New Highly Sensitive and Specific Raman Probe for Live Cell Imaging of Mitochondrial Function.

For Raman hyperspectral detection and imaging in live cells, it is very desirable to create novel probes with strong and unique Raman vibrations in the biological silent region (1800-2800 cm-1). The use of molecular probes in Raman imaging is a relatively new technique in subcellular research; however, it is developing very rapidly. Compared with the label-free method, it allows for a more sensitive and selective visualization of organelles within a single cell. Biological systems are incredibly complex and heterogeneous. Directly visualizing biological structures and activities at the cellular and subcellular levels remains by far one of the most intuitive and powerful ways to study biological problems. Each organelle plays a specific and essential role in cellular processes, but importantly for cells to survive, mitochondrial function must be reliable. Motivated by earlier attempts and successes of biorthogonal chemical imaging, we develop a tool supporting Raman imaging of cells to track biochemical changes associated with mitochondrial function at the cellular level in an in vitro model. In this work, we present a newly synthesized highly sensitive RAR-BR Raman probe for the selective imaging of mitochondria in live endothelial cells.

Automatic subtyping of Diffuse Large B-cell Lymphomas (DLBCL): Raman-based genetic and metabolic classification.

Diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin's lymphoma in adults, is a genetically and metabolically heterogeneous group of aggressive malignancies. The complexity of their molecular composition and the variability in clinical presentation make clinical diagnosis and treatment selection a serious challenge. The challenge is therefore to quickly and correctly classify DLBCL cells. In this work, we show that Raman imaging is a tool with high diagnostic potential, providing unique information about the biochemical components of tumor cells and their metabolism. We present models of classification of lymphoma cells based on their Raman spectra. The models automatically and efficiently identify DLBCL cells and assign them to a given cell-of-origin (COO) subtype (activated B cell-like (ABC) or germinal center B cell-like (GCB)) or, respectively, to a comprehensive cluster classification (CCC) subtype (OxPhos/non-OxPhos). In addition, we describe each lymphoma subtype by its unique spectral profile, linking it to biochemical, genetic, or metabolic features.