Decoding human fetal liver haematopoiesis

doi:10.1038/s41586-019-1652-y

. 2019 Oct;574(7778):365-371.

doi: 10.1038/s41586-019-1652-y. Epub 2019 Oct 9.

Decoding human fetal liver haematopoiesis

Dorin-Mirel Popescu^#¹, Rachel A Botting^#¹, Emily Stephenson^#¹, Kile Green¹, Simone Webb¹, Laura Jardine¹, Emily F Calderbank², Krzysztof Polanski³, Issac Goh¹, Mirjana Efremova³, Meghan Acres¹, Daniel Maunder¹, Peter Vegh¹, Yorick Gitton⁴, Jong-Eun Park³, Roser Vento-Tormo³, Zhichao Miao^{3

5}, David Dixon¹, Rachel Rowell¹, David McDonald¹, James Fletcher¹, Elizabeth Poyner^{1

6}, Gary Reynolds¹, Michael Mather¹, Corina Moldovan⁷, Lira Mamanova³, Frankie Greig¹, Matthew D Young³, Kerstin B Meyer³, Steven Lisgo⁸, Jaume Bacardit⁹, Andrew Fuller¹, Ben Millar¹, Barbara Innes¹, Susan Lindsay⁸, Michael J T Stubbington³, Monika S Kowalczyk¹⁰, Bo Li^{10

11}, Orr Ashenberg¹⁰, Marcin Tabaka¹⁰, Danielle Dionne¹⁰, Timothy L Tickle^{10

12}, Michal Slyper¹⁰, Orit Rozenblatt-Rosen¹⁰, Andrew Filby¹, Peter Carey¹³, Alexandra-Chloé Villani^{14

11}, Anindita Roy¹⁵, Aviv Regev^{10

16}, Alain Chédotal⁴, Irene Roberts^{15

17

18}, Berthold Göttgens², Sam Behjati^{19

20}, Elisa Laurenti²¹, Sarah A Teichmann^{22

23}, Muzlifah Haniffa^{24

25

26}

Affiliations

¹ Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK.
² Department of Haematology and Wellcome and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
³ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
⁴ Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.
⁵ European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK.
⁶ Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
⁷ Department of Pathology, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
⁸ Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK.
⁹ School of Computing, Newcastle University, Newcastle upon Tyne, UK.
¹⁰ Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
¹¹ Data Sciences Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
¹² Haematology Department, Royal Victoria Infirmary, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
¹³ Broad Institute of Harvard and MIT, Cambridge, MA, USA.
¹⁴ Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA.
¹⁵ Department of Paediatrics, University of Oxford, Oxford, UK.
¹⁶ Howard Hughes Medical Institute, Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁷ MRC Molecular Haematology Unit and Department of Paediatrics, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
¹⁸ BRC Blood Theme, NIHR Oxford Biomedical Centre, Oxford, UK.
¹⁹ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. sb31@sanger.ac.uk.
²⁰ Department of Paediatrics, University of Cambridge, Cambridge, UK. sb31@sanger.ac.uk.
²¹ Department of Haematology and Wellcome and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. el422@cam.ac.uk.
²² Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. st9@sanger.ac.uk.
²³ Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK. st9@sanger.ac.uk.
²⁴ Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK. m.a.haniffa@newcastle.ac.uk.
²⁵ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. m.a.haniffa@newcastle.ac.uk.
²⁶ Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK. m.a.haniffa@newcastle.ac.uk.

^# Contributed equally.

PMID: 31597962
PMCID: PMC6861135
DOI: 10.1038/s41586-019-1652-y

Decoding human fetal liver haematopoiesis

Dorin-Mirel Popescu et al. Nature. 2019 Oct.

. 2019 Oct;574(7778):365-371.

doi: 10.1038/s41586-019-1652-y. Epub 2019 Oct 9.

Authors

Affiliations

¹ Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK.
² Department of Haematology and Wellcome and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
³ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
⁴ Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.
⁵ European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK.
⁶ Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
⁷ Department of Pathology, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
⁸ Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK.
⁹ School of Computing, Newcastle University, Newcastle upon Tyne, UK.
¹⁰ Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
¹¹ Data Sciences Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
¹² Haematology Department, Royal Victoria Infirmary, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
¹³ Broad Institute of Harvard and MIT, Cambridge, MA, USA.
¹⁴ Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA.
¹⁵ Department of Paediatrics, University of Oxford, Oxford, UK.
¹⁶ Howard Hughes Medical Institute, Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁷ MRC Molecular Haematology Unit and Department of Paediatrics, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
¹⁸ BRC Blood Theme, NIHR Oxford Biomedical Centre, Oxford, UK.
¹⁹ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. sb31@sanger.ac.uk.
²⁰ Department of Paediatrics, University of Cambridge, Cambridge, UK. sb31@sanger.ac.uk.
²¹ Department of Haematology and Wellcome and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. el422@cam.ac.uk.
²² Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. st9@sanger.ac.uk.
²³ Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK. st9@sanger.ac.uk.
²⁴ Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK. m.a.haniffa@newcastle.ac.uk.
²⁵ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. m.a.haniffa@newcastle.ac.uk.
²⁶ Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK. m.a.haniffa@newcastle.ac.uk.

^# Contributed equally.

PMID: 31597962
PMCID: PMC6861135
DOI: 10.1038/s41586-019-1652-y

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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Conflict of interest statement

Competing Interests

None declared

Figures

**Extended Data Figure 1. Single cell transcriptome map of fetal liver.**
a, Fetal skin and kidney haematopoietic cells visualised by UMAP. Colours indicate cell state. Inset: colours indicate tissue type. b, UMAP visualisation of yolk sac haematopoietic cells. Colours indicate cell state. Inset: colours indicate location within yolk sac. c, UMAP visualisation of 3’ liver 10x cells post batch correction, coloured by sample. d, UMAP visualisation (top) of 3’ 10x liver sample sex mixing grouped by developmental stage, and violin plots (bottom) showing ln-normalised median expression of *XIST* (green) and *RSP4Y1* (purple), which marks female and male samples respectively. e, UMAP visualisation of fetal liver composition by developmental stage. Colours indicate cell state. f, UMAP visualisation 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; **** p < 0.001).

**Extended Data Figure 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 RNAseq data (in coloured triangles) and liver erythroblastic island (EI) populations (early, mid and late erythroids, VCAM1⁺ EI macrophages), Kupffer cells and endothelium validated by SS2 (in colour) overlaid on whole liver SS2 populations (grey). c, Dot plot showing representative median scaled ln-normalised gene expression of 100 FACS-isolated liver cells based on marker gene expression in Figure 2a. Gene expression indicated by spot size and colour intensity. d, Dot plot showing median scaled ln-normalised gene expression of FACS sorted single cells from liver erythroblastic island (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 Figure 2a. Gene expression frequency (% cells within cell type expressing) 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 5x magnification with zoom of insets on right at 20x magnification (1μm/pixel). Bile ducts are marked with an *.

**Extended Data Figure 3. Fetal liver and NLT haematopoiesis.**
a, PAGA analysis of fetal liver HSC/MPP, erythroid, megakaryocyte and mast cell lineages from Figure 3a. Lines symbolise connection; line thickness corresponds to the level of connectivity (thin to thick = low to high PAGA connectivity). b, Heat map showing min-max normalised 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, asterisk (*) mark genes not previously implicated for the respective lineages. c, FDG visualisation 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 bar, 100μm. g, Representative light sheet fluorescence microscopy of embryo (5 PCW) hand skin. Scale bar, 5μm; TO-PRO-3/nuclei = red, GYPA = green (see also Supplementary Video 2). < indicates extravascular nucleated erythroid cells. h, Stacked barplots (right) of the mean +/- s.d. percent 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-normalised median expression of *MKI67* transcript (right) in corresponding liver vs NLT cell types (total percent of *MKI67* expressing cells stated above plots; each dot represents a single cell). * p < 0.05; ** p < 0.01; *** p < 0.005.

**Extended Data Figure 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, 50μm. 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). Asterisk (*) indicate protein complexes. Violin plots show ln-normalised median gene expression value of VCAM1 and ITGA4 in cells analysed by CellPhoneDB (marked 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. Zoom in of insets (right) with coloured arrows indicating erythroblast (yellow) and VCAM1⁺ EI macrophage (purple). Scale bar, 100μm. c, Representative gating strategy used to visualise fetal liver erythroid cells, VCAM1⁺ EI macrophages, Kupffer cells, Mono-macs, and mast cells. d, Bright field, 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, Barplots showing the mean +/- s.d. proportion of each cell type within the single cell gate (white) or doublet gate (grey); * p = 0.0194. g, Comparison of macrophage and erythroid gene expression in mouse macrophages (red) and EI macrophages (blue), n = 3 from Li *et al*.¹⁹.

**Extended Data Figure 5. Lymphoid lineages in fetal liver and NLT.**
a, PAGA analysis of fetal liver HSC/MPP and lymphoid cell types from **Figure 1b** showing changes over four developmental stages. Lines symbolise connection; line thickness corresponds to the level of connectivity (thin to thick = low to high PAGA connectivity). b, Feature plots and c, violin plots showing ln-normalised 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; **** p < 0.0001. d, Dot plot showing median scaled ln-normalised 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 normalised 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/Pre B cells and B cells by Giemsa stain after cytospin. f, ln-normalised expression (mean +/- s.e.m.) of *TNFSF13B* in Kupffer cells and *NFKBIA* in HSC/MPPs and cells in the B cell lineage across 4 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-normalised median expression of selected known NK (left) and ILC precursor (right) marker genes and selected differentially expressed genes between liver (red), skin (blue) and kidney (green) visualised by violin plots (*** p < 0.005; **** p < 0.001). i, Violin plots showing ln-normalised median expression of selected known ILC and NK cell genes expressed in ILC precursors from fetal liver, skin, and kidney.

**Extended Data Figure 6. Tissue signatures in developing myeloid cells.**
a, Diffusion map of fetal liver HSC/MPP, progenitors and precursors from **1b. b,** Heat map showing min-max normalised expression (p < 0.001) of dynamically variable genes from pseudotime analysis for monocyte, DC1 and DC2 inferred trajectories. Transcription factors in bold, * mark genes not previously implicated for the respective lineages. c, Heat map visualisation 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-normalised median expression of 3 known marker genes & 3 differentially expressed genes in corresponding myeloid populations across fetal liver, skin and kidney visualised by violin plots (* p < 0.05; *** p < 0.005; **** p < 0.0001).

**Extended Data Figure 7. HSC/MPP differentiation potential by gestation.**
a, Experimental design for single cell transcriptome and culture of fetal liver cells from 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 visualised using FDG, point shape corresponds to sequencing type (triangle = SS2 plate data, circle = 10X data). c, Stacked barplot 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 6c) that differentiated along 3 lineages (defined as sum of Ery/NK/My and Ery/Meg/My colonies) branches (*** p < 0.005). g, Percentage of colonies containing NK cells following B/NK optimised culture of 10 cells from ‘HSC pool’ gate (* p < 0.05, ** p < 0.01). h, Percentage (Mean +/- s.e.m.) of HSC/MPP and early progenitors in fetal liver, yolk sac, cord blood and adult bone marrow expressing *MKI67* (* p < 0.05, ** p < 0.01, **** p < 0.001). i, Mean +/- s.d. percentage 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: Ki67^-DAPI^-, G₁: Ki67⁺DAPI^-, S-G₂-M: Ki67⁺DAPI⁺ (left)).

