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Single-cell analyses reveal the clonal and molecular aetiology of Flt3L-induced emergency dendritic cell development

Nature Cell Biology volume 23, pages 219–231 (2021)Cite this article

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Abstract

Regulation of haematopoietic stem and progenitor cell (HSPC) fate is crucial during homeostasis and under stress conditions. Here we examine the aetiology of the Flt3 ligand (Flt3L)-mediated increase of type 1 conventional dendritic cells (cDC1s). Using cellular barcoding we demonstrate this occurs through selective clonal expansion of HSPCs that are primed to produce cDC1s and not through activation of cDC1 fate by other HSPCs. In particular, multi/oligo-potent clones selectively amplify their cDC1 output, without compromising the production of other lineages, via a process we term tuning. We then develop Divi-Seq to simultaneously profile the division history, surface phenotype and transcriptome of individual HSPCs. We discover that Flt3L-responsive HSPCs maintain a proliferative ‘early progenitor’-like state, leading to the selective expansion of multiple transitional cDC1-primed progenitor stages that are marked by Irf8 expression. These findings define the mechanistic action of Flt3L through clonal tuning, which has important implications for other models of ‘emergency’ haematopoiesis.

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Fig. 1: Supra-physiological levels of Flt3L drive the expansion of BM HSPC populations and promote emergency cDC1 development.
Fig. 2: Cellular barcoding reveals minimal contribution of clonal cDC1 fate activation to emergency cDC1 generation.
Fig. 3: Selective clonal expansion is the major driver of emergency cDC1 generation.
Fig. 4: Divi-Seq reveals increased cell division in HSPCs during the early Flt3L response.
Fig. 5: Maintenance of a progenitor-like state in Flt3L-treated cells.
Fig. 6: Emergence of a cDC1 precursor after short-term exposure to Flt3L.
Fig. 7: Flt3L treatment alters the expression of cDC1 genes.
Fig. 8: Flt3L stimulation selectively expands Irf8-expression cDC1-primed progenitors.

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Data availability

Divi-Seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under the accession code GSE147977. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

All of the R packages that were used are available online, as described in the Methods. Customized code used to analyse Divi-Seq and barcoding data are available on GitHub https://github.com/DawnSLin/Emergency_DC_Development.

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Acknowledgements

We thank the Walter and Eliza Hall Bioservices facility, FACS laboratory and S. Wilcox for technical support. We thank S. Heinzel, F. Vaillant and J. Visvader for providing critical reagents as well as technical and intellectual advice. We thank K. Shortman for insightful discussions and critical feedback on the paper. This work was supported by grants from the National Health and Medical Research Council (NHMRC), Australia (grant nos GNT1062820, GNT1100033, GNT1101378, GNT1124812 and GNT1145184), the Australia Research Council’s special initiative Stem Cells Australia, and through a funded research agreement with Gilead Inc. P.D.H is supported by an NHMRC fellowship. J.R. is supported by a Victorian Cancer Agency (VCA) grant (grant no. ECSG18020).

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Authors and Affiliations

  1. Immunology Division, Walter and Eliza Hall Institute, Parkville, VIC, Australia

    Dawn S. Lin, Luyi Tian, Sara Tomei, Daniela Amann-Zalcenstein, Tom S. Weber, Jaring Schreuder, Philip D. Hodgkin, Stephen L. Nutt & Shalin H. Naik

  2. Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia

    Dawn S. Lin, Luyi Tian, Sara Tomei, Daniela Amann-Zalcenstein, Tom S. Weber, Olivia J. Stonehouse, Samir Taoudi, Matthew E. Ritchie, Philip D. Hodgkin, Ashley P. Ng, Stephen L. Nutt & Shalin H. Naik

