Decoding human fetal liver haematopoiesis

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Abstract

Definitive haematopoiesis in the fetal liver supports self-renewal and differentiation of haematopoietic stem cells and multipotent progenitors (HSC/MPPs) but remains poorly defined in humans. Here, using single-cell transcriptome profiling of approximately 140,000 liver and 74,000 skin, kidney and yolk sac cells, we identify the repertoire of human blood and immune cells during development. We infer differentiation trajectories from HSC/MPPs and evaluate the influence of the tissue microenvironment on blood and immune cell development. We reveal physiological erythropoiesis in fetal skin and the presence of mast cells, natural killer and innate lymphoid cell precursors in the yolk sac. We demonstrate a shift in the haemopoietic composition of fetal liver during gestation away from being predominantly erythroid, accompanied by a parallel change in differentiation potential of HSC/MPPs, which we functionally validate. Our integrated map of fetal liver haematopoiesis provides a blueprint for the study of paediatric blood and immune disorders, and a reference for harnessing the therapeutic potential of HSC/MPPs.

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Fig. 1: Single-cell transcriptome map of fetal liver.
Fig. 2: Multi-modal and spatial validation of cell types.
Fig. 3: Fetal liver and NLT haematopoiesis.
Fig. 4: Lymphoid lineages in fetal liver and NLT.
Fig. 5: Tissue signatures in developing myeloid cells.
Fig. 6: HSC/MPP differentiation potential by gestation.

Data availability

The raw sequencing data, expression count data with cell classifications are deposited at ArrayExpress with accession code E-MTAB-7407.

Code availability

All scripts are available at https://github.com/haniffalab/FCA_liver.

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Acknowledgements

This publication is part of the Human Cell Atlas (www.humancellatlas.org/publications). We thank the Newcastle University Flow Cytometry Core Facility, Bioimaging Core Facility, Genomics Facility, NUIT for technical assistance, School of Computing for access to the High-Performance Computing Cluster and Newcastle Molecular Pathology Node Proximity Laboratory; A. Farnworth for clinical liaison, S. Hambleton for primary immunodeficiency expertise, H. Chen for immunohistochemistry assistance and M. Belle and S. Fouquet for light-sheet fluorescence microscopy assistance. The human embryonic and fetal material was provided by the Joint MRC–Wellcome (MR/R006237/1) Human Developmental Biology Resource (www.hdbr.org). We acknowledge funding from the Wellcome Human Cell Atlas Strategic Science Support (WT211276/Z/18/Z); M.H. is funded by Wellcome (WT107931/Z/15/Z), The Lister Institute for Preventive Medicine and NIHR and Newcastle Biomedical Research Centre; S.A.T. is funded by Wellcome (WT206194), ERC Consolidator and EU MRG-Grammar awards; S.B. is funded by Wellcome (WT110104/Z/15/Z) and St. Baldrick’s Foundation; E.L. is funded by a Wellcome Sir Henry Dale and Royal Society Fellowship, European Haematology Association, Wellcome and MRC to the Wellcome–MRC Cambridge Stem Cell Institute and BBSRC; A. Regev is funded by the Manton Foundation and Klarman Cell Observatory.

Author information

M.H., S.A.T. and E.L. conceived and directed the study. M.H., S.A.T., E.L. and R.A.B. designed the experiments. Samples were collected by S. Lisgo and S. Lindsay, isolated by R.A.B. and libraries prepared by E.S., L.M., D.-M.P., R.V.-T., J.-E.P. and J.F. Flow cytometry and FACS experiments were performed by R.A.B., E.F.C., L.J., D. Maunder. and A. Filby. Imaging mass cytometry experiments were performed by M.A., B.M., B.I., D. McDonald and A. Fuller. Cytospins were performed by D.D. and J.F. and in vitro culture differentiation experiments were performed by L.J., D. Maunder. and E.F.C. Immunohistochemistry was performed by B.I., M.A., F.G. and C.M.; P.C. and M.A. interpreted immunohistochemistry and developmental pathology sections. Y.G. and A.C. performed and interpreted light-sheet fluorescence microscopy experiments. M.S.K., B.L., O.A., M.T., D.D., T.L.T., M.S., O.R.-R. and A. Regev. generated adult and cord blood scRNA-seq datasets. D.-M.P., K.G., K.P., S.W., I.G., M.E., P.V., M.D.Y., Z.M. and J.B performed the computational analysis. M.H., D.-M.P., R.A.B., B.G., E.L, I.R., A. Roy, E.F.C., L.J., A.-C.V., R.R., E.P., M.M., J.-E.P., G.R., K.B.M., M.J.T.S., A. Filby., K.G., S.W., I.G., S.B. and J.B. interpreted the data. M.H., L.J., R.A.B., E.S., D.-M.P., B.G., E.L., I.R., K.G., S.W., I.G., A.-C.V. and A. Roy. wrote the manuscript. All authors read and accepted the manuscript.

