1. Stem Cells and Regenerative Medicine
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External signals regulate continuous transcriptional states in hematopoietic stem cells

  1. Eva M Fast
  2. Audrey Sporrij
  3. Margot Manning
  4. Edroaldo Lummertz Rocha
  5. Song Yang
  6. Yi Zhou
  7. Jimin Guo
  8. Ninib Baryawno
  9. Nikolaos Barkas
  10. David Scadden
  11. Fernando Camargo
  12. Leonard I Zon  Is a corresponding author
  1. Department of Stem Cell and Regenerative Biology, Harvard University, United States
  2. Laboratório de Imunobiologia, Departmento de Microbiologia, Imunologia e Parasitologia, Universidade Federal de Santa Catarina, Brazil
  3. Stem Cell Program and Division of Hematology/Oncology, Howard Hughes Medical Institute, Boston's Children's Hospital and Dana Farber Cancer Institute, Harvard Medical School, United States
  4. Medical Devices Research Centre, National Research Council Canada, Canada
  5. Childhood Cancer Research Unit, Department of Children's and Women's Health, Karolinska Institutet, Sweden
  6. Broad Institute of Harvard and MIT, United States
  7. Harvard University, United States
  8. Children's Hospital Harvard Med Sch, United States
  9. Stem Cell Program and Hematology/Oncology, Boston Children's Hospital, United States
Research Article
Cite this article as: eLife 2021;10:e66512 doi: 10.7554/eLife.66512
5 figures, 1 table and 12 additional files

Figures

Figure 1 with 4 supplements
Hematopoietic stem cells (HSCs) are transcriptionally heterogeneous and niche perturbations rapidly shift cells into different states.

(A) Schematic of stimulant treatment before HSC and multipotent progenitor (MPP) isolation, see also Figure 1—figure supplement 1. (B) Uniform manifold approximation and projection (UMAP) plot of HSC clusters (n = 15,355 cells), with 16,16-dimethyl prostaglandin E2 (dmPGE2)-induced cluster (red) traced with a dashed line, see also Figure 1—figure supplement 2A-G. (C) UMAP plot with transcriptional scores for each cluster. (D) Heatmap of selected enriched genes for each HSC cluster and treatment (columns, scaled expression) averaged gene expression for all cells within a cluster and treatment (rows, only clusters shown with >20 cells), see also Figure 1—figure supplement 2I and Figure 1—figure supplement 4. (E) UMAP density graphs of HSC distribution for each external stimulant. (F) Proportion of HSCs within clusters for each perturbation. (G) Proportion of HSCs of each perturbation within a cluster normalized for total cell number per treatment. (H) Heatmap with number of common genes between the 100 top induced genes per HSC treatment (rows) and HSC clusters (columns), false discovery rate (FDR)-corrected hypergeometric p-values < 0.01 are italicized, exact p-values in Figure 1—source data 1. For separate analysis of male and female HSCs, see Figure 1—figure supplement 3.

Figure 1—figure supplement 1
Functional characterization of hematopoietic stem cells (HSCs) confirms high regenerative capacity.

(A) Sorting scheme of multipotent progenitors (MPPs) and HSCs. Cells were lineage depleted prior to sort. (B) Schematic of limiting dilution transplantation assay (LDTA) experiment. (C) Chimerism and lineage distribution per mouse 4 months post-transplant. (D) Extreme limiting dilution assay (ELDA) for LDTA experiment. (E) Percentage of MPPs and HSCs within Lin-, c-Kit+, Sca1+ (LSK) cells at baseline and after niche stimulation (pooled cells from five mice for each condition). (F) Proportion of each surface phenotype within all five experimental conditions after computational reassembly of the LSK compartment.

Figure 1—figure supplement 2
Evaluation of single-cell RNA sequencing (scRNA-Seq) clustering with independent replicates, candidate genes, and transcriptional scores.

