hardware and software to perform the procedures in this article:
You also need the following skills:
Create a Demonstration Application
REFERENCESFor additional information, click the article number below
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HOWTO: Use the Registry API to Save and Retrieve Setting
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Nvidia this week reported $977.2 million in total revenue for its second quarter of fiscal 2014 ended July 28, 2013, up 2.4 percent from $954.7 million the prior quarter though down 6.4 percent from $1.04 billion in the same period a year ago. This led to a 23.8 percent sequential jump in profit to $96.4 billion, or $0.16 per share. Not too shabby considering Tegra sales fell off a cliff last quarter.
The GPU maker's Tegra Processor business raked in just $52.6 million in revenue in Q2, down a whopping 49 percent sequentially and a stomach churning (for Nvidia investors) 70.7 percent year-over-year.
"These decreases were largely due to lower unit shipments of Tegra 3 processors as customers ramped down production of Tegra 3-based smartphones and tablets, as expected," Nvidia said in a statement. "They also reflect lower revenue associated with the sale of embedded products and game controls, offset by increased revenue from automotive products."
Nvidia's GPU business fared far better, generating $858.6 million in revenue, up 9.3 percent sequentially and 7.5 percent year over-year. There was a slight decrease in notebook GPU revenue, but on the desktop, GeForce GPU revenue jumped 13.8 percent sequentially and 3.9 percent year-on-year, Nvidia said.
[1] http://www.maximumpc.com/user/paul_lilly
[2] http://www.maximumpc.com/tags/nvidia
[3] http://nvidianews.nvidia.com/Releases/NVIDIA-Reports-Financial-Results-for-Second-Quarter-Fiscal-2014-a05.aspx
[4] https://plus.google.com/113266473617484509826?rel=author
[5] https://twitter.com/#!/paul_b_lilly
[6] http://www.facebook.com/Paul.B.Lilly
[7] http://www.maximumpc.com/tags/financial_report
[8] http://www.maximumpc.com/tags/gpu
[9] http://www.maximumpc.com/tags/hardware
[10] http://www.maximumpc.com/tags/revenue
[11] http://www.maximumpc.com/tags/tegra
[12] http://www.maximumpc.com/articles/news
[13] http://www.maximumpc.com/user/login?&commentfragment=comments_top_anchor


text:
Looking for a vacuum cleaner?
Whether you're looking for a traditional upright vacuum, a nimble stick model, or a low-maintenance robot vac, this list has an option that's right for you.
Asus moves beyond the Transformer Prime with a device that starts as a phone and can end as a notebook. CNET goes hands on at Mobile World Congress.


text:
X-ray observations shed new light on dark matter
New analyses of the x-ray and gamma-ray emissions from the center of the Milky Way galaxy, the Andromeda galaxy, and the Perseus galaxy cluster have detected significant signs of two possible dark matter particles. One is likely a 7.1 keV sterile neutrino, and the other appears to be a 35 GeV WIMP (Weakly Interacting Massive Particle). Read More
Archaeology vs. Physics: Conflicting roles for old lead
The study of archaeology has long been carried out using tools from the physics lab. Among these are carbon-14 dating, thermoluminescence dating, x-ray photography, x-ray fluorescence elemental analysis, CAT and MRI scanning, ground-penetrating sonar and radar, and many others. What is less well known is that archaeology has also made substantial contributions to physics. This is the story of old lead; why it is important to physics, and what ethical problems it presents to both sciences. Read More
Revamped Fermilab neutrino beam offers hope for new physics
Particle physicists have been eagerly awaiting the first trials of the new Main Injector neutrino beam at the Fermi National Accelerator Laboratory in the US. This new facility is the result of reconfiguration of the Fermilab particle accelerators in the wake of the shutdown of the Tevatron in 2011. The new beam source is now online, and is well on route to becoming the world's most intense focused neutrino source. Read More
New results from CERN today would appear to confirm that last year's findings by the OPERA experiment which appeared to suggest that neutrinos could travel faster than light were incorrect. A faulty element of the experiment's fiber optic timing system has been cited by CERN as a likely cause for the error. Read More
Neutrinos have been in the news recently, and although it appears that they probably do not travel faster than light, they still hold court as three of the strangest of the known subatomic particles. Undeterred by these arcane particles, Fermilab scientists have succeeded in communicating with neutrino pulses through 240 meters (262 yards) of rock at a rate of 0.1 bits per second. Read More
An audacious project to construct a vast infrastructure housing a neutrino observatory at the bottom of the Mediterranean Sea is being undertaken by a consortium of 40 institutes and universities from ten European countries. The consortium claims that KM3NeT, as it is known, will "open a new window on the Universe," as its "several" cubic kilometer observatory detects high-energy neutrinos from violent sources in outer space such as gamma-ray bursts, colliding stars and supernovae. Read More
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text:
Northern Railway zone
Northern Railway उत्तर रेलवे |
1-Northern Railway
Dates of operation | 14 April 1952– |
Track gauge | Mixed |
Headquarters | New Delhi Railway Station |
Website | http://www.nr.indianrail.gov.in/ |
The Northern Railways is one of the 16 zones and the northernmost zone of the Indian Railways. Its headquarters is in Delhi at Baroda House & New Delhi Railway Station.
