Bone marrow transplantation therapy relies on the life-long regenerative capacity of haematopoietic stem cells (HSCs)1,2. HSCs present a complex variety of regenerative behaviours at the clonal level, but the mechanisms underlying this diversity are still undetermined3,4,5,6,7,8,9,10,11. Recent advances in single-cell RNA sequencing have revealed transcriptional differences among HSCs, providing a possible explanation for their functional heterogeneity12,13,14,15,16,17. However, the destructive nature of sequencing assays prevents simultaneous observation of stem cell state and function. To solve this challenge, we implemented expressible lentiviral barcoding, which enabled simultaneous analysis of lineages and transcriptomes from single adult HSCs and their clonal trajectories during long-term bone marrow reconstitution. Analysis of differential gene expression between clones with distinct behaviour revealed an intrinsic molecular signature that characterizes functional long-term repopulating HSCs. Probing this signature through in vivo CRISPR screening, we found the transcription factor TCF15 to be required and sufficient to drive HSC quiescence and long-term self-renewal. In situ, Tcf15 expression labels the most primitive subset of true multipotent HSCs. In conclusion, our work elucidates clone-intrinsic molecular programmes associated with functional stem cell heterogeneity and identifies a mechanism for the maintenance of the self-renewing HSC state.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Raw data and counts matrices are available at the GEO (GSE134242). The LARRY barcoding tool is available at Addgene (no. 140024). Data analyses are available at the following links: http://github.com/rodriguez-fraticelli/Tcf15_HSCs and https://github.com/AllonKleinLab/StemCellTransplantationModel. Source data are provided with this paper.
Code, processed data and analyses are available at http://github.com/rodriguez-fraticelli/Tcf15_HSCs and https://github.com/AllonKleinLab/StemCellTransplantationModel.
Haas, S., Trumpp, A. & Milsom, M. D. Causes and consequences of hematopoietic stem cell heterogeneity. Cell Stem Cell 22, 627–638 (2018).
Laurenti, E. & Göttgens, B. From haematopoietic stem cells to complex differentiation landscapes. Nature 553, 418–426 (2018).
Naik, S. H. et al. Diverse and heritable lineage imprinting of early haematopoietic progenitors. Nature 496, 229–232 (2013).
Dykstra, B. et al. Long-term propagation of distinct hematopoietic differentiation programs in vivo. Cell Stem Cell 1, 218–229 (2007).
Sun, J. et al. Clonal dynamics of native haematopoiesis. Nature 514, 322–327 (2014).
Dharampuriya, P. R. et al. Tracking the origin, development, and differentiation of hematopoietic stem cells. Curr. Opin. Cell Biol. 49, 108–115 (2017).
Kent, D. G. et al. Prospective isolation and molecular characterization of hematopoietic stem cells with durable self-renewal potential. Blood 113, 6342–6350 (2009).
Carrelha, J. et al. Hierarchically related lineage-restricted fates of multipotent haematopoietic stem cells. Nature 554, 106–111 (2018).
Rodriguez-Fraticelli, A. E. et al. Clonal analysis of lineage fate in native haematopoiesis. Nature 553, 212–216 (2018).
Yamamoto, R. et al. Clonal analysis unveils self-renewing lineage-restricted progenitors generated directly from hematopoietic stem cells. Cell 154, 1112–1126 (2013).
Yamamoto, R. et al. Large-scale clonal analysis resolves aging of the mouse hematopoietic stem cell compartment. Cell Stem Cell 22, 600–607.e4 (2018).
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).
Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535–1548.e16 (2018).
Dahlin, J. S. et al. A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice. Blood 131, e1–e11 (2018).
Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).
Cabezas-Wallscheid, N. et al. Vitamin A–retinoic acid signaling regulates hematopoietic stem cell dormancy. Cell 169, 807–823.e19 (2017).
Wilson, N. K. et al. Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell 16, 712–724 (2015).
Weinreb, C., Rodriguez-Fraticelli, A. E., Camargo, F. D. & Klein, A. M. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science 67, eaaw3381 (2020).
Verovskaya, E. et al. Heterogeneity of young and aged murine hematopoietic stem cells revealed by quantitative clonal analysis using cellular barcoding. Blood 122, 523–532 (2013).
Cheung, A. M. S. et al. Analysis of the clonal growth and differentiation dynamics of primitive barcoded human cord blood cells in NSG mice. Blood 122, 3129–3137 (2013).
Lu, R., Neff, N. F., Quake, S. R. & Weissman, I. L. Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding. Nat. Biotechnol. 29, 928–933 (2011).
McKenzie, J. L., Gan, O. I., Doedens, M., Wang, J. C. Y. & Dick, J. E. Individual stem cells with highly variable proliferation and self-renewal properties comprise the human hematopoietic stem cell compartment. Nat. Immunol. 7, 1225–1233 (2006).
Biasco, L. et al. In vivo tracking of human hematopoiesis reveals patterns of clonal dynamics during early and steady-state reconstitution phases. Cell Stem Cell 19, 107–119 (2016).