  3. Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, Australia

    Dawn S. Lin, Luyi Tian, Sara Tomei & Olivia J. Stonehouse

  4. Epigenetics and Development Division, Walter and Eliza Hall Institute, Parkville, VIC, Australia

    Luyi Tian, Olivia J. Stonehouse, Samir Taoudi & Matthew E. Ritchie

  5. Single Cell Open Research Endeavour (SCORE), Walter and Eliza Hall Institute, Parkville, VIC, Australia

    Daniela Amann-Zalcenstein, Tracey M. Baldwin & Shalin H. Naik

  6. Blood Cells and Blood Cancer Division, Walter and Eliza Hall Institute, Parkville, VIC, Australia

    Tracey M. Baldwin & Ashley P. Ng

  7. Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia

    Jai Rautela & Nicholas D. Huntington

  8. Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia

    Jai Rautela & Nicholas D. Huntington

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Contributions

D.S.L. designed and performed most experiments, did the analysis and wrote the manuscript. S. Tomei, D.A-Z., T.M.B., J.S., O.J.S., J.R. and A.P.N. assisted with experiments. L.T. and T.S.W. assisted with data analysis. N.D.H., A.P.N., S.L.N., S. Taoudi, M.E.R. and P.D.H. provided critical input into the experimental design and analysis. S.H.N. conceptualized and supervised the study and wrote the manuscript.

Corresponding author

Correspondence to Shalin H. Naik.

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Competing interests

J.R. and N.D.H. are founders and shareholders of oNKo-Innate Pty. Ltd. This project was partly funded through a research agreement between Gilead Sciences, Inc. and S.H.N. The other authors declare no competing interests.

Additional information

Peer review information Nature Cell Biology thanks Simon Haas, Caetano Reis e Sousa, Samantha A. Morris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 High levels of Flt3L promote cell division and DC generation in vitro.

a-d, CD11b−cKit+Sca1+ HSPCs were labelled with CTV and were cultured (~ 1 × 103 cells per well) with RPMI supplemented with either high (2 μg/ml) or low (2 ng/ml) concentrations of Flt3L in vitro. Cells were serially sampled at each time point from each well (50% of contents) and analysed by flow cytometry daily from days 1-7. a. Changes in CTV intensity of total live cells over time. b. Stacked histogram showing percentage of cells in each division peak from a. n=3 wells per condition. Data shown are representative of two independent experiments. c. Flow cytometry plots comparing upregulation of CD11c or MHCII to CTV profiles on day 4. Numbers depict % of cells within the parent gate. d. Total inferred number of live cells (left) or DCs (right) in both conditions over time. FACS plots in (a, c) show representative plots of one well per condition. Histograms (b) show mean ± s.e.m. Scatter plot (d) show individual replicates where lines connect output from the same well serially sampled over time and numbers extrapolated from the number of prior sampling events; n=3 wells per condition. Data shown are representative of two independent experiments.

Extended Data Fig. 2 Flow cytometric plots of BM HSPC populations.

a. Gating strategy to isolate different HSPC populations from BM. Numbers depict percentage of cells from parent gate. b. Flow cytometry analysis of BM HSPC populations from mice treated with PBS or Flt3L for 5 days. This gating strategy is similar to a, except the exclusion of Flt3 antibody staining, as Flt3 is poorly detected in cells treated with Flt3L. Numbers shown in all FACS plots represent percentage of cells from parent gate. Cell numbers from each population are shown in Fig. 1f.

Extended Data Fig. 3 Gating strategy to isolate mature progeny populations from the spleen.

a. General workflow to fractionate total splenocytes into DC, myeloid, lymphoid/erythroid fractions. Note: red cell lysis was excluded if erythroid lineage was included in analysis. b. General pre-gating strategy to define counting beads and live cells from each fraction. Beads were added to all fractions after enrichment (before final staining) and gated as FSCloSSChi to allow estimation of cell numbers. c. Gating strategy to isolate cDC1s, cDC2s and pDCs from CD11c and Siglec-H - enriched fraction. d. Gating strategy to isolate eosinophils (eos), monocytes (mon) and neutrophils (neu) from CD11b-enrich fraction. e. Gating strategy to isolate B cells and erythroid blast (EryB) from final flow through fraction. f. Example plot showing gating strategy to identify donor-derived cells from each defined population. Numbers shown in all FACS plots represent percentage of cells from parent gate.

Extended Data Fig. 4 Clonal emergency DC development in different barcoding experimental settings.

