High-resolution Raman imaging of >300 patient-derived cells from nine different leukemia subtypes: a global clustering approach

Leukemia comprises a diverse group of bone marrow tumors marked by immature cell proliferation. Current diagnosis involves identifying leukemia subtypes through visual assessment of blood and bone marrow smears, a subjective and time-consuming method. Our study introduces the characterization of different leukemia subtypes using a global clustering approach of Raman hyperspectral maps of cells. We analyzed bone marrow samples from 19 patients, each presenting one of nine distinct leukemia subtypes, conducting high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. An automated pre-processing pipeline followed by a single-step global clustering approach performed over the entire dataset identified relevant cellular components (cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO) and hemoglobin (HB)) enabling the unsupervised creation of high-quality pseudo-stained images at the single-cell level. Furthermore, this approach provided a semi-quantitative analysis of cellular component distribution, and multivariate analysis of clustering results revealed the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.