SVD-clustering, a general image-analyzing method explained and demonstrated on model and Raman micro-spectroscopic maps

Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era

Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps.

Raman spectroscopy based molecular signatures of methamphetamine and HIV induced mitochondrial dysfunction

METH and HIV Tat treatment results in increased oxidative stress which affects cellular metabolism and causes DNA damage in the treated microglia. Both, METH ± HIV Tat impair mitochondrial respiration, leading to dysfunction in bioenergetics and increased ROS in microglial cells. Our data indicate that mitochondrial dysfunction may be key to the METH and/or HIV Tat-induced neuropathology. METH and/or HIV Tat induced changes in the protein, lipid and nucleotide concentration in microglial cells were measured by Raman Spectroscopy, and we speculate that these fundamental molecular-cellular changes in microglial cells contribute to the neuropathology that is associated with METH abuse in HIV patients.

Investigation of Raman Spectroscopic Signatures with Multivariate Statistics: An Approach for Cataloguing Microbial Biosignatures

Spectroscopic instruments are increasingly being implemented in the search for extraterrestrial life. However, microstructural spectral analyses of alien environments could prove difficult without knowledge on the molecular identification of individual spectral signatures. To bridge this gap, we introduce unsupervised K-means clustering as a statistical approach to discern spectral patterns of biosignatures without prior knowledge of spectral regions of biomolecules. Spectral profiles of bacterial isolates from analogous polar ice sheets were measured with Raman spectroscopy. Raman analysis identified carotenoid and violacein pigments, and key cellular features including saturated and unsaturated fats, triacylglycerols, and proteins. Principal component analysis and targeted spectra integration biplot analysis revealed that the clustering of bacterial isolates was attributed to spectral biosignatures influenced by carotenoid pigments and ratio of unsaturated/saturated fat peaks. Unsupervised K-means clustering highlighted the prevalence of the corresponding spectral peaks, while subsequent supervised permutational multivariate analysis of variance provided statistical validation for spectral differences associated with the identified cellular features. Establishing a validated catalog of spectral signatures of analogous biotic and abiotic materials, in combination with targeted supervised tools, could prove effective at identifying extant biosignatures.

Deconstruction of Obscure Features in SVD-Decomposed Raman Images from P. chrysogenum Reveals Complex Mixing of Spectra from Five Cellular Constituents

Raman imaging has transcended in recent times from being an analytical tool to a molecular profiling technique. Biomedical applications of this technique often rely on singular-value decomposition (SVD), principal component analysis (PCA), etc. for data analysis. These methods, however, obliterate the molecular information contained in the original Raman data leading to speculative interpretations based on relative intensities. In the present study, SVD analysis of the Raman images from Penicillium chrysogenum resulted in 11 spectral components and corresponding images with highly distorted spectral features and complex image contrast, respectively. To interpret the SVD results in molecular terms, we have developed a combined multivariate approach. By applying this methodology, we have successfully extracted the contribution of five biomolecular constituents of the P. chrysogenum filamentous cell to the SVD vectors. Molecular interpretability will help SVD/PCA surpass the realm of variance-based classification to a more meaningful molecular domain.

Iron-binding cellular profile of transferrin using label-free Raman hyperspectral imaging and singular value decomposition (SVD)

Serum transferrin (Tf) is the essential iron transport protein in the body. Transferrin is responsible for the sequestration of free iron in serum and the delivery of iron throughout the body and into cells, where iron is released inside a mildly acidified endosome. Altered iron distributions are associated with diseases such as iron-overload, cancer, and cardiovascular disease. The presence of free iron is linked to deleterious redox reactions, inside and outside cells and organelles. As Tf iron release is pH dependent, any changes in intraorganelle and extracellular pH, often associated with disease progression, could inhibit normal iron delivery or accelerate iron release in the wrong compartment. However, imaging approaches to monitor changes in the iron-bound state of Tf are lacking. Recently, Raman spectroscopy has been shown to measure iron-bound forms of Tf in solution, intact cells and tissue samples. Here, a biochemical Raman assay has been developed to identify iron-release from Tf following modification of chemical environment. Quantitative singular value decomposition (SVD) method has been applied to discriminate between iron-bound Tf samples during endocytic trafficking in intact cancer cells subjected to Raman hyperspectral confocal imaging. We demonstrate the strength of the SVD method to monitor pH-induced Tf iron-release using Raman hyperspectral imaging, providing the redox biology field with a novel tool that facilitates subcellular investigation of the iron-binding profile of transferrin in various disease models.