2026-08-06 バッファロー大学（UB）
Figure 1 shows how fluorescent labels help identify cell structures while collecting biochemical data, creating the examples needed to train the AI model. Figure 3 shows how AI distinguishes cell structures using their natural biochemical signatures alone. Credit: University at Buffalo.
＜関連情報＞
- https://www.buffalo.edu/news/releases/2026/08/AI-Ramanomics-studying-living-cells.html
- https://pubs.acs.org/acsodf/article/11/24/35079/5166670/Integrating-Artificial-Intelligence-with
人工知能とラマノミクスを統合し、生細胞内の生化学的環境をラベルフリーでモニタリングすることで、細胞診断と分子医学を進歩させる Integrating Artificial Intelligence with Ramanomics for Label-Free Monitoring of Biochemical Environment in Live Cells to Advance Cellular Diagnostics and Molecular Medicine
Varun Chandola;Andrey N. Kuzmin;Artem Pliss;Alexander Baev;Giovana R. Teixeira;Paras N. Prasad
ACS Omega Published:June 06, 2026
DOI:https://doi.org/10.1021/acsomega.5c12148
Abstract
Raman spectrometry, with its capability to noninvasively characterize the molecular composition of microscopic subcellular volumes, including single organelles in live cells, has revolutionized cell biology research. Being introduced as a label-free approach for biochemical imaging, the practical applications of Raman spectrometry still often include the fluorescence probes for the localization of organelles and other subcellular domains of interest. Aiming to overcome this limitation, we report on the development of an artificial intelligence/machine learning approach for true label-free identification of different types of subcellular structures. Here, we explore the application of machine learning (ML) to learn the relationship between a set of biochemical parameters in single organelles of live cells. The biochemical parameters are extracted by Ramanomics, an optical Omics technology, from Raman spectra of single organelles of live cells of different cell lines. Several classification algorithms, such as neural networks, Random Forests, support vector machines, logistic regression, and Gaussian process classification, are evaluated. We report the performance of the best classifier, a shallow neural network, to classify the type of organelle using the biochemical parameters. Evaluation is done using k-fold cross-validation (k = 10), and the final output classification is compared against the ground truth. The k-fold cross-validation shows that the NN-based classifier has significant accuracy (∼90%) to distinguish between different organelles using Ramanomics measurements. Our approach allows us to identify the precise location of separate organelles by local Raman measurement without labeling.