(A–D) Comparison of clustering of control hematopoietic stem cells (HSCs) in two biological replicates. Uniform manifold approximation and projection (UMAP) plots for replicates 1 (A, n = 2382 cells) and 2 (B, n = 5334 cells) and summarized cell proportions in each cluster (C). (D) Heatmap with number of common genes between the 100 top enriched genes for Replicate 1 (rows) and Replicate 2 (columns) clusters, false discovery rate (FDR)-corrected hypergeometric p-values < 0.01 are italicized, exact p-values listed in Figure 1—source data 1. (E) UMAP plot with expression of representative genes for each HSC cluster. (F) UMAP plots with expression of previously described HSC markers. (G) Violin plot of transcriptional scores in HSC clusters computed from HSC ‘activation’ cluster with highest mean ‘Activated’ score (besides the ‘Activated’ cluster) indicated with an asterisk, for exact p-values and confidence intervals, see Figure 1—source data 1. (H) Proportion of 16,16-dimethyl prostaglandin E2 (dmPGE2) and control cells within clusters split by surface phenotype for HSCs and multipotent progenitors 1 (MPP1s). (I) Unified heatmap with top 100 genes per cluster and treatment. Single-cell expression is averaged within a single-cell cluster, scaled to z-scores and similar genes (columns) and clusters (rows) are aggregated by hierarchical clustering. Top row (‘specific’) indicates the cluster or treatment each gene is assigned to, the color scheme is the same as in Figure 1F (clusters) and Figure 1G (treatments).

Figure 1—figure supplement 3
Minimal sexual dimorphism in hematopoietic stem cells (HSCs) and Lin-, c-Kit+, Sca1+ (LSKs) in steady state and upon stimulation.

(A) Xist expression, classification of male and female cells and male and female cells plotted separately. (B) Stacked violin plots of all consistent sexually dimorphic genes (red; female, blue; male) for two independent biological replicates. (C–D) Proportions of male and female HSCs (C) and LSK cells (D) within clusters for each drug treatment (p-value(DPA) > 0.05 for all male vs. female comparisons). (E) Heatmap of average expression for ‘opposite directionality’ genes in HSCs for indomethacin (‘Indo’) and control. (F) Scatter plot of differential expression coefficient (converted to log2 scale) induced by stimulants in HSCs (F) and LSKs (G) between male (y-axis) and female (x-axis). Solid red line indicates equal expression coefficients (coef female = coef male) and dashed line indicates a twofold deviation (2*coef female = coef male or vice versa). Green arrowhead indicates ‘opposite directionality’ genes in indomethacin (shown also in E).

Figure 1—figure supplement 4
Heatmaps of differentially expressed genes in hematopoietic stem cells (HSCs) enables identification of genes and single-cell clusters with similar expression patterns.

(A–D) Heatmap of differentially expressed genes between external stimulants and control in HSCs. Single-cell expression is averaged within a single-cell RNA sequencing (scRNA-Seq) cluster, scaled to z-scores and similar genes (rows) and clusters (columns) are aggregated by hierarchical clustering. Row label (‘specific’) in black indicates HSC-specific genes, gray label marks genes differentially expressed in both HSCs and Lin-, c-Kit+, Sca1+ (LSKs). (A) Poly (I:C) at 1.5-fold cutoff. (B) Granulocyte colony-stimulating factor (G-CSF) at 1.2-fold cutoff, (C) 16,16-Dimethyl prostaglandin E2 (dmPGE2) at 1.5-fold cutoff, (D) indomethacin at 1.2-fold cutoff.

Figure 2 with 1 supplement
Poly(I:C), granulocyte colony-stimulating factor (G-CSF), and indomethacin induce cluster-specific transcriptional changes in hematopoietic stem cells (HSCs).

(A) Dot plot of representative genes from poly(I:C) treated and control HSC clusters (scaled expression across columns). (B) Dot plot of representative genes from the G-CSF-treated and control HSC clusters (scaled expression across columns). (C–J) Diffusion pseudotime analysis. Uniform manifold approximation and projection (UMAP) plot of Fos expression in control (C) and upon indomethacin (D) treatment, see also Figure 2—figure supplement 1A-B. Diffusion map embedding with combined expression of top ‘Activated’ genes to select root cell (E) and cells colored by pseudotime (F). Kernel density of pseudotime distribution comparing indomethacin and control (G, asterisk: p-value [Mann–Whitney U-test] = 5.8*10–12) and G-CSF and control (H). Average expression of Fos (I) and Ly6a (J) across cells ranked by pseudotime (cells split into 10 bins to decrease noise), change in transcript levels indicated by asterisk in I, see also Figure 2—figure supplement 1C-D. (K) Histogram of FOS levels via intracellular fluorescence-activated cell sorting (FACS) of HSCs, ‘no stain’ is FACS-negative control, ‘control’ is FOS in untreated mice. (L) Normalized mean fluorescent intensity (MFI) for FOS in control and indomethacin-treated HSCs (p-value = 6.2 * 10–3, Welch-corrected t-test, asterisk) and LSK cells (p-value = 6.6 * 10–3, Welch-corrected t-test, asterisk) across two independent biological replicate experiments, n(mice) = 20.

