The replicative senescence during in vitro expansion severely limits the clinical application of human umbilical cord mesenchymal stem cells (hUC-MSCs). Existing senescence assessment methods still face significant limitations in terms of noninvasive and real-time quantification. In this study, using atomic force microscopy (AFM), we systematically characterized the nanomorphology and mechanical properties of naive and senescent hUC-MSCs. The results revealed that senescent cells exhibited significantly increased height, surface roughness, adhesion, and elastic modulus compared to naive cells, along with enhanced bundling and formation of F-actin stress fibers. These findings show a new "senescence-associated mechanical phenotype" unique to hUC-MSCs. Notably, the hypoxic intervention effectively reversed these senescence-related mechanical changes, demonstrating the high sensitivity of AFM in detecting senescence. To achieve precise quantitative assessment of cell senescence, we also developed a deep learning model based on a variational autoencoder (VAE), which successfully established continuous low-dimensional representations of the age-related mechanical phenotype. This work exhibited excellent predictive performance and generalization ability under different culture conditions, enabling accurate prediction and early identification of hUC-MSCs senescence.
Fu et al. (Wed,) studied this question.