Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures.

ABSTRACT:

Background

Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs.

Results

We have developed a successful morphology-based Cascade region-based convolution neura

SUBMITTER: He L 

PROVIDER: S-EPMC10762950 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature