Differentiation of transplanted haematopoietic stem cells tracked by single-cell transcriptomic analysis
How transplanted haematopoietic stem cells (HSCs) behave soon after they reside in a preconditioned host has not been studied due to technical limitations. Here, using single-cell RNA sequencing, we first obtained the transcriptome-based classifications of 28 haematopoietic cell types. We then applied them in conjunction with functional assays to track the dynamic changes of immunophenotypically purified HSCs in irradiated recipients within the first week after transplantation. Based on our transcriptional classifications, most homed HSCs in bone marrow and spleen became multipotent progenitors and, occasionally, some HSCs gave rise to megakaryocytic–erythroid or myeloid precursors. Parallel in vitro and in vivo functional experiments supported the paradigm of robust differentiation without substantial HSC expansion during the first week. Therefore, this study uncovers the previously inaccessible kinetics and fate choices of transplanted HSCs in myeloablated recipients at early stage, with implications for clinical applications of HSCs and other stem cells.
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Fig. 1: Single-cell transcriptomes of iHSCs.
Fig. 2: Single-cell transcriptomes of iMPPs and trajectory analysis of haematopoietic cells.
Fig. 3: Cellular composition of engrafted cells following HSC transplantation.
Fig. 4: Cell division of donor-derived cells after transplantation.
Fig. 5: Temporal fate alteration and differentiation tendency of transplanted HSCs.
Fig. 6: Functional assessments for donor-derived cells after transplantation.
Fig. 7: Emergence of lineage precursors at early phase of HSC transplantation.
The RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE116530 . Source data for Figs. 1 – 7 and Extended Data Figs. 3 – 5 , 8 and 9 are provided with the paper. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.
The computational code used in this study can be obtained by request to P.Z. (email@example.com).
Eaves, C. J. Hematopoietic stem cells: concepts, definitions, and the new reality. Blood 125, 2605–2613 (2015).
Laurenti, E. & Gottgens, B. From haematopoietic stem cells to complex differentiation landscapes. Nature 553, 418–426 (2018).
Chabannon, C. et al. Hematopoietic stem cell transplantation in its 60s: a platform for cellular therapies. Sci. Transl Med. 10, eaap9630 (2018).
Srour, E. F. et al. Homing, cell cycle kinetics and fate of transplanted hematopoietic stem cells. Leukemia 15, 1681–1684 (2001).
Shen, H. et al. An acute negative bystander effect of gamma-irradiated recipients on transplanted hematopoietic stem cells. Blood 119, 3629–3637 (2012).
Mendelson, A. & Frenette, P. S. Hematopoietic stem cell niche maintenance during homeostasis and regeneration. Nat. Med. 20, 833–846 (2014).
Morrison, S. J. & Scadden, D. T. The bone marrow niche for haematopoietic stem cells. Nature 505, 327–334 (2014).
Forsberg, E. C., Serwold, T., Kogan, S., Weissman, I. L. & Passegue, E. New evidence supporting megakaryocyte-erythrocyte potential of Fflk2/Flt3+ multipotent hematopoietic progenitors. Cell 126, 415–426 (2006).
Shin, J. Y., Hu, W., Naramura, M. & Park, C. Y. High c-Kit expression identifies hematopoietic stem cells with impaired self-renewal and megakaryocytic bias. J. Exp. Med. 211, 217–231 (2014).
Wang, L. D. & Wagers, A. J. Dynamic niches in the origination and differentiation of haematopoietic stem cells. Nat. Rev. Mol. Cell Biol. 12, 643–655 (2011).
Abkowitz, J. L., Golinelli, D., Harrison, D. E. & Guttorp, P. In vivo kinetics of murine hemopoietic stem cells. Blood 96, 3399–3405 (2000).
Bernitz, J. M., Kim, H. S., MacArthur, B., Sieburg, H. & Moore, K. Hematopoietic stem cells count and remember self-renewal divisions. Cell 167, 1296–1309.e10 (2016).
Trumpp, A., Essers, M. & Wilson, A. Awakening dormant haematopoietic stem cells. Nat. Rev. Immunol. 10, 201–209 (2010).
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
Drissen, R. et al. Distinct myeloid progenitor-differentiation pathways identified through single-cell RNA sequencing. Nat. Immunol. 17, 666–676 (2016).
Giladi, A. et al. Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis. Nat. Cell Biol. 20, 836–846 (2018).
Gur-Cohen, S. et al. PAR1 signaling regulates the retention and recruitment of EPCR-expressing bone marrow hematopoietic stem cells. Nat. Med. 21, 1307–1317 (2015).
Kent, D. G. et al. Prospective isolation and molecular characterization of hematopoietic stem cells with durable self-renewal potential. Blood 113, 6342–6350 (2009).
Yang, L. et al. Identification of Lin−Sca1+kit+CD34+Flt3– short-term hematopoietic stem cells capable of rapidly reconstituting and rescuing myeloablated transplant recipients. Blood 105, 2717–2723 (2005).
Yamamoto, R. et al. Clonal analysis unveils self-renewing lineage-restricted progenitors generated directly from hematopoietic stem cells. Cell 154, 1112–1126 (2013).
Wilson, A. et al. Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair. Cell 135, 1118–1129 (2008).
Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).
Akashi, K., Traver, D., Miyamoto, T. & Weissman, I. L. A clonogenic common myeloid progenitor that gives rise to all myeloid lineages. Nature 404, 193–197 (2000).
Kondo, M., Weissman, I. L. & Akashi, K. Identification of clonogenic common lymphoid progenitors in mouse bone marrow. Cell 91, 661–672 (1997).
Chambers, S. M. et al. Hematopoietic fingerprints: an expression database of stem cells and their progeny. Cell Stem Cell 1, 578–591 (2007).
Pronk, C. J. et al. Elucidation of the phenotypic, functional, and molecular topography of a myeloerythroid progenitor cell hierarchy. Cell Stem Cell 1, 428–442 (2007).
Venezia, T. A. et al. Molecular signatures of proliferation and quiescence in hematopoietic stem cells. PLoS Biol. 2, e301 (2004).
Cheng, H. et al. Leukemic marrow infiltration reveals a novel role for Egr3 as a potent inhibitor of normal hematopoietic stem cell proliferation. Blood 126, 1302–1313 (2015).
Kubota, Y., Osawa, M., Jakt, L. M., Yoshikawa, K. & Nishikawa, S. Necdin restricts proliferation of hematopoietic stem cells during hematopoietic regeneration. Blood 114, 4383–4392 (2009).
Dong, F. et al. Mouse acute leukemia develops independent of self-renewal and differentiation potentials in hematopoietic stem and progenitor cells. Blood Adv. 3, 419–431 (2019).
Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).
Min, I. M. et al. The transcription factor EGR1 controls both the proliferation and localization of hematopoietic stem cells. Cell Stem Cell 2, 380–391 (2008).
Copley, M. R. et al. The Lin28b–let-7–Hmga2 axis determines the higher self-renewal potential of fetal haematopoietic stem cells. Nat. Cell Biol. 15, 916–925 (2013).
Masumi, A. et al. Interferon regulatory factor-2 induces megakaryopoiesis in mouse bone marrow hematopoietic cells. FEBS Lett. 583, 3493–3500 (2009).
Haug, J. S. et al. N-cadherin expression level distinguishes reserved versus primed states of hematopoietic stem cells. Cell Stem Cell 2, 367–379 (2008).
Wilkinson, A. C. et al. Long-term ex vivo haematopoietic-stem-cell expansion allows nonconditioned transplantation. Nature 571, 117–121 (2019).
Grinenko, T. et al. Hematopoietic stem cells can differentiate into restricted myeloid progenitors before cell division in mice. Nat. Commun. 9, 1898 (2018).
Grover, A. et al. Erythropoietin guides multipotent hematopoietic progenitor cells toward an erythroid fate. J. Exp. Med. 211, 181–188 (2014).
Tusi, B. K. et al. Population snapshots predict early haematopoietic and erythroid hierarchies. Nature 555, 54–60 (2018).
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M. & Klein, A. M. Fundamental limits on dynamic inference from single-cell snapshots. Proc. Natl Acad. Sci. USA 115, E2467–E2476 (2018).
Plett, P. A., Frankovitz, S. M. & Orschell-Traycoff, C. M. In vivo trafficking, cell cycle activity, and engraftment potential of phenotypically defined primitive hematopoietic cells after transplantation into irradiated or nonirradiated recipients. Blood 100, 3545–3552 (2002).
Hu, L. et al. Antioxidant N-acetyl-l-cysteine increases engraftment of human hematopoietic stem cells in immune-deficient mice. Blood 124, e45–e48 (2014).
Ema, H. et al. Adult mouse hematopoietic stem cells: purification and single-cell assays. Nat. Protoc. 1, 2979–2987 (2006).
Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).
Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).
Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Research 5, 2122 (2016).
Kowalczyk, M. S. et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860–1872 (2015).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Zhang, B. et al. Altered microenvironmental regulation of leukemic and normal stem cells in chronic myelogenous leukemia. Cancer Cell 21, 577–592 (2012).
We thank T. Green for constructive suggestions on our data and A. Wilkinson for sharing the PVA in vitro culture protocol. We are grateful to our lab members and collaborators for their insightful discussions during the course of this work and in the preparation of this manuscript. This work was supported by the grants from the National Key R&D Program of China (2016YFA0100600, 2017YFA0103400, 2017YFA0104900, 2019YFA0110203, 2018YFA0107801 and 2015CB964400), the National Natural Science Foundation of China (81421002, 81730006, 81922002, 81890990, 81861148029, 81670105 and 81870086), the CAMS Initiative for Innovative Medicine (2016-I2M-1-017, 2017-I2M-1–015, 2017-I2M-3-009 and 2019-I2M-1-006), Distinguished Young Scholars of Tianjin (19JCJQJC63400), CAMS Fundamental Research Funds for Central Research Institutes (2019RC310003), and the Atlas of Blood Cell Alliance. F.K.H. was funded by a MRC studentship (MR/K500975/1), and F.K.H. and B.G. were supported by infrastructure funding from the Wellcome and MRC to the Wellcome & MRC Cambridge Stem Cell Institute (203151/Z/16/Z, MC_PC_12009).
These authors contributed equally: Fang Dong, Sha Hao, Sen Zhang, Caiying Zhu, Hui Cheng.
These authors jointly supervised this work: Tao Cheng, Ping Zhu, Berthold Göttgens.
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
Center for Stem Cell Medicine, Chinese Academy of Medical Sciences, Tianjin, China
Department of Stem Cell and Regenerative Medicine, Peking Union Medical College, Tianjin, China
, Ping Zhu
& Tao Cheng
Cambridge University Department of Hematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
Fiona K. Hamey
& Berthold Göttgens
Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China
& Yu Lan
Beijing Advanced Innovation Center for Genomics, Biomedical Institute for Pioneering Investigation via Convergence, College of Life Sciences, Peking University, Beijing, China
, Ji Dong
& Fuchou Tang
CAS Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
F.D., S.H., S.Z., C.Z. and H.C. designed and performed the experiments, analysed the data and wrote the manuscript. X.W., A.G. and F.W. helped with the mouse experiments and flow cytometry. Y.G., Z.Y. and C.W. helped with single-cell sequencing. F.K.H. and J.D. helped with the scRNA-seq data analysis and assisted with the manuscript. J.W., B.L., Y.L., H.E. and F.T. assisted with the manuscript. B.G. and P.Z. performed the bioinformatics analysis and assisted with the manuscript. T.C. conceived the study, designed the experiments, interpreted the results, wrote the paper and oversaw the research project.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Flow cytometry plots of 28 cell populations isolated from bone marrow (BM) or spleen (SP) of mice for scRNA-seq.