Scala, S. et al. Dynamics of genetically engineered hematopoietic stem and progenitor cells after autologous transplantation in humans. Nat. Med. 24, 1683–1690 (2018).
Lu, R., Czechowicz, A., Seita, J., Jiang, D. & Weissman, I. L. Clonal-level lineage commitment pathways of hematopoietic stem cells in vivo. Proc. Natl Acad. Sci. USA 116, 1447–1456 (2019).
Qian, H. et al. Critical role of thrombopoietin in maintaining adult quiescent hematopoietic stem cells. Cell Stem Cell 1, 671–684 (2007).
Yoshihara, H. et al. Thrombopoietin/MPL signaling regulates hematopoietic stem cell quiescence and interaction with the osteoblastic niche. Cell Stem Cell 1, 685–697 (2007).
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).
Vitali, C. et al. SOCS2 controls proliferation and stemness of hematopoietic cells under stress conditions and its deregulation marks unfavorable acute leukemias. Cancer Res. 75, 2387–2399 (2015).
Jeong, M. et al. Thioredoxin-interacting protein regulates hematopoietic stem cell quiescence and mobilization under stress conditions. J. Immunol. 183, 2495–2505 (2009).
Matsumoto, A. et al. p57 is required for quiescence and maintenance of adult hematopoietic stem cells. Cell Stem Cell 9, 262–271 (2011).
Laurenti, E. et al. Hematopoietic stem cell function and survival depend on c-Myc and N-Myc activity. Cell Stem Cell 3, 611–624 (2008).
Cabezas-Wallscheid, N. et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 15, 507–522 (2014).
Lauridsen, F. K. B. et al. Differences in cell cycle status underlie transcriptional heterogeneity in the HSC compartment. Cell Rep. 24, 766–780 (2018).
Pietras, E. M. et al. Functionally distinct subsets of lineage-biased multipotent progenitors control blood production in normal and regenerative conditions. Cell Stem Cell 17, 35–46 (2015).
Säwen, P. et al. Murine HSCs contribute actively to native hematopoiesis but with reduced differentiation capacity upon aging. eLife 7, e41258 (2018).
Yu, V. W. C. et al. Epigenetic memory underlies cell-autonomous heterogeneous behavior of hematopoietic stem cells. Cell 168, 944–945 (2017).
Balazs, A. B., Fabian, A. J., Esmon, C. T. & Mulligan, R. C. Endothelial protein C receptor (CD201) explicitly identifies hematopoietic stem cells in murine bone marrow. Blood 107, 2317–2321 (2006).
Pina, C., May, G., Soneji, S., Hong, D. & Enver, T. MLLT3 regulates early human erythroid and megakaryocytic cell fate. Cell Stem Cell 2, 264–273 (2008).
Uckelmann, H. et al. Extracellular matrix protein Matrilin-4 regulates stress-induced HSC proliferation via CXCR4. J. Exp. Med. 213, 1961–1971 (2016).
Qian, P. et al. Retinoid-sensitive epigenetic regulation of the Hoxb cluster maintains normal hematopoiesis and inhibits leukemogenesis. Cell Stem Cell 22, 740–754.e7 (2018).
Laurenti, E. et al. CDK6 levels regulate quiescence exit in human hematopoietic stem cells. Cell Stem Cell 16, 302–313 (2015).
Osawa, M., Hanada, K., Hamada, H. & Nakauchi, H. Long-term lymphohematopoietic reconstitution by a single CD34-low/negative hematopoietic stem cell. Science 273, 242–245 (1996).
Gekas, C. & Graf, T. CD41 expression marks myeloid-biased adult hematopoietic stem cells and increases with age. Blood 121, 4463–4472 (2013).
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
Hill, A. J. et al. On the design of CRISPR-based single-cell molecular screens. Nat. Methods 15, 271–274 (2018).
Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).
Rowton, M. et al. Regulation of mesenchymal-to-epithelial transition by PARAXIS during somitogenesis. Dev. Dyn. 242, 1332–1344 (2013).
Burgess, R., Cserjesi, P., Ligon, K. L. & Olson, E. N. Paraxis: a basic helix-loop-helix protein expressed in paraxial mesoderm and developing somites. Dev. Biol. 168, 296–306 (1995).
Davies, O. R. et al. Tcf15 primes pluripotent cells for differentiation. Cell Rep. 3, 472–484 (2013).
Seita, J. et al. Gene Expression Commons: an open platform for absolute gene expression profiling. PLoS ONE 7, e40321 (2012).
Yamada, T., Park, C. S. & Lacorazza, H. D. Genetic control of quiescence in hematopoietic stem cells. Cell Cycle 12, 2376–2383 (2013).
Nakamura-Ishizu, A., Takizawa, H. & Suda, T. The analysis, roles and regulation of quiescence in hematopoietic stem cells. Development 141, 4656–4666 (2014).
Opferman, J. T. et al. Obligate role of anti-apoptotic MCL-1 in the survival of hematopoietic stem cells. Science 307, 1101–1104 (2005).
Menendez-Gonzalez, J. B. et al. Gata2 as a crucial regulator of stem cells in adult hematopoiesis and acute myeloid leukemia. Stem Cell Rep. 13, 291–306 (2019).
Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).
Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).
Biddy, B. A. et al. Single-cell mapping of lineage and identity in direct reprogramming. Nature 564, 219–224 (2018).
Zilionis, R. et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12, 44–73 (2017).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Weinreb, C., Wolock, S. & Klein, A. M. SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics 34, 1246–1248 (2018).
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).
Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).
Kirschner, K. et al. Proliferation drives aging-related functional decline in a subpopulation of the hematopoietic stem cell compartment. Cell Rep. 19, 1503–1511 (2017).
Wilson, A. et al. Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair. Cell 135, 1118–1129 (2008).
Haas, S. et al. Inflammation-induced emergency megakaryopoiesis driven by hematopoietic stem cell-like megakaryocyte progenitors. Cell Stem Cell 17, 422–434 (2015).
Tusi, B. K. et al. Population snapshots predict early haematopoietic and erythroid hierarchies. Nature 555, 54–60 (2018).
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
Grimes, H. L. et al. Single cell transcriptome-based dissection of lineage fate decisions in myelopoiesis. Exp. Hematol. 42, S21 (2014).
Farbehi, N. et al. Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury. eLife 8, e43882 (2019).
Naik, S. H., Schumacher, T. N. & Perié, L. Cellular barcoding: a technical appraisal. Exp. Hematol. 42, 598–608 (2014).
Gerrits, A. et al. Cellular barcoding tool for clonal analysis in the hematopoietic system. Blood 115, 2610–2618 (2010).
Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).
A.E.R.-F. acknowledges support by the Life Sciences Research Foundation Merck Fellowship, the European Molecular Biology Organization Long-term Fellowship (ALTF 675-2015), and the NIH NHLBI K99/R00 award (K99HL146983). A.E.R.F is a Scholar of the American Society of Hematology and a Special Fellow of the Leukemia and Lymphoma Society (3391-19). S.-W.W. is supported by a Damon Runyon Cancer Research Foundation Computational Biology Fellowship. A.M.K., S.-W.W. and C.W. acknowledge support by NIH grants R33CA212697-01 and 1R01HL14102-01, the Harvard Stem Cell Institute Blood Program Pilot grant DP-0174-18-00, and the Chan-Zuckerberg Initiative grant 2018-182714. S.L. was supported by a Senior Fellowship from the Wellcome Trust WT103789AIA. F.D.C. was supported by NIH grants HL128850-01A1 and P01HL13147. F.D.C. is a scholar of the Howard Hughes Medical Institute and the Leukemia and Lymphoma Society. We acknowledge C.-Y. Lin for generating Tcf15-Venus mice; the assistance of R. Mathieu and the Flow Cytometry Core at Boston Children’s Hospital; the assistance of A. Ratner and the Harvard Medical School Single Cell Core; the assistance of the Harvard Biopolymers Facility for high-throughput sequencing; and members of the Camargo and Klein lab for helpful discussions, method development and scripts. Illustrations were created with BioRender.
A.M.K. is a co-founder of 1cellbio, Ltd. The other authors declare no competing interests.
Peer review information Nature thanks Thomas Höfer, Samantha A. Morris and Leïla Périé 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 peripheral blood engraftment for barcode-expressing cells (EGFP+) in two representative experiments. b, Merged cluster labelling of the data set, indicating the localization of HSCs (pink) and Progenitors (grey) in the single cell map plotted using SPRING. c, Merged cluster labelling, indicating the localization of Erythroid (Ery), Basophil (Ba), Dendritic cell (preDC), Granulocyte-Monocyte (GM), B-cell (preB) and Megakaryocyte (Mk) progenitors. d, Cluster distribution comparison of barcoded (blue) and non-barcoded (red) cells. Mean ± s.d. % of cells assigned to each cluster (n = 2 independent experiments). e, Barcode library diversity estimation, showing cumulative barcode frequency at different barcode abundances (binned). 96% of the library is represented by barcodes with a freq <0.00001. f, Barcode library diversity estimation, showing the barcode overlap between independent experiments. Average overlap is 1.3%. g, Barcode silencing estimation, showing the % of barcodes detected in the genomic DNA of EGFP-negative cells by quantitative PCR. A calibration curve using sorted numbers of EGFP-positive cells is shown in blue. Mean ± s.d. of n = 3 independent animals are shown. Lines represent linear regression from the data. h, Differences in barcode detection efficiency. The histogram represents the proportion of barcoded cells in each population as detected by scRNaseq (HSCs, MPP, Mk, GM, Ery, Ba, preDC and preB). Data shown are mean ± s.d. from 3 independent experiments. The data are shown normalized by the proportion of barcoded HSCs (72.3% ± 5.5%). The mean efficiency drops for the preDC and preB populations, but it is not significant (paired two-sided t-tests, P = 0.07, P = 0.17). i, Mean ± s.d. % of shared DNaseq reads and scRNaseq cells across barcodes in progenitors (n = 3 independent experiments). j, Distribution of progeny frequencies for all clones (quantified by scRNaseq), and labelled according to their presence or absence in DNaseq barcodes. Box plot shows median and interquartile range. Error bars are min/max values. *** P < 0.01 two-sided t-test (ndetected = 137, nnot-detected = 50). k, Distribution of progeny frequencies for all barcodes (quantified by DNaseq), and labelled according to their presence or absence in scRNaseq-recovered barcodes. Box plot shows median and interquartile range. Error bars are min/max values. *** P < 0.01 two-sided t-test (ndetected = 127, nnot-detected = 286). l, Correlation of DNaseq and RNA-seq barcode frequencies (n = 429). Pearson correlation (r) is shown. Line represents simple linear regression of the data. A pseudocount of 0.0001 is used for plotting clones undetected in either set. m, Correlation of DNaseq and RNA-seq measurements of HSC output activity for all HSC clones (n = 136). Pearson correlation (r) is shown. Line represents simple linear regression of the data. A pseudocount of 0.01 is used to plot clones with output = 0. Source data
Extended Data Fig. 2 Description of HSC heterogeneity according to their output activity and clone size.
a, Histogram showing % of cells (right) and % of clones (left) in progenitors that are not detected in HSCs (n = 3 independent experiments). Whereas some clones are not detected in HSCs (orange bar, left), these are typically single cell clones and minimally contribute to progenitor cellularity (orange bar, right). pclones = 0.022 and pcells < 0.001. Holm-Sidak multiple-test corrected t-test. b, Scatter plot showing correlation between HSC clone size, hi (expressed as fraction of total HSCs in each experiment), and clonal output activity, ki (fraction of total progenitors), for each detected clone (data are pooled from 5 mice). Pearson correlation r = 0.59 (n = 226 clones, from all 3 independent experiments). A pseudocount of 0.0001 is used for progeny frequency to display the zeros (clones with no output). c, Scatter plot showing HSC clone sizes and their range of differentiated output activity. Pearson correlation r = -0.097 (slope non-significantly different than zero, P = 0.1449, n = 226 clones). A pseudocount of 0.01 is used for output activity to display clones for which progeny is not detected. The binned average and range are shown in blue (HSC frequency bins are [0.0001-0.005], n = 127, [0.005-0.01], n = 33, [0.01-0.05], n = 52 [0.05-1], n = 14). d, Single cell maps showing the clonal HSC output activity values for each single cell. Low-output clones are shown on the left and high-output clones are shown on the right. For each population (HSCs, Mk, Ery, Ly and Neu), the percentage of cells that belongs to clones of the indicated behaviour class is shown. Scale range, 0 (red) to 2 or more (blue). Plotted single cells are randomly subsampled (n = 2000) without replacement. e, Single cell maps showing the clonal HSC Mk-bias values for each single cell. Non-biased multilineage clones are shown on the left and Mk-biased (bias >1) clones are shown on the right. For each population (HSCs, Mk, Ery, Ly and Neu), the percentage of cells that belongs to clones of the indicated behaviour class is shown. Scale range, 0 (green) to 2.5 or more (pink). Plotted single cells are randomly subsampled (n = 2000) without replacement. f, Pearson correlation between the output activity and the average signature score of each clone, for different computed signatures as in Fig. 1. Black bars indicate mean of 3 independent experiments. Source data
a, SPRING plot showing the localization of the four reproducible HSC subclusters, HSC1-4. The plot is representative of one of three experiments with similar results. b, Marker gene expression for HSC subclusters. c, Violin plots showing the values for output activity, Mk-bias, and the scores of different HSC behaviour signatures. Violin plots show all the data (min-to-max) and are representative from one of 3 independent experiments (nHSC1 = 2206, nHSC2 = 577, nHSC3 = 1794, nHSC4 = 649). DPA results (P-values) are indicated for each HSC cluster in order from HSC1 to HSC4. Low-output: 0.0023, 0.0051, <0.0001, 0.0114. High-output: <0.0001, 0.3883, <0.0001, 0.0006. Mk-bias: 0.0002, 0.0172, 0.0516, 0.0182. Multilineage: 0.2257, 0.0763, 0.4374, 0.1977. d, SPRING plot showing distribution of native LT-HSCs (n = 1) mapped by approximate nearest neighbours (see Methods). e, Cluster distribution of native LT-HSCs (blue dots) compared to transplant HSCs (black dots). Mean ± S.D., n = 3. Chi-square test (transplant HSCs vs. native LT-HSCs), Pexp1 = 10−8, Pexp2 = 0.0007, Pexp3 = 0.0483. Source data
a, Scatter plot showing the Pearson correlation between expansion of HSC clones in each secondary recipient (R1 and R2, n = 133 clones). b, Scatter plot showing the Pearson correlation between HSC clone size in primary and secondary recipients (n = 485 clones). The grey dots are clones only detected in either primary or secondary recipients, using pseudocount of 0.1 to plot in logarithmic scale. c, Histogram depicting the values for clone size correlations between the designated populations. The experimental data are shown in blue, and the data (range) from the null equipotent model is shown in pink (1σ). d, Scatter plot of relative HSC output activity in the primary transplant (1T output) vs. clone expansion in secondary recipients (2T expansion). Clonal expansion (2T/1T clone size) is used, instead of absolute clone size, to account for the effect of 1T clone size on the estimation of engraftment capacity. To avoid numerical divergence, pseudocount = 1 is added before taking the ratio. High-output