Northern Railways is one of nine old zones of Indian Railways and also the biggest in terms of network having 6807 kilometre route.[1] It covers the states of Jammu and Kashmir, Punjab, Haryana, Himachal Pradesh, Uttarakhand and Uttar Pradesh and the Union territories of Delhi and Chandigarh.
The railway zone was created on 14 April 1952 by merging Jodhpur Railway, Bikaner Railway, Eastern Punjab Railway and three divisions of the East Indian Railway north-west of Mughalsarai (Uttar Pradesh). It includes the first passenger railway line in North India, which opened from Allahabad and Kanpur on 3 March 1859.[2] The Zonal Headquarters Office of Northern Railways is at Baroda House, New Delhi, and divisional headquarters are located at Ambala (Haryana), Delhi, Firozpur (Punjab), Lucknow (Uttar Pradesh) and Moradabad (Uttar Pradesh).
The first passenger railway line in North India opened from Allahabad to Kanpur on 3 March 1859. This was followed in 1889, by the Delhi–Ambala–Kalka line. Northern Railways previously consisted of eight divisional zones: Allahabad, Bikaner, Jodhpur, Delhi, Moradabad, Ferozpur, Ambala, and Lucknow, spanning most of North India. With the re-organisation of zones by the Indian Railways, Northern Railway zone came to its present form on 14 April 1952 and it now consists of five divisional zones.
On 19 February 1986, Northern Railways was the first zone to introduce the computerized passenger reservation system and was the first zone to do so. To facilitate the Unreserved travellers to also plan their journey ahead, Northern Railway introduced the Unreserved Ticketing System (UTS) whereby the unreserved rail passenger can purchase an unreserved ticket 3 days in advance from the current booking counters.
The first diesel and electric locomotive simulators in India at the Tughlaqabad and Kanpur locomotive sheds were introduced by Northern Railways. These help upgrading the skills of the working and new drivers, providing them training for high speed train operation. All workshops, Diesel sheds and Air brake freight depts. are ISO 9000 certified. Diesel shed, Tughlaqabad has the distinction of being the first diesel shed to get ISO 14000 certification on Indian Railway.
There are eight workshops operated by Northern Railways
Workshop | Location | State |
Locomotive Workshop | Charbagh Railway Station, Lucknow | Uttar Pradesh |
Carriage & Wagon Workshop[3] | Alambagh, Lucknow | Uttar Pradesh |
Carriage & Wagon Workshop | Yamunanagar-Jagadhari Railway Station, Yamunanagar | Haryana |
Carriage & Wagon Workshop | Kalka | Haryana |
Bridge Workshop | Jalandhar Cantonment | Punjab |
Bridge Workshop | Lucknow | Uttar Pradesh |
Engineering workshop | Jalandhar | Punjab |
Signal & Telecom Workshop | Ghaziabad | Uttar Pradesh |
Zonal Railway Training Institute (ZRTI) at Chandausi, Moradabad UP is the oldest (and North India's only) Railway Training Institute, where group C railway employees like Driver, Guard, Ticket Checker, Station Master, Traffic Inspector, Commercial Inspector, JE (signal & Telecom) etc. are trained for initial, promotional and refresher courses. ZRTI is an ISO 9001 Institute. Applicants pass All India Railway Examinations to get admission into the institute. ZRTI is headed by a senior Civil Services Officer.[4]
Several facilities are provided for the satisfaction of customers such as Interactive Voice Response Systems (IVRS), Electronic Display Systems at stations, Recorded Coach Guidance Display System, Reservation Availability Position Information Display (RAPID), Self Dial Telephone Reservation Enquiry Booths, Automatic Teller Machines (ATM) and Money Changing Facilities at Stations.
