This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Researchers at Karolinska Institutet and Stockholm University have developed a new tool that estimates a cell's biological age by analyzing its gene activity. In an article published in the journal Advanced Science, they also demonstrate how the tool can be used to identify substances that influence cellular aging.
Aging is a major risk factor for many diseases, but there is still a lack of methods that can reliably measure how quickly cells age. In the new study, the researchers present Pasta, a freely available tool that calculates a cell's biological age based on which genes are active or inactive. To develop the tool, the researchers analyzed gene activity data from more than 17,000 tissue samples taken from healthy people.
The model was then tested on several independent datasets. "Previous tools for measuring biological age often only work for a single tissue or type of data, which limited their usefulness. We built Pasta to be broadly applicable across many tissues, cell types and laboratory techniques, so that all research groups can apply it to the data they already have.
With this tool, we can track how cells change over time and gain insights into the mechanisms driving aging," says lead author Jérôme Salignon, a researcher at the Department of Medicine, Huddinge, Karolinska Institutet Salignon led the study together with Federico Pietrocola, a senior researcher at the Department of Cell and Molecular Biology, Karolinska Institutet, and Christian G. Riedel, a professor at Stockholm University and a senior researcher at the Department of Medicine, Huddinge, Karolinska Institutet. The researchers observed that cells with high biological age often exhibited increased activity in genes linked to DNA damage and cellular stress.
The tool was also able to distinguish between older, so-called senescent cells and more youthful, stem cell-like cells. In the next stage, Pasta was used to analyze more than 3 million gene profiles from public databases where cells had been exposed to thousands of drugs and genetic alterations. The analysis identified substances and biological signaling pathways that appeared to increase or decrease the cells' biological age.
Some of the results were subsequently confirmed in laboratory experiments on human cells. "Reliably determining the biological age of cells has long been a major challenge. We can now do this using gene expression data—a type of data that is already routinely generated in a great many research studies.
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