Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems1. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a ‘single-cell HCL analysis’ pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.
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The CNGB Nucleotide Sequence Archive accession number is CNP0000325 (https://db.cngb.org/search/?q=CNP0000325). The GEO accession number is GSE134355. The human DGE data are available at https://figshare.com/articles/HCL_DGE_Data/7235471. The mouse DGE data are available at https://figshare.com/articles/MCA_DGE_Data/5435866. Source Data for Figs. 2, 4 and Extended Data Figs. 2, 5, 7, 8, 9, 10, 11 are provided with the paper. HCL data can also be accessed at http://bis.zju.edu.cn/HCL/ or https://db.cngb.org/HCL/.
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We thank G-BIO (Hangzhou), Sidansai Biotechnology (Shanghai), BGI (Shenzhen) and CNGB (Shenzhen) for supporting this project; Vazyme 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; and Y. Zhu, J. Zhu, L. Huang, L. Shao, Z. Wang, H. Huang, X. Wu, W. Lin, M. Bai, Q. Sun, X. Wu, M. Yao, F. Zhu, Z. Li, L. Huang, L. Shao, Z. Wang and X. Chen for help with sample collection. This publication is part of the Human Cell Atlas: www.humancellatlas.org/publications/. G.G. is a participant of the Human Cell Atlas Project (International), the Alliance for Atlas of Blood Cells (China), and the Cell Atlas Project (Zhejiang University Stem Cell Institute). This work was supported by the National Natural Science Foundation of China (grants 91842301, 81770188, 31722027, 31922049, 31701290, and 31871473), the National Key Research and Development Program (grants 2018YFA0107804, 2018YFA0107801, 2018YFA0800503, and 2018YFC1005003), the Zhejiang Provincial Natural Science Foundation of China (grant R17H080001), and the Fundamental Research Funds for the Central Universities (G.G.).
The authors declare no competing interests.
Peer review information Nature thanks Berthold Gottgens, Rosario Isasi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Comparison of t-SNE maps for lung data sets from Tabula Muris (10X Genomics), down-sampled Tabula Muris data (10X Genomics), and MCA data (microwell-seq). Note that downsampling in sequencing depth does not affect cell-type clusters in the Tabula Muris data. Notably, lung data from MCA (sequenced at lower depth) detects more cell-type clusters, including important lung epithelial cells such as AT1 cells, club cells and biopotent progenitors. b, Numbers of cells processed by 31 December 2019 at the HCL for each tissue type. c, Venn diagrams of gene numbers detected in bulk RNA sequencing and microwell-seq (genes with fewer than three counts are excluded). Scatter plots on the right show high correlations (more than 0.8) of average gene expression between bulk RNA sequencing and microwell-seq. We analysed 17,058 genes for kidney and 16,910 genes for lung. d, The percentage of cell types recovered in sub-samples of adult lung, kidney and adrenal gland single-cell data. The major cell-type numbers in representative tissues are near plateau at around 8,000 cells; we collected more than 10,000 cells per tissue on average. e, A Seurat analysis of donor batch effect from four fetal kidney samples (n = 22,439 cells) and three adult kidney samples (n = 22,692 cells). The mixing of different donor single cells in each cell-type cluster suggests a relatively low batch effect in the data. The cluster contribution bar charts on the right suggest that one of the fetal kidney donors lacks C19 and one of the adult kidney donors lacks C22.
a, Verification of pseudo-cell analysis for network interpretation from HCL data. Left, numbers of genes in single cell, pseudo-cell 5, pseudo-cell 10, pseudo-cell 20, pseudo-cell 50 and pseudo-cell 100 from mouse lung single-cell data14,15 generated by 10X Genomics, Smart-seq2 and microwell-seq. Genes were calculated in each cell or pseudo-cell. Sample sizes for each box from left to right were: 10X Genomics: 5,449, 1,089, 540, 272, 112 and 62; Smart-seq2: 1,620, 324, 158, 83, 33 and 20; microwell-seq: 6,940, 1,390, 686, 349, 142 and 81. Right, silhouette value in single cell, pseudo-cell 5, pseudo-cell 10, pseudo-cell 20, pseudo-cell 50 and pseudo-cell 100 from mouse lung single-cell data. A high silhouette value represents good separation. Sample sizes for each box from left to right were: 6,940, 1,390, 686, 349, 142 and 81. Box plots: centre line, median; boxes, first and third quartiles of the distribution; whiskers, highest and lowest data points within 1.5 × IQR. b, t-SNE map of HCL pseudo-cell data showing improved cell-type clustering (n = 30,053 pseudo-cells). c, TF–TF correlation heat map covering 1,521 human TFs generated using HCL pseudo-cell data. The correlation data are listed in Supplementary Table 2. d, Representative TF network in the HCL (PCC > 0.5). Note that the HCL TF network is highly related within small modules but discrete among different modules.
t-SNE maps for single-cell data from fetal skin 2 (a; n = 5,294 cells), fetal brain 5 (b, n = 5,096), fetal pancreas 2 (c, n = 6,939), fetal female gonad 1 (d, n = 2,710), fetal rib 3 (e, n = 4,560), fetal male gonad 1 (f, n = 3,358), chorionic villus 1 (g, n = 9,898), and fetal calvaria 1 (h, n = 5,129). Cells are coloured by cell-type cluster.
t-SNE maps for single-cell data from adult gallbladder 1 (a, n = 9,769 cells), adult liver 4 (b, n = 4,384), adult transverse colon 1 (c, n = 5,765), adult duodenum 1 (d, n = 4,681), adult ileum 2 (e, n = 3,367), adult trachea 2 (f, n = 9,949), adult thyroid gland 1 (g, n = 6,319), and adult peripheral blood 1 (h, n = 2,719). Cells are coloured according to cell-type cluster.
a, t-SNE map of fetal kidney 4 single-cell data (n = 4,511 cells). The experiment was replicated four times with similar results. b, t-SNE map of adult kidney 2 single-cell data (n = 8,877 cells). The experiment was replicated three times with similar results. c, t-SNE map of fetal lung 1 single-cell data (n = 4,526 cells). The experiment was replicated twice with similar results. d, t-SNE map of adult lung 1 single-cell data (n = 8,426 cells). The experiment was replicated three times with similar results. All cells in a–d are coloured according to cell-type cluster. e–h, Ligand and receptor analysis of fetal kidney 4 (e), adult kidney 2 (f), fetal lung 1 (g) and adult lung 1 (h) using the method CellPhoneDB. The colours represent cell types; line thickness indicates the degree of association between cell types.
a, t-SNE map of adult pleura 1 single-cell data (n = 19,695 cells). Cells are coloured according to cell-type cluster in a, c and d. b, Gene expression heat map showing the top 20 differentially expressed genes for each cell cluster in adult pleura 1. Dark blue, high expression; light blue, low expression. Representative genes are labelled in the corresponding area on the right. c, t-SNE map of adult omentum 3 single-cell data (n = 1,354 cells). d, t-SNE map of fetal muscle single-cell data (n = 18,345 cells). e, Feature plot in the t-SNE map of adult lung 1 single-cell data (n = 8,426 cells). Cells are coloured according to the expression of the indicated marker genes. f, Immunofluorescence assay for the epithelial cell marker KRT17 and the CXC chemokine CXCL2 in human adult lung tissue. Scale bar, 50 μm. The experiment was replicated three times with similar results.
a, b, t-SNE maps of single-cell data for human tissue-specific stromal cells (n = 9,452 cells). Cells are coloured according to stromal cell subtype (a) or tissue type (b). c, Bar plots showing the contributions of donors to each of the stromal and endothelial cell clusters.
a, Heat map showing the similarity (PCC) between cell types of adult kidney and fetal kidney. Blue, low similarity; red, high similarity. b, Heat map showing the similarity (PCC) between cell types of adult lung and fetal lung. Blue, low similarity; red, high similarity. c, d, Branching gene expression trajectory analysis of non-immune cells in fetal and adult human tissues using PAGA. c, Coloured by developmental stages; d, coloured by cell lineages. Differential gene expression analysis was performed for representative lineage progression; top markers for fetal (light blue) and adult (dark blue) cells are shown. Marker lists are provided in the source data. e, Single-cell entropy of non-immune cells in each fetal and adult tissue pair (right) and for combined adult and fetal data (left) measured by SLICE54. Box plots: centre line, median; boxes, first and third quartiles of the distribution; whiskers, highest and lowest data points within 1.5 × IQR. Sample from left to right: 2,770, 3,557, 522, 279, 340, 193, 270, 38, 885, 631, 208, 353, 187, 111, 268, 112, 294, 314, 583, 739.
a, scHCL results for bulk human ES cell data (n = 17 samples). Each row represents one cell type in our reference. Each column represents a bulk sample. PCC was used to evaluate cell-type gene expression similarity. Red, high correlation; grey, low correlation. Some cell-type data come from published works, as denoted by first author name: Zhong20, Li25, Segerstolpe27, Baron29, VentoTormo37, Camp42, LaManno43, Han57. b, scHCL results for colorectal cancer organoid data (n = 15 samples), as in a. c, scHCL results for liver bud organoid data (n = 465 cells), as in a except that each column represents a single cell in the customer data set. d, scHCL results for cerebral organoid data (n = 508 cells), as in c. e, t-SNE map of single-cell data for day 20 embryoid bodies differentiated from human iPS cells (n = 9,140 cells). Cells are coloured according to cell-type cluster. f, scHCL results obtained using the data set of day 20 embryoid bodies differentiated from human iPS cells (n = 9,140 cells), as in c. g, Cell–cell correlation network for embryoid body. Each node represents a pseudo-cell in each cell type.
