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Single-cell transcriptomic analysis identifies an immune-prone population in erythroid precursors during human ontogenesis

Nature Immunology volume 23, pages 1109–1120 (2022)Cite this article

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Abstract

Nonimmune cells can have immunomodulatory roles that contribute to healthy development. However, the molecular and cellular mechanisms underlying the immunomodulatory functions of erythroid cells during human ontogenesis remain elusive. Here, integrated, single-cell transcriptomic studies of erythroid cells from the human yolk sac, fetal liver, preterm umbilical cord blood (UCB), term UCB and adult bone marrow (BM) identified classical and immune subsets of erythroid precursors with divergent differentiation trajectories. Immune-erythroid cells were present from the yolk sac to the adult BM throughout human ontogenesis but failed to be generated in vitro from human embryonic stem cells. Compared with classical-erythroid precursors, these immune-erythroid cells possessed dual erythroid and immune regulatory networks, showed immunomodulatory functions and interacted more frequently with various innate and adaptive immune cells. Our findings provide important insights into the nature of immune-erythroid cells and their roles during development and diseases.

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Fig. 1: Erythroid precursors from YS, FL and neonatal UCB are heterogenous.
Fig. 2: Unique developmental properties of classical-erythroid precursors from YS, FL and neonatal UCB.
Fig. 3: Comparative analysis of erythroid cells derived from hESC in vitro and those originating from YS, FL, and UCB.
Fig. 4: Characterization of the immune-erythroid cluster.
Fig. 5: Immune-erythroid cells persist in adult BM.
Fig. 6: Cell–cell interactions between immune-erythroid precursors and an array of immune cells.

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Data availability

All scRNA-seq data have been uploaded to the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) and the National Omics Data Encyclopedia (https://www.biosino.org/node/index) under the accession numbers GSE144024 and GSE149938 for the Gene Expression Omnibus and OEP002022 for the National Omics Data Encyclopedia. Datasets including the integrated gene expression data, cell type and dimensionality reduction information are available in figshare repository (Rdata files: https://doi.org/10.6084/m9.figshare.19747204.v1; Loom files: https://doi.org/10.6084/m9.figshare.19746328.v1). The following databases and datasets were used in this study: GRCh38 human reference genome (https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-GRCh38-2020-A.tar.gz); In silico human surfaceome (http://wlab.ethz.ch/surfaceome/); HumanTFDB3.0 (http://bioinfo.life.hust.edu.cn/HumanTFDB/); the Molecular Signatures Database (http://www.gsea-msigdb.org/gsea/msigdb/search.jsp); and the annotation GTF files (https://www.gencodegenes.org/). Source data are provided with this paper.

Code availability

Publicly available packages were used to perform the analysis of scRNA-seq data. Detailed Python and R scripts for figures are available at https://github.com/Changlu-Xu/scRNA-seq_human_erythroid.

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Acknowledgements

This study was supported by grants from by National Key Research and Development Program of China (2021YFA1100102 to B.L., 2020YFA0112402 to Y.Lan and 2021YFA1100703 to H.W.), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-040 to L.S., 2021-I2M-1-073 to J.Z.), the National Natural Science Foundation of China (81870099 to J.Z., 31930054 to B.L., 81890991 to Y.Lan, 31871173 to Y.Lan, 81870089 to L.S. and 82125003 to J.Z.), the Haihe Laboratory of Cell Ecosystem Innovation Fund (HH22KYZX0017 to L.S. and HH22KYZX0031 to J.Z.), the Beijing-Tianjin-Hebei basic research project (19JCZDJC65700 to J.Z.), the Peking Union Medical College subject construction project (201920101401 to J.Z.), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07S347 to Y.Lan), the Tianjin Municipal Science and Technology Commission (20JCYBJC00240 to H.W.) and the State Key Laboratory of Experimental Hematology Research (ZK21-09 to L.S.). We thank Hongbo Hu and Xiaoming Feng for their excellent discussion and comments, J. Tamanini of Insight Editing London for assistance in revising the manuscript and all members in Shi lab for their kind assistance in this study.

Author information

Author notes

  1. These authors contributed equally: Changlu Xu, Jian He, Hongtao Wang, Yingnan Zhang, Jing Wu, Lu Zhao.

  2. These authors jointly supervised this work: Yu Lan, Jiaxi Zhou, Bing Liu, Lihong Shi.

Authors and Affiliations

  1. State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China

    Changlu Xu, Hongtao Wang, Yingnan Zhang, Jing Wu, Yue Li, Jie Gao, Guangfeng Geng, Bingrui Wang, Xiaoyuan Chen, Zhaofeng Zheng, Biao Shen, Fang Dong, Shihui Ma, Erlie Jiang, Tao Cheng, Jiaxi Zhou, Bing Liu & Lihong Shi

  2. CAMS Center for Stem Cell Medicine, Department of Stem Cell and Regenerative Medicine, PUMC, Tianjin, China

    Changlu Xu, Hongtao Wang, Yingnan Zhang, Jing Wu, Yue Li, Jie Gao, Guangfeng Geng, Bingrui Wang, Xiaoyuan Chen, Zhaofeng Zheng, Biao Shen, Fang Dong, Shihui Ma, Erlie Jiang, Tao Cheng, Jiaxi Zhou & Lihong Shi

  3. State Key Laboratory of Proteomics, Academy of Military Medical Sciences, Academy of Military Sciences, Beijing, China

    Jian He, Zhijie Bai & Bing Liu

  4. Laboratory of Basic Medicine, The General Hospital of Western Theater Command, Chengdu, China

    Jian He

  5. Tianjin Central Hospital of Gynecology Obstetrics, Tianjin, China

    Lu Zhao, Hua Yang & Shujuan Shi

  6. Laboratory of Experimental Hematology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China

    Yang Zeng & Bing Liu

  7. Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China

    Yu Lan & Bing Liu

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  1. Changlu Xu
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Contributions

L.S., B.L., J.Z. and Y.Lan designed and supervised the study; C.X., H.W., J.H., Y.Zhang, J.W., L.Z., Y.Li, J.G., G.G., B.W., X.C., Z.Z., B.S., E.J., H.Y., S.S., Y.Zeng and Z.B. performed the samples preparation and conducted the scRNA-seq; Y.Zhang, J.W., Y.Li and J.G., F.D. and S.M. performed the functional experiments; C.X., J.H. and H.W. performed the bioinformatics analysis with help from L.S., B.L., J.Z., Y.Lan and T.C.; and L.S., B.L., J.Z. and Y.Lan wrote the manuscript.

Corresponding authors

Correspondence to Yu Lan, Jiaxi Zhou, Bing Liu or Lihong Shi.

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Nature Immunology thanks Sing Sing Way and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Ioana Visan was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Strategies to capture the molecular repertoire of erythroid cells from distinct developmental stages.

a, Schematic illustration of the experimental design. b, c, Flow cytometry plots showing the gating strategies for isolation of erythroid precursors (GYPAloCD71+ and GYPAhiCD71+) and progenitors (early erythroid progenitors (EEP): CD34+CD36−CD123−GYPA− and later erythroid progenitors (LEP): CD34−CD36+CD123−GYPA−) from FL and UCB; red boxes showing the sorted cell populations. d-f, t-SNE plots of cells collected from YS (d, left), FL (e, left) and UCB (f, left). The erythroid cell cluster was defined according to the marker genes (right). Ery, erythroid cells. g, Representative significantly enriched GO terms of erythroid cluster; dot size represents the count of identified signature genes and dot color indicates the adjusted P value. P values were determined by hypergeometric test and adjusted for multiple testing using the Benjamini–Hochberg method. h, Violin plots showing the number of UMIs captured in erythroid precursors of YS, FL and UCB. i, FeaturePlots showing the expression of GYPA in YS, FL and UCB cells. j, Spearman correlation analysis of GYPA+ erythroid precursors of each individual sample from YS, FL and UCB. k, UMAP plots showing each individual sample. l, Stacked bar chart indicating the proportion of cell clusters at YS, FL and UCB. m, Heatmap showing the relative expression (scaled by row) of signature genes in each cluster. n, Boxplot illustrating the erythroid maturation score (GO:0043249) for each cluster (C1, n = 8,373 cells; C2, n = 10,328 cells; C3, n = 10,918 cells; C4, n = 1502 cells). The horizontal line across the box indicates the median value and the box represents the first and third quartiles. o, Correlation analysis between UCB erythroid precursors in each cluster and the FACS-sorted, stage-defined erythroid precursors23. The circle area represents the absolute value of the corresponding correlation coefficient. p, Pseudotime analysis of C4 cells. C4 cells were divided into relatively immature ‘C4-A’ (Pseudotime < 20) and mature ‘C4-B’ (Pseudotime ≥ 20) groups. q, The dynamic expression of HBB and HBA2 along the inferred pseudotime axis. r, Violin plots showing the enrichment of C2 and C3 signature (adopted from Extended Data Fig. 1m) for the indicated group of cells.