Northern Railways implemented the Route Relay Interlocked system (RRI) at New Delhi Railway Station which is a modern signaling system for enhancing efficiency and safety in the operations. This Route Relay Interlocked system at New Delhi is one of the world's largest route relay interlocking system certified by the Guinness Book of Records. Northern Railway is equipped with 40 Route Relay Interlocking systems including the system at Delhi Main.
Delhi Suburban Railway services is a commuter rail service operated by Northern Railway. It covers the city state of Delhi, along with the adjoining districts of Faridabad, Ghaziabad and other adjoining places in Haryana and Uttar Pradesh. These services are mostly run using EMU and MEMU rakes. In 2009 Ladies Special trains were introduced between New Delhi and Palwal. Two more ladies special trains will be connecting from the city to Ghaziabad and Panipat.
Starting with a part of Firozpur division of the Northern Railway zone, the line has been under construction since 1983; the Jammu Udhampur Srinagar Baramulla Railway Link (JUSBRL) of the Kashmir Railway is under construction in perhaps the most difficult terrain on the Indian subcontinent. The Northern Railway reached another landmark achievement by extending rail services in the Kashmir Valley on 28 October 2009 by commencing rail services between Anantnag and Qazigund of the Qazigund-Baramula rail project.[5] Pir Panjal Railway Tunnel, the 10.96 km long railway tunnel, passes through the Pir Panjal Range of middle Himalayas in Jammu and Kashmir. It is a part of its Udhampur–Srinagar–Baramulla rail link project, opened in October 2011, India's longest and Asia's second longest railway tunnel and reduced the distance between Quazigund and Banihal to only 11 km .[6]
Notable trains[edit]
Some of the important trains connecting Delhi with other cities of India are
Luxury trains[edit]
Rajdhani Express, Shatabdi Express, Palace on Wheels, Mahaparinirvana Express
Passenger trains[edit]
Himsagar Express, New Delhi–Jabalpur Express, Prayagraj Express, Kashi Vishwanath Express,Bareilly–New Delhi Intercity Express, Shan-e-Punjab Superfast Exp, New Delhi–Amritsar Superfast Exp., Jammu Mail., Shalimar Exp., Golden Temple Mail.,Sangam Exp., Nauchandi Express.,Rajya Rani Express.
See also[edit]
- Indian Railways
- New Delhi Railway Station
- Delhi Railway Junction
- Sarai Rohilla Railway Station
- Charbagh Railway Station, Lucknow
- ^ iloveindia.com. "Northern Indian Railway".
- ^ Asiatradehub.com.com. "India – Infrastructure Railways".
- ^ Northern Railway Carriage and wagon Workshop Lucknow
- ^ [1]
- ^ Sify.com. "Tracking Jammu and Kashmir's rail history".
- ^ "India's longest railway tunnel unveiled in Jammu & Kashmir". The Times of India. 14 October 2011. Retrieved 14 October 2011.
RRC Northern Railway Group D Admit Card 2014
External links[edit]


text:
Import Auto Parts offers auto trim retail service in Lakeland, FL. more
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Definition: n. pl. People that are outside of your group or party, or outside of any party perhaps, ie. those who roll alone, especially on Xbox Live.
Example: We just raped those guys in that last game, they must have all been randoms. Who are these randoms we keep running into?
Prices shown in USD.
