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Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development

Nature Genetics volume 54, pages 1051–1061 (2022)Cite this article

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An Author Correction to this article was published on 03 August 2022

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Abstract

Waddington’s epigenetic landscape is a metaphor frequently used to illustrate cell differentiation. Recent advances in single-cell genomics are altering our understanding of the Waddington landscape, yet the molecular mechanisms of cell-fate decisions remain poorly understood. We constructed a cell landscape of mouse lineage differentiation during development at the single-cell level and described both lineage-common and lineage-specific regulatory programs during cell-type maturation. We also found lineage-common regulatory programs that are broadly active during the development of invertebrates and vertebrates. In particular, we identified Xbp1 as an evolutionarily conserved regulator of cell-fate determinations across different species. We demonstrated that Xbp1 transcriptional regulation is important for the stabilization of the gene-regulatory networks for a wide range of mouse cell types. Our results offer genetic and molecular insights into cellular gene-regulatory programs and will serve as a basis for further advancing the understanding of cell-fate decisions.

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Fig. 1: Single-cell transcriptional atlas of mouse differentiation.
Fig. 2: Cellular heterogeneity in mouse tissues.
Fig. 3: Analysis of regulatory programs in MCDA.
Fig. 4: Global characteristics of cell differentiation across species.
Fig. 5: Inference of gene regulation during cell-fate decisions across species.
Fig. 6: ScRNA-seq and high-resolution MS revealed gene and protein changes in Xbp1−/− embryos.

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Data availability

The data generated in the present study can be downloaded from the NCBI’s Gene Expression Omnibus under accession nos. GSE176063 and GSE178217. The raw and processed files of MCDA are at accession no. GSE176063. The raw and processed files of WT and Xbp1 KO embryos are at accession no. GSE178217. Processed count matrices and cell annotations are provided on the figshare website (https://figshare.com/s/340e8e7f349559f61ef6), including the development stage, tissue of origin, lineage information and cell-type annotations. We have provided separate datasets for each tissue and the merged datasets for the MCDA. We have also provided an interactive website (http://bis.zju.edu.cn/MCA) to enable public access to the data. The proteomics data was provided in the Proteomics Identifications Database (PRIDE) under accession no. PXD032847. The following publicly available datasets were used in the study: Mus_musculus. GRCm38.88 genome, Mus_musculus_10090 database, AnimalTFDB 3.0 database, STRING database (v.1.1), eggNOG database (v.5.0), Ensembl v.96; the S. mediterranea dataset generated by Plass et al.12 (accession no. GSE103633), the C. elegan dataset generated by Packer et al.15 (accession no. GSE126954.); the C. intestinalis dataset generated by Cao et al.16 (accession no. GSE131155); the H. vulgaris dataset generated by Siebert et al.13 (accession no. GSE121617); the D. rerio dataset generated by Li et al.42 (GSE178151); the H. sapiens dataset generated by Han et al.14 (GSE134355); and part of the M. musculus dataset (E14.5 and adult) generated by Han et al.11 (accession nos. GSE108097 and GSE134355). The mouse scATAC-seq dataset was generated by Cusanovich et al.63 (accession no. GSE111586, https://atlas.gs.washington.edu/mouse-atac/data) and Di Bella et al.64 (accession no. GSE153164), and the human scATAC-seq dataset by Domcke et al.65 (descartes.brotmanbaty.org).

Code availability

Detailed code is available at GitHub (https://github.com/ggjlab/MCDA) and Zenodo (https://zenodo.org/record/6548256#.Yn92F-hBw2w)95.

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Acknowledgements

G.G. is a participant of the Human Cell Atlas Project. We thank J. Wu, H. Wu, J. Liu, D. Wang, S. Xiong, Y. Huang and P. Xu for support on the project. We thank: G-BIO (Hangzhou), BGI (Shenzhen) and CNGB (Shenzhen) for supporting this project; Vazyme (Nanjing) for supplying the customized enzymes in the study; the Core Facilities of Zhejiang University School of Medicine for technical support; the Center of Cryo-Electron Microscopy at Zhejiang University for computational support; PTM Biolab (Hangzhou) for performing the LC–MS proteomic analysis; and Gempharmatech (Nanjing) for producing the KO mice. Funding was provided by: National Natural Science Foundation of China (grant nos. 31930028 to G.G., 31871473 to X.H., 31922049 to X.H., 91842301 to G.G., 32001068 to H.C. and 32000461 to J.W.), the Funds for Creative Research Groups of China (grant no. T2121004 to G.G.), National Key Research and Development Program (grant nos. 2018YFA0107804 to G.G., 2018YFA0107801 to X.H. and 2018YFA0800503 to G.G.); and Fundamental Research Funds for the Central Universities (to G.G.).

Author information

Author notes

  1. These authors contributed equally: Lijiang Fei, Haide Chen, Lifeng Ma, Weigao E, Renying Wang, Xing Fang, Ziming Zhou.

Authors and Affiliations

  1. Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Lijiang Fei, Haide Chen, Lifeng Ma, Weigao E, Renying Wang, Xing Fang, Ziming Zhou, Huiyu Sun, Xinru Wang, Chengxuan Yu, Yuqing Mei, Danmei Jia, Xiaoping Han & Guoji Guo

  2. Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China

    Haide Chen, Jingjing Wang, Mengmeng Jiang, Tingyue Zhang & Guoji Guo

  3. Zhejiang University–University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China

    Guoji Guo

  4. Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, China

    Guoji Guo

  5. Institute of Hematology, Zhejiang University, Hangzhou, China

    Guoji Guo

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  1. Lijiang Fei
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Contributions

G.G. conceived the project. X.H., H.C., X.F., Z.Z., R.W. and L.F. performed the experiments. L.F., L.M., W.E., H.S., J.W., X.W., C.Y. and Y.M. performed the single-cell data processing, clustering analyses, gene-regulated analyses and cell-type annotation. G.G., L.F., H.C., L.M., W.E. and X.F. wrote the paper. M.J., D.J. and T.Z. performed the sequencing experiments. L.F., L.M. and W.E. preserved and made available the data, code and materials on publication. G.G., X.H., H.C. and J.W. acquired the funds.

Corresponding authors

Correspondence to Xiaoping Han or Guoji Guo.

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The authors declare no competing interests.

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Nature Genetics thanks Malte Spielmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Construction of the MCDA.

a, Hierarchical trees showing the relationship between 95 cell types in MCDA, colored by lineage. b, t-SNE visualization of 520,801 single cells from seven developmental stages of mice, colored by lineage. They share the same color legend of lineages. c, t-SNE visualization of 520,801 single cells from different developmental stages of mice, colored by tissue. d, Heatmaps showing the number of differentially expressed genes (DEGs) in each developmental stage across the ten tissues of mice. DEGs between two stages of cells were identified using a Wilcoxon rank sum test. e, Summary of the GO enrichment analysis performed on the DEGs in each developmental stage. f, Visualization of the top 10 principal components of PCA in MCDA. Colors represent tissues, which is the same in Extended Data Fig. 1c. g, Lollipop chart displaying the gene expression variance explained by residuals (that is, biological and technical noise) or experimental factors such as tissue, stage, gender, and their respective combinations. Items like “tissue and gender” are variances explained by interactions of two factors instead of the union of two factors. h, UMAP visualization of 57,118 single cells in the kidneys at 7 different time points, colored by stage. i, Summary of the GO enrichment analysis performed on the DEGs in the kidneys across different stages. The red marks the go terms related to physiological functions of renal functions.

Extended Data Fig. 2 t-SNE maps for examples of analyzed tissues in MCDA.

t-SNE maps for single-cell data from brain at P0 (a, n =9,265 cells), P10 (b, n = 6,100 cells), P21 (c, n = 4,433 cells) stages, heart at P0 (d, n = 3,948 cells), P10 (e, n = 5,383 cells), P21 (f, n = 4,054 cells) stages, intestine at P0 (g, n = 9,101 cells), P10 (h, n = 17,909 cells), P21 (i, n = 9,365 cells) stages, kidney at P0 (j, n = 13,155 cells), P10 (k, n = 12,129 cells), P21 (l, n = 5,700 cells) stages, liver at P0 (m, n = 9,980 cells), P10 (n, n = 9,259 cells), P21 (o, n = 5,867 cells) stages, lung at P0 (p, n = 5,906 cells), P10 (q, n = 11,314 cells), P21 (r, n = 6,391 cells) stages, and pancreas at P0 (s, n = 5,639 cells), P10 (t, n = 11,007 cells) stages.

Extended Data Fig. 3 t-SNE maps for examples of analyzed tissues in MCDA.

t-SNE maps for single-cell data from pancreas at P21 (a, n = 4,858 cells) stages, stomach at P0 (b, n = 4,073 cells), P10 (c, n = 22,599cells), P21(d, n = 9,945 cells) stages, testes at P0 (e, n = 9,034 cells), P10 (f, n = 15,808 cells), P21 (g, n = 9,095 cells) stages, uterus at P0 (h, n = 4,561 cells), P10 (i, n = 4,841 cells), P21 (j, n = 9,077 cells) stages, and embryo at E10.5 (k, n = 26,551 cells) and E12.5 (l, n = 72,792 cells) stages.

Extended Data Fig. 4 Examples of novel cell populations.

a, Feature plots in the t-SNE map of P10 lung (n = 11,314 cells). Cells are colored according to the expression of the indicated marker genes or two genes. The red boxes magnify the co-expressed cell types in the tissues. b, Immunofluorescence assay for the club cell marker gene Scgb1a1 (green) and goblet cell marker gene Tff2 (yellow) in P10 lung. The red boxes indicate the co-expressed locations. The experiment was replicated three times with similar results. Scale bar, 20 μm. c, d, Left: feature plots of Afp in the t-SNE map of P0 pancreas (c, n = 5,639 cells), P10 pancreas (d, n = 11,007 cells). Cells are colored according to the expression of Afp. Right: immunofluorescence assay for the hepatocyte marker gene Afp (green) in P0 (c) pancreas and P10 (d) pancreas. The experiment was replicated three times with similar results. Scale bar, 20 μm. e, Heatmap shows the differentially expressed genes between liver hepatocytes and pancreas hepatocyte-like cells at the P0 stage. Wilcoxon rank-sum test (two-sided) was performed to identify differentially expressed genes and p-value adjustment was performed using bonferroni correction (p adjusted values < 0.05, fold change > = 2). f, Heatmap shows the differentially expressed genes between liver hepatocytes and pancreas hepatocyte-like cells at the P10 stage. Wilcoxon rank-sum test (two-sided) was performed to identify differentially expressed genes and p-value adjustment was performed using bonferroni correction (p adjusted values < 0.05, fold change > 2).

Extended Data Fig. 5 Entropy estimations of the MCDA using.

a, Entropy measurement of cells in MCDA using the SLICE method. The color represents the stage. P-values are from a two -sided Wilcoxon rank sum test comparing entropies of two different development stages (n = 60,065 cells, ns: not significant, p-value > 0.05, * p-value ≤  0.05, ** p-value ≤  0.01, *** p-value ≤  0.001, **** p-value ≤  0.0001). The exact p-values were displayed in the Source Data. Box plots: center line, median; boxes, first and third quartiles of the distribution; whiskers, highest and lowest data points within 1.5 × IQR. The same statistical analysis was performed for Extended Data Fig. 5a-d. b, Entropy measurement of each lineage in MCDA using the SLICE method. The color represents the stage (epithelial: n = 13,642 cells, neuron: n = 3,638 cells, immune: n = 15,719 cells, muscle n = 2,592 cells, stromal: n = 8,541 cells, endothelial: n = 4,528 cells, other: n = 2,626 cells, erythroid: n = cells, proliferating: n = 3,442 cells, secretory: n = 2,892 cells, germline: n = 5,480 cells). c, Entropy measurement of cells in MCDA using the StemID method (n = 60,065 cells). The color represents the stage. d, Entropy measurement of each lineage in MCDA using the StemID method. The color represents the stage (epithelial: n = 13,642 cells, neuron: n = 3,638 cells, immune: n = 15,719 cells, muscle n = 2,592 cells, stromal: n = 8,541 cells, endothelial: n = 4,528 cells, other: n = 2,626 cells, erythroid: n = cells, proliferating: n = 3,442 cells, secretory: n = 2,892 cells, germline: n = 5,480 cells). e, Boxplots displaying the sensitivity, specificity, FPR (False Positive Rate), and PRAUC (Precision-Recall Area Under Curve) of two methods with different inputs to detect tissue-specific TFs in MCDA (n = 9 tissues per box). Methods represented are running VIPER-DOROTHEA with pseudo cells (pseudo_VIPER-DOROTHEA) or single cells (single_VIPER-DOROTHEA), running SCENIC with pseudo cells (pseudo_SCENIC) or single cells(single_SCENIC). The union of the two methods with single cells (single_union (ABC)) was the union of collection ABC. And the intersection of the two methods with single cells (single_intersection (A)) is the collection A. Box plots: center line, median; boxes, first and third quartiles of the distribution; point, tissues in MCDA. The results indicate SCENIC with single-cell datasets performs better in specificity and PRAUC than VIPER-DOROTHEA. The union of two methods achieves over 75% sensitivity in identifying regulatory programs while the intersection of two methods achieves the highest specificity. f, Heatmap of aggregated module activities of TFs clustered by fuzzy c-means showing variation by stage and lineage from VIPER-DOROTHEA. g, Boxplot showing the module activity scores in module 14 (n = 56 TFs) and module 15 (n = 36 TFs) per lineage per stage in SECNIC. Red lines mark the zero line. Colors from blue to yellow represent the 7 development stages from E10.5 to adult stage. Box plots: center line, median; boxes, first and third quartiles of the distribution; whiskers, highest and lowest data points within 1.5 × IQR. h, Venn diagrams of the numbers of overlapping genes between housekeeping TFs and commonly upregulated TFs (TFs in module 14, collection ABC) in MCDA. i, Heatmap showing commonly upregulated TFs (TFs in module 14, collection ABC) with regard to expression levels in MCDA. The color displays the Spearman correlation between aggregated TF expression levels in tissue-lineage against development stages (labeled as 1 to 7 to represent E14.5 to adult). Red blocks indicate the TFs display the upregulated expression patterns in the specific lineages of tissues.

Source data

Extended Data Fig. 6 Analysis of the developmental branch across species.

a, Circos plot showing the subphyla, species, tissues/lineages, and time points of the single-cell dataset used in the cross-species analysis. b–d, Radial network plot showing the inferred relationships among cell types of invertebrates (b, H. vulgaris c, C. elegans d, S. mediterraneaia). Dot representing cell types, colored by lineage. e, Sankey plot showing the inferred relationships among cell types in fetal and adult human lungs.

Extended Data Fig. 7 Cross-species analysis of commonly upregulated and downregulated genes.

a, b, Entropy measurement of each lineage in H. sapiens (a) and D. rerio (b) using the CCAT methods (H. sapiens: immune, n = 26,976 cells, stromal, n = 11,278 cells, muscle, n = 5,450 cells, epithelial, n = 20,347 cells, erythroid, n = 1,897 cells, neuron, n = 4,659 cells, endothelial n = 7,475 cells, proliferating, n = 3,421 cells, secretory, n = 3,708 cells; D. rerio: epithelial, n = 36,243 cells, stromal, n = 8,801 cells, erythroid, n = 693 cells, others, n = 3,454 cells, muscle, n = 4,140, neuron: n = 10,363 cells, immune: n = 10,104 cells). The color represents the stage. P-values were from a two-sided Wilcoxon rank sum test comparing entropies of two different development stages. Box plots: center line, median; boxes, first and third quartiles of the distribution; whiskers, highest and lowest data points within 1.5 × IQR. c, d, Venn plots showing the downregulated (c) and upregulated (d) genes in 7 species (homologous genes of humans, p-adj < 0.1). e, Bar plot showing the numbers of conserved upregulated and conserved downregulated genes per species, which were homologous genes of humans. f, Boxplots showing the number of log10 protein–protein interactions of commonly upregulated genes (at least 3 species, n = 59), commonly downregulated genes (at least 3 species, n = 524), other conserved genes (at least 3 species and homologous to human genes, n = 12,543), and other genes (n = 17,839). P-values were from a twosided Wilcoxon rank sum test comparing log10 PPI numbers of two different gene types. Box plots: center line, median; boxes, first and third quartiles of the distribution; whiskers, highest and lowest data points within 1.5 × IQR. g, Bar plot showing the gene composition of conserved upregulated genes (at least 3 species, n = 59 genes), conserved downregulated genes (at least 3 species, n = 524 genes), other conserved genes (in at least 3 species and homologous to human genes, n = 12,543 genes), and other genes (n = 17,839 genes). Gene categories were colored by mean values of log10 PPI number (blue: less PPIs, red: more PPIs). h, i, Bubble plot showing the GO terms of commonly downregulated (h) and upregulated (i) genes. The bubble color indicates the value representing the proportion of selected GO term in the EBI GOA database for the human. Higher value implies more general terms, lower implies more specific ones. The bubble size indicates the frequency of the GO term in the underlying GOA database. Hypergeometric test was performed to identify significant go terms and benjamini-hochberg correction was used to adjust p-values. j, Heatmap showing the cell type frequencies of commonly upregulated genes in 7 species.