**Extended Data Figure 8. Expression of known Primary Immunodeficiency (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 **Figure 1b**. Early L/T L, Early lymphoid/T lymphocyte. Gene expression frequency (% cells within cell type expressing) indicated by spot size and expression level by colour intensity.

**Extended Data Figure 9. FACS gating strategy for scRNA-seq analysis.**
a, Gating strategy used to FACS-isolate cells for droplet-(10x) and plate-based scRNA-seq (Smart-seq2) for samples F2-F17. b, Gating strategy used to FACS-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 Figure 6c. d, Flow cytometry gating strategy used to identify B and NK colonies cultured *in vitro* from 10 cells as shown in Figure 6e.

**Figure 1. Single cell transcriptome map of fetal liver.**
a, Schematic of tissue processing and cell isolation for scRNA-seq profiling of fetal liver, skin and kidney across four developmental stages (7-8, 9-11, 12-14, and 15-17 post conception weeks (PCW)), and yolk sac from 4-7 PCW. SS2, Smart-seq2. b, UMAP visualisation of fetal liver cells from 10x using 3’ chemistry. Colours indicate cell state. HSC/MPP, haematopoietic stem cell/multipotent progenitor; ILC, innate lymphoid cell; NK, natural killer cell; Neut-myeloid, neutrophil-myeloid; DC, dendritic cell; pDC, plasmacytoid DC; Mono-mac, monocyte-macrophage; EI, erythroblastic island; Early L/TL, Early lymphoid/T lymphocyte; MEMP, megakaryocyte-erythroid-mast cell progenitor. Statistical significance of cell frequency change by stage shown in parentheses (negative binomial regression with bootstrap correction for sort gates; * p < 0.05, *** p < 0.001, and **** p < 0.0001 as per SI Table 8) with up/down arrows to indicate positive/negative coefficient of change, respectively. c, Liver composition by developmental stage as the mean percentage of each population per stage corrected by CD45⁺/CD45^- sort fraction. Colours indicate cell states as shown in b.

**Figure 2. Multi-modal and spatial validation of cell types.**
a, Median scaled ln-normalised gene expression of 48 selected differentially expressed genes for the liver cell states from 1b visualised by dot; asterisk (*) indicates markers used for FACS-isolation of cells. Gene expression frequency (% cells within cell type expressing) indicated by spot size and expression level by colour intensity. Neut-my, Neutrophil-myeloid; prec., precursor; mono, monocyte. b, Representative Giemsa-stained cytospins showing morphology of populations isolated by FACS based on differentially expressed genes with * in a. Scale bar, 10μm.

**Figure 3. Fetal liver and NLT haematopoiesis.**
a, Force-directed graph (FDG) visualisation of all haematopoietic cells from **1b. b,** Dot plot showing the median scaled ln-normalised expression of globin genes encoding haemoglobin subunits; *HBZ* and *HBE1* (Gower 1), *HBE1* and *HBA1* (Gower 2) and *HBA1* and *HBG2* (fetal) in liver, skin, and yolk sac erythroid lineages (MEMP, early, mid and late erythroids). Gene expression frequency (% cells within cell type expressing) indicated by spot size and expression level by colour intensity.

**Figure 4. Lymphoid lineages in fetal liver and NLT.**
a, FDG visualisation of fetal liver HSC/MPP and lymphoid cell types from 1b showing changes over four developmental stages. b, FDG visualisation of fetal liver and corresponding skin, kidney and yolk sac lymphoid cells.

**Figure 5. Tissue signatures in developing myeloid cells.**
a, FDG visualisation of HSC/MPP, myeloid progenitors, monocytes and macrophages from fetal liver, decidua/placenta and yolk sac. Mac, Macrophage; Monocyte prec., Monocyte precursor; Neut-myeloid prog., Neutrophil-myeloid progenitor. b, PAGA connectivity scores of the populations shown in a.

**Figure 6. HSC/MPP differentiation potential by gestation.**
a, FDG visualisation of liver HSC/MPP and early haematopoietic progenitor populations from Figure 1b. b, Violin plots showing ln-normalised median gene expression of statistically significant, dynamically variable genes that are up or downregulated during HSC/MPP transition to neutrophil-myeloid progenitors, MEMP and pre pro-B cells from fetal liver. Bar and ‘ns’ indicate not significant. H/M, HSC/MPP. c, Stacked barplot of all different types of colonies generated by single ‘HSC pool’ gate cells in an assay with MS5 stroma. * p < 0.05, *** p < 0.001, individual samples shown in Extended Data 7c. The colour of the stat bar corresponds to the type of colony tested vs all others. My, Myeloid; Ery, Erythroid, Meg, Megakaryocyte. d, Percentage of colonies generated by single ‘HSC pool’ gate cells containing erythroid cells (defined as the sum of Ery, Ery/Meg, Ery/Meg/My, Ery/My, Ery/NK and Ery/NK/My colonies shown in c), *** p < 0.001. My, Myeloid; Ery, Erythroid; Meg, Megakaryocyte. e, Percentage of colonies containing B cells following culture in B/NK optimized conditions from 10 cells from ‘HSC pool’ gate (** p < 0.01). f, Mean +/- s.d. percentage of cells in the G₀ phase of the cell cycle assessed using Ki67 and DAPI flow cytometry analysis (* p = 0.0136). g, ln-normalised median expression of selected genes in yolk sac progenitors, cord blood HSC and adult bone marrow HSC with significant differential expression compared to fetal liver HSC/MPP, visualised by violin plots (**** p < 0.0001).

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References

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1. Parekh C, Crooks GM. Critical Differences in Hematopoiesis and Lymphoid Development Between Humans and Mice. J Clin Immunol. 2013;33:711–715. - PMC - PubMed
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1. Kashem SW, Haniffa M, Kaplan DH. Antigen-Presenting Cells in the Skin. Annu Rev Immunol. 2017;35:469–499. - PubMed

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[1] Jagannathan-Bogdan M, Zon LI. Hematopoiesis. Development. 2013;140:2463. - PMC - PubMed

[2] Jagannathan-Bogdan M, Zon LI. Hematopoiesis. Development. 2013;140:2463. - PMC - PubMed

[3] Parekh C, Crooks GM. Critical Differences in Hematopoiesis and Lymphoid Development Between Humans and Mice. J Clin Immunol. 2013;33:711–715. - PMC - PubMed

[4] Parekh C, Crooks GM. Critical Differences in Hematopoiesis and Lymphoid Development Between Humans and Mice. J Clin Immunol. 2013;33:711–715. - PMC - PubMed

[5] Ivanovs A, et al. Human haematopoietic stem cell development: from the embryo to the dish. Development. 2017;144:2323–2337. - PubMed

[6] Ivanovs A, et al. Human haematopoietic stem cell development: from the embryo to the dish. Development. 2017;144:2323–2337. - PubMed

[7] Holt PG, Jones CA. The development of the immune system during pregnancy and early life. Allergy. 2001;55:688–697. - PubMed

[8] Holt PG, Jones CA. The development of the immune system during pregnancy and early life. Allergy. 2001;55:688–697. - PubMed

[9] Kashem SW, Haniffa M, Kaplan DH. Antigen-Presenting Cells in the Skin. Annu Rev Immunol. 2017;35:469–499. - PubMed

[10] Kashem SW, Haniffa M, Kaplan DH. Antigen-Presenting Cells in the Skin. Annu Rev Immunol. 2017;35:469–499. - PubMed

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Decoding human fetal liver haematopoiesis

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Decoding human fetal liver haematopoiesis

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