邮件由 email@example.com 发出。我们决不会发送垃圾邮件。


text:
BEST WESTERN Wheatland Inn
- Continental Breakfast
- Swimming pool
- Free High-Speed Internet
- Business Services
- Fitness/Health center
- Wheelchair accessibility
TravelPod Member ReviewsBEST WESTERN Wheatland Inn Colfax
If you are traveling to Palouse this is the place to stay! Clean, comfortable with a great staff!
This review is the subjective opinion of a TravelPod member and not of TravelPod.com.
Great place to stay if you are touring the Palouse area
This review is the subjective opinion of a TravelPod member and not of TravelPod.com.
We were lucky enough to get a room here in Colfax at the last minute. We have reserved a room at a motel in Pullman, WA which we decided we could not stay at once we arrived and saw our room. The BW in Colfax was one of the cleanest hotels we have ever stayed in. We are BIG fans of the free breakfast and BW did not disappoint. Excellent service, fantastic food, and easy drive to the WSU Campus. Thanks!
This review is the subjective opinion of a TravelPod member and not of TravelPod.com.
TripAdvisor Reviews BEST WESTERN Wheatland Inn Colfax
Travel Blogs from Colfax
A fun and fabulous day in Palouse
The weather was perfect. The light gorgeous. The scenery breathtaking. Old barns, houses and granaries nestled in the undulating hills. The sky a beautiful shade of blue with billowing white puffy clouds. After breakfast, our first stop was a supermarket where we loaded up a cooler with turkey, cheese fresh fruit, and cookies. We drove all over: …
Time to Say Goodbye
... 45 pm), we landed at Philadelphia Airport and headed for immigration and customs. Immigration lines were pretty fast, as were customs. But we were sad to realize our two seed packets from a Scottish garden shop weren't allowed, since they didn't have the right seal on them. Oh, well, glad we didn't buy more than two.
Almost two hour layover, then a short flight to Reagan National Airport in Washington. I really liked the high, arched ceiling and yellow and ...
Last full day in Palouse
Got up around 7:30 and left the motel around 8:30. Our plan today was to drive towards east, explore the local roads, and capture the scenes as much as we could.
We took Rt 27 towards Palouse and turned left onto Lange Rd towards Garfield. Rt 27 is a beautiful road. There are rolling fields of many different colors on both sides, but it was difficult to pull to the side to take photos. Lange Rd is a gravel road and took us through a canyon which was in the middle of the ...
2 Falls weekend
... crab off.
This morning decided to hit a Denny's. First time in a long time that we had great service! Decided to come home Stevens Pass. I had my jacket on the whole day. On the other side of the pass we stopped at another falls, called Deception Falls. Chris went down and snapped a picture for us.
Stopped at a HD shop in Renton, and wow they had the exact bike that Chris and I have been looking for. Don't think we will ...
Leaving Home
The day before we left for our California adventure was very busy. We moved all of our things out of our house. In so doing, it seems the Chewy's subaru, Ruby, was packed to the brim with ****. It was our first adventure, and I must say that the first thing we learned about travel is that it is ideal to be a minimalist. Live and learn. We were also leaving ...


text:
Eminem has one of the highest-anticipated new albums of 2013... and still, there's no official details in regards to release date, album name, tracklist, etc. NOTHING. But Twitter fans still anxiously await fresh beats from the Detroit rapper... and Mstars is here to satisfy those Slim Shady enthusiasts while they (im)patiently wait! Take a look back at some of Em's top performances from MTV's Video Music Awards over the past several years... will Shady hit the VMA stage in 2013 to surprise fans with some new music?
Like Us on Facebook
Although Eminem's hype man Mr. Porter claims that Shady probably won't drop his new album anytime this summer, Twitter fans are still hoping for a July 2 release date. Check it out:
@NorthernBarbie_: "I can't wait for eminem to release his new album. #detroit."
@ImShaneMichael: "Eminem needs to release his new album I'm stoked."
@paulwheatthins: "Eminem. New Album. July 2nd."
@Lou_Savvvy11: "Eminem kills any verse and any song he's on. He needs to make a new album already."
Here's everything that fans need to know about Eminem's much-hyped about new album (so far):
Eminem July Release Date, New Music Leak RUMORS: Rapper Denace Speaks Out, 'Wannabe Crap is Bull!'
