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Abstract
Purpose: Vibrational spectroscopy enables the label-free characterization of cells and tissue by probing the biochemical composition. Here, we evaluated these techniques to identify glioblastoma stem cells.
Materials and methods: The biochemical fingerprints of glioblastoma cells were established in human cell lines with high and low content of CD133 (cluster of differentiation 133)-positive cells using attenuated total reflection Fourier-transform infrared (ATR FT-IR) on vital cells and FT-IR mapping, which delivers spatially resolved spectroscopic datasets. After data preprocessing, unsupervised cluster analysis was applied. CD133 was addressed with flow cytometry and immunohistochemistry and used as a stemness marker.
Results: In all preparations, the algorithm was able to correctly classify the spectra, differentiating CD133-rich and -poor populations. The main spectral differences were found in the region of 1000 cm− 1 to 1150 cm− 1 that can be assigned to vibrations of chemical bonds of DNA, RNA, carbohydrates and phospholipids. Interestingly, this spectral region is a key feature to discern glioblastoma from normal brain parenchyma, as FT-IR spectroscopic mapping of experimental brain tumors demonstrated.
Conclusions: We were able to show biochemical differences between glioblastoma cell populations with high and low content of cancer stem cells that are presumably related to changes in the RNA/DNA content.
Acknowledgements
We gratefully acknowledge Professor Valdas Sablinskas of the University of Vilnius to allow us using the FT-IR imaging spectrometer. The authors would like to thank Elke Leipnitz for excellent technical assistance. This research was funded by the German Research Foundation (DFG), German Ministry for Education, Research and Technology (BMBF) and The Association of German Engineers (VDI) (mediCARS, A.Z. 13N10777).
Declaration of interest
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.