|
|||||
|
|
Integration of Raman spectra with transcriptome data in glioblastoma multiforme defines tumour subtypes and predicts patient outcome |
|
| Authors: | Pierre-Jean Le Reste Eleftherios Pilalis Marc Aubry Mari McMahon Luis Cano Amandine Etcheverry Aristotelis Chatziioannou Eric Chevet Alain Fautrel |
| Affiliation: | 1. Department of Neurosurgery, University Hospital, Rennes, France;2. e-NIOS PC, Kallithea-Athens, Greece Contribution: Conceptualization (equal), Investigation (equal), Methodology (equal), Project administration (supporting), Supervision (supporting), Visualization (supporting), Writing - review & editing (supporting);3. REACT – Rennes Brain Cancer Team, Rennes, France IGDR CNRS, University of Rennes, Rennes, France Contribution: Conceptualization (supporting), Investigation (equal), Methodology (equal), Writing - review & editing (equal);4. INSERM U1242, University of Rennes, Rennes, France REACT – Rennes Brain Cancer Team, Rennes, France Centre de Lutte Contre le Cancer Eugene Marquis, Rennes, France Contribution: Investigation (supporting), Methodology (supporting);5. H2P2 Platform, UMS CNRS 3480 – INSERM 018, University of Rennes, Rennes, France Contribution: Investigation (supporting), Methodology (supporting);6. REACT – Rennes Brain Cancer Team, Rennes, France IGDR CNRS, University of Rennes, Rennes, France Contribution: Investigation (supporting), Methodology (supporting);7. e-NIOS PC, Kallithea-Athens, Greece Contribution: Conceptualization (supporting), Funding acquisition (equal), Investigation (supporting), Methodology (supporting), Project administration (equal), Writing - review & editing (equal);8. INSERM U1242, University of Rennes, Rennes, France;9. H2P2 Platform, UMS CNRS 3480 – INSERM 018, University of Rennes, Rennes, France Contribution: Conceptualization (equal), Investigation (equal), Methodology (equal), Project administration (equal), Visualization (equal), Writing - review & editing (equal) |
| 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. |
| Keywords: | data integration glioblastoma Raman spectroscopy |