Label-free Raman imaging defines distinct cell populations in human skin
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Abstract
Understanding cellular heterogeneity in human skin is crucial for regenerative medicine and tissue engineering. In this study, we applied label-free Raman imaging to visualize molecular features corresponding to the three-dimensional architecture of the epidermis. Spatially resolved Raman spectra, combined with multivariate data analysis, enabled the identification of cell-layer–specific molecular signatures. Based on the region-specific spectra analysis, component C5 was predominantly localized to the basal layer within rete ridges and was characterized by β-sheet–enriched keratin features. This spatially restricted distribution reflects the molecular microenvironment of epidermal stem cell niches, suggesting that C5 may serve as a biomarker for basal stem cell populations associated with skin undulations. These findings provide insight into the molecular basis of epidermal architecture and demonstrate the potential of Raman spectroscopy as a label-free tool for evaluating stem cell localization and differentiation status.
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Raman spectral datasets corresponding to MCR-derived components were extracted and subjected to PCA using the nonlinear iterative partial least squares (NIPALS) algorithm. PCA, widely used in chemometrics, facilitates the identification and interpretation of the spectral variation within a Raman dataset24. PCA represents spectral information as vectors termed principal components (PCs). The results are typically visualized using a scores plot, where one principal component is plotted against another and each spectrum is represented by an individual point. The corresponding PC loadings plot highlights the peaks that significantly influence the score values, enabling interpretation of molecular differences between groups25. Data analysis was performed using the Scikit-learn package in Python.
I wonder if you have considered wither blind …
Raman spectral datasets corresponding to MCR-derived components were extracted and subjected to PCA using the nonlinear iterative partial least squares (NIPALS) algorithm. PCA, widely used in chemometrics, facilitates the identification and interpretation of the spectral variation within a Raman dataset24. PCA represents spectral information as vectors termed principal components (PCs). The results are typically visualized using a scores plot, where one principal component is plotted against another and each spectrum is represented by an individual point. The corresponding PC loadings plot highlights the peaks that significantly influence the score values, enabling interpretation of molecular differences between groups25. Data analysis was performed using the Scikit-learn package in Python.
I wonder if you have considered wither blind source separation or machine learning methods to try to identify specific spectral signatures for your differing cell types? Non-negative matrix factorization is possibly particularly interesting here as it can separate out the spectral signals that vary across the cell types.
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