Extended Data Fig. 2 Comparative studies of human primitive and definitive erythroid cells.

a, Boxplots showing the log-transformed, normalized expression of ENO1 and LDHA in C1 cells of YS, FL and UCB (YS C1, n = 1,636 cells; FL C1, n = 5,344 cells; UCB C1, n = 1,393 cells). The horizontal line across the box indicates the median value and the box represents the first and third quartiles. b, UMAP visualization of the distribution of integrated GYPA+ erythroid precursors in distinct cell cycle phases. c, Stacked bar chart (left) displaying the proportion of cells at different cell cycle phases in each designated cluster. Pie plot (right) depicts the proportion of C2 cells originating from YS, FL and UCB. d, Heatmap showing the relative expression (scaled by row) of the top 10 signature genes in C3 cells of YS. e, Dot plot showing the expression of HBE1 and HBG1/2 in human YS and FL erythroid precursors. Cells in C1 to C4 were classified as primitive and definitive erythroid precursors according to the relative expression of HBE1 and HBG1/2. f, Venn diagram illustrating the common and unique genes expressed in human YS primitive and FL definitive erythroid precursors. g, Dot plot depicting the expression of genes specific for human YS primitive or FL definitive erythroid cells. Dot color represents the relative expression level and dot size represents the percentage of cells expressing this gene. h, Heatmap showing the relative expression (scaled by row) of DEGs identified by comparing human YS primitive and FL definitive erythroid cells. Representative key transcription factors (TFs) and genes encoding cell surface markers (Surface) were listed in red and blue, respectively. i, GO enrichment analysis of DEGs identified by comparing human YS primitive and FL definitive erythroid cells. P values were determined by hypergeometric test.

Extended Data Fig. 3 Characteristics of erythroid cells differentiated from hESCs in vitro.

a, c, e, UMAP plots showing clusters of hematopoietic cells derived from hESC in vitro differentiation at days 4 (Day 12 + 4), 11 (Day 12 + 11) and 16 (Day 12 + 16), respectively. MK, megakaryocyte; GMP, granulocyte-monocyte progenitor. b, d, f, Dot plots showing the marker genes of each defined cluster. g, Boxplot showing the number of genes captured in each cluster identified in Fig. 3b (hESC C1, n = 9,697 cells; hESC C2, n = 7,832 cells; hESC C3, n = 3,939 cells). The thick horizontal line across the box indicates the median value and the box represents the first and third quartiles. h, Violin plot showing the scores of primitive and definitive hESC-derived erythrocytes using the DEGs of YS primitive and FL definitive erythrocytes generated from Extended Data Fig. 2h. i, Representative GO terms enriched based on the DEGs between corresponding clusters from hESC-derived (left) and FL (right) erythrocytes. P values were determined by hypergeometric test. j, Dot plot showing the expression of representative genes of the terms enriched in (i). Among these, genes associated with apoptotic signaling pathway (BAX, BCL2L1), lysosome (ASAH1, AP3S1), autophagy (ATG12, GABARAP), ferroptosis (FTH1, FTL), cholesterol biosynthesis (MSMO1, FDFT1), regulation of protein stability (CCT6A, CALR), cell cycle phase transition (CCNB1, CDC27) and regulation of RNA splicing (SF3B4, SRSF2) are shown.

Extended Data Fig. 4 Immunomodulatory roles of the GYPA+CD71+CD63+ cells in UCB.