clones are top 40% clones ranked by their 1T activities, and the remaining 60% are classified as low-output clones. Red triangles show the mean ± s.d. 2T expansion for each category (n = 485 clones, combined from both recipients). e, Scatter plot showing relative 1T output activity across different lineages for all 1T clones and secondary engrafting clones (R1 and R2 shown separately). Bar indicates mean output value. f, Fold-change in the HSC cluster distribution showing the enrichment of secondary transplantation capacity in HSC-1/2/3/4 subclusters. Bars indicate mean ± s.d. (n = 2). Chi-square test P = 0.009 (observed vs. expected distribution). See data availability statement for source data of secondary transplantation assays. Source data
a, Single cell plots of transplanted and barcoded HSCs showing the scores of previously published HSC signatures. Pietras et al. 2014 HSC signature is derived from comparison of Flt3-CD48-CD150+ LSKs (HSCs) versus all other progenitor populations. Lauridsen et al. 2019 dormant HSC (dHSC) signature is derived from comparison of RA-CFPdim HSCs, which are enriched in quiescent HSCs, versus RA-CFPpositive HSCs, which are enriched in cycling HSCs. Giladi et al. 2018 StemScore is derived from single cell data analysis of genes correlating with Hlf expression in naive HSCs. Wilson et al. 2015 MolO signature is derived from single cell expression data of index-sorted LT-HSCs. Cabezas-Wallscheid et al. 2017 label-retaining HSC signature is derived all HSC genes significantly upregulated in H2B-GFPhi label-retaining HSCs, compared to H2B-GFPlow. b, Single cell plot showing the 2T-engrafting signature score, derived from the comparison of serially repopulating HSC clones and non-serially repopulating clones (Fig. 2). c, Pearson correlation between the 2T-engraftment long-term repopulating signature score and the indicated HSC signature scores. Low-output, high-output, Mk-biased and Multilineage signature scores are derived from the analyses shown in Fig. 1. Black bars indicate mean of 3 independent experiments. Source data
Extended Data Fig. 6 Tcf15 expression is restricted to HSCs, and it is highest in the low-output clones.
a, Localization of expression of Tcf15 along the single cell manifold using SPRING. Major cluster groups are labelled. The plot shows cells from one of 3 experiments with similar results (n = 16976 cells). b, Localization of expression of Tcf15 along the single cell manifold in the Dahlin et al. 2018 data set using Scanpy (n = 44802 cells pooled from 6 animals). Major cluster groups are labelled. c, Localization of Tcf15 expression along the bone marrow FACS-pure populations in Gene Expression Commons. d, Expression levels of Tcf15 in the different HSC subclusters. Violin plots show all the data (min-to-max). The scale (width) of the violin plot is adjusted to show the same total area for each subcluster (nHSC1 = 10815, nHSC2 = 2265, nHSC3 = 2867, nHSC4 = 900). Tcf15 expression scale is log (normalized UMI). DPA results (P-values) testing enrichment of Tcf15hi (>5 UMI) cells across each HSC cluster are, in order, from cluster HSC1 to HSC4: <0.0001, 0.4843, <0.0001, 0.0009. * indicates enrichment in HSC1. e, Selected genes enriched in Tcf15hi HSCs and Tcf15neg HSCs. f, Single cell plot of the Tcf15hi signature score, using genes enriched in Tcf15-expressing cells (z-score >0.3). g, Pearson correlation between the Tcf15hi signature score and the indicated HSC signature scores. Bars indicate average of n = 3 independent experiments. Low-output, high-output, Mk-biased and Multilineage signature scores are derived from the analyses shown in Fig. 1. h, SPRING plots showing distribution of Tcf15hi HSC clones and their progeny (purple) compared to the rest of HSCs (light grey) in primary transplants. Major cluster groups are labelled. Cells shown are from a representative experiment of 3 independent experiments with similar results (n = 16976 cells). i, Violin plot showing the average distribution of Tcf15 expression levels in low-output (n = 123) versus high-output (n = 101) HSC clones taken from 3 independent experiments with similar results. Violin plot shows all data, with median (dashed line) and quartiles (dotted lines). *P = 0.0165 (two-sided unpaired t-test). j, Violin plot showing the distribution of relative output activity in Tcf15hi (n = 95) versus Tcf15neg (n = 129) HSC clones. Violin plot shows all data, with median (dashed line) and quartiles (dotted lines). *P = 0.0015 (two-sided unpaired t-test). Source data
