Human trophectoderm becomes multi-layered by internalisation at the polar region

To implant in the uterus, mammalian embryos form blastocysts comprising trophectodermsurrounding an inner cell mass, confined to the polar region by the expanding blastocoel. The mode of implantation varies between species. Murine embryos maintain a single layered trophectoderm until they implant in the characteristic thick deciduum, whereas humanblastocysts attach via polar trophectoderm directly to the uterine wall. Usingimmunofluorescence of rapidly isolated inner cell masses, blockade of RNA and protein synthesis in whole embryos, or 3D visualisation of immunostained embryos we provide evidence of multi-layering in human polar trophectoderm before implantation. This may be required for rapid uterine invasion to secure the developing human embryo and initiateformation of the placenta. Using sequential fluorescent labelling, we demonstrate that the majority of inner trophectoderm in human blastocysts arises from existing outer cells with no evidence of conversion from the inner cell mass in the context of the intact embry

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

Peer reviewed: TrueAcknowledgements: We are grateful to Kenneth Jones, Ayaka Yanagida and Lawrence Bates for assistance with human embryo thawing; Peter Humphreys, Darran Clement (Cambridge Stem Cell Institute imaging facility) and Ann Wheeler (Institute of Genetics and Cancer) for help with microscopy, and core facilities at both Institutes; and Sophie Kraunsoe for assistance in image analysis. We thank all the staff at our participating assisted conception clinics and the patients who kindly donated their embryos to our research.Funder: Microsoft Research; doi: http://dx.doi.org/10.13039/100006112Funder: University of Cambridge; doi: http://dx.doi.org/10.13039/501100000735Funder: University of Edinburgh; doi: http://dx.doi.org/10.13039/501100000848Successful 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, 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 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. Entropy sorting thus provides a powerful approach for maximising information extraction from high dimensional datasets such as scRNA-seq data

The transition from local to global patterns governs the differentiation of mouse blastocysts

During mammalian blastocyst development, inner cell mass (ICM) cells differentiate into epiblast (Epi) or primitive endoderm (PrE). These two fates are characterized by the expression of the transcription factors NANOG and GATA6, respectively. Here, we investigate the spatio-temporal distribution of NANOG and GATA6 expressing cells in the ICM of the mouse blastocysts with quantitative three-dimensional single cell-based neighbourhood analyses. We define the cell neighbourhood by local features, which include the expression levels of both fate markers expressed in each cell and its neighbours, and the number of neighbouring cells. We further include the position of a cell relative to the centre of the ICM as a global positional feature. Our analyses reveal a local three-dimensional pattern that is already present in early blastocysts: 1) Cells expressing the highest NANOG levels are surrounded by approximately nine neighbours, while 2) cells expressing GATA6 cluster according to their GATA6 levels. This local pattern evolves into a global pattern in the ICM that starts to emerge in mid blastocysts. We show that FGF/MAPK signalling is involved in the three-dimensional distribution of the cells and, using a mutant background, we further show that the GATA6 neighbourhood is regulated by NANOG. Our quantitative study suggests that the three-dimensional cell neighbourhood plays a role in Epi and PrE precursor specification. Our results highlight the importance of analysing the three-dimensional cell neighbourhood while investigating cell fate decisions during early mouse embryonic development

insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology

ABSTRACT A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells’ spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences.</jats:p

insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology.

Peer reviewed: TrueAcknowledgements: The authors thank the members of the Fletcher and Nichols groups for their helpful feedback in the preparation of the manuscript, especially Ian Groves and Lawrence Bates. Collaboration between the Fletcher and Nichols groups was made possible through a Company of Biologists Travelling Fellowship awarded to S.E.S. (DEVTF-180513). S.E.S. also acknowledges a Sir Henry Wellcome Postdoctoral Fellowship (224070/Z/21/Z). A.G.F. acknowledges support from the Biotechnology and Biological Sciences Research Council (BB/V018647/1 and BB/R016925/1).Funder: Company of Biologists; doi: http://dx.doi.org/10.13039/501100000522Funder: University of Cambridge; doi: http://dx.doi.org/10.13039/501100000735A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells' spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences

insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology

Peer reviewed: TrueAcknowledgements: The authors thank the members of the Fletcher and Nichols groups for their helpful feedback in the preparation of the manuscript, especially Ian Groves and Lawrence Bates. Collaboration between the Fletcher and Nichols groups was made possible through a Company of Biologists Travelling Fellowship awarded to S.E.S. (DEVTF-180513). S.E.S. also acknowledges a Sir Henry Wellcome Postdoctoral Fellowship (224070/Z/21/Z). A.G.F. acknowledges support from the Biotechnology and Biological Sciences Research Council (BB/V018647/1 and BB/R016925/1).Funder: Company of Biologists; doi: http://dx.doi.org/10.13039/501100000522Funder: University of Cambridge; doi: http://dx.doi.org/10.13039/501100000735A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells’ spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences