Integration of Raman spectra with transcriptome data in glioblastoma multiforme defines tumour subtypes and predicts patient outcome. Issue 23 (12th November 2021)

• Record Type: Journal Article
• Authors: Le Reste, Pierre‐Jean; Pilalis, Eleftherios; Aubry, Marc; McMahon, Mari; Cano, Luis; Etcheverry, Amandine; Chatziioannou, Aristotelis; Chevet, Eric; Fautrel, Alain
• Is Part Of: Journal of cellular and molecular medicine. Volume 25:Issue 23(2021)
• Page Start: 10846
• Page End: 10856
• Publication Date: 2021-11-12
• Subjects: data integration -- glioblastoma -- Raman spectroscopy; Cytology; Medicine; Molecular Biology; Cytologie -- Périodiques; Médecine -- Périodiques; Biologie moléculaire -- Périodiques; Cytology -- Periodicals; Medicine -- Periodicals; Molecular biology -- Periodicals; 611.01805
• DOI: 10.1111/jcmm.16902
• Languages: English
• ISSNs: 1582-1838

Abstract

Abstract: Raman spectroscopy is an imaging technique that has been applied to assess molecular compositions of living cells to characterize cell types and states. However, owing to the diverse molecular species in cells and challenges of assigning peaks to specific molecules, it has not been clear how to interpret cellular Raman spectra. Here, we provide firm evidence that cellular Raman spectra (RS) and transcriptomic profiles of glioblastoma can be computationally connected and thus interpreted. We find that the dimensions of high‐dimensional RS and transcriptomes can be reduced and connected linearly through a shared low‐dimensional subspace. Accordingly, we were able to predict global gene expression profiles by applying the calculated transformation matrix to Raman spectra and vice versa. From these analyses, we extract a minimal gene expression signature associated with specific RS profiles and predictive of disease outcome.