a, Volcano-plot showing the multiple comparison-corrected (Bonferroni) unique t-test for each gene in a representative population (LS-K+CD41-, Myeloid progenitors). Two-sided test, n = 6 independent mice. b, SPRING plot localization of sgControl vs. sgTcf15 cells using inDrop. Identified branches are labelled by marker gene expression. Plot is representative from one of n = 2 independent single-cell experiments (each experiment from 3 mice combined). c, Quantification of peripheral blood engraftment as %EGFP+ cells (of all CD45.2+), comparing sgControl (blue) and sgTcf15 (red) donor cells. *P = 0.0017 (two-sided unpaired t-test, nsgControl = 4 and nsgTcf15 = 5 animals). Lines indicate mean per group. d, FACS plots showing Lin- cKit-enriched bone marrow staining for LSKs in primary recipients. Only EGFP+ cells are shown in the plots. Plots are taken from representative one animal per group from n = 3 experiments. e, Quantification of bone-marrow engraftment as Mean ± s.d. %EGFP+ cells (of all bone marrow) in each designated compartment. *significant discoveries. PLT-HSC < 0.0001, PMPP1 = 0.0237, PMPP2 = 0.1427, PMPP3/4 = 0.5190, PMyP = 0.1206, PMkP = 0.5190, PGM = 0.0002, PpreB < 0.0001 (two-sided Holm-Sidak multiple-corrected t-test, n = 3). f, Phenotype quantification as Mean ± s.d. % of donor LSKs in primary recipients corresponding to each SLAM gate (LT-HSC, MPP1, MPP2, MPP3/4). *significant P-value PLT-HSC < 0.0001, PMPP1 = 0.0001, PMPP2 = 0.7152, PMPP3/4 = 0.0428 (two-sided Holm-Sidak multiple-corrected t-test, n = 3). g, FACS scatter plots of sgControl and sgTcf15 EGFP+ LSKs, stained with DAPI and Ki-67 to evaluate cell cycle status. Plots are taken from representative one animal per group taken from 3 independent experiments. Source data
a, Micrographs of liquid cultures of control TetO-Tcf15 cells. LT-HSCs (1000 cells) from M2rtTA mice were transduced with GFP-carrying lentiviral vectors expressing either a control sgRNA or TetO-Tcf15. Cells were sorted immediately into 1 μg/ml Dox-supplemented STEMspan + SCF/Flt3L/TPO and cultured for 7 days. Images are representative of 5 independent experiments with similar results. b, Quantification of liquid culture cellularity by measuring the area of the liquid colonies from 5 independent experiments. Mean ± s.d. is indicated. Control HSC cultures are shown in black, and TetO-Tcf15 HSC cultures are shown in green. *P < 0.0001 (unpaired two-sided t-test). c, Experimental setup to evaluate the effect of Tcf15 overexpression. d, Quantification of TetO-Tcf15 EGFP+ cells in peripheral blood. Time-point 0 reflects the lentiviral transduction efficiency evaluated from a remainder of non-transplanted cultured HSCs. Untreated (Dox-) controls (n = 5) were compared with Dox-treated (Dox+) mice (n = 5). Line represents mean. Arrow indicates time point of Dox addition in the Dox-treated mice. *** Two-way ANOVA test (genotype x time-factor) P = 0.0127. e, FACS contour plots of Dox-treated TetO-Tcf15 bone marrow cells at 16 wk. Left panels show Lin- EGFP- control cells. Right panels show Lin- EGFP+ TetO-Tcf15 cells. Plots are representative from 3 independent experiments. f, Fraction of TetO-Tcf15 EGFP+ cells in different bone marrow populations at 16wk (nDox- = 5, nDox+ = 3). Mean ± s.d. *two-sided unpaired t-test. P-values are PLT-HSC = 0.0144, PMyP = 0.0010, PGM = 0.0091, PpreB = 0.0032. g, Quantification of % of all Lin- EGFP+ cells that belong to the LT-HSC or MPP1(ST-HSC) fraction (nDox- = 5, nDox+ = 3). Mean ± s.d. *two-sided unpaired Holm-Sidak-corrected multiple comparisons t-test. P-values are PLT-HSC = 0.0062, and PMPP1 = 0.0157. h, Quantification of LT-HSC, MPP1, MPP2 and MPP3/4 as % of all donor LSK, comparing EGFP+ (treated and untreated) and EGFP- cells (nDox- = 5, nDox+ = 3). Mean ± s.d. *two-sided unpaired Holm-Sidak-corrected multiple comparisons t-test. P-values are PLT-HSC = 0.0042, and PMPP3-4 = 0.0001. i, Quantification of cell cycle phase (G0, G1, G2/M) in LT-HSCs, comparing donor EGFP+ (Dox-treated and untreated) and EGFP- cells (nDox- = 5, nDox+ = 3). Mean ± s.d. *two-sided unpaired Holm-Sidak-corrected multiple comparisons t-test, PG0 = 0.0148, PG1 = 0.1127, PG2/S/M = 0.4815. j, Competitive secondary transplantation of cKit cells derived from Dox-supplemented TetO-Tcf15 mice. EGFP+ cKit+ cells were FACS-purified from Dox-treated primary recipients from experiment in Extended Data Fig. 8c. These cells were transplanted competitively against the same number of cKit cells isolated from a CD45.2+ wild-type donor (same gate), with an additional 250,000 of CD45.1 nucleated whole bone marrow cells (WBM). k, Quantification of EGFP+ CD45.2+ secondary engraftment showing higher repopulation from TetO-Tcf15 cKit+ cells (EGFP positive), which outcompete WT cKit+ cells (EGFP negative). Line represents mean (n = 4 independent experiments). One-way t-test (vs. null hypothesis of 50% engraftment) P = 10−202. Source data