a, The regulatory network of the selected regulons and their targets in immune-erythroid cluster. b, Enriched KEGG pathways of the target genes in (a). P values: hypergeometric test and Benjamini–Hochberg method for multiple testing. c, Enriched GO terms in the ‘Classical-Ery’ and ‘Immune-Ery’ clusters from Fig. 4k. P values: hypergeometric test and Benjamini–Hochberg method for multiple testing. d, Flow cytometry plots showing GYPA+CD71−CD63+ enucleated erythrocytes from UCB. New methylene blue staining of GYPA+CD71− cells; scale bar, 10 mm. Bar graph showing the percentage of CD63+ in GYPA+CD71− cells in UCB (n = 3 samples). Data = mean ± SEM. e, Differentially expressed immune signature genes between two groups. f, Flow cytometry plot depicting GYPA+CD71+ erythroid cells at day 14 of differentiation. Pie chart showing the composition of erythroid precursors. Wright-Giemsa staining showing their morphology (n = 3 samples); scale bar, 20 μm. g, t-SNE visualization of clusters in day 14 differentiated erythroid precursors. h, FeaturePlots showing the expression of indicated genes. i, Schematic diagram showing the experimental design. j, Heatmap showing the relative expression of immune-related genes in PBMCs with or without LPS stimulation by low-input RT-qPCR. k, Boxplot showing the expression of key immune-related genes (listed in j) in each indicated group (n = 3 independent experiments). The horizontal line indicates the median value; the box represents the first and third quartiles. P values (left, 0.034; right, 0.016): two-sided Wilcoxon rank-sum test. l, Cytokines secreted by PBMCs treated with or without LPS (n = 2 independent experiments). m, Flow cytometry plots illustrating CD71+TO+CD63+ and CD71+TO+CD63− reticulocytes from CD71 enriched, Hoechst− UCB MNCs. New methylene blue staining and Wright-Giemsa staining of CD71+TO+CD63+ and CD71+TO+CD63− reticulocytes (n = 3 samples); scale bars, 10 μm. n, Cytokine production from the indicated groups (n = 2 independent experiments). o, Schematic diagram showing the experimental design. p, Flow cytometry plots illustrating the TNF expression in the indicated groups. q, Bar graph indicating the percentage of TNF expressing cells in indicated groups (n = 5 independent experiments). Data = mean ± SEM. P values (P < 0.0001 for both): unpaired one-way ANOVA. * P < 0.05; *** P < 0.001.

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Extended Data Fig. 5 Characteristics of erythroid precursors from preterm UCB.

a, Representative flow cytometry plots showing the gating strategy for isolation of erythroid cells from preterm UCB (PT-UCB) (n = 3 samples), similar to Extended Data Fig. 1b, c. b, UMAP plot of erythroid cell cluster (red). Ery, erythroid cells. c, FeaturePlot showing GYPA expression in PT-UCB cells. d, Violin plot showing the number of UMI captured in erythroid precursors of PT-UCB. e, UMAP visualization of clusters of erythroid precursors from PT-UCB. f, UMAP plots showing each individual sample of PT-UCB. g, Heatmap showing the relative expression (scaled by row) of marker genes of each cluster identified in PT-UCB. h, GSEA plots depicting the enrichment of the immune-related functional pathways in PT-UCB C4 cells. i, Heatmap showing the relative expression (scaled by row) of the designated immune-related genes from C1 to C4 cells of PT-UCB. j, Dot plot showing the expression of key transcription factors in PT-UCB C4 cells. k, Representative flow cytometry plots of GYPA+CD71+CD63+ immune-erythroid cells of PT-UCB (left). Bar graph showing the percentage of CD63+ immune-erythroid cells among the GYPA+CD71+ erythroid precursors of PT-UCB (right). Data = mean ± SEM (n = 3 samples). l, Stacked bar chart indicating the proportion of clusters at YS, FL, PT-UCB and term UCB developmental stages. m, Boxplots showing the scores of activation of immune response and cytokine-mediated signaling pathway, among C4 cells of YS, FL, PT-UCB and term UCB (YS C4, n = 315 cells; FL C4, n = 968 cells; UCB C4, n = 219 cells; PT-UCB C4, n = 146 cells). The thick horizontal line across the box indicates the median value and the box represents the first and third quartiles. n, Jaccard similarity analysis of the immune-erythroid clusters among YS, FL, PT-UCB and term UCB based on expression of the signature genes in corresponding immune-erythroid cells. o, Volcano plot showing the DEGs between C4 cells from PT-UCB and term UCB. DEGs with absolute log-transformed fold change > 0.25 and adjusted P < 0.05 (determined by two-sided Wilcoxon Rank Sum test and adjusted using bonferroni correction) defined as significant. The top five DEGs are shown.

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Extended Data Fig. 6 Isolation and clustering of terminally differentiated erythroid precursors from adult BM.