Figure 2—figure supplement 1
Indomethacin affects transcriptional state of immediate early genes (IEGs).

(A) Uniform manifold approximation and projection (UMAP) plot with expression of selected ‘Activated’ genes in control (A) and indomethacin (B). Average expression of the same genes across cells ranked by pseudotime (cells split into 10 bins to decrease noise) comparing indomethacin and control (C, difference indicated by asterisk) or granulocyte colony-stimulating factor (G-CSF) and control (D).

Figure 3 with 2 supplements
Comparative analysis of Lin-, c-Kit+, Sca1+ (LSK) response to external stimulants.

(A) Heatmap with number of common genes between the 100 top enriched genes for LSK (rows) and hematopoietic stem cell (HSC) (columns) clusters, false discovery rate (FDR)-corrected hypergeometric p-values < 0.01 are italicized, exact p-values listed in Figure 3—source data 1. (B) Uniform manifold approximation and projection (UMAP) plot of LSK clustering (n = 8191 cells), with induced clusters by 16,16-dimethyl prostaglandin E2 (dmPGE2) (red) and poly(I:C) (pink and purple) traced with dashed line, see also Figure 3—figure supplement 1. (C) Proportion of LSK cells within clusters for each perturbation. (D) Proportion of LSK cells of each perturbation within a cluster normalized for total cell number per treatment. (E) Heatmap of selected enriched genes for each LSK cluster and treatment (columns, scaled expression) averaged gene expression for all cells within a cluster and treatment (rows, only clusters shown with >20 cells), see also Figure 3—figure supplement 2. (F) Heatmap with number of common genes between the 100 top induced genes per LSK treatment (rows) and LSK clusters (columns), FDR-corrected hypergeometric p-values < 0.01 are italicized, exact p-values listed in Figure 3—source data 1. (G) UMAP density graphs of LSK distribution for each external stimulant.

Figure 3—figure supplement 1
Multipotent progenitor (MPP) surface marker expression validates Lin-, c-Kit+, Sca1+ (LSK) cluster definitions.

(A) Schematic of LSK pooling and cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) surface hashtag (hashtag oligonucleotide [HTO]) methodology. (B) Uniform manifold approximation and projection (UMAP) plots with expression of selected genes in LSK cells. (C) UMAP plot of surface receptor phenotypes in LSK cells. (D) UMAP density graphs visualizing the distribution of cells by surface phenotype. (E) Stacked violin plots of gene expression for surface markers within MPPs and hematopoietic stem cells (HSCs). (F) Proportion of LSK cells belonging to different clusters for each surface phenotype. (G) Proportion of surface phenotypes within each LSK cluster. (H) Violin plot of transcriptional scores computed from HSC ‘quiescent’ cluster in LSKs with the two highest mean ‘quiescent’ scores indicated with asterisks, for exact p-values and confidence intervals, see Figure 3—source data 1.

Figure 3—figure supplement 2
Heatmaps of differentially expressed genes in Lin-, c-Kit+, Sca1+ (LSKs) enable identification of genes and single-cell clusters with similar expression patterns.

(A–D) Heatmap of differentially expressed genes between external stimulants and control in LSKs. Single-cell expression is averaged within a single-cell RNA sequencing (scRNA-Seq) cluster, scaled to z-scores and similar genes (rows) and clusters (columns) are aggregated by hierarchical clustering. Row label (‘specific’) in black indicates LSK-specific genes, gray label marks genes differentially expressed in both hematopoietic stem cells (HSCs) and LSKs. (A) Poly (I:C) at 1.5-fold cutoff. (B) Granulocyte colony-stimulating factor (G-CSF) at 1.5-fold cutoff, (C) 16,16-dimethyl prostaglandin E2 (dmPGE2) at 1.5-fold cutoff.

Lin-, c-Kit+, Sca1+ (LSK) and hematopoietic stem cell (HSC) cluster-specific differential gene expression cannot be explained by receptor expression.

(A–D) Stacked bar graphs with proportion of differentially expressed genes that are unique for HSCs (purple), LSKs (green) or common (gray) upon granulocyte colony-stimulating factor (G-CSF) (A), poly(I:C) (B), Indo (C), or 16,16-dimethyl prostaglandin E2 (dmPGE2) (D) treatment. Below each bar graph the total number of differentially expressed genes (‘genes #’) for each fold-change (‘cutoff’) is listed. (E–F) Violin plots of receptor expression in control HSCs (E) and LSKs (F) split by cluster (only clusters with >20 cells displayed).

