Published on: Nov 18, 2025
A research team at Yale has developed an innovative imaging technique that uncovers previously invisible links between aging, disease, and genetic activity within human cells.
Leveraging a new machine learning–based approach, the scientists discovered that standard tissue samples viewed under a microscope can reveal far more than their physical structure — including genetic variants, patterns of gene expression, and even an estimate of a person’s biological age.
Our study demonstrates that ordinary tissue images contain patterns capable of reliably predicting gene expression and estimating age — details that cannot be detected by the naked eye, said lead author Ran Meng, a postdoctoral researcher in Yale’s Department of Molecular Biophysics and Biochemistry and the computational biology and biomedical informatics program.
“The enhanced image quality allowed us to connect genetic features, Meng explained. These models can process massive datasets with high accuracy and highlight the specific image regions that drive predictions toward an older or younger age.
The technique has the potential to transform diagnostic practice by using routine pathology slides to identify signs of disease risk early, based on subtle abnormalities in tissue patterns. The study was published in the Proceedings of the National Academy of Sciences.
One of the fundamental concepts in genetics is the connection between genotype and phenotype, said co-author Mark Gerstein, the Albert L. Williams Professor of Biomedical Informatics at Yale School of Medicine, who also holds appointments in molecular biophysics and biochemistry, computer science, and statistics and data science.
Source: https://news.yale.edu/2025/11/18/uncovering-hidden-cellular-connections-bridge-aging-and-disease
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