Delamination of trophoblast-like syncytia from the amniotic ectodermal analogue in human primed embryonic stem cell-based differentiation model

Human primed embryonic stem cells (ESCs) are known to be converted to cells with several trophoblast properties, but it has remained controversial whether this phenomenon represents the inherent differentiation competence of human primed ESCs to trophoblast lineages. In this study, we report that chemical blockage of ACTIVIN/NODAL and FGF signals is sufficient to steer human primed ESCs into GATA3-expressing cells that give rise to placental hormone-producing syncytia analogous to syncytiotrophoblasts of the post-implantation stage of the human embryo. Despite their cytological similarity to syncytiotrophoblasts, these syncytia arise from the non-trophoblastic differentiation trajectory that recapitulates amniogenesis. These results provide insights into the possible extraembryonic differentiation pathway that is unique in primate embryogenesis

RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations

Abstract Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems

RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations

Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems.摂動に基づく遺伝子制御ネットワーク推定 --数理モデルによる自動決定--. 京都大学プレスリリース. 2024-01-04.Gene expression technology set to semi-automation: KyotoU develops RENGE to infer gene regulatory networks efficiently and accurately. 京都大学プレスリリース. 2024-03-14