The importance of this approach lies in the fact that it does not only monitor active genes, but also reveals cell components, including lipids, proteins, and nucleic acids, providing a more comprehensive picture of the changes that accompany aging.
A common lipid fingerprint
The researchers tested the technique on lung and skin samples from young and old mice, and observed a spectral signal associated with lipids in senescent cells in both tissues, despite the differences in gene expression patterns between them. The same signal also appeared during wound healing.
Integrating chemical data with genetic information increased the accuracy of classifying senescent cells by about 5.5%, reaching an overall accuracy of about 76% in lung tissue and 65% in skin, compared to relying on genetic data alone.
However, the researchers emphasize that the results reveal an association between the lipid fingerprint and cellular aging, and do not prove that lipids are the direct cause behind it, nor have the molecules responsible for this signal been precisely identified yet.
Towards more accurate monitoring of aging
Cellular aging is defined as a state in which cells stop dividing in response to damage or stress, and it may play a useful and temporary role in wound healing and tissue repair. However, their accumulation with age may contribute to chronic inflammation and organ function decline.
Despite the promising results, the technique is still in its early research stages, as experiments were limited to tissue samples from mice, and it faces challenges related to slow scanning, small imaging field, and the limited number of cells available for analysis.
The study results were published in the journal "Nature Aging," and this technique may in the future allow for monitoring chemical changes in tissues and evaluating the efficacy of experimental drugs that target senescent cells, in addition to studying wound healing, tissue fibrosis, and skin aging.
