Crucially, meticulous data pre-processing, minimizing experimental variability, played a pivotal role in this process. To address this issue, we implemented an automatic preprocessing pipeline applied to the entire dataset, eliminating the need for manual or sample-specific interventions. Interestingly, the definition and optimization of the abovementioned pre-processing pipeline triggered the development of a web-based free tool ("RamApp") for the pre-processing and intuitive analysis of hyperspectral data 52 . It is also important to say that, even considering the large amount of data analyzed here, we included a limited number of patients and cells for each subtype, as well as a relatively high number of different subtypes, preventing the validation of a Ramanbased classification of leukemia subtypes using training and test datasets, or "leave one patient out" strategies. A larger number of cells and patients per subtype would have enhanced the validation and allowed for the development of a more accurate classification model, but availability of patient's samples and time-demanding procedures for data acquisition played an important role. For instance, the collection of highresolution Raman maps is currently hindered by the intrinsic slow acquisition speed of standard spontaneous Raman approaches, limiting the amount of data that can be gathered. New approaches, such as coherent Raman microscopy 9 , eventually coupled with microfluidic approaches , hold promise in improving the speed and potential of Raman techniques.