A new view of senescent cells through RamanOmics: a cutting-edge tool for integrating biochemical and spatial data

Aging and tissue repair involve complex and coordinated biochemical and spatial changes.

To create a more complete picture of senescence, scientists from across Mass General Brigham, Massachusetts Institute of Technology and Harvard Medical School developed a platform called RamanOmics that examines cells’ gene activity, chemistry and location within tissue, incorporating machine learning to integrate these data.

RamanOmics is the first method to jointly map biochemical and molecular features of senescence. Their goal was to identify and characterize senescent cells more comprehensively than is possible using genomic data alone.

The team applied RamanOmics to examine aging and wound healing in lung and skin tissue from mice. They found that senescent cells were associated with different biological changes depending on the tissue type and that senescent cells in young tissues may be helpful and reparative, while those in older tissues become more dysfunctional.

One of the most important findings was that senescent cells are defined by distinct biochemical changes as well as genetic ones, particularly changes related to lipid remodeling. The researchers combined a recurring lipid-associated chemical signature with other features into a machine learning-derived “barcode” that could identify senescent cells in their natural environment.

Overall, RamanOmics helps to capture the underlying biochemical remodeling that occurs during senescence, providing a new framework for studying aging and tissue repair.

Published in Nature Aging on September 21, 2026 | Read the paper: “RamanOmics decodes the spatial vibrational–molecular architecture of senescence in aging and repair”

Summary reviewed by: Jian Shu, PhD, senior author