Predicting multipotency of human adult stem cells derived from various donors through deep learning.

ABSTRACT: Adult stem cell-based therapeutic approaches have great potential in regenerative medicine because of their immunoregulatory properties and multidifferentiation capacity. Nevertheless, the outcomes of stem cell‑based therapies to date have shown inconsistent efficacy owing to donor variation, thwarting the expectation of clinical effects. However, such donor dependency has been elucidated by biological consequences that current research could not predict. Here, we introduce cellular morphology-based prediction to determine the multipotency rate of human nasal turbinate stem cells (hNTSCs), aiming to predict the differentiation rate of keratocyte progenitors. We characterized the overall genes and morphologies of hNTSCs from five donors and compared stemness-related properties, including mu

SUBMITTER: Kim H 

PROVIDER: S-EPMC9749643 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature