Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment.

ABSTRACT: Manufacturing regenerative medicine requires continuous monitoring of pluripotent cell culture and quality assessment while eliminating cell destruction and contaminants. In this study, we employed a novel method to monitor the pluripotency of stem cells through image analysis, avoiding the traditionally used invasive procedures. This approach employs machine learning algorithms to analyze stem cell images to predict the expression of pluripotency markers, such as OCT4 and NANOG, without physically interacting with or harming cells. We cultured induced pluripotent stem cells under various conditions to induce different pluripotent states and imaged the cells using bright-field microscopy. Pluripotency states of induced pluripotent stem cells were assessed using invasive methods, including

SUBMITTER: Akiyoshi R 

PROVIDER: S-EPMC11231322 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature