Abstract
Organogenesis is a highly complex and precisely regulated process. Here we profiled the chromatin accessibility in >350,000 cells derived from 13 mouse embryos at four developmental stages from embryonic day (E) 10.5 to E13.5 by SPATAC-seq in a single experiment. The resulting atlas revealed the status of 830,873 candidate cis-regulatory elements in 43 major cell types. By integrating the chromatin accessibility atlas with the previous transcriptomic dataset, we characterized cis-regulatory sequences and transcription factors associated with cell fate commitment, such as Nr5a2 in the development of gastrointestinal tract, which was preliminarily supported by the in vivo experiment in zebrafish. Finally, we integrated this atlas with the previous single-cell chromatin accessibility dataset from 13 adult mouse tissues to delineate the developmental stage-specific gene regulatory programmes within and across different cell types and identify potential molecular switches throughout lineage development. This comprehensive dataset provides a foundation for exploring transcriptional regulation in organogenesis.
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Data availability
All raw single-cell sequencing data and processed data, generated in this study, are available through the Gene Expression Omnibus under accession (GSE216371). There are no restrictions on data availability or use. The imaging figure in Fig. 4e,f and Extended Data Fig. 5, detailed cell annotation, metadata of MOPA cCREs, metadata of merged cCREs across foetal and adult stages and the results of motif enrichment analysis in cis-regulatory modules of Fig. 2j are available at FigShare (https://doi.org/10.6084/m9.figshare.25465480.v1). The gene expression datasets of MOCA were downloaded from https://oncoscape.v3.sttrcancer.org/atlas.gs.washington.edu.mouse.rna/downloads The gene expression datasets of MOCA with deeper sequencing (TOME) were downloaded from http://tome.gs.washington.edu/. The scATAC-seq datasets of adult mice were downloaded from https://atlas.gs.washington.edu/mouse-atac/data/, and their coordinates were mapped using LiftOver (https://genome.ucsc.edu/cgi-bin/hgLiftOver) to mm10 with default parameters. The reference in the file for LiftOver was downloaded from the UCSC genome browser (https://hgdownload.soe.ucsc.edu/goldenPath/mm9/liftOver/mm9ToMm10.over.chain.gz). The scATAC-seq datasets of early mouse organogenesis at E8.25 were downloaded from http://bioinformatics.stemcells.cam.ac.uk/Files_for_transfer/Open/SRP211872/. scRNA-seq datasets of the mouse cell atlas were downloaded via http://bis.zju.edu.cn/MCA/index.html. scRNA-seq datasets of Mus musculus were downloaded from Tabula Muris via https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733. scRNA-seq datasets of early mouse organogenesis at E8.25 were downloaded from ArrayExpress (Accession number: E-MTAB-6153). The Mouse Organogenesis Spatiotemporal Transcriptomic Atlas (MOSTA) datasets were downloaded from https://db.cngb.org/stomics/mosta/spatial.html. Source data are provided with this paper.
Code availability
The code used for the analysis is available in the GitHub repository (https://github.com/Lan-lab/SPATAC-seq).
References
Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).
Preissl, S., Gaulton, K. J. & Ren, B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat. Rev. Genet. https://doi.org/10.1038/s41576-022-00509-1 (2022).
Gorkin, D. U. et al. An atlas of dynamic chromatin landscapes in mouse fetal development. Nature 583, 744–751 (2020).
Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019).
Fei, L. et al. Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development. Nat. Genet. 54, 1051–1061 (2022).
Ibarra-Soria, X. et al. Defining murine organogenesis at single-cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation. Nat. Cell Biol. 20, 127–134 (2018).
Pijuan-Sala, B. et al. Single-cell chromatin accessibility maps reveal regulatory programs driving early mouse organogenesis. Nat. Cell Biol. 22, 487–497 (2020).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Qiu, C. et al. Systematic reconstruction of cellular trajectories across mouse embryogenesis. Nat. Genet. 54, 328–341 (2022).
Jiang, S. et al. Single-cell chromatin accessibility and transcriptome atlas of mouse embryos. Cell Rep. 42, 112210 (2023).
Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e1618 (2020).
Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).
Lohoff, T. et al. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat. Biotechnol. 40, 74–85 (2022).
Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373, 111–117 (2021).
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e1721 (2022).
Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681���686 (2022).
Sun, K. et al. Mapping the chromatin accessibility landscape of zebrafish embryogenesis at single-cell resolution by SPATAC-seq. Nat. Cell Biol. https://doi.org/10.1038/s41556-024-01449-0 (2024).
Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871.e858 (2018).
Abascal, F. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).
Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324.e1318 (2018).
Visel, A., Minovitsky, S., Dubchak, I. & Pennacchio, L. A. VISTA Enhancer Browser—a database of tissue-specific human enhancers. Nucleic Acids Res. 35, D88–92, (2007).
Kvon, E. Z. et al. Progressive loss of function in a limb enhancer during snake evolution. Cell 167, 633–642.e611 (2016).
Kvon, E. Z. et al. Comprehensive in vivo interrogation reveals phenotypic impact of human enhancer variants. Cell 180, 1262–1271.e1215 (2020).
Sarropoulos, I. et al. Developmental and evolutionary dynamics of cis-regulatory elements in mouse cerebellar cells. Science https://doi.org/10.1126/science.abg4696 (2021).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e1022 (2018).
Mulvaney, J. & Dabdoub, A. Atoh1, an essential transcription factor in neurogenesis and intestinal and inner ear development: function, regulation, and context dependency. J. Assoc. Res. Otolaryngol. 13, 281–293 (2012).
Burda, P., Laslo, P. & Stopka, T. The role of PU.1 and GATA-1 transcription factors during normal and leukemogenic hematopoiesis. Leukemia 24, 1249–1257 (2010).
Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).
Pevny, L. H. & Nicolis, S. K. Sox2 roles in neural stem cells. Int. J. Biochem. Cell Biol. 42, 421–424 (2010).
Braun, T. & Gautel, M. Transcriptional mechanisms regulating skeletal muscle differentiation, growth and homeostasis. Nat. Rev. Mol. Cell Biol. 12, 349–361 (2011).
Iwasaki, H. et al. Distinctive and indispensable roles of PU.1 in maintenance of hematopoietic stem cells and their differentiation. Blood 106, 1590–1600 (2005).
Edmondson, D. G., Lyons, G. E., Martin, J. F. & Olson, E. N. Mef2 gene expression marks the cardiac and skeletal muscle lineages during mouse embryogenesis. Development 120, 1251–1263 (1994).
Barbieri, C. E. & Pietenpol, J. A. p63 and epithelial biology. Exp. Cell. Res. 312, 695–706 (2006).
Ikonomou, L. et al. The in vivo genetic program of murine primordial lung epithelial progenitors. Nat. Commun. 11, 635 (2020).
Bouchard, M., Souabni, A., Mandler, M., Neubüser, A. & Busslinger, M. Nephric lineage specification by Pax2 and Pax8. Genes Dev. 16, 2958–2970 (2002).
Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).
Zheng, W. et al. The role of Six1 in mammalian auditory system development. Development 130, 3989–4000 (2003).
Ramachandran, K. et al. Dynamic enhancers control skeletal muscle identity and reprogramming. PLoS Biol. 17, e3000467 (2019).
Asp, P. et al. Genome-wide remodeling of the epigenetic landscape during myogenic differentiation. Proc. Natl Acad. Sci. USA 108, E149–E158 (2011).
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Nguyen, P. D. et al. Muscle stem cells undergo extensive clonal drift during tissue growth via Meox1-mediated induction of G2 cell-cycle arrest. Cell Stem Cell 21, 107–119.e106 (2017).
Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Han, X. et al. Mapping the mouse cell atlas by microwell-Seq. Cell 172, 1091–1107.e1017 (2018).
Schaum, N. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).
Cantù, C. et al. Sox6 enhances erythroid differentiation in human erythroid progenitors. Blood 117, 3669–3679 (2011).
Dumitriu, B. et al. Sox6 cell-autonomously stimulates erythroid cell survival, proliferation, and terminal maturation and is thereby an important enhancer of definitive erythropoiesis during mouse development. Blood 108, 1198–1207 (2006).
Inoue, A. et al. Elucidation of the role of LMO2 in human erythroid cells. Exp. Hematol. 41, 1062–1076.e1061 (2013).
Liu, N. et al. Direct promoter repression by BCL11A controls the fetal to adult hemoglobin switch. Cell 173, 430–442.e417 (2018).
Uda, M. et al. Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of beta-thalassemia. Proc. Natl Acad. Sci. USA 105, 1620–1625 (2008).
Canver, M. C. et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192–197 (2015).
Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).
Baranasic, D. et al. Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis-regulatory elements. Nat. Genet. 54, 1037–1050 (2022).
Kimmel, C. B., Ballard, W. W., Kimmel, S. R., Ullmann, B. & Schilling, T. F. Stages of embryonic development of the zebrafish. Dev. Dyn. 203, 253–310 (1995).
Pack, M. et al. Mutations affecting development of zebrafish digestive organs. Development 123, 321–328 (1996).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e727 (2018).
Haeussler, M. et al. The UCSC Genome Browser database: 2019 update. Nucleic Acids Res. 47, D853–d858 (2019).
Tosches, M. A. et al. Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science 360, 881–888 (2018).
Han, X. et al. Construction of a human cell landscape at single-cell level. Nature 581, 303–309 (2020).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).
Qiu, C. et al. A single-cell time-lapse of mouse prenatal development from gastrula to birth. Nature 626, 1084–1093 (2024).
Acknowledgements
We thank C. Liu from the School of Medicine of Tsinghua University for helpful discussions and feedback. We thank Y. Dong from the School of Medicine of Tsinghua University for her kind assistance with embryo dissection. We thank R. Xu from the School of Medicine of Tsinghua University for his kind assistance in uploading Biwig files into UCSC’s genome browser. This work was supported by grants (grant no. 81972680 to X. Lan) from the National Natural Science Foundation of China, grants (grant no. 61020100119 to X. Lan) from Tsinghua University-Peking University Jointed Center for Life Science, a start-up fund for X. Lan from Tsinghua University-Peking University Joined Center for Life Science, and grants for K.S. from Awarded Center for Life Sciences ‘Excellent Grade Post-Doctoral Fellowship’ Tsinghua University.
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K.S. and X. Lan designed the experiments. K.S. and X. Liu performed the experiments. K.S. analysed the data. K.S. and X. Lan wrote the manuscript.
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Extended data
Extended Data Fig. 1 Data quality of MOPA.
a-b, Sequencing fragment distributions across TSSs (a) and fragment size frequencies (b) of hepatocytes and definitive erythroid cells. c, Dot plot illustrating the TSS enrichment score vs unique nuclear fragments per cell in hepatocytes and definitive erythroid cells. Nuclei that TSS enrichment > 4 and > 1,000 fragments were selected for further analysis. The dot color represents the density in arbitrary units of points in the plot. d, Uniform manifold approximation and projection (UMAP) visualization of single-nucleus chromatin accessibility of mouse organogenesis colored by doublet enrichment (estimated by demuxlet61), FRIP, number of unique fragments, and TSS enrichment score. e, UMAP showing the distribution of nuclei in each embryo of four developmental stages. f, Aggregate chromatin accessibility profiles for 43 main cell types at s Hba locus (left) and Hbb locus (right). g, Barplot showing the cell ratio of each cell type at each time point. h. Heatmaps of mean gene expression (left) or gene activity score (right) for the top 500 differentially expressed genes (selected from scRNA-seq) across 43 main cell types. Gene expression matrix derived from TOME dataset8. Each row represents a gene. Source numerical data are provided as source data.
Extended Data Fig. 2 The comparison between pseudo-cell and single-cell of MOCA as the reference of label transfer.
a, UMAP visualization of MOCA pseudo-cell data of 43 main cell types showing improved cell-type clustering (n = 400 pseudo-cells in each cell type), colored by cell-type assignment. Random sampling 5 cells in the same cell type from the MOCA dataset were aggregated to make one pseudo-cell (See Method). b, Uniform manifold approximation and projection (UMAP) visualization of integrated results of subsampling cells from scATAC-seq (this study) and pseudocells from scRNA-seq (TOME) for the 43 major cell types (see Methods), colored by omics (right) and cell-type assignment (left). c, Violin plot showing the number of genes and unique molecular identifiers (UMIs) in raw single cells and pseudo-cells. d, Confusion matrix comparing the annotation of scATAC cells using marker genes in Fig. 1c and transferred labels of scATAC cells with the nearest scRNA-seq cell following the integration of the scATAC and raw MOCA single-cell dataset (up) or and pseudocell scRNA-seq dataset (bottom). Coloring indicates the proportion of cells mapping for a given pair. e, Density plot showing the distribution of predicted scores of each cell calculated by the label transfer algorithm in Seurat packages. Red and cyan dotted lines indicate the median predicted scores of pseudocell and single cell as a reference, respectively. Source numerical data are provided as source data.
Extended Data Fig. 3 The subtype annotations of epithelial cells by label transfer.
a, UMAP visualization of integrated results of cells from scATAC-seq (this study) and pseudocells from MOCA epithelial cells, colored by omics (left) and cell-type assignment (right). b, Aggregate chromatin accessibility profiles for 22 epithelial subtypes at several marker gene loci. c, Violin plot showing the expression of epithelial marker genes across 22 epithelial subtypes using an integrated gene expression matrix. d, Heatmap showing Pearson’s correlation coefficients between average gene activity scores and gene expression of the top 500 differentially expressed genes (selected from scRNA-seq) across 22 epithelial subtypes. Each row represents a cell type in scATAC-seq data, and each column represents a cell type in scRNA-seq data. e, Heatmaps of mean gene expression (left) or gene activity score (right) for the top 500 differentially expressed genes (selected from scRNA-seq) across 22 epithelial subtypes. Each row represents a gene. Source numerical data are provided as source data.
Extended Data Fig. 4 The subtype annotations of epithelial and endothelial cells by label transfer.
a, UMAP visualization of integrated results of cells from scATAC-seq (this study) and pseudocells from MOCA of endothelial cells, colored by omics (left) and cell-type assignment (right). b, UMAP visualization showing the enrichment of marker genes of several endothelial cell types. c, Heatmap showing Pearson’s correlation coefficients between average gene activity scores and gene expression of the top 500 differentially expressed genes (selected from scRNA-seq) across 10 subtypes of endothelial cells. Each row represents a cell type in scRNA-seq data and each column represents a cell type in scATAC-seq data. d, Heatmaps of mean gene expression (left) or gene activity score (right) for the top 500 differentially expressed genes (selected from scRNA-seq) across 10 subtypes of endothelial cells. Each row represents a gene. Source numerical data are provided as source data.
Extended Data Fig. 5 The enhancer activity validation of 14 accessible chromatin regions.
a, Genome browser tracks of aggregate chromatin accessibility profiles for 43 cell types at the Ryr2 locus. The red box represents the genomic position of the selected putative Ryr2 enhancer (ACR14). b, The mouse enhancer activity of 14 candidate ACRs in mid-gestation (E11.5) mouse embryos. Numbers of embryos with LacZ activity in the corresponding tissue over the total number of transgenic embryos screened are indicated. Source numerical data are provided as source data.
Extended Data Fig. 6 Supplemental analyses of lineage-specific transcriptional regulators in mouse organogenesis.
a, Hierarchical clustering of transcription factor (TF) motif based on Pearson correlation coefficients in average motif activity across 43 major cell types. b, TF footprints of several lineages or cell-type specific TF motifs in the 43 main cell types. The Tn5 insertion bias track is shown below. c, DNA binding-site motif of Klf family TFs from the CIS-BP database27. d, Violin plot showing the expression of Klf family genes across 43 main cell types using the scRNA-seq dataset of TOME9. e, UMAP showing the motif deviation scores of Klf family motifs. f, Violin plot showing the cognate gene expression of selected TF motifs in Fig. 3c across 43 major cell types in the TOME scRNA-seq dataset9. g, Spatial visualization of the expression of several lineages or cell-type specific genes from E10.5 to E13.5. Source numerical data are provided as source data.
Extended Data Fig. 7 Identification of key cCREs and TFs for distinct epithelial subtype.
a, UMAP visualization showing the motif deviation scores (first row) and integrated gene expression (second row) of several cell-type or lineage-specific TF motifs in epithelial cells. b, TF footprints of Trp63 and Grhl1 motifs across 22 subtypes of epithelial cells. The Tn5 insertion bias track is shown below. c, X–Y plots showing the RNA expression levels (x-axis) and the transcription factor (TF) motif enrichment Z-scores (mean values) (y-axis) for several cell-type specific TFs. d, Heatmap summary of cCRE-to-gene links (n = 73,104) at 50 kb resolution where chromatin accessibility is highly correlated with target gene expression. Shown on the left are Z scores for scATAC-seq peak accessibility and on the right are Z scores for scRNA-seq expression. e, Bar plot of enriched gene ontology (GO) terms using genes whose number of linked cCREs was greater than 5 in epithelial cells. The y-axis indicates the GO terms; the x-axis indicates the adjusted P-value (one-sided hypergeometric distribution test). f, HOMER motif analysis of the top transcription factor motif enriched in stomach epithelium linked cCREs (n = 913), endocrine epithelium-linked cCREs (n = 706) and intestinal epithelium-linked cCREs (n = 808), detected using HOMER enrichment analysis with the one-sided hypergeometric distribution test with no adjustment for multiple-hypothesis testing. Source numerical data are provided as source data.