a, Tcf15-Venus knock-in mouse allele. The open-reading frame of monomeric Venus fluorescent protein is knocked-in replacing the start codon in the first exon of the Tcf15 locus. b, FACS plot of Tcf15-Venus knock-in mouse reporter bone marrow, stained with Lineage markers. Bone marrow from a wild-type BL/6J mouse is used as a negative control. The YFP channel was used to detect expression of Venus fluorescent protein. Plots are representative of 3 independent experiments with similar results. c, Quantification of %Venus+ cells in Lin- vs. Lin+ bone marrow, comparing Tcf15-Venus reporter and negative control mice (n = 3). Mean ± s.d. ***Holm-Sidak-corrected multiple comparison two-sided t-test P = 0.0243. d, Quantification of %Venus+ cells in Lin-Sca1+cKit+ (LSK), Lin-Sca1-cKit+ (MyP) and Lin-Sca1-cKit- (Kit-). Mean ± s.d. ***unpaired two-sided t-test, P = 0.0021 (n = 3). e, Quantification of distribution of Lin- Venus+ cells from Tcf15-Venus knock-in reporter bone marrow (measured as % Live Lin-). BL/6J bone marrow cells are shown for comparison, as negative controls. Mean ± s.d. (n = 3). f, FACS plot of Tcf15-Venus knock-in reporter LSK cells, stained for LSK SLAM markers to show YFP (Venus) expression in different SLAM compartments. BL/6J bone marrow LSK cells are used as a negative control. Plots shown are representative of 3 independent experiments with similar results. g, Donor engraftment in primary competitive transplantation, measured as % of peripheral blood CD45.2+ leukocytes. Bars indicate mean ± s.d. (n = 4). h, Engraftment in bone marrow, measured as total CD45.2+ cells at 3-4 months post transplantation. Mean ± s.d. (n = 4). *Holm-Sidak-corrected multiple comparison unpaired two-sided t-test, P = 0.0223. i, Automated peripheral blood counts of mice reconstituted with Venus+ or Venus- HSCs. The scale is shared for all measurements, but the units are indicated for each population after the labels. *Holm-Sidak-corrected multiple comparison two-sided t-test PWBC = 0.0006, PLY = 0.0056. j, FACS plots showing bone marrow Lin- analysis of primary recipients transplanted with Venus+ HSCs. Left panels show cKit vs. Sca1 staining of all cKit+ cells. Right panel shows SLAM (CD48, CD150) staining of LSK cells. Plots shown are representative of 3 independent experiments with similar results. k, FACS plots showing bone marrow Lin- analysis of primary recipients transplanted with Venus- HSCs. Left panels show cKit vs. Sca1 staining of all cKit+ cells. Right panel shows SLAM (CD48, CD150) staining of LSK cells. Plots shown are representative of 3 independent experiments with similar results. l, Quantification of % of bone marrow myeloid (GM, Gr-1+), lymphoid (B, CD19+) and erythroid (Ery, Ter119+) cells from Venus+ vs. Venus- primary recipients. Mean ± s.d. (n = 3). *Holm-Sidak corrected multiple comparison two-sided t-test. PB = 0.0002, PEry = 0.0166, PGM = 0.0125. m, Quantification of FACS gate in (J, left panels) showing % of all cKit cells that are LSK. Mean ± s.d. (n = 3). ***unpaired two-sided t-test. PB = 0.0054. n, Quantification of % of donor-derived LSK cells belonging to each SLAM population. Mean ± s.d. (n = 3). *Holm-Sidak corrected multiple comparison two-sided t-test. PLT-HSC = 0.0010, PMPP1 = 0.0806, PMPP2 = 0.6026, PMPP3-4 < 0.0001. o, Quantification of % Venus+ cells in each CD45.2+ LSK SLAM subpopulation, comparing recipients transplanted with 100 Venus+ vs. Venus- HSCs. Mean ± s.d. (n = 3). *Holm-Sidak corrected multiple comparison two-sided t-test. PLT-HSC < 0.0001, PMPP1 = 0.0002, PMPP2 = 0.8157, PMPP3-4 = 0.8820. p, Donor engraftment in secondary competitive transplantation, measured as % of peripheral blood CD45.2+ granulocytes. Mean ± s.d. (nVenus+ = 4, nVenus- = 5). Line connects the means at each time point. ***paired two-sided t-test P < 0.0001. Source data
Supplementary Methods. Description of single cell barcode data analysis for statistical modelling.
