Molecular monitoring of glioblastoma's immunogenicity using a combination of Raman spectroscopy and chemometrics.

Affiliation

Robert C(1), Tsiampali J(2), Fraser-Miller SJ(1), Neumann S(3), Maciaczyk D(3), Young SL(4), Maciaczyk J(5), Gordon KC(6).
Author information:
(1)Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, Dunedin, New Zealand.
(2)Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany.
(3)Department of Pathology, University of Otago, Dunedin, New Zealand.
(4)School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
(5)Department of Neurosurgery, University Hospital Bonn, 53179 Bonn, Germany; Department of Surgical Sciences, University of Otago, Dunedin, New Zealand. Electronic address: [Email]
(6)Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, Dunedin, New Zealand. Electronic address: [Email]

Abstract

Raman spectroscopy (RS) has been used as a powerful diagnostic and non-invasive tool in cancer diagnosis as well as in discrimination of cancer and immune cells. In this study RS in combination with chemometrics was applied to cellular Raman spectral data to distinguish the phenotype of T-cells and monocytes after incubation with media conditioned by glioblastoma stem-cells (GSCs) showing different molecular background. For this purpose, genetic modulations of epithelial-to-mesenchymal transition (EMT) process and expression of immunomodulator CD73 were introduced. Principal component analysis of the Raman spectral data showed that T-cells and monocytes incubated with tumour-conditioned media (TCMs) of GSCs with inhibited EMT activator ZEB1 or CD73 formed distinct clusters compared to controls highlighting their differences. Further discriminatory analysis performed using linear discriminant analysis (LDA) and support vector machine classification (SVM), yielded sensitivities and specificities of over 70 and 67% respectively upon validation against an independent test set. Supporting those results, flow cytometric analysis was performed to test the influence of TCMs on cytokine profile of T-cells and monocytes. We found that ZEB1 and CD73 influence T-cell and monocyte phenotype and promote monocyte differentiation into a population of mixed pro- and anti-tumorigenic macrophages (MΦs) and dendritic cells (DCs) respectively. In conclusion, Raman spectroscopy in combination with chemometrics enabled tracking T-cells and monocytes.