From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives

In recent years, Raman spectroscopy has been more and more frequently applied to address research questions in neuroscience. As a non-destructive technique based on inelastic scattering of photons, it can be used for a wide spectrum of applications including neurooncological tumor diagnostics or analysis of misfolded protein aggregates involved in neurodegenerative diseases. Progress in the technical development of this method allows for an increasingly detailed analysis of biological samples and may therefore open new fields of applications. The goal of our review is to provide an introduction into Raman scattering, its practical usage and also commonly associated pitfalls. Furthermore, intraoperative assessment of tumor recurrence using Raman based histology images as well as the search for non-invasive ways of diagnosis in neurodegenerative diseases are discussed. Some of the applications mentioned here may serve as a basis and possibly set the course for a future use of the technique in clinical practice. Covering a broad range of content, this overview can serve not only as a quick and accessible reference tool but also provide more in-depth information on a specific subtopic of interest

Differentiation of primary CNS lymphoma and glioblastoma using Raman spectroscopy and machine learning algorithms

Objective and Methods: Timely discrimination between primary CNS lymphoma (PCNSL) and glioblastoma is crucial for diagnostics and therapy, but most importantly also determines the intraoperative surgical course. Advanced radiological methods allow this to a certain extent but ultimately, biopsy is still necessary for final diagnosis. As an upcoming method that enables tissue analysis by tracking changes in the vibrational state of molecules via inelastic scattered photons, we used Raman Spectroscopy (RS) as a label free method to examine specimens of both tumor entities intraoperatively, as well as postoperatively in formalin fixed paraffin embedded (FFPE) samples. Results: We applied and compared statistical performance of linear and nonlinear machine learning algorithms (Logistic Regression, Random Forest and XGBoost), and found that Random Forest classification distinguished the two tumor entities with a balanced accuracy of 82,4% in intraoperative tissue condition and with 94% using measurements of distinct tumor areas on FFPE tissue. Taking a deeper insight into the spectral properties of the tumor entities, we describe different tumor-specific Raman shifts of interest for classification. Conclusions: Due to our findings, we propose RS as an additional tool for fast and non-destructive, perioperative tumor tissue discrimination, which may augment treatment options at an early stage. RS may further serve as a useful additional tool for neuropathological diagnostics with little requirements for tissue integrity

Extracellular vesicle secretion by leukemia cells in vivo promotes CLL progression by hampering antitumor T-cell responses

Small extracellular vesicles (sEV, or exosomes) communication among cells in the tumor microenvironment has been modeled mainly in cell culture, while their relevance in cancer pathogenesis and progression in vivo is less characterized. Here we investigated cancer-microenvironment interactions in vivo using mouse models of chronic lymphocytic leukemia (CLL). sEV isolated directly from CLL tissue were enriched in specific miRNA and immune checkpoint ligands. Distinct molecular components of tumor-derived sEV altered CD8+ T-cell transcriptome, proteome and metabolome leading to decreased functions and cell exhaustion ex vivo and in vivo. Using antagomiRs and blocking antibodies, we defined specific cargo-mediated alterations on CD8+ T-cells. Abrogating sEV biogenesis by Rab27a/b knockout dramatically delayed CLL pathogenesis. This phenotype was rescued by exogenous leukemic sEV or CD8+ T-cell depletion. Finally, high expression of sEV-related genes correlated with poor outcomes in CLL patients, suggesting sEV profiling as prognostic tool. In conclusion, sEV shape the immune microenvironment during CLL progression.info:eu-repo/semantics/publishe