Correspondence to Sam Behjati or Elisa Laurenti or Sarah A. Teichmann or Muzlifah Haniffa.

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Competing interests

A. Regev is a founder and equity holder of Celsius Therapeutics and an SAB member of Neogene Therapeutics, ThermoFisher Scientific and Syros Pharmaceuticals.

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Extended data figures and tables

Extended Data Fig. 1 Single-cell transcriptome map of fetal liver.

a, Fetal skin and kidney haematopoietic cells visualized by UMAP. Colours indicate cell state. Inset, colours indicate tissue type. b, UMAP visualization of yolk sac haematopoietic cells. Colours indicate cell state. Inset, colours indicate location within yolk sac. c, UMAP visualization of 3′ liver 10x cells after batch correction, coloured by sample. d, UMAP visualization (top) of 3′ 10x liver sample sex mixing grouped by developmental stage, and violin plots (bottom) showing ln-normalized median expression of XIST (green) and RSP4Y1 (purple), which mark female and male samples, respectively. e, UMAP visualization of fetal liver composition by developmental stage. Colours indicate cell state. f, UMAP visualization of fetal liver cells profiled using Smart-seq2. Colours indicate cell states as shown in e. g, Frequency (mean ± s.e.m.) of B cells in the CD34 cells detected in 6–19 PCW fetal livers by flow cytometry (*P < 0.05, ***P = 0.003 and ****P < 0.001). Source data

Extended Data Fig. 2 Transcriptome validation of fetal liver cells.

a, Assessment of 48 genes from the 4,471 highly variable genes by using a random forest classifier to assign cell labels, where ‘true cell label’ indicates the manual annotation based on the full list of variable genes. b, Comparison of representative mini bulk RNA-seq data (in coloured triangles) and liver EI populations (early, mid and late erythroids, VCAM1+ EI macrophages), Kupffer cells and endothelium validated by Smart-seq2 (SS2) (in colour) overlaid on whole liver SS2 populations (grey). c, Dot plot showing representative median-scaled ln-normalized gene expression of 100 FACS-isolated liver cells based on marker gene expression in Fig. 2a. Gene expression indicated by spot size and colour intensity. d, Dot plot showing median-scaled ln-normalized gene expression of FACS-sorted single cells from liver EI populations (early, mid and late erythroids, VCAM1+ EI macrophages), Kupffer cells and endothelium shown as coloured dots in b based on marker gene expression in Fig. 2a. Gene-expression frequency (per cent of cells within cell type expressing the gene) indicated by spot size and expression level by colour intensity. e, Overlay pseudo-colour Hyperion representative images for 8 PCW and 15 PCW fetal liver. Far left images are shown at 5× magnification with zoom of insets on right at 20× magnification (1 μm per pixel). Asterisks indicate bile ducts.

Extended Data Fig. 3 Fetal liver and NLT haematopoiesis.

a, PAGA analysis of fetal liver HSC/MPP, erythroid, megakaryocyte and mast cell lineages from Fig. 3a. Lines show connections; line thickness corresponds to the level of connectivity (low (thin) to high (thick) PAGA connectivity). b, Heat map showing min − max normalized expression of statistically significant (P < 0.001), dynamically variable genes from pseudotime analysis for erythroid, megakaryocyte and mast cell inferred trajectories. Transcription factors in bold, asterisks mark genes not previously implicated for the respective lineages. c, FDG visualization of fetal liver, skin and kidney HSC/MPP, MEMP, erythroid, megakaryocyte and mast cell lineages. d, Heat map showing the scaled ln-expression of selected marker genes in fetal liver, NLT and yolk sac subsets. e, PAGA connectivity scores of HSC/MPP, erythroid, megakaryocyte and mast cell lineages between fetal liver, skin, kidney (K) and yolk sac. f, Representative immunohistochemical staining of sequential sections of 8 PCW fetal skin for endothelium (CD34+) and erythroblasts (nucleated and GYPA+), nuclei stained with blue alkaline phosphatase. Zoom in of insets (right) bordered with black (top) indicate nucleated cells stained positive for GYPA within CD34+ blood vessels, and those bordered with red (bottom) indicate nucleated GYPA+ cells outside CD34+ blood vessels. Scale bars, 100 μm. g, Representative confocal fluorescence microscopy of embryo (5 PCW) hand skin. Scale bar, 10 μm; red, TO-PRO-3 nuclei; green, GYPA (see also Supplementary Video 2 showing light-sheet fluorescence microscopy). The arrowhead indicates extravascular nucleated erythroid cells. h, Stacked bar plots (left) showing percentage (mean ± s.d.) of fetal liver (red), skin (blue) and kidney (green) HSC/MPP, MEMP, erythroid, megakaryocyte and mast cells in each stage of the cell cycle (G1 (navy), G2M (blue) and S (white) phase), and ln-normalized median expression of MKI67 transcript (right) in corresponding liver vs NLT cell types (total percent of MKI67-expressing cells shown above plots; each dot represents a single cell). *P < 0.05, **P < 0.01 and ***P < 0.005. Source data

