A Yale research team has created a new imaging technique that reveals the hidden connections between aging, disease, and genetic activity in human cells.
Using a novel machine learning approach, the team found that tissue samples, under a microscope, can reveal genetic variants, gene activity, and even estimates of a person's age.
"Our study shows that ordinary tissue images contain patterns that can reliably predict gene expression and reveal a person's age - information that was previously hidden to the naked eye," said study lead author Ran Meng, a postdoctoral researcher in Yale's Department of Molecular Biophysics and Biochemistry and program in computational biology and biomedical informatics.
"The improved image quality let us link genetic features." Meng added. "And the models are able to crunch a great amount of data very accurately and shine a light on the image regions that push the prediction toward an older or younger age."
The new technique could lead to the development of better diagnostic practices using routine pathology slides, and the ability to predict disease risk by spotting abnormal tissue patterns early. The research is published in the Proceedings of the National Academy of Sciences.
"One key aspect of genetics is the genotype-phenotype connection," said co-author Mark Gerstein, the Albert L. Williams Professor of Biomedical Informatics at Yale School of Medicine who is also a professor of molecular biophysics and biochemistry; of computer science; and of statistics and data science in Yale's Faculty of Arts and Sciences.
