Cargando…
A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems
The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-bat...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244346/ https://www.ncbi.nlm.nih.gov/pubmed/37280224 http://dx.doi.org/10.1038/s41421-023-00543-1 |
_version_ | 1785054619115716608 |
---|---|
author | Yang, Xiaochun Chen, Daichao Sun, Qiushi Wang, Yao Xia, Yu Yang, Jinyu Lin, Chang Dang, Xin Cen, Zimu Liang, Dongdong Wei, Rong Xu, Ze Xi, Guangyin Xue, Gang Ye, Can Wang, Li-Peng Zou, Peng Wang, Shi-Qiang Rivera-Fuentes, Pablo Püntener, Salome Chen, Zhixing Liu, Yi Zhang, Jue Zhao, Yang |
author_facet | Yang, Xiaochun Chen, Daichao Sun, Qiushi Wang, Yao Xia, Yu Yang, Jinyu Lin, Chang Dang, Xin Cen, Zimu Liang, Dongdong Wei, Rong Xu, Ze Xi, Guangyin Xue, Gang Ye, Can Wang, Li-Peng Zou, Peng Wang, Shi-Qiang Rivera-Fuentes, Pablo Püntener, Salome Chen, Zhixing Liu, Yi Zhang, Jue Zhao, Yang |
author_sort | Yang, Xiaochun |
collection | PubMed |
description | The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications. |
format | Online Article Text |
id | pubmed-10244346 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-102443462023-06-08 A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems Yang, Xiaochun Chen, Daichao Sun, Qiushi Wang, Yao Xia, Yu Yang, Jinyu Lin, Chang Dang, Xin Cen, Zimu Liang, Dongdong Wei, Rong Xu, Ze Xi, Guangyin Xue, Gang Ye, Can Wang, Li-Peng Zou, Peng Wang, Shi-Qiang Rivera-Fuentes, Pablo Püntener, Salome Chen, Zhixing Liu, Yi Zhang, Jue Zhao, Yang Cell Discov Article The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications. Springer Nature Singapore 2023-06-06 /pmc/articles/PMC10244346/ /pubmed/37280224 http://dx.doi.org/10.1038/s41421-023-00543-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yang, Xiaochun Chen, Daichao Sun, Qiushi Wang, Yao Xia, Yu Yang, Jinyu Lin, Chang Dang, Xin Cen, Zimu Liang, Dongdong Wei, Rong Xu, Ze Xi, Guangyin Xue, Gang Ye, Can Wang, Li-Peng Zou, Peng Wang, Shi-Qiang Rivera-Fuentes, Pablo Püntener, Salome Chen, Zhixing Liu, Yi Zhang, Jue Zhao, Yang A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems |
title | A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems |
title_full | A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems |
title_fullStr | A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems |
title_full_unstemmed | A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems |
title_short | A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems |
title_sort | live-cell image-based machine learning strategy for reducing variability in psc differentiation systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244346/ https://www.ncbi.nlm.nih.gov/pubmed/37280224 http://dx.doi.org/10.1038/s41421-023-00543-1 |
work_keys_str_mv | AT yangxiaochun alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT chendaichao alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT sunqiushi alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT wangyao alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT xiayu alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT yangjinyu alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT linchang alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT dangxin alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT cenzimu alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT liangdongdong alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT weirong alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT xuze alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT xiguangyin alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT xuegang alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT yecan alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT wanglipeng alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT zoupeng alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT wangshiqiang alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT riverafuentespablo alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT puntenersalome alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT chenzhixing alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT liuyi alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT zhangjue alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT zhaoyang alivecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT yangxiaochun livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT chendaichao livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT sunqiushi livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT wangyao livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT xiayu livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT yangjinyu livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT linchang livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT dangxin livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT cenzimu livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT liangdongdong livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT weirong livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT xuze livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT xiguangyin livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT xuegang livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT yecan livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT wanglipeng livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT zoupeng livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT wangshiqiang livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT riverafuentespablo livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT puntenersalome livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT chenzhixing livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT liuyi livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT zhangjue livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems AT zhaoyang livecellimagebasedmachinelearningstrategyforreducingvariabilityinpscdifferentiationsystems |