Figure 5 with 1 supplement
Heterogeneous distribution of interferon signaling response element (ISRE) and AP-1 motif in hematopoietic stem cells (HSCs) and Lin-, c-Kit+, Sca1+ (LSKs) and specific motif co-occurrences in HSCs.

(A) Schematic of downstream transcriptional signaling pathways for externalstimulants. (B–C) Uniform manifold approximation and projection (UMAP) plot of HSC (B) single-cell chromatin accessibility sequencing (scATAC-Seq) clusters (n = 730 cells) or LSK (C) scATAC-Seq clusters (n = 10,750 cells), see also Figure 5—figure supplement 1A-B. (D–E) Violin plots of transcription factor (TF) motif scores enriched in HSCs (D) and LSKs (E) with selected significant p-values (logistic regression) indicated by asterisks, see also Supplementary file 11 and Figure 5—figure supplement 1C-D.

Figure 5—figure supplement 1
Uniform distribution of motif activity immediately downstream of external stimulants and differential enrichment for secondary signals in hematopoietic stem cells (HSCs) and Lin-, c-Kit+, Sca1+ (LSKs).

(A) Schematic of fluorescence-activated cell sorting (FACS) and analysis of the single-cell chromatin accessibility sequencing (scATAC-Seq) experiment. (B) Uniform manifold approximation and projection (UMAP) plot of combined LSKs (n = 10,750 cells) colored by sorted HSCs or multipotent progenitors (MPPs). (C–D) UMAP plots of various transcription factor (TF) motif scores in HSCs (C) or LSKs (D). (E–F) Violin plot (E) and UMAP plot (F) of SMAD TF motif score in HSCs.

