Deep Learning-Assisted Nanomechanical Evaluation of Replicative Senescence in Human Umbilical Cord Mesenchymal Stem Cells.

Abstract

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.