Multi-omics profiling of mouse gastrulation at single-cell resolution

Abstract

Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan and is associated with major transcriptional changes [1][2][3][4][5] . Global epigenetic reprogramming accompanies these changes [6][7][8] , but the role of the epigenome in regulating early cell fate choice remains unresolved, and the coordination between different molecular layers is unclear. Here we describe the first single cell triple-omics map of chromatin accessibility, DNA methylation and RNA expression during the onset of gastrulation in mouse embryos. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements at enhancer marks, driven by TET-mediated demethylation, and a concomitant increase of accessibility. In striking contrast, the methylation and accessibility landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or remodelled prior to cell fate decisions, providing the molecular logic for a hierarchical emergence of the primary germ layers.Recent technological advances have enabled the profiling of multiple molecular layers at single cell resolution 9-13 , providing novel opportunities to study the relationship between the transcriptome and epigenome during cell fate decisions. We applied scNMT-seq (singlecell Nucleosome, Methylome and Transcriptome sequencing 12 ) to profile 1,105 single cells isolated from mouse embryos at four developmental stages (Embryonic Day (E) 4.5, E5.5, E6.5 and E7.5) which comprise the exit from pluripotency and primary germ layer specification (Figure 1a-d, Extended Data Fig. 1). Cells were assigned to a specific lineage by mapping their RNA expression profiles to a comprehensive single-cell atlas 4 from the same stages, when available, or using marker genes (Extended Data Fig. 2). By performing Argelaguet et al.

MONET: Multi-omic patient module detection by omic selection

“…One module covered only DNA methylation. This module comprised cells from different cell types at E7.5, again highlighting that while the transcriptional signatures of different cell types differ at that stage, the promoter methylation profile of the different germ layers is still quite similar 27 . Overall, these results demonstrate that Monet can be applied and lead to insights in diverse biological scenarios.…”

“…Finally, we applied Monet to single-cell data. Argelaguet et al recently developed scNMT, a method that measured gene expression, DNA methylation and DNA accessibility at single cell resolution, and applied it to mouse embryos at embryonic days 4.5-7.5 27 . We applied Monet to the gene expression and promoter methylation data of 619 single cells (Fig 5b, 5c).…”

Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data

“…7). Although part of the populations from E6.5 and E7.5 has some overlap in the integrated data based on MAPLE input, this outcome is due to the biological nature of the data, as it is observed in all data modalities including gene expression (Argelaguet et al 2019) .…”

“…Single-cell multi-omics studies suggest significant correlation (both positive and negative) between expression and gene body methylation for only a limited number of genes Angermueller, Clark, et al 2016) . As a repressive marker, mean promoter methylation is significantly negatively correlated with gene expression only in a fraction of promoters (Angermueller, Clark, et al 2016;Clark et al 2018;Argelaguet et al 2019) in individual cells. As a result, there is not a straightforward approach for inferring gene activity levels using single-cell DNA methylation datasets.…”

Concurrent stem- and lineage-affiliated chromatin programs precede hematopoietic lineage restriction

“…The transition from HSC to frank lineages is multi-faceted, and therefore reliance on a single set of features is unlikely to reveal the full extent of molecular changes. We anticipate that future single cell multi-omics approaches extracting several epigenomic features such as DNA methylation, and chromatin accessibility (Argelaguet et al 2019) while simultaneously coupled to lineage tracing might yield molecular signatures that more accurately correspond to, temporally precede and hence empower the transition in potency. The scATAC-seq itself provides an elegant solution by utilizing reads mapped to the mitochondrial genome to clonally track the lineages while simultaneously reading out the chromatin features within the same cell (Ludwig et al 2019;Xu et al 2019).…”