a, t-SNE analysis of 333,778 single cells from the MCA data, with 104 main cell-type clusters labelled. b, t-SNE analysis of 333,778 single cells from the MCA data, with tissue types labelled. c, t-SNE map of pseudo-cell 20 data for mouse (n = 16,740 pseudo-cells). Pseudo-cells are coloured according to cell-type cluster. d, Heat map showing the conserved cell types in human and mouse adult kidney. AUROC scores were calculated from the Spearman correlation between human and mouse adult kidney pseudo-cells (n = 1,197 pseudo-cells). e, Heat map showing conserved cell types in human and mouse fetal kidney. AUROC scores were calculated from the Spearman correlation between human and mouse fetal kidney pseudo-cells (n = 1,459 pseudo-cells).
a, b, Binary regulon activity t-SNE maps for human and mouse based on 259 human regulons (a) and 248 mouse regulons (b), created with R package SCENIC. Each dot represents a pseudo-cell of 20 in the HCL or MCA cell clusters. The t-SNE maps were created using binary regulon activity matrices from 17,028 human pseudo-cells and 16,740 mouse pseudo-cells. c, Binary RASs for human special regulon BHLHE41 and mouse special regulon Olig1 in the regulon activity t-SNE maps (n = 17,028 for human; n = 16,740 for mouse). d, Binary RASs for human special regulon FOXO1 and mouse special regulon Dlx5 in the regulon activity t-SNE maps (n = 17,028 for human; n = 16,740 for mouse). e, Binary RASs for human special regulon ZNF230 and mouse special regulon Mlxipl in the regulon activity t-SNE maps (n = 17,028 for human; n = 16,740 for mouse). f, Binary RASs for regulons IRF8/Irf8 and GATA1/Gata1 in the regulon activity t-SNE maps (n = 17,028 for human; n = 16,740 for mouse). Note that in the regulation of antigen-presenting endothelial cells, the IRF8/Irf8 regulon is conserved. In the regulation of erythroid cells, the GATA1/Gata1 regulon is conserved.
The table contains the age (gestational age for fetal tissues), gender, cause of death, medical history of the donors, sample location, source, dissociation, concentration and digestion time of the tissues. Information about sequencing titration experiment, the analysed cell number, experimental batches, and average transcript number (UMI) are also provided. All used 0ligonucleotide sequences are listed in the last sheet of the table.
Supplementary Table 2 | Differentially expressed genes detected in 102 human cell types from HCL and 104 mouse cell types from MCA
P values were calculated by the Wilcoxon rank-sum test (n = gene name, l = logFc, s = score, p = P value). Statistical tests were two-sided. Tissue contribution for each of the HCL clusters and HCL TF-TF correlation data matrix are also provided.
Supplementary Table 3 | Differentially expressed genes detected in each cell type for all tissues or cell lines in HCL datasets
Genes were selected by log foldchange > 0.25, Bonferroni-adjusted P < 0.1, expressed in at least 15% of cells in either population (Seurat FindAllMarkers). Log foldchange (avg_logFC) was calculated as the arithmetic mean of ln cpm values of one population minus the arithmetic mean of ln cpm values of the other populations. P value (p_val) was calculated by the Wilcoxon rank-sum test. Statistical tests were two-sided. Sample size for each tissue type was listed in Supplementary Table 1.
Supplementary Table 4 | Sheet 1: List of novel cell populations revealed from the HCL study. Sheets 2 and 3: Differentially expressed genes detected in the cross-tissue comparison of stromal (n=9,452) and endothelial (n=7,140) cells
Genes are selected by log foldchange > 0.25, Bonferroni-adjusted P < 0.1, expressed in at least 15% of cells in either population (Seurat FindAllMarkers). Log foldchange (avg_logFC) was calculated as the arithmetic mean of ln cpm values of one population minus the arithmetic mean of ln cpm values of the other populations. P value (p_val) was calculated by the Wilcoxon rank-sum test. Statistical tests were two-sided. Sheet 4 and 5: Differentially expressed genes between fetal and adult stage for 85 pairs of fetal-to-adult cell types. Genes are ranked by the number of times they appear as fetal or adult differentially expressed genes. Statistical tests were two-sided. Commonly enriched genes for fetal cells are listed on sheet 4; commonly enriched genes for adult cells are listed on sheet 5.
The sources of published datasets used in scHCL are listed. The relationship of the corresponding conserved cell types among human and mouse are measured using AUROC scores in MetaNeighbor. The TFs are grouped into 140 orthologous regulons and then clustered into 15 orthologous TF-TF regulon modules using clustering method ‘complete’. The TF module activity in each cluster is listed; a comparison between human and mouse regulons is also provided.
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Han, X., Zhou, Z., Fei, L. et al. Construction of a human cell landscape at single-cell level. Nature 581, 303–309 (2020). https://doi.org/10.1038/s41586-020-2157-4
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