The changing mouse embryo transcriptome at whole tissue and single-cell resolution

Abstract

During mammalian embryogenesis, differential gene expression gradually builds the identity and complexity of each tissue and organ system 1. Here we systematically quantified mouse polyA-RNA from day 10.5 of embryonic development to birth, sampling 17 tissues and organs. The resulting developmental transcriptome is globally structured by dynamic cytodifferentiation, body-axis and cell-proliferation gene sets that were further characterized by the transcription factor motif codes of their promoters. We decomposed the tissue-level transcriptome using single-cell RNA-seq (sequencing of RNA reverse transcribed into cDNA) and found that neurogenesis and haematopoiesis dominate at both the gene and cellular levels, jointly accounting for one-third of differential gene expression and more than 40% of identified cell types. By integrating promoter sequence motifs with companion ENCODE epigenomic profiles, we identified a prominent promoter de-repression mechanism in neuronal expression clusters that was attributable to known and novel repressors. Focusing on the developing limb, single-cell RNA data identified 25 candidate cell types that included progenitor and differentiating states with computationally inferred lineage relationships. We extracted cell-type transcription factor networks and complementary sets of candidate enhancer elements by using single-cell RNA-seq to decompose integrative cis-element (IDEAS) models that were derived from whole-tissue epigenome chromatin data. These ENCODE reference data, computed network components and IDEAS chromatin segmentations are companion resources to the matching epigenomic developmental matrix, and are available for researchers to further mine and integrate. Hierarchical transcription programs regulate mammalian histogenesis, a spatiotemporally coordinated process of changing cell identities, numbers and locations 1. Contemporary RNA-seq time-courses can comprehensively quantify expression trajectories, including the transcriptional regulators that drive patterning, cell-type specification and differentiation and their regulatory targets. Here we systematically map the mouse polyadenylated RNA transcriptome, tracking 12 major tissues from embryonic day (E) 10.5 to birth (postnatal day (P) 0) (Fig. 1a, b, Extended Data Fig. 1a) to cover much of organogenesis and histogenesis. Pertinent to integrative regulatory analysis and modelling, these RNA expression data are part of the ENCODE Consortium mouse embryo project, which provides companion genome-wide microRNA, DNA methylation, histone mark, and chromatin accessibility datasets for the same sample matrix 2. To better interpret the core sample set, we added five additional organs at P0, sampling seventeen tissues in all. As these whole-tissue data are intended for community use, including integration with high-resolution single-cell transcriptomes, we chose a widely used RNA-seq method that is robust at both bulk sample and single-cell scales 3 and has been used for other single-cell RNA-seq (scRNA-seq) experiments in ENCODE...

Expanded encyclopaedias of DNA elements in the human and mouse genomes

“…We deepened human transcriptome annotations by combining RAMPAGE with 333 60 33 833 1 6 1 3 1 8 1 8 9 0 1 4 8 1 0 0 1 1,317 8 2 1 6 1 9 9 4 178 2 8 8 3 3 6 4 7 2 9 1 168 190 87 Table 1a). We also systematically expanded the mouse transcriptome by performing bulk RNA-seq and microRNA-seq on 17 developing tissues, some on multiple embryonic days, augmented by single-cell RNA-seq on the developing limb 16,21 ( Supplementary Table 1b, c). These new data enhance and expand our knowledge of transcribed elements, including precise mapping of promoters and splicing isoforms to improve gene and transcript annotation, as well as deepening our knowledge of diverse noncoding transcripts.…”

“…A two-dimensional, epigenetic state segmentation model, IDEAS 53 , served as the basis for regulatory region annotation and target gene assessments in mouse haematopoiesis 28 . In the developing mouse limb, IDEAS elements from bulk epigenomic data were deconvolved into specific cell type assignments by using single-cell RNA-seq 16 .…”

“…Across multiple data types, the increase in the scale of experimental data has provided new insights into genome organization and function, and catalysed new capabilities for deriving biological understandings and principles, as illustrated below and detailed in accompanying papers [7][8][9][10][11][12][13][14][15][16] . In summary, we:…”

“…• Characterize the landscape of 3D chromatin interactions across 24 different cell types 13 . • Expand annotation of mouse chromatin modification, DNA accessibility, DNA methylation, and RNA transcription landscapes in early developmental stages not readily accessible in human [14][15][16][17] . To enhance the utility and accessibility of ENCODE data for studies of gene regulation, in this report, we have now:…”

Spatiotemporal DNA methylome dynamics of the developing mouse fetus

“…We performed whole-genome bisulfite sequencing (WGBS) to generate base-resolution methylome maps. In other papers published as part of ENCODE 23,24 , the same tissue samples were profiled using chromatin immunoprecipitation with sequencing (ChIP-seq), assay for transposase-accessible chromatin data using sequencing (ATAC-seq) 23,25 and RNA sequencing (RNA-seq) 24 to identify histone modification, chromatin accessibility and gene expression landscapes, respectively.…”