Scientists at the Massachusetts Institute of Technology have proposed a way to recognize senescent cells in living tissues without destroying them. The method combines Raman microscopy, which reads the chemical composition of cells from the spectrum of reflected light, with gene expression data and machine learning. The result is a unique "barcode" — a set of spectral features by which "zombie cells" can be distinguished from ordinary ones.
With age, cells accumulate in the body that have stopped dividing but have not died. They release inflammatory signals and contribute to the development of cancer, fibrosis and other age-related disorders. Classical markers, such as the proteins p16 and p21, require fixation and destruction of the sample, so they cannot be used for diagnostics during life. The new approach solves this problem: the Raman spectrum captures changes in lipids and other molecules without interfering with the cell.
The researchers compared skin and lung tissues of young and old mice. In the aged samples, the synthesis and accumulation of lipids increased noticeably, and in the skin, pathways associated with muscle contraction and remodeling of the extracellular matrix intensified. In the lungs, genes of the immune response and inflammation were activated. These biochemical shifts formed the basis of the training set for the algorithm.
Machine learning identified from thousands of spectral peaks a few key ones that most accurately correlate with the state of cellular aging. Now, according to the authors' design, it is enough to record a spectrum in several characteristic ranges to determine with high probability whether a cell has entered a senescent state. The method has so far been tested only in mice and under laboratory conditions.
The advantage of the approach is obvious: it does not require a biopsy and in the future will make it possible to track the dynamics of the accumulation of zombie cells in real time. However, the data are limited to animal models, and transferring it to humans will require validation on human tissues and clinical trials. In addition, Raman microscopy has not yet become a routine tool in the clinic — the equipment is expensive and requires special training.
If the method confirms its reliability, it could change the understanding of how to measure the biological age of tissues. Instead of indirect blood markers or destructive tests, it will become possible to "read" the state of cells directly and non-invasively. This will bring closer the moment when therapies aimed at removing or rejuvenating senescent cells can rely on precise, individual data about their distribution in the body.
