The impact of cell fixation on coherent anti-stokes Raman scattering signal intensity in neuronal and glial cell lines

Effects of Chemical Fixatives on Kinetic Measurements of Biomolecular Interaction on Cell Membrane

Understanding the interaction between ligands and membrane proteins is important for drug design and optimization. Although investigation using live cells is desirable, it is not feasible in some circumstances and cell fixation is performed to reduce cell motion and degradation. This study compared the effects of five fixatives, i.e., formaldehyde vapor (FV), paraformaldehyde (PFA), acetone, methanol, and ethanol, on kinetic measurements via the LigandTracer method. We found that all five fixatives exerted insignificant effects on lectin-glycan interaction. However, antibody-receptor interaction is markedly perturbed by coagulant fixatives. The acetone fixation changed the binding of the anti-human epidermal growth factor receptor 2 (HER2) antibody to HER2 on the cell membrane from a 1:2 to a 1:1 binding model, while methanol and ethanol abolished the antibody binding possibly by removal of the HER2 receptors on the cell membrane. The capability of binding was retained when methanol fixation was performed at lower temperatures, albeit with a binding model of 1:1 instead. Moreover, whereas cell morphology does not exert a substantial impact on lectin-glycan interaction, it can indeed modify the binding model of antibody-receptor interaction. Our results provided insights into the selection of fixatives for cell-based kinetic studies.

Analytical Techniques for Single-Cell Biochemical Assays of Lipids

Lipids are essential cellular components forming membranes, serving as energy reserves, and acting as chemical messengers. Dysfunction in lipid metabolism and signaling is associated with a wide range of diseases including cancer and autoimmunity. Heterogeneity in cell behavior including lipid signaling is increasingly recognized as a driver of disease and drug resistance. This diversity in cellular responses as well as the roles of lipids in health and disease drive the need to quantify lipids within single cells. Single-cell lipid assays are challenging due to the small size of cells (∼1 pL) and the large numbers of lipid species present at concentrations spanning orders of magnitude. A growing number of methodologies enable assay of large numbers of lipid analytes, perform high-resolution spatial measurements, or permit highly sensitive lipid assays in single cells. Covered in this review are mass spectrometry, Raman imaging, and fluorescence-based assays including microscopy and microseparations.

Phenotype Identification of HeLa Cells Knockout CDK6 Gene Based on Label-Free Raman Imaging

Identifying cell phenotypes is essential for understanding the function of biological macromolecules and molecular biology. We developed a noninvasive, label-free, single-cell Raman imaging analysis platform to distinguish between the cell phenotypes of the HeLa cell wild type (WT) and cyclin-dependent kinase 6 (CDK6) gene knockout (KO) type. Via large-scale Raman spectral and imaging analysis, two phenotypes of the HeLa cells were distinguished by their intrinsic biochemical profiles. A significant difference was found between the two cell lines: large lipid droplets formed in the knockout HeLa cells but were not observed in the WT cells, which was confirmed by Oil Red O staining. The band ratio of the Raman spectrum of saturated/unsaturated fatty acids was identified as the Raman spectral marker for HeLa cell WT or gene knockout type differentiation. The interaction between organelles involved in lipid metabolism was revealed by Raman imaging and Lorentz fitting, where the distribution intensity of the mitochondria and the endoplasmic reticulum membrane decreased. At the same time, lysosomes increased after the CDK6 gene knockout. The parameters obtained from Raman spectroscopy are based on hierarchical cluster analysis and one-way ANOVA, enabling highly accurate cell classification.

Bayesian Quantification for Coherent Anti-Stokes Raman Scattering Spectroscopy

We propose a Bayesian statistical model for analyzing coherent anti-Stokes Raman scattering (CARS) spectra. Our quantitative analysis includes statistical estimation of constituent line-shape parameters, the underlying Raman signal, the error-corrected CARS spectrum, and the measured CARS spectrum. As such, this work enables extensive uncertainty quantification in the context of CARS spectroscopy. Furthermore, we present an unsupervised method for improving spectral resolution of Raman-like spectra requiring little to no a priori information. Finally, the recently proposed wavelet prism method for correcting the experimental artifacts in CARS is enhanced by using interpolation techniques for wavelets. The method is validated using CARS spectra of adenosine mono-, di-, and triphosphate in water, as well as equimolar aqueous solutions of d-fructose, d-glucose, and their disaccharide combination sucrose.

Nerve Segmentation with Deep Learning from Label-Free Endoscopic Images Obtained Using Coherent Anti-Stokes Raman Scattering

Semantic segmentation with deep learning to extract nerves from label-free endoscopic images obtained using coherent anti-Stokes Raman scattering (CARS) for nerve-sparing surgery is described. We developed a CARS rigid endoscope in order to identify the exact location of peripheral nerves in surgery. Myelinated nerves are visualized with a CARS lipid signal in a label-free manner. Because the lipid distribution includes other tissues as well as nerves, nerve segmentation is required to achieve nerve-sparing surgery. We propose using U-Net with a VGG16 encoder as a deep learning model and pre-training with fluorescence images, which visualize the lipid distribution similar to CARS images, before fine-tuning with a small dataset of CARS endoscopy images. For nerve segmentation, we used 24 CARS and 1,818 fluorescence nerve images of three rabbit prostates. We achieved label-free nerve segmentation with a mean accuracy of 0.962 and an F1 value of 0.860. Pre-training on fluorescence images significantly improved the performance of nerve segmentation in terms of the mean accuracy and F1 value (p<0.05). Nerve segmentation of label-free endoscopic images will allow for safer endoscopic surgery, while reducing dysfunction and improving prognosis after surgery.

Raman spectroscopy and neuroscience: from fundamental understanding to disease diagnostics and imaging

Neuroscience would directly benefit from more effective detection techniques, leading to earlier diagnosis of disease. The specificity of Raman spectroscopy is unparalleled, given that a molecular fingerprint is attained for each species. It also allows for label-free detection with relatively inexpensive instrumentation, minimal sample preparation, and rapid sample analysis. This review summarizes Raman spectroscopy-based techniques that have been used to advance the field of neuroscience in recent years.

Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives

This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.

Cellular transformations in near-infrared light-induced apoptosis in cancer cells revealed by label-free CARS imaging

Photobiomodulation (PBM) involves light to activate cellular signaling pathways leading to cell proliferation or death. In this work, fluorescence and Coherent anti-Stokes Raman Scattering (CARS) imaging techniques were applied to assess apoptosis in human cervical cancer cells (HeLa) induced by near infrared (NIR) laser light (808 nm). Using the Caspase 3/7 fluorescent probe to identify apoptotic cells, we found that the pro-apoptotic effect is significantly dependent of irradiation dose. The highest apoptosis rate was noted for the lower irradiation doses, that is, 0.3 J/cm² (~58%) and 3 J/cm² (~28%). The impact of light doses on proteins/lipids intracellular metabolism and distribution was evaluated using CARS imaging, which revealed apoptosis-associated reorganization of nuclear proteins and cytoplasmic lipids after irradiation with 0.3 J/cm² . Doses of NIR light causing apoptosis (0.3, 3 and 30 J/cm² ) induced a gradual increase in the nuclear protein level over time, in contrast to proteins in cells non-irradiated and irradiated with 10 J/cm² . Furthermore, irradiation of the cells with the 0.3 J/cm² dose resulted in lipid droplets (LDs) accumulation, which was apparently caused by an increase in reactive oxygen species (ROS) generation. We suggest that PBM induced apoptosis could be caused by the ability of NIR light to trigger excessive LDs formation which, in turn, induces cellular cytotoxicity.