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

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.

Keywords: automation; gastrulation; high-content analysis; image analysis; machine learning; organoid; pluripotent stem cells; screen; self-organization; stem cell-based embryo model.