Will Eminem live-up to his rap legacy? Here's a round-up of unforgettable Slim Shady performances at the MTV VMAs:
And who can forget Eminem's Grammy performance with Elton John in 2001...
Wouldn't it be great if Shady surprised fans with some new music at the 2013 VMAs? Unfortunately, that probably won't happen... Em will be touring in Europe:
August 15, 2013: Kempische Steenweg, Kiewit (Pukkelpop 2013)
August 17, 2013: Slane, Ireland
August 20, 2013: Dumbreck Road, Glasgow Eminem
August 21, 2013: Bramham Park, Leeds (Leeds Festival 2013)
August 22, 2013: Stade De France, La Plaine St Denis
August 23, 2013: Bramham Park, Wetherby (Leeds Festival 2013)
August 24, 2013: Reading, Reading (Reading Festival 2013)
August 25, 2013: Bramham Park, Leeds (Leeds Festival 2013)
The 2013 VMAs air on Sunday, August 25 from Brooklyn's Barclay's Center... wonder when Em will hit the stage again in the US?


text:
SUPER sweet and lightning fast shipping! Thanks Melanie!
The bobbys are too big in the waist so I found a pair of 25's that should work! Thanks again! :)
great seller, fast shipping
Hey, got the jeans, thanks!
Melanie is awesome! The Bohos are beautiful!!
I love them. Been wanting a pair for a long time now. Thanks again!!
quick payment. smooth transaction. :)
thank you!
Thank you for the great deal! Fast shipping and great communication! :)
Love the shirt. Thank you for sending it so quickly! It arrived at the perfect time :)
A great experience, Such a sweetie too, thank you!!
thnks again!!
Melanie is the best! :D Really!
Thanks SO much, youre awesome !
Love the jeans! So nice!
Thanks for the great jeans, just gotta have the waist taken in and they'll be perfect everywhere. And thanks for the nice note on the bag!!
Super fast shipping, great person to work with! Thanks!!
Thanks again!!!
Awesome seller & awesome person- shipping was faster than expected-thx a bnch!
Thank you so much! I couldn't be happier. You've been a wealth of knowledge! You have no idea how refreshing it is to have someone give honest answers and not just look out for their own "bottom line." (You know--none of the pressure, pressure, buy, buy crap!). I'll be on the look out for more of your closet purges!
Wonderful experience. The jeans are perfect. Great communication and fast ship
Thank you so much. I LOVE these jeans.
Thanks so much for the fast shipping! Jeans are awesome and very pretty!!! :) :)
Hi Melanie,
I got the jeans!! woohoo!! They're so pretty. And the shipping was quick too. Thanks for sending them out right away. Take Care!
Shirts arrived in record time and in the condition mentioned! Great!
Thanks again for the cute shirts!
great top exactly as describe thx so much mel!
hi mel got the top today its great! but the bust is a bit small i can see why you needed to get rid of it... I'm a 34 A (sad I know) and a sz 2/0 but its still kind of tight on me after a padded bra!
BUt none the less i love it
thx alot and you have cute clothes
Love the tops. Great deal and fast shipping
I can't wait for the weekend to wear my new tops. Thanks!
cute shirts, fast shipping, thanks melanie!
super sweet & so nice to work with! All HF members should be like you! :)
Great top, Fast shipping,Thank you!!!!!!!!!!!!!
Thanks so much!
Great transaction! Great communication! A+
Thanks so much and enjoy!
Mel's the best! Great communication, pretty jeans, and lightening fast shipping
I got the jeans yesterday and they're really cute. I was shocked at how quickly they arrived. Thanks Mel :)
Really nice buyer. Would love to work with again.
No additional comments.


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Genre/Form: | Biography History Registers |
Named Person: | William Nicholson; William Nicholson |
Document Type: | Article |
All Authors / Contributors: |
Alice Kinyoun Houts; Hazel Eastman; Daughters of the American Revolution. Missouri Society. |
OCLC Number: | 697174305 |
In: | Houts, Alice Kinyoun, comp |
Notes: | "Data secured from the different DAR chapters of the State society." |
Description: | p. 174; 23 cm. |
Other Titles: | Soldiers of the American Revolution buried in the State of Missouri. |
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