Resistance to Naive and Formative Pluripotency Conversion in RSeT Human Embryonic Stem Cells.
Kevin G ChenKory J JohnsonKyeyoon ParkDragan MaricForest YangWen Fang LiuYang C FannBarbara S MallonPamela Gehron RobeyPublished in: bioRxiv : the preprint server for biology (2024)
One of the most important properties of human embryonic stem cells (hESCs) is related to their primed and naive pluripotent states. Our previous meta-analysis indicates the existence of heterogeneous pluripotent states derived from diverse naive protocols. In this study, we have characterized a commercial medium (RSeT)-based pluripotent state under various growth conditions. Notably, RSeT hESCs can circumvent hypoxic growth conditions as required by naive hESCs, in which some RSeT cells (e.g., H1 cells) exhibit much lower single cell plating efficiency, having altered or much retarded cell growth under both normoxia and hypoxia. Evidently, hPSCs lack many transcriptomic hallmarks of naive and formative pluripotency (a phase between naive and primed states). Integrative transcriptome analysis suggests our primed and RSeT hESCs are close to the early stage of post-implantation embryos, similar to the previously reported primary hESCs and early hESC cultures. Moreover, RSeT hESCs did not express naive surface markers such as CD75, SUSD2, and CD130 at a significant level. Biochemically, RSeT hESCs exhibit a differential dependency of FGF2 and co-independency of both Janus kinase (JAK) and TGFbeta signaling in a cell-line-specific manner. Thus, RSeT hESCs represent a previously unrecognized pluripotent state downstream of formative pluripotency. Our data suggest that human naive pluripotent potentials may be restricted in RSeT medium. Hence, this study provides new insights into pluripotent state transitions in vitro.
Keyphrases
- embryonic stem cells
- hiv infected
- endothelial cells
- early stage
- single cell
- induced apoptosis
- systematic review
- induced pluripotent stem cells
- antiretroviral therapy
- cell cycle arrest
- squamous cell carcinoma
- randomized controlled trial
- rna seq
- lymph node
- electronic health record
- machine learning
- cell proliferation
- deep learning