Extended Data Fig. 8 Lineage-specific regulators among different species.

a, Network plot showing the reliable and biologically plausible matches of lineages from 7 species using Metaneighbor and SAMap (sm: S. mediterranea, ce: C. elegans, hy: H. vulgaris, ci: C. intestinalis, ze: D. rerio, mo: Mus M. musculus, hu: H. sapiens, the abbreviations are the same in Extended Data Fig. 8). b, UMAP showing the combination projection of seven species based on pseudo-bulk cells, colored by species. c, UMAP showing the combination projection based on pseudo-bulk cells, colored by meta-lineages. d–j, Heatmaps showing the sequence similarities (log values) of development-related lineage-specific TFs within the meta-lineage across species: stromal (d), endothelial (e), muscle (f), stem/germline (g), neural (h), immune (i), and epithelial (j).

Extended Data Fig. 9 scRNA-seq revealed the changes in Xbp1-/- embryos.

a, Western blot for the knockout experiment. The molecular weight markers were labeled. The experiment was replicated three times with similar results. b, A igv view of mapped reads in the Xbp1 gene in the sequencing data of the WT and KO embryos. The left one shows the entire Xbp1 gene. The right one shows the marked red region which is the exon1 and exon2 region of Xbp1. The exon2 region shows no read coverage, which indicates that the exon2 (97 bp) has been completely disrupted in KO embryos. The blue lines link the different parts of reads that, by definition, map on several exons. The left and right genome browser tracks share the same y axis. c, Xbp1-/- embryos at E12.5. The arrows represent dead embryos. d, Scatter plot showing the cell composition proportions of differential cell types between KO and WT embryos on E12.5 (WT: n = 4, KO: n = 5, FDR < 0.01). e–f, Entropy measurement of each cluster in Fig. 6b using the StemID (e, n = 93,246 cells) and SLICE (f, n = 93,246 cells) methods. They share the same text in the x coordinates. P-values are from a two-sided Wilcoxon rank sum test comparing entropies of two different groups from each cluster (ns: not significant, p-value > 0.05, * p-value ≤  0.05, ** p-value ≤  0.01, *** p-value ≤  0.001, **** p-value ≤  0.0001). The exact p values were displayed in the Source Data. Box plots: center line, median; boxes, first and third quartiles of the distribution; whiskers, highest and lowest data points within 1.5 × IQR.

Source data

Extended Data Fig. 10 High-resolution MS revealed the protein changes in Xbp1-/- embryos.

a, Heatmap illustrating the proteins that were differentially expressed in Xbp1−/− embryos and wild-type embryos (the colors represent the z-scores of the protein expression). A two-sided t-test is performed for comparing protein levels of KO embryos to WT embryos (p-value ≤ 0.05, fold change ≥ 1.5). b, Volcano plot showing the differentially expressed proteins in Xbp1−/− embryos and WT embryos. The lines mark thresholds for log values of the p-value and fold change. The dots of text annotations are genes that are canonical Xbp1 targets related to the unfolded protein response (UPR). The yellow and blue dots are genes with significantly upregulated genes in KO embryos and WT embryos respectively. c, mESCs and Xbp1−/− mESCs grown in mESCs medium for 3 days and showing no visible differences in cell morphology. The experiment was replicated three times with similar results. Scale bar, 50 μm. d, qPCR analysis of Nanog, Oct4, and Sox2 expression in mESCs and Xbp1−/− mESCs showing no significant differences (normalized by the expression level of Gapdh, n = 3 per box). A two-sided Wilcoxon rank sum test is performed for comparing gene expression levels of wild-type and knockout mESCs (p-value ≥ 0.05: not significant, mean ± s.d.).

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7 and 9–23.

Supplementary Data

Supplementary Table 8 DEGs in each tissue of each stage. Wilcoxon’s rank-sum test (two-sided) was performed to identify DEGs and p-value adjustment was performed using Bonferroni’s correction.

Source data

Source Data Fig. 3

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Statistical source data.

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Source Data Fig. 6

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Source Data Extended Data Fig. 5

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Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Unprocessed western blots.

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Fei, L., Chen, H., Ma, L. et al. Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development. Nat Genet 54, 1051–1061 (2022). https://doi.org/10.1038/s41588-022-01118-8

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