From Single Molecular Detail to Subcellular Dynamics: Real-Time Kinetics Study of dPA Uptake with Raman-based Spectroscopy.
bioRxiv 2025.11.21.689687
https://doi.org/10.1101/2025.11.21.689687
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Raman spectroscopy supported by machine learning reveals changes in balance of macromolecules in diabetic rat serum.
Anal Bioanal Chem 417, 6655–6663 (2025). https://doi.org/10.1007/s00216-025-06156-9
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In type 2 diabetes mellitus (T2DM), changes in glucose and lipid levels in serum are the first signals of disease progression. In this article, Raman spectroscopy supported with machine learning (ML) was used both to determine the level of imbalance between major metabolites caused by the disease and to investigate which metabolites were impacted the most. The principal component analysis separated the T2DM and control rats by PC1 and PC3. The bands that enabled that separation were assigned to all four main metabolite groups—amino acids (745–763 cm−1, 985–1027 cm−1), amides (1199–1258 cm−1, 1574–1600 cm−1, 1653–1712 cm−1), polysaccharides (1104–1192 cm−1), and lipids (1442–1472 cm−1, 2831–3041 cm−1). Moreover, diabetes induction caused major correlation changes between both proteins (amide) and the general amino acid band, with the organic matter band (CH band), which mainly arises due to lipid presence. Moreover, four different machine algorithms were employed to detect changes between the groups. AdaBoost showed the best performance in classifying the serum samples from diabetic and control rats (F1 = 0.84), which was further refined when only the relevant bands were used for learning and classification (F1 = 0.94). The bands responsible for the separation suggest that T2DM induces alterations in the protein profile, including changes in the levels of aromatic amino acids, as well as in both the quantity and composition of lipids, which is further confirmed by the ML classification.
Lymphocytes in infectious mononucleosis analyzed by Raman microspectroscopy.
Journal of Molecular Structure
Volume 1352, Part 2, 15 February 2026, 144563
https://doi.org/10.1016/j.molstruc.2025.144563
RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis.
Anal. Chem. 2024, 96, 21, 8492–8500
https://doi.org/10.1021/acs.analchem.4c00383
https://ramanspy.readthedocs.io/
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Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.
Review: Advances in synovial fluid analysis for the diagnosis of crystal arthropathies.
Arthritis Care Res. Accepted Author Manuscript. 2025
https://doi.org/10.1002/acr.25698
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Posthandling Spectral Information Enhancement for Single Cell Raman Molecular Mapping Analysis.
Anal. Chem. 2025
https://doi.org/10.1021/acs.analchem.5c03915
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Biochemical analysis of living systems such as single cells benefits greatly from the label-free and low-invasive molecular mapping with Raman microspectroscopy. Sets of Raman spectra at different spatial points are analyzed to generate Raman molecular maps corresponding to specific chemical species. However, human error and subjective data analysis can be technical issues that limit interpretation and its validity. Here, we present an objective data analysis scheme for postprocessing large data sets of Raman spectra for molecular mapping of living cells. The process comprises three steps: (i) Denoising the spectral data set using low-rank approximation; (ii) obtaining an objective background from data points outside the target cell; (iii) subtracting the thus obtained background using Hypothetical Addition Multivariate Analysis with Numerical Differentiation (HAMAND) via an automatically determined coefficient. Through the present analysis, minor Raman peaks, as indiscernible as they are, can be identified and precisely mapped. We demonstrate a quantitative discussion of cellular components after extracting contributions only from a single target cell from a Raman mapping image where multiple cells or parts of other cells are present. This work opens an improved analysis workflow for accurate spectroscopic analysis of living cells with the advantage of identifying minor Raman peaks unambiguously.
A point-of-care diagnostic for drug-induced liver injury using surface-enhanced Raman scattering lateral flow immunoassay.
Nat Commun 16, 6223 (2025).
https://doi.org/10.1038/s41467-025-61600-9
Espectroscopia biofototérmica e óptica para o estudo de
Formulações farmacêuticas e materiais para odontologia.
Monique de Souza
http://www.pfi.uem.br/wp-content/uploads/2025/09/Tese-Monique-de-Souza.pdf in Portuguese
VidyaKasam Education
Mathrubhumi English
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