High-resolution Raman imaging of >300 cells from human patients affected by 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 a novel approach for 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 with nine distinct subtypes, conducting high-resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. A nine-step automated pre-processing pipeline and global clustering identified relevant cellular components, enabling the creation of high-quality pseudostained images at the single cell level. This approach provides a semi-quantitative analysis of cellular component distribution, and multivariate analysis of clustering results reveals the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.