(a-c) Standard setting as in Figs. 3 and 4 (day -3; 5 Gy irradiation; i.v. transplantation), with analysis of erythroid lineage. n = 5 mice per treatment, pooled from two independent experiments. (d-f) Intra-BM (not i.v.) transplantation, same irradiation regimen (day -3; 5 Gy), with analysis of erythroid lineage. n = 6 mice per treatment, pooled from two independent experiments. (g-i) Lorw dose irradiation (day -3; 2 Gy; i.v.). n = 5 PBS-treated mice and n = 6 Flt3L-treated mice, pooled from two independent experiments. (a, b, d, e, g & h) Fold change in number of cells (a, d & g) or barcodes (b, e & h) from each cell type produced by Flt3L-treated mice compared to the average of PBS-treated mice from the same experiment; mean ± s.e.m., P-values from two-tailed unpaired t-tests. (c, f & i) Heatmaps of all barcodes from the corresponding experiments. (e, f, h & i) Note that barcode data filtering was not applied due to low read counts and/or low correlation between technical replicates in a number of samples in these experimental settings.

Source data

Extended Data Fig. 5 Distribution of clone size from each fate cluster.

Violin plots showing the distribution of number of cells produced per clone (clone size) from all PBS- or Flt3L-treat clones in each fate cluster, from cluster 5-17. Results for cluster 1-4 is shown in Fig. 4f. Average fold change in clone size (Flt3L/PBS) and P-values from two-tailed unpaired t-tests are shown for each pair. See Source Data Extended Fig. 5 for details.

Source data

Extended Data Fig. 6 Flow-cytometric analysis of cells from Divi-Seq experiments.

a, Gating strategy used during index sorting of single donor-derived cells for Divi-Seq experiments in Fig. 4. As donor cells were CTV-labelled prior to transplantation, CTV+ cells were sorted. b, Gating strategy used to remove potential dead (based on FSC, SSC and PI) and contaminated endogenous cells (CD45.1lowCD45.2+) after sorting. c. Flow-cytometric analysis of HSPC populations from the endogenous or donor-derived compartment, numbers depict percentage of cells from the endogenous or donor-derived compartment, respectively. d. Estimated cell number of each phenotypically defined HSPC population from the donor-derived compartment. e. estimated number of cells in each cluster from BM (tibia, femur and ilium; donor-derived) of each mouse. from the second Divi-Seq experiment. (d-e) n = 3 mice per treatment from the second Divi-Seq experiment; mean ± s.e.m., P-values from two-tailed unpaired t-tests with Holm-Sidak correction for multiple comparison.

Source data

Extended Data Fig. 7 Assessing similarity to known cell types using SingleR.

A heatmap showing similarity scores to known cell types (compared to Immgen database) of each single cell computed by SingleR, with the following annotation per cell: 1) Cluster ID identified by Seurat as in Fig. 4c; 2) Cell type labels identified by SingleR; 3) PBS or Flt3L treatment. Note: Information regarding expression of surface markers and cluster marker genes in Fig. 4c and Supplementary Table 1 is sufficient for accurate cell type annotation of Seurat clusters, independent of SingleR analysis. SingleR analysis is performed and shown to further validate the annotation.

Extended Data Fig. 8 Gene expression of cells from different cluster in Divi-Seq.

Violin plots showing gene expression of cells from different clusters. Each dot represents a cell. Colour depicts PBS or Flt3L treatment. Large black dot indicates mean expression. See Source Data Extended Fig. 8 for details.

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Supplementary Tables

Supplementary Table 1. List of marker genes for clusters defined by Seurat. Cluster marker genes were identified using the FindAllMarker function in Seurat via differential expression analysis, which used non-parameteric Wilcoxon’s rank-sum test to compute P values. Adjustments for multiple testing were made and are presented as p_val_adj. Cluster 0, HSC/MPP-like; Cluster 1, lymphoid progenitor-like; Cluster 2, cDC-like; Cluster 3, myeloid progenitor-like; Cluster 4, monocyte-like; Cluster 5, pDC-like and Cluster 6, neutrophil-like. Supplementary Table 2. List of antibodies used.

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Lin, D.S., Tian, L., Tomei, S. et al. Single-cell analyses reveal the clonal and molecular aetiology of Flt3L-induced emergency dendritic cell development. Nat Cell Biol 23, 219–231 (2021). https://doi.org/10.1038/s41556-021-00636-7

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