Raman Imaging of Biomedical Samples. In: Toporski J, Dieing T, Hollricher O, editors. Confocal Raman Microscopy. Cham: Springer International Publishing; 2018. pp. 307-46. [DOI: 10.1007/978-3-319-75380-5_14]

Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning

Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.

Rapid Approach for Detection of Antibiotic Resistance in Bacteria Using Vibrational Spectroscopy

Here, we applied vibrational spectroscopy to investigate the drug response following incubation of S. aureus with oxacillin. The main focus of this work was to identify the chemical changes caused by oxacillin over time and to determine the feasibility of the spectroscopic approach to detect antimicrobial resistance. The oxacillin-induced changes in the chemical composition of susceptible bacteria, preceding (and leading to) the inhibition of growth, included an increase in the relative content of nucleic acids, alteration in the α-helical/β-sheet protein ratio, structural changes in carbohydrates (observed via changes in the band at 1035 cm^-1), and significant thickening of the cell wall. These observations enabled a dose-dependent discrimination between susceptible bacteria incubated with and without oxacillin after 120 min. In methicillin resistant strains, no spectral differences were observed between cells, regardless of drug exposure. These results pave the way for a new, rapid spectroscopic approach to detect drug resistance in pathogens, based on their early positive/negative drug response.

Depression as is Seen by Molecular Spectroscopy. Phospholipid- Protein Balance in Affective Disorders and Dementia

There is an expanding field of research investigating the instrumental methods to measure the development of affective disorders. The goal of the commentary is to turn the attention of medical practitioners at the molecular spectroscopy techniques (FTIR, Raman and UV-Vis) that can be applied for monitoring and quantification of the phospholipid-protein balance in human blood serum of depressed patients. Even facial overview of cited original research strongly suggests that disturbed phospholipid-protein balance could be one of the biomarkers of affective disorders. The blood serum monitoring of depressed patients would serve as a tool for more effective holistic therapy.