Self-organization of embryonic stem cells into a reproducible embryo model through epigenome editing

Abstract

Embryonic stem cells (ESCs) can self-organize in vitro into developmental patterns with spatial organization and molecular similarity to that of early embryonic stages. This self-organization of ESCs requires transmission of signaling cues, via addition of small molecule chemicals or recombinant proteins, to induce distinct embryonic cellular fates and subsequent assembly into structures that can mimic aspects of early embryonic development. During natural embryonic development, different embryonic cell types co-develop together, where each cell type expresses specific fate-inducing transcription factors through activation of non-coding regulatory elements and interactions with neighboring cells. However, previous studies have not fully explored the possibility of engineering endogenous regulatory elements to shape self-organization of ESCs into spatially-ordered embryo models. Here, we hypothesized that cell-intrinsic activation of a minimum number of such endogenous regulatory elements is sufficient to self-organize ESCs into early embryonic models. Our results show that CRISPR-based activation (CRISPRa) of only two endogenous regulatory elements in the genome of pluripotent stem cells is sufficient to generate embryonic patterns that show spatial and molecular resemblance to that of pre-gastrulation mouse embryonic development. Quantitative single-cell live fluorescent imaging showed that the emergence of spatially-ordered embryonic patterns happens through the intrinsic induction of cell fate that leads to an orchestrated collective cellular motion. Based on these results, we propose a straightforward approach to efficiently form 3D embryo models through intrinsic CRISPRa-based epigenome editing and independent of external signaling cues. CRISPRa-Programmed Embryo Models (CPEMs) show highly consistent composition of major embryonic cell types that are spatially-organized, with nearly 80% of the structures forming an embryonic cavity. Single cell transcriptomics confirmed the presence of main embryonic cell types in CPEMs with transcriptional similarity to pre-gastrulation mouse embryos and revealed novel signaling communication links between different embryonic cell types. Our findings offer a programmable embryo model and demonstrate that minimum intrinsic epigenome editing is sufficient to self-organize ESCs into highly consistent pre-gastrulation embryo models.

Figure 1.. Design and validation of doxycycline-inducible CRISPRa mESCs.

A) Overview of plasmids and integration methods. B) Immunofluorescence stainings of Gata6, Foxa1 and Sox17 for comparison of control and Gata6 gRNA mESCs treated with doxycycline for up to 4 days. C) Quantification of Gata6 induction and related PrE-cell markers over time. D) Immunofluorescence stainings of Cdx2, Gata3 and Tfap2c for comparison of control and Cdx2 gRNA mESCs treated with doxycycline for up to 4 days. E) Quantification of Cdx2 induction and related TS-cell markers over time.

Figure 2.. 2D Micropattern organization of control-, Cdx2- and Gata6-induced mESCs.

A) Overview of cells used and growth protocol micropattern chip. B) Representative immunofluorescence staining of 80um, 140um, 225um and 500um regions (top to bottom), showing markers for each mESC lines, Pou5f1, Cdx2 and Foxa1 respectively. 80um, n=62. 140um, n=35. 225um, n=25. 500um, n=4. C) Density plot showing average distribution of cells in normalized coordinate space for each marker and different sized regions. D) Distribution of total cells positive for each marker per region. E) Ratio of total cells positive for each marker per region.

Figure 3.. Live cell imaging and tracking of micropattern organization.

A) Overview of modified sgRNA constructs for tracking and imaging design. B) Example timelapse images from a +dox (left) and C) −dox (right) micropattern, showing phase contrast (top), sgGata6 mESCs (middle) and sgCdx2 mESCs (bottom). For sgGata6 mESC and sgCdx2 mESC panels, top rows are fluorescent signals, middle rows are segmented nuclei, and bottom rows are cell tracking over time. D) Cell proliferation as measured by the number of sgCdx2-mESCs and sgGata6-mESCs over time. E) Average mean square displacement of sgCdx2-mESCs and sgGata6-mESCs over time. F) Directionality of displacements as measured by mean degree angles over time. n=11 micropattern regions per analysis. +dox sgCdx2 labels across all timepoints n=6,856, +dox sgGata6 labels across all timepoints n=3478, −dox sgCdx2 labels across all timepoints n=7,482, −dox sgGata6 labels across all timepoints n=4918.

Figure 4.. Generation and single-cell analysis of CRISPRa-programmed embryo models (CPEMs).

A) Overview of cells used for differentiation B) Differentiation protocol and representative confocal image of a CPEM with each cell type present, stained by Pou5f1, Cdx2 and Foxa1. C) Quantification of cell density for each marker across CPEMs using z-stack confocal imaging (n=30, n=21,477 nuclei labels), left to right shows increasing depth with 3.89um z-stack step-size, 28 density maps across the z-axis are displayed. D) Analysis of cavity formation by analyzing DAPI stain in the center stack position of CPEMs, showing an example with (left) and without (right) cavity. E) Quantification and clustering of cavity formation in 2 clusters, and F) ratio of the clusters (n=30). G) Number and H) ratio of Cdx2, Foxa1 and Pou5f1 labeled cells identified in CPEMs (n=30). I) Mean number of Cdx2, Foxa1 and Pou5f1 labeled cells across imaged z-positions (n=30). J) Relative distribution of Cdx2, Foxa1 and Pou5f1 labeled cells across imaged z-positions (n=30), scaled to maximum within each group.

Figure 5.. Single cell RNA-seq analysis of 3D CPEMs and ligand-receptor interactions.

A) UMAP and clustering analysis of single cell RNA-seq data of mixed cell culture CPEMs, showing feature maps for Nanog, Gata6 and Cdx2. B) Dotplot showing a set of gene marker levels for Epi, TS and PrE cells. C) CellChat analysis showing ligand-receptor interactions between cell clusters of pathways known in early embryogenesis. D) CellChat analysis showing ligand-receptor interactions between cell clusters of potential new signaling communications in early embryogenesis.