Unsupervised unmixing of Raman microspectroscopic images for morphochemical analysis of non-dried brain tumor specimens

Raman microspectroscopic imaging provides molecular contrast in a label-free manner with subcellular spatial resolution. These properties might complement clinical tools to diagnose tissue and cells in the future. Eight Raman spectroscopic images were collected with 785 nm excitation from five non-dried brain specimens immersed in aqueous buffer. The specimens were assigned to molecular and granular layers of cerebellum, cerebrum with and without scattered tumor cells of astrocytoma grade 3, ependymoma grade 2, astrocytoma grade 3 and glioblastoma multiforme with subnecrotic and necrotic regions. In contrast to dried tissue section, these samples were not affected by drying effects such as crystallization of lipids or denaturation of proteins and nucleic acids. The combined data sets were processed using the hyperspectral unmixing algorithms N-FINDR and VCA. Both unsupervised approaches calculated seven endmembers that reveal the abundance plots and spectral signatures of cholesterol, cholesterol ester, nucleic acids, carotene, proteins, lipids and buffer. The endmembers were correlated with Raman spectra of reference materials. The focus of the single mode laser near 1 µm and the step size of 2 µm were sufficiently small enough to resolve morphological details such as cholesterol ester islets and cell nuclei. The results are compared for both unmixing algorithms and with previously reported supervised spectral decomposition techniques.