Highly purified 28 populations including: a-c, 5 haematopoietic stem cell (HSC) populations (BM): HSCLT, Fraction I, Fraction III, ESLAM, ESLAMSK. a, c, d, 9 multipotent progenitor (MPP) populations (BM): HSCST, LMPP, MPP1, MPP2, MPP3, MPP4, Fraction II, HPC2 and HPC3. e, h, 4 committed progenitor populations (BM): Common Myeloid Progenitor (CMP), Granulocyte Macrophage Progenitor (GMP), Megakaryocyte Erythrocyte Progenitor (MEP), and Common Lymphoid Progenitor (CLP). f, 2 erythroid populations (BM): Erythrocyte A and Erythrocyte B (EryA and EryB). g, 3 mature myeloid cell populations (BM): Granulocyte, Monocyte and Macrophage, 1 Megakaryocyte population (BM): Megakaryocytes (Mk). i, j, k, 4 mature lymphoid cell populations (SP): B cell, CD4+ T cell, CD8+ T cell and Natural Killer (NK) cell. 2 independent experiments for cell sorting.
Extended Data Fig. 2 The comparisons of scRNA-seq data with published results in the same immunophenotypical cell populations.
a, Diffusion map comparisons were performed on HSCLT, LMPP, MEP and GMP cells in Sonia Nestorowa’s paper (PMID:27365425) and this study. b, Visualization of cell types defined in Sonia Nestorowa’s paper by force-directed graph with broad gating (LSK cells) and each highlighted with red colour. c, Immunophenotypical HSCs and MPPs sorted by FACS in this study were projected onto Sonia Nestorowa’s data, and each cell population was found in the k-nearest neighbours of cell types defined in Sonia Nestorowa’s data 22 . Cells were coloured by log-transformed score.
Extended Data Fig. 3 Clustering of committed progenitor cells and lineage cells according to the transcriptome profiling.
a, Heatmap and unsupervised hierarchical clustering of diversely expressed genes in 4 immunophenotypical committed progenitors (CPs) (CMP, GMP, MEP and CLP). Representative GO enrichments of specifically expressed genes for each cluster were listed on the right. 3 types of CPs (tCP1, 2 and 3) were thus grouped according to transcriptome profiling. b, Compositions of tCPs by immunophenotypical CPs. Cell numbers were n=39/26/30 for tCP1/2/3. c, Heatmap and unsupervised hierarchical clustering of diversely expressed genes in 3 immunophenotypical megakaryocytes and erythrocytes (MEs). Representative GO enrichments of specifically expressed genes for each cluster were listed on the right respectively. 3 types of MEs (tME1, 2 and 3) were thus grouped according to transcriptome profiling. d, Compositions of tMEs by immunophenotypical MEs. Cell numbers were n=41/29/19 for tME1/2/3. e, Heatmap and unsupervised hierarchical clustering of diversely expressed genes in 3 immunophenotypical granulocytes and macrophages/monocytes (GMs). Representative GO enrichments of specifically expressed genes for each cluster were listed on the right respectively. 3 types of GMs (tGM1,2 and 3) were thus grouped according to transcriptome profiling. f, Compositions of tGMs by immunophenotypical GMs. Cell numbers were n=46/24/36 for tGM1/2/3. g, Heatmap and unsupervised hierarchical clustering of diversely expressed genes in 4 immunophenotypical lymphocytes including B, CD4+ T, CD8+ T and NK cells (Lyms). Representative GO enrichments of specifically expressed genes for each cluster were listed on the right respectively. 4 types of Lyms (tLym1, 2, 3 and 4) were thus grouped according to transcriptome profiling. h, Compositions of tLyms by immunophenotypical Lyms (1-4). Cell numbers were n=48/42/41/36 for tLym1/2/3/4. Source data
Extended Data Fig. 4 Enriched GO terms from each classified cell population and validation of cell type classifications and annotations.
a, Enriched GO terms from each cell type based on specifically expressed genes. Top five GO terms enriched for each gene sets specifically expressed in each cluster under homeostasis. Dot color indicates the logarithmic transformed adjusted P value. Dot size indicates enrichment score estimated by Enrichr. Cell numbers were n=189/93/23 for tHSC1/2/3, n=100/145/95/93/75 for tMPP1/2/3/4/5/, n=39/26/30 for tCP1/2/3, n=41/29/19 for tME1/2/3, n=46/24/36 for tGM1/2/3, n=48/42/41/36 for tLym1/2/3/4 (logarithmic transformed adjusted P values with Benjamini-Hochberg correction). b, Heatmap showed the prediction results of cell type determination by the classifier build on the unique gene expressions of haematopoietic cells under homeostasis. Source data
Extended Data Fig. 5 Cell cycle analysis of different cell types based on transcriptome profiling and characteristics comparison among subtypes of tHSCs and tMPPs.