Extended Data Fig. 4 Investigation of interactions between fetal liver macrophages and erythroid cells.

a, Representative immunohistochemical staining of fetal liver for erythroblasts and macrophages with GYPA and CD68, respectively. Scale bar, 50 μm. Statistically significantly (P < 0.05) enriched receptor–ligand interactions from CellPhoneDB between VCAM1+ EI macrophages (purple) and two erythroid populations (early and mid; red) (n = 14 biologically independent samples). Asterisks indicate protein complexes. Violin plots show ln-normalized median gene-expression value of VCAM1 and ITGA4 in cells analysed by CellPhoneDB (indicated by # in dot plot). b, Representative immunohistochemical staining of sequential sections of 8 PCW fetal liver for VCAM1+ EI macrophages (VCAM1+) and CD49d+GYPA+ cells with nuclei stained using blue alkaline phosphatase. Right, zoom in of insets, with coloured arrows indicating erythroblast (yellow) and VCAM1+ EI macrophage (purple). Scale bars, 100 μm. c, Representative gating strategy used to visualize fetal liver erythroid cells, VCAM1+ EI macrophages, Kupffer cells, monocyte-macrophages and mast cells. d, Bright field (BF), VCAM1 (CD106), CD34, CD45, KIT (CD117), GYPA, CD14 and HLA-DR images for each cell type within gates shown in c. e, Representative bright-field images of cells found within the single-cell and doublet gates. f, Bar plots showing the proportion of each cell type within the single-cell gate (white) or doublet gate (grey) (mean ± s.d.); *P = 0.0194. g, Comparison of macrophage and erythroid gene expression in mouse macrophages (red) and EI macrophages (blue), n = 3 from ref. 20. Source data

Extended Data Fig 5 Lymphoid lineages in fetal liver and NLT.

a, PAGA analysis of fetal liver HSC/MPP and lymphoid cell types from Fig. 1b showing changes over four developmental stages. Lines symbolize connection; line thickness corresponds to the level of connectivity (low (thin) to high (thick) PAGA connectivity). b, c, Feature plots (b) and violin plots (c) showing ln-normalized median expression of selected known NK, ILC and T cell genes over gestation for early lymphoid/T lymphocyte cluster; **P < 0.001, ***P < 0.005 and ****P < 0.0001. d, Dot plot showing median-scaled ln-normalized median expression of V(D)J transcripts in fetal liver lymphoid cell types. Gene expression indicated by spot size and colour intensity. e, Heat map showing normalized expression of statistically significant, dynamically variable genes from pseudotime analysis for B cell lineage inferred trajectory (likelihood ratio test). Transcription factors are in bold. Morphology of liver pro-B and pre-B cells and B cells by Giemsa stain after cytospin. f, ln-normalized expression (mean ± s.e.m.) of TNFSF13B in Kupffer cells and NFKBIA in HSC/MPPs and cells in the B cell lineage across four developmental stages spanning 6–17 PCW; trend lines showing linear regression. g, PAGA connectivity scores of HSC/MPP and lymphoid cells from fetal liver, skin, kidney and yolk sac. h, ln-normalized median expression of selected known NK (top) and ILC precursor (bottom) marker genes and selected DEGs between liver (red), skin (blue) and kidney (green) visualized by violin plots (***P < 0.005 and ****P < 0.001). i, Violin plots showing ln-normalized median expression of selected known ILC and NK cell genes expressed in ILC precursors from fetal liver, skin and kidney. Source data

Extended Data Fig. 6 Tissue signatures in developing myeloid cells.

a, Diffusion map of fetal liver HSC/MPP, progenitors and precursors from Fig. 1b. b, Heat map showing min − max normalized expression (P < 0.001) of dynamically variable genes from pseudotime analysis for monocyte, DC1 and DC2 inferred trajectories. Transcription factors in bold, asterisks mark genes not previously implicated for the respective lineages. c, Heat map visualization comparing scaled expression of the top marker genes of decidua–placenta (red), fetal liver (black) and yolk sac (purple) progenitor and myeloid populations. d, PAGA connectivity scores of HSC/MPP and myeloid cells from fetal liver, skin and kidney. e, ln-normalized median expression of three known marker genes and three DEGs in corresponding myeloid populations across fetal liver, skin and kidney visualized by violin plots (*P < 0.05, ***P < 0.005 and ****P < 0.0001).