a, Representative flow cytometry plots showing the gating strategy for isolation of erythroid precursors from adult BM (n = 7 samples). b, UMAP plots of each individual sample from BM. c, Hierarchical clustering of GYPA+ erythroid precursors from YS, FL, PT-UCB, term UCB and adult BM. d, e, Boxplot showing the number of genes captured (d) and erythroid maturation score (e) in BM erythroid precursors (C1, n = 578 cells; C2, n = 302 cells; C3, n = 771 cells; C4, n = 553 cells). The horizontal line indicates the median value; the box represents the first and third quartiles. f, Heatmap showing the relative expression (scaled by row) of the top 10 signature genes of each indicated cluster. g, Boxplots showing the enriched scores of the activation of immune responses and cytokine-mediated signaling pathway in the four clusters (C1, n = 578 cells; C2, n = 302 cells; C3, n = 771 cells; C4, n = 553 cells). The horizontal line indicates the median level; the box represents the first and third quartiles. P values were determined by two-sided Wilcoxon rank-sum test (all P values < 10−16). h, Representative flow cytometry plots illustrating the TNF expression. The upper panel showing the TNF expression in BM-derived GYPA+CD71+ and GYPA+CD71+CD63− cells cultured alone. The lower panel showing the TNF expression in CD11b+ cells from groups of CD11b+ cells cultured alone, co-cultured with BM-derived GYPA+CD71+CD63+ cells or GYPA+CD71+CD63− cells. i, Bar graph indicating the percentage of TNF expressing cells in each indicated group from Extended Data Fig. 6h. Each dot indicates an independent experiment (n = 5 independent experiments). Data are presented as the mean ± SEM. P values were determined by unpaired one-way ANOVA (P = 0.0373, CD11b+ vs. CD11b+ + GYPA+CD71+CD63+; P = 0.0008, CD11b+ + GYPA+CD71+CD63− vs. CD11b+ + GYPA+CD71+CD63+). * P < 0.05, *** P < 0.001. j, Representative GO terms enriched from the common and developmental stage-specific signature genes in immune-erythroid cells in Fig. 5k. P values were determined by hypergeometric test and adjusted for multiple testing using the Benjamini–Hochberg method.

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Extended Data Fig. 7 Cell–cell interaction analysis of classical and immune-erythroid precursors with other immune cells in BM, UCB and FL.

a, d, The gating strategy for isolation of GYPA− cells from single-cell suspensions of FL (a) and mononuclear cells of UCB (d). b, e, UMAP showing the clusters of GYPA− cells from FL (b) and UCB (e). NK/T, natural killer T cell; Mac, macrophage; MEMP, MK-erythroid-mast cell progenitor; HPC, hematopoietic progenitor cell; EC, endothelial cell; Gran-Mono, granulocyte and monocyte. c, f, Track plots showing the expression of feature genes in specific clusters. Gene expression levels in each cell were represented by line height. g, i, j, Dot plots showing the significantly enriched ligand–receptor pairs between immune-erythroid cells and designated immune cells or between classical-erythroid cells and immune cells in BM (g), YS (i) and UCB (j). Dot size indicates the permutation P value and color indicates the mean expression of genes in each ligand–receptor pair. The representative pairs are labeled with asterisks. The P value for a given receptor–ligand complex is calculated on the basis of the proportion of the means that are as high as or higher than the actual mean. h, Violin plots showing the expression of representative ligand–receptor pair genes in immune-erythroid cells and other immune cells in BM.

Supplementary information

Supplementary Information

This zip file contains Supplementary Tables 1–7. Supplementary Table 1: Details of human samples used in the present study. Related to Figs. 1, 4, and 5 and Extended Data Figs. 1 and 5–7. Supplementary Table 2: Cell annotations of scRNA-seq. Related to Figs. 1 and 3–5 and Extended Data Figs. 1 and 3–7. Supplementary Table 3: Differentially expressed genes among the clusters in YS, FL, PT-UCB, UCB and BM. Related to Figs. 1, 4 and 5 and Extended Data Figs. 1 and 5–7. P values were determined by two-sided Wilcoxon rank-sum test and adjusted using bonferroni correction. Supplementary Table 4: Differentially expressed genes among the clusters in hESC-HCs and HSPC-derived cells. Related to Fig. 3 and Extended Data Figs. 3 and 4. P values were determined by two-sided Wilcoxon rank-sum test and adjusted using bonferroni correction. Supplementary Table 5: Enriched regulons and their target genes. Related to Extended Data Fig. 4. Supplementary Table 6: Expression of significant interaction gene pairs in YS, FL, UCB and BM. Related to Fig. 6 and Extended Data Fig. 7. Supplementary Table 7: Gene set source and information. Related to Figs. 2, 4 and 5, and Extended Data Figs. 1, 2 and 4–6.

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Xu, C., He, J., Wang, H. et al. Single-cell transcriptomic analysis identifies an immune-prone population in erythroid precursors during human ontogenesis. Nat Immunol 23, 1109–1120 (2022). https://doi.org/10.1038/s41590-022-01245-8

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