a, Cell cycle distributions (G0, G1, S and G2/M) of cell populations based on expression levels of known genes related to cell cycle. b, GSEA results of HSC state (HSC signature and quiescence) and lineage differentiation (Megakaryocytic, erythroid, myeloid and lymphoid) associated gene sets among tHSC1, 2, and 3 under homeostasis. Cell numbers were n=189/93/23 for tHSC1/2/3 (Permutation test, two-side). c, Box plots showing the expression level (mean normalized expressed of each gene sets) of HSC signature, proliferation, CLP, pre-megakaryocyte-erythrocyte, megakaryocyte progenitor, and pre-granulocyte/monocyte commitment related genes in all single cells of tMPP populations under homeostasis Box represents the first and the third quartile of the expression levels and whiskers above and below the box show the locations of the minimum and maximum values except outliers. The line in the box represents the median value and the black circles represent outliers. Cell numbers were n=100/145/95/93/75 for tMPP1/2/3/4/5 (unpaired t-test, two-side). Source data
Extended Data Fig. 6 Specifically expressed genes on HSPC cell populations.
a, The expression of specially expressed genes in HSPCs (Procr, Esam, Mmrn1, Sult1a1) were displayed using published data (Nestorowa’s paper, PMID:27365425, n=1656). Colors indicate the gene expression levels. b, Expression levels of genes (Procr, Esam, Mmrn1, Sult1a1) and other representative genes (Necdin, Dst, and Tgm2) were shown on the t-SNE map in this study (n=1270). Colors indicate the gene expression levels. Top-left panel showed the cell type annotations with indicated colors.
Extended Data Fig. 7 Differentially expressed genes and transcription factor regulation of engrafted cells.
a, Differentially expressed genes in transplanted ESLAMSK cells compared with donor-derived GFP+ cells at indicated time points. b, Enriched transcription factor regulation in donor-derived GFP+ cells at indicated time points. Representative genes for myeloid, erythroid and self-renewal signatures were listed on the right with dynamic changes.
Extended Data Fig. 8 Validation of cell type classification after transplantation and comparisons of apoptosis and autophagy related gene sets in tHSC1/2 after transplantation.
a–d, Heatmap showed the consistent expression patterns of cell type specifically expressed genes under homeostasis (Ctrl) and after transplantation (Tx) for each cell type, indicating high accuracy of cell classification of single cells after transplantation. a, tHSCs, b, tMPPs, c, tMEs, d, tGMs. Colors from purpose to red indicate low to high gene expression levels. e, Additional 137 donor-derived cells were collected from BM and SP of 3 individual recipient mice at day 1 after transplantation and single-cell RNA-seq was performed on these cells. f. GSEA results of apoptosis and autophagy related gene sets in Tx tHSC1/2 comparing with ESLAMSK tHSC1/2 (n=16/15 for Tx tHSC1/2, n=34/22 for ESLAMSK tHSC1/2, Permutation test, two-side). Source data
Extended Data Fig. 9 Characteristics of tMPPs after transplantation.
a, Cell cycle analysis (S/G2/M) of Tx tMPPs compared with the counterparts under homeostasis. b, GSEA of indicated gene signatures (HSC signature, proliferation, megakaryocytic, erythroid, myeloid and lymphoid) comparing Tx tMPPs with tMPPs under homeostasis respectively. Red squares indicate positive enrichment of Tx tMPPs and blue squares indicate negative enrichment of Tx tMPPs (Permutation test, two-side). c, The expression of lineage specific transcription factors or marker genes on tMPP populations after transplantation (Tx) and the counterparts under homeostasis (Ctrl). Boxplot displaying distribution of normalized expressed of each gene within cluster. Box represents the first and the third quartile of the expression levels and whiskers below and above the box show the locations of the minimum and maximum values except outliers. The line in the box represents the median value and the black circles represent outliers. *, p