Extended Data Fig. 7 HSC/MPP differentiation potential by gestation.

a, Experimental design for single-cell transcriptome and culture of fetal liver cells from the representative FACS gates illustrated. b, Alignment of 349 scRNA-seq-profiled cells from FACS gates in a with 10x-profiled HSC/MPPs and early progenitors visualized using FDG; point shape corresponds to sequencing type (triangle, SS2 plate data; circle, 10x data). c, Stacked bar plot of all different types of colonies generated by single HSC pool gate cells (gate defined in a). d, Stacked bar plot of all different types of colonies generated by single HSC pool gate cells without MS5 stroma layer (gate defined in a) by stage (left) and in individual samples (right), *P < 0.05. e, Percentage of colonies generated by single HSC pool cells without MS5 stroma layer containing erythroid cells (sum of ery, ery–meg, ery–meg–my and ery–my colonies shown in c); **P < 0.01. f, Percentage of colonies from single-cell culture (shown in Fig. 6c) that differentiated along three lineage (defined as sum of ery, NK and my, and ery, meg and my colonies) branches (***P < 0.005). g, Percentage of colonies containing NK cells following B/NK optimized culture of ten cells from the HSC pool gate (*P < 0.05 and **P < 0.01). h, Percentage (mean ± s.e.m.) of HSC/MPPs and early progenitors in fetal liver, yolk sac, cord blood and adult bone marrow expressing MKI67 (*P < 0.05, **P < 0.01 and ****P < 0.001). i, Percentage (mean ± s.e.m.) of CD34+CD38 and CD34+CD38+cells in the indicated cell cycle phases (right) as determined by flow cytometry analysis (left; representative plot of n = 8 biologically independent samples) (G0, Ki67DAPI; G1, Ki67+DAPI; S–G2–M, Ki67+DAPI+ (left)). Source data

Extended Data Fig. 8 Expression of known PID-linked genes in fetal liver.

Dot plots showing relative expression of genes known to be associated with major PID disease categories in fetal liver cell types from Fig. 1b. Gene-expression frequency (per cent of cells within the cell type expressing the gene) is indicated by spot size and expression level is indicated by colour intensity.

Extended Data Fig. 9 FACS gating strategy for scRNA-seq analysis.

a, Gating strategy used to isolate cells for droplet (10x) and plate-based scRNA-seq (Smart-seq2) for samples F2–F17. b, Gating strategy used to isolate cells for cytospins, scRNA-seq (Smart-seq2) and 100-cell RNA-seq. c, Flow cytometry gating strategy used to identify the colonies cultured in vitro from single cells as shown in Fig. 6c. d, Flow cytometry gating strategy used to identify B and NK colonies cultured in vitro from 10 cells as shown in Fig. 6e.

Supplementary information

Supplementary Figures

Giemsa-stained cytospin images of fetal liver cell states. Raw Giemsa-stained cytospin images as shown in Fig. 2b and Extended Data 5e, where left and right corresponds to their position in Fig. 2b. a, NK cells (left and right). b, pDC precursor (left and right). c, DC1 (left and right). d, DC2. e, Monocytes (left and right). f, Mono-Mac (left and right). g, Kupffer cells (left and right). h, Mast cells (left and right). i, Early erythroid cells (left and right). j, Mid erythroid cells. k, Late erythroid cells (left and right). l, Endothelial cells. m, Fibroblasts (left and right). n, Hepatocytes. o, pre/pro B cells. p, B cells (left and right). All images for a cell state were taken from the same slide at 100x magnification. Scale bar, 10μm.

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-13 with a guide.

Supplementary Tables

This file contains Supplementary Tables 14-22 with a guide.

Supplementary Video 1

Animated force-directed graph of all liver cell states. Animated FDG visualization of fetal liver cell states shown in Fig. 1b. Video can also be found on the web portal.

Supplementary Video 2

3D analysis of fetal skin erythropoiesis. Animation of 3D light-sheet fluorescence microscopy analysis corresponding to Extended Data 3g. Parallel movies of maximum intensity projection rendering (left) and additional shading processes to emphasize 3D rendering (right). < indicates extravascular nucleated erythroid cells.

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Popescu, D., Botting, R.A., Stephenson, E. et al. Decoding human fetal liver haematopoiesis. Nature 574, 365–371 (2019) doi:10.1038/s41586-019-1652-y

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