AI and 'Ramanomics' could eliminate a major obstacle to studying living cells
Researchers at the University at Buffalo have developed a new imaging method combining AI and Raman spectroscopy to study living cells. This technique identifies cellular structures by their natural biochemical signatures, eliminating the need for potentially harmful fluorescent dyes.
Why it matters
This advancement could significantly improve the accuracy of biological research and drug discovery by allowing for non-invasive, high-precision cellular analysis.
edited by Lisa Lock , reviewed by Robert Egan
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Add as preferred source The left image shows how fluorescent labels help identify cell structures while collecting biochemical data, creating the examples needed to train the AI model. The image on the right shows how AI distinguishes cell structures using their natural biochemical signatures alone. Credit: University at Buffalo, from ACS Omega (2026). DOI: 10.1021/acsomega.5c12148. Fluorescent dyes have long been used in biological research to identify and visualize structures within living cells. Although effective, they have several drawbacks, including altering the cells under study, limiting the number of structures that can be examined at once and reducing measurement accuracy.
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