Supplementary Figure 1. Sorting strategy. Bone marrow cells were stained with fluorescently-labeled monoclonal antibodies against mature lineage markers, cKit, Sca-1, CD48 and CD150, and sorting gates were drawn on the basis of differential expression of these surface markers. EGFP+ cells (barcoded cells) were sorted to include the top 10% of the EGFP- population. For achieving the optimal cell concentration required for the inDrops device, when EGFP+ HSC cell numbers were low, these cells were diluted with EGFP- cells at the proportions indicated in Supplementary table 10.
Supplementary Table 1. Annotation markers. This is a list of gene markers used to annotate clusters in each experiment.
Supplementary Table 2. Differential gene expression between HSC categories defined by clonal analysis. Differential gene expression of low-output versus high-output, Mk-biased versus multilineage and secondary-engrafting versus non-engrafting HSC clones (in different subtables). Rows 1-4 are gene names, combined test-score, log2fold-change and adjusted p-value. Rows 5-6 show number of cells (n) used per category per test. Rest of rows show list of differentially expressed genes for each signature. Benjamini-Hochberg multiple comparison adjusted t-test p-value is reported. Subtable “signature comparisons” shows the number of shared genes between signatures and their observed enrichment over random overlap. Subtable “previous signatures” shows lists of signatures from previous publications, the number of shared genes between the clonal HSC behavior signatures, and their observed enrichment over random overlap.
Supplementary Table 3. Differential gene expression between transcriptional HSC clusters. Rows 1-4 are gene names, combined test-score, log2fold-change and adjusted p-value. Rows 6-7 show number of cells (n) used per category per test. Rest of rows show list of differentially expressed genes for each signature. Benjamini-Hochberg multiple comparison adjusted t-test p-value is reported.
Supplementary Table 4. List of candidates and evaluation of the inclusion criteria for the in vivo CRISPR screening. Genes and their criteria are labeled in green (good), yellow (intermediate), or red (poor).
Supplementary Table 5. List of single guide RNAs used for the in vivo CRISPR screening.
Supplementary Table 6. CROPseq screening DNAseq analysis. These are the raw deep sequencing counts for the CROPseq screening. Numbers 78, 79, 80, 83, 84 and 85 are experimental Dox-treated mice. PBM, peripheral blood myeloid. PBL, peripheral blood lymphoid. Neu, bone marrow neutrophils. Mo, bone marrow monocytes. Mk, bone marrow megakaryocytes. Ery, bone marrow erythroblasts. MPP, bone marrow multipotent progenitors. MyP, bone marrow myeloid progenitors (Lin-Sca1-cKit+). preB, bone marrow B-cell progenitors. preT, bone marrow CD3+ T-cells. Lib1/2/3 are three replicates of the sequenced sgRNA plasmid library.
Supplementary Table 7. Differential single cell gene expression of sgTcf15 vs. sgControl cells. Rows 1-4 are gene names, combined test-score, log2fold-change and adjusted p-value. Rows 5-6 show number of cells (n) used per category per test. Benjamini-Hochberg multiple comparison adjusted t-test p-value is reported.
Supplementary Table 8. Differential single cell gene expression of TetO-Tcf15 vs. wild-type cells. Rows 1-4 are gene names, combined test-score, log2fold-change and adjusted p-value. Rows 5-6 show number of cells (n) used per category per test. Benjamini-Hochberg multiple comparison adjusted t-test p-value is reported.
Supplementary Table 9. Tcf15-regulated gene set and gene term enrichment analysis. The ranked list of genes was analyzed with Toppgene (http://toppgene.cchmc.org). For each gene term, ontology or list, the sample size (Hit Count in query list), the p-values (combined [1 - similarity score] for each gene) and Benjamini-Hochberg and Benjamini-Yekuteli adjusted FDR (q-values) are shown.
Supplementary Table 10. QC and filtering data. This data is provided separately for each library.
Supplementary Table 11. Clonal parameters calculated for each clone. These are clonal analysis lists, containing all the parameters for each clone in each experiment. Unpaired two-sided t-test p-values (non-adjusted) and t-statistic for output activity and Mk-bias are reported for each clone (see methods). Mean, SD, CV, Min, Max and Sample size (n) values are shown for each parameter in a separate subtable.
Supplementary Table 12. Whitelist of LARRY barcodes.
Supplementary Table 13. Antibodies. This is the list of antibodies used in this study.
About this article
Cite this article
Rodriguez-Fraticelli, A.E., Weinreb, C., Wang, S. et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature 583, 585–589 (2020). https://doi.org/10.1038/s41586-020-2503-6
Mechanisms of Ageing and Development (2020)