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Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro

Guo, Jianying and Wang, Peizhe and Sozen, Berna and Qiu, Hui and Zhu, Yonglin and Zhang, Xingwu and Ming, Jia and Zernicka-Goetz, Magdalena and Na, Jie (2021) Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro. Stem Cell Reports, 16 (5). pp. 1331-1346. ISSN 2213-6711. PMCID PMC8185434. doi:10.1016/j.stemcr.2021.03.018. https://resolver.caltech.edu/CaltechAUTHORS:20210504-094816688

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Abstract

Stem cell-based embryo models by cultured pluripotent and extra-embryonic lineage stem cells are novel platforms to model early postimplantation development. We showed that induced pluripotent stem cells (iPSCs) could form ITS (iPSCs and trophectoderm stem cells) and ITX (iPSCs, trophectoderm stem cells, and XEN cells) embryos, resembling the early gastrula embryo developed in vivo. To facilitate the efficient and unbiased analysis of the stem cell-based embryo model, we set up a machine learning workflow to extract multi-dimensional features and perform quantification of ITS embryos using 3D images collected from a high-content screening system. We found that different PSC lines differ in their ability to form embryo-like structures. Through high-content screening of small molecules and cytokines, we identified that BMP4 best promoted the morphogenesis of the ITS embryo. Our study established an innovative strategy to analyze stem cell-based embryo models and uncovered new roles of BMP4 in stem cell-based embryo models.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.stemcr.2021.03.018DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185434PubMed CentralArticle
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=139379Related ItemData
ORCID:
AuthorORCID
Sozen, Berna0000-0001-5834-5819
Zernicka-Goetz, Magdalena0000-0002-7004-2471
Na, Jie0000-0003-1820-0548
Additional Information:© 2021 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Received 20 October 2020, Revised 17 March 2021, Accepted 17 March 2021, Available online 22 April 2021. This work was supported by the National Key R&D Program of China (grants 2017YFA0102802 and 2019YFA0110001) to J.N., Wellcome Trust (098287/Z/12/Z, 108438/C/15/Z) and Curci Foundation grants to M.Z.-G., an NSFC grant (32000610) to J.G. J.G. is supported by postdoctoral fellowships from Tsinghua-Peking Center for Life Sciences. We thank Dr. P. Liu and Professor S. Ding from the School of Pharmaceutical Sciences, Tsinghua University, Beijing, China, for the mouse iPSC lines. Mingyao Cui from the Institute of Molecular Medicine, Peking University, Beijing, China, for assistance in RNA-seq experiments. Data and code availability: The RNA high-throughput sequencing data are publicly available at the National Center for Biotechnology Information with Gene Expression Omnibus, accession no. GSE 139379. The algorithms developed by this study are listed in the supplemental information. Author contributions: J.G., P.W., M.Z.-G., and J.N. conceived the study and designed experiments. J.G. performed mouse PSC, ESC, TSC, and XEN cell culture, ITX/ETX and ITS/ETS embryo assembly and characterization, HCS, and image analysis. J.G., P.W., and Y.Z. performed single iPSC, TSC, and ITS-iPSC, ITS-TSC structure collection, RNA-seq library construction, and data analysis. B.S. performed ETX and ETS embryo assembly experiments. H.Q. and X.Z. assisted with HCS data analysis and graphic presentation. J.G., P.W., and J.N. wrote the manuscript. The author declare that they have no competing interests.
Funders:
Funding AgencyGrant Number
National Key Research and Development Program of China2017YFA0102802
National Key Research and Development Program of China2019YFA0110001
Wellcome Trust098287/Z/12/Z
Wellcome Trust108438/C/15/Z
Shurl and Kay Curci FoundationUNSPECIFIED
National Natural Science Foundation of China32000610
Tsinghua-Peking Center for Life SciencesUNSPECIFIED
Subject Keywords:stem cell-based embryo model; pluripotent stem cells; machine learning; gastrulation; self-organization; organoid; screen; high-content analysis; image analysis; automation
Issue or Number:5
PubMed Central ID:PMC8185434
DOI:10.1016/j.stemcr.2021.03.018
Record Number:CaltechAUTHORS:20210504-094816688
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210504-094816688
Official Citation:Jianying Guo, Peizhe Wang, Berna Sozen, Hui Qiu, Yonglin Zhu, Xingwu Zhang, Jia Ming, Magdalena Zernicka-Goetz, Jie Na, Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro, Stem Cell Reports, Volume 16, Issue 5, 2021, Pages 1331-1346, ISSN 2213-6711, https://doi.org/10.1016/j.stemcr.2021.03.018. (https://www.sciencedirect.com/science/article/pii/S221367112100148X)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108960
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:05 May 2021 18:24
Last Modified:21 Jun 2021 19:53

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