Naive pluripotent stem cell-based models capture FGF-dependent human hypoblast lineage specification.

The hypoblast is an essential extraembryonic tissue set aside within the inner cell mass in the blastocyst. Research with human embryos is challenging. Thus, stem cell models that reproduce hypoblast differentiation provide valuable alternatives. We show here that human naive pluripotent stem cell (PSC) to hypoblast differentiation proceeds via reversion to a transitional ICM-like state from which the hypoblast emerges in concordance with the trajectory in human blastocysts. We identified a window when fibroblast growth factor (FGF) signaling is critical for hypoblast specification. Revisiting FGF signaling in human embryos revealed that inhibition in the early blastocyst suppresses hypoblast formation. In vitro, the induction of hypoblast is synergistically enhanced by limiting trophectoderm and epiblast fates. This finding revises previous reports and establishes a conservation in lineage specification between mice and humans. Overall, this study demonstrates the utility of human naive PSC-based models in elucidating the mechanistic features of early human embryogenesis.

Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting.

Analysis of single cell transcriptomics (scRNA-seq) data is typically performed after subsetting to highly variable genes (HVGs). Here, we show that Entropy Sorting provides an alternative mathematical framework for feature selection. On synthetic datasets, continuous Entropy Sort Feature Weighting (cESFW) outperforms HVG selection in distinguishing cell-state-specific genes. We apply cESFW to six merged scRNA-seq datasets spanning human early embryo development. Without smoothing or augmenting the raw counts matrices, cESFW generates a high-resolution embedding displaying coherent developmental progression from eight-cell to post-implantation stages and delineating 15 distinct cell states. The embedding highlights sequential lineage decisions during blastocyst development, while unsupervised clustering identifies branch point populations obscured in previous analyses. The first branching region, where morula cells become specified for inner cell mass or trophectoderm, includes cells previously asserted to lack a developmental trajectory. We quantify the relatedness of different pluripotent stem cell cultures to distinct embryo cell types and identify marker genes of naïve and primed pluripotency. Finally, by revealing genes with dynamic lineage-specific expression, we provide markers for staging progression from morula to blastocyst.

Evidence implicating sequential commitment of the founder lineages in the human blastocyst by order of hypoblast gene activation.

Successful human pregnancy depends upon rapid establishment of three founder lineages: the trophectoderm, epiblast and hypoblast, which together form the blastocyst. Each plays an essential role in preparing the embryo for implantation and subsequent development. Several models have been proposed to define the lineage segregation. One suggests that all lineages specify simultaneously; another favours the differentiation of the trophectoderm before separation of the epiblast and hypoblast, either via differentiation of the hypoblast from the established epiblast, or production of both tissues from the inner cell mass precursor. To begin to resolve this discrepancy and thereby understand the sequential process for production of viable human embryos, we investigated the expression order of genes associated with emergence of hypoblast. Based upon published data and immunofluorescence analysis for candidate genes, we present a basic blueprint for human hypoblast differentiation, lending support to the proposed model of sequential segregation of the founder lineages of the human blastocyst. The first characterised marker, specific initially to the early inner cell mass, and subsequently identifying presumptive hypoblast, is PDGFRA, followed by SOX17, FOXA2 and GATA4 in sequence as the hypoblast becomes committed.

Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo.

A major challenge in single-cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present entropy sorting (ES), a mathematical framework that distinguishes genes indicative of cell identity. ES achieves this in an unsupervised manner by quantifying if observed correlations between features are more likely to have occurred due to random chance versus a dependent relationship, without the need for any user-defined significance threshold. On synthetic data, we demonstrate the removal of noisy signals to reveal a higher resolution of gene expression patterns than commonly used feature selection methods. We then apply ES to human pre-implantation embryo single-cell RNA sequencing (scRNA-seq) data. Previous studies failed to unambiguously identify early inner cell mass (ICM), suggesting that the human embryo may diverge from the mouse paradigm. In contrast, ES resolves the ICM and reveals sequential lineage bifurcations as in the classical model. ES thus provides a powerful approach for maximizing information extraction from high-dimensional datasets such as scRNA-seq data.

Assessment of engineered cells using CellNet and RNA-seq.

CellNet is a computational platform designed to assess cell populations engineered by either directed differentiation of pluripotent stem cells (PSCs) or direct conversion, and to suggest specific hypotheses to improve cell fate engineering protocols. CellNet takes as input gene expression data and compares them with large data sets of normal expression profiles compiled from public sources, in regard to the extent to which cell- and tissue-specific gene regulatory networks are established. CellNet was originally designed to work with human or mouse microarray expression data for 21 cell or tissue (C/T) types. Here we describe how to apply CellNet to RNA-seq data and how to build a completely new CellNet platform applicable to, for example, other species or additional cell and tissue types. Once the raw data have been preprocessed, running CellNet takes only several minutes, whereas the time required to create a completely new CellNet is several hours.