Raman Spectroscopy Imaging As a Tool to Classify Diffused Large B-Cell Lymphoma Subtypes

INTRODUCTION: Diffuse large B-cell lymphoma (DLBCL) is a clinically heterogenous, aggressive disease. The ‘cell-of-origin’ classification (COO) distinguishes two major DLBCL subtypes, GCB (germinal center B cell-like) and ABC (activated B cell-like) that are derived from distinct putative cell of origin, differ in prognosis and transcriptional pattern. Comprehensive cluster classification (CCC) based on unbiased “consensus clustering” of transcriptomic microarray data has led to the identification of subtypes that differ in energy source utilization and bioenergetic profiles The oxPhos DLBCL subtype, unlike non-oxPhos, is characterized by increased mitochondrial oxidative phosphorylation and energy transduction. Although COO class assignment can be achieved with multiple clinical diagnostic tools (including transcript-based or immunohistochemical approaches), there is no clinical diagnostic tool appropriate for COO classification.

In Raman effect, laser light, as a result of internal oscillations of molecules, is scattered with a change in wavelength, giving a spectrum characteristic for a given substance. This allows the chemical composition of mixtures and even cells to be determined on the basis of the obtained spectra. Thus, it also allows to determine differences in metabolism, including energy metabolism of lymphoma cells. The aim of the study was to use the RS images to recognize and classify DLBCL cells.

MATERIALS AND METHODS: DLBCL cell lines (OCI-Ly4, Pfeiffer, Toledo, DHL4, DHL6 , OCI-Ly1 and OCI-Ly7) with known COO and CCC subtype assignments, and B cells isolated from the blood of healthy donors were used. For Raman analysis cells were fixed in 0.5% glutaraldehyde and imaged at 533 and 633 nm emission wavelengths under a WITec Raman microscope. The collected spectral images were subjected to chemometric analyses: characteristic bands analysis, k-means cluster analysis (KMCA), principal component analysis (PCA) and least squares discriminant analysis (PLS-DA) using dedicated software.

RESULTS: The analysis of Raman spectra revealed clear changes in the structure and composition of cells after neoplastic transformation. Chemometric analysis of spectral bands showed greater proportion of proteins (1008, 1041 and 1660 cm-1) and lipids (1440, 1240 and 2850 cm-1) in DLBCL cells then B cells from healthy donors. More nucleic acids were observed (733, 790 , 1583 cm-1) in normal B cells that at least partially resulted from the greater ratio of the nucleus to the cytoplasm. The differences resulted in clear separation of malignant and healthy cells in PCA and PLS-DA. Similar approach allowed to characterize differences between ABC vs GCB subtype cell lines and oxPhos vs non-oxPhos subtype. Based on the specific differences between healthy B cells and DLBCL cells a predictive model was built using PLS-DA that accurately determines the chance of the analyzed cell, first, being a lymphoma cell, and second, belonging to the ABC vs GCB or oxPhos vs non-oxPhos DLBCL subtype. The good performance of the constructed predictive model was evaluated by prediction on unknown samples with sensitivity and specificity of 98%.

CONCLUSIONS: We present a model of a classification of B cell lymphomas based on label-free Raman spectral imagines, capable of automatic and efficient identification of DLBCL cells spectra. In addition, we describe each lymphoma subtype by its unique spectral profile, linking it to biochemical features.

Acknowledgements. The studies were performed as a part of the “ Label-free and rapid optical imaging, detection and sorting of leukemia cells” project carried out within the Team-Net programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.