Extended Data Fig. 8 The activity of Nr5a2 in zebrafish intestinal cells.
a-b, t-SNE projection of cells at 24 hours post fertilization (hpf; a) and 72 hpf (b) in zebrafish, colored by cell type annotation. LLP, Lateral line primordium; DN, Differentiating neurons; RPE, Retina pigmented epithelium; FP, Floor plate; Dien, Diencephalon; Telen, Telencephalon; P. arch, Pharyngeal arch; Epi, Epidermal; YSL, yolk syncytial layer; LP, Lateral plate. c, t-SNE projection of cells at 24 hpf (a) and 72 hpf (b) in zebrafish, colored by the gene activity scores or the motif activity score of Nr5a2. The cells in the green circles were zebrafish intestinal cells. d, DNA binding-site motif of mouse Nr5a2 from the CIS-BP database27 (left) and zebrafish nr5a2 from Baranasic, et al.58.
Extended Data Fig. 9 The definition of the trajectories of myogenesis.
a, UMAP visualization showing the enrichment of 14 myogenesis-related gene modules. A full list of genes in the module is from Cao, et al.8. b, Pseudotime representation of four potentially differential paths of myocytes. The line represents a double-spline fitted trajectory across pseudotime. c, Luciferase activities of 18 tested ACRs in C2C12 myoblast and C2C12 induced myotubes. The y-axis represents the fold change of normalized luciferase activity (Firefly/Renilla) over control. Each column represents the mean ± SD of three independent experiments (Paired two-sided Student’s t-test). *P < 0.05, **P < 0.01. The exact P values of basic vector and ACR1-ACR18 are 1.00, 0.00965, 0.00895, 0.0116, 0.195, 0.0155, 0.0504, 0.221, 0.0181, 0.0839, 0.868, 0.0105, 0.100, 0.0106, 0.983, 0.298, 0.0602, 0.00600, 0.00749. Source numerical data are provided as source data.
Extended Data Fig. 10 Clustering analysis of sci-ATAC-seq data from 13 adult mouse tissues and embryos at E8.25.
a, t-SNE visualization of the atlas of single-cell chromatin accessibility of adult mouse across 13 different tissues, colored by cell types defined in Cusanovich, et al.20. (left) and tissues (right). b, UMAP visualization of the atlas of single-cell chromatin accessibility of mouse embryos at E8.25, colored by cell types defined in Pijuan-Sala, et al.7. c, UMAP visualization of 43,3345 nuclei from fetal and adult stages, split by life stage and colored 30 adult cell types defined in Cusanovich, et al.20. (first row) and 43 main fetal cell types (second row).
Supplementary information
Supplementary Information
Supplementary Notes 1–5 and Figs. 1–6.
Supplementary Tables 1–10
Supplementary Table 1. SPATAC primers and barcodes for each stage in mouse organogenesis. Supplementary Table 2. Enhancer validation of 14 putative ACRs. Supplementary Table 3. The average of TF-motif activity score and gene expression matrix across 43 major cell types. Supplementary Table 4. The correlation between TF motif activity and TF gene expression across 43 major cell types. Supplementary Table 5. TF-motif activity matrix across 22 subtypes of epithelial cells. Supplementary Table 6. The correlation between TF motif activity and TF gene expression in epithelial cells. Supplementary Table 7. The cCREs-to-gene links in epithelial cells. Supplementary Table 8. The Pearson correlation coefficient between gene activity score versus gene expression in epithelial cells. Supplementary Table 9. The top 500 differentially expressed genes in epithelial cells. Supplementary Table 10. Primers used for myocyte enhancer validation.
Source data
Source Data for Figs. 1–7 and Extended Data Figs. 1–7
Statistical source data.
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Sun, K., Liu, X. & Lan, X. A single-cell atlas of chromatin accessibility in mouse organogenesis. Nat Cell Biol 26, 1200–1211 (2024). https://doi.org/10.1038/s41556-024-01435-6
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DOI: https://doi.org/10.1038/s41556-024-01435-6