Tables

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Mus musculus)Male and femaleReplicate 1Jackson LaboratoryRRID:IMSR_JAX:016617
Genetic reagent (Mus musculus)Male and femaleReplicate 2, CD45.2 (transplant recipients)Jackson LaboratoryRRID:IMSR_JAX:000664Used for pharmacological perturbations
Genetic reagent (Mus musculus)Female onlyTransplant donorsJackson LaboratoryRRID:IMSR_JAX:002014
AntibodyAnti-CD117 (c-Kit), ACK2, APC (rat monoclonal)Thermo Fisher Scientific(17-1172-83)RRID:AB_469434FACS (1:100)
AntibodyAnti-CD11b/Mac1, M1/70, eFluor 450 (rat monoclonal)Thermo Fisher Scientific(48-0112-80)RRID:AB_1582237FACS (1:100)
AntibodyAnti-CD11b/Mac1, M1/70, PE-Cyanine5 (rat monoclonal)Thermo Fisher Scientific(15-0112-83)RRID:AB_468715FACS (1:100)
AntibodyAnti-CD11b/Mac1, M1/70, Alexa Fluor 700 (rat monoclonal)BD Pharmingen(557960)RRID:AB_396960FACS (1:300)
AntibodyAnti-CD135 (Flt3), A2F10, PE (rat monoclonal)Thermo Fisher Scientific(12-1351-81)RRID:AB_465858FACS (1:100)
AntibodyAnti-CD150, TC15-12F12.2, PE/Cy7 (rat monoclonal)Biolegend(115914)RRID:AB_439797FACS (1:100)
AntibodyAnti-CD3, 17A2, APC (rat monoclonal)Thermo Fisher Scientific(17-0032-82)RRID:AB_10597589FACS (1:100)
AntibodyAnti-CD34, RAM34, eFluor 450 (rat monoclonal)Thermo Fisher Scientific(48-0341-80)RRID:AB_2043838FACS (1:33)
AntibodyAnti-CD34, RAM34, FITC (rat monoclonal)Thermo Fisher Scientific(11-0341-85)RRID:AB_465022FACS (1:33)
AntibodyAnti-CD3e, 145–2C11, eFluor 450 (armenian hamster monoclonal)Thermo Fisher Scientific(48-0031-80)RRID:AB_10733280FACS (1:100)
AntibodyAnti-CD3e, 145–2C11, PE-Cyanine5 (armenian hamster monoclonal)Thermo Fisher Scientific(15-0031-83)RRID:AB_468691FACS (1:100)
AntibodyAnti-CD45.1, A20, FITC (mouse monoclonal)BD Pharmingen(553775)RRID:AB_395043FACS (1:100)
AntibodyAnti-CD45.2, 104, PE (mouse monoclonal)BD Pharmingen(560695)RRID:AB_1727493FACS (1:100)
AntibodyAnti-CD45R (B220), RA3-6B2, eFluor 450 (rat monoclonal)Thermo Fisher Scientific(48-0452-80)RRID:AB_1548763FACS (1:100)
AntibodyAnti-CD45R (B220), RA3-6B2, PE-Cyanine5 (rat monoclonal)Thermo Fisher Scientific(15-0452-83)RRID:AB_468756FACS (1:100)
AntibodyAnti-CD45R/(B220), RA3-6B2, pacific Blue (rat monoclonal)Biolegend(103227)RRID:AB_492876FACS (1:100)
AntibodyAnti-CD48, HM48-1, Alexa Fluor 700 (armenian hamster monoclonal)Biolegend(103425)RRID:AB_10612754FACS (1:100)
AntibodyAnti-CD5, 53–7.3, eFluor 450 (rat monoclonal)Thermo Fisher Scientific(48-0051-80)RRID:AB_1603252FACS (1:100)
AntibodyAnti-CD8a, 53–6.7, eFluor 450 (rat monoclonal)Thermo Fisher Scientific(48-0081-80)RRID:AB_1272235FACS (1:100)
AntibodyAnti-c-Fos, H15-S, FITC (rabbit monoclonal)Abcam(ab175647)RRID:AB_2893164FACS (10 µl for
1 MIO cells)
AntibodyAnti-Ly-6A/E (Sca-1), D7, PE-eFluor 610 (rat monoclonal)Thermo Fisher Scientific(61-5981-80)RRID:AB_2574647FACS (1:100)
AntibodyAnti-Ly-6A/E (Sca-1), D7, APC/Cy7 (rat monoclonal)Biolegend(108125)RRID:AB_10639725FACS (1:100)
AntibodyAnti-Ly-6G (Gr-1), RB6-8C5, eFluor 450 (rat monoclonal)Thermo Fisher Scientific(48-5931-80)RRID:AB_1548797FACS (1:100)
AntibodyAnti-Ly-6G (Gr-1), RB6-8C5, PE-Cyanine5 (rat monoclonal)Thermo Fisher Scientific(15-5931-83)RRID:AB_468814FACS (1:100)
AntibodyAnti-Ly-6G (Gr-1), RB6-8C5, PE-Cyanine7 (rat monoclonal)Thermo Fisher Scientific(25-5931-82)RRID:AB_469663FACS (1:100)
AntibodyAnti-TER-119/Erythroid Cells, TER-119, eFluor 450 (rat monoclonal)Thermo Fisher Scientific(48-5921-80)RRID:AB_1518809FACS (1:100)
AntibodyAnti-TER-119/Erythroid Cells, TER-119, PE-Cyanine5 (rat monoclonal)Thermo Fisher Scientific(15-5921-83)RRID:AB_468811FACS (1:100)
AntibodyAnti-TER-119/Erythroid Cells, TER-119, APC/Cy7 (rat monoclonal)Biolegend(116223)RRID:AB_2137788FACS (1:100)
AntibodyTotalSeq-A0301 anti-mouse Hashtag 1 Antibody, M1/42; 30-F11 (rat monoclonal)Biolegend(155801)RRID:AB_2750032Cell hashing (1 µg
per reaction)
AntibodyTotalSeq-A0302 anti-mouse Hashtag 2 Antibody, M1/42; 30-F12 (rat monoclonal)Biolegend(155803)RRID:AB_2750033Cell hashing (1 µg
per reaction)
AntibodyTotalSeq-A0303 anti-mouse Hashtag 3 Antibody, M1/42; 30-F13 (rat monoclonal)Biolegend(155805)RRID:AB_2750034Cell hashing (1 µg
per reaction)
AntibodyTotalSeq-A0304 anti-mouse Hashtag 4 Antibody, M1/42; 30-F14 (rat monoclonal)Biolegend(155807)RRID:AB_2750035Cell hashing (1 µg
per reaction)
Reagent, commercialStreptavidin, -, PE-Cyanine5Thermo Fisher(15-4317-82)RRID:AB_10116415FACS (1:100)
Reagent, commercialStreptavidin, -, eFluor 450Thermo Fisher Scientific(48-4317-82)RRID:AB_10359737FACS (1:100)
Commercial assay or kitscRNA-Seq kit V2 – replicate 110× GenomicsPN-120267
Commercial assay or kitscRNA-Seq kit V3– replicate 210× GenomicsPN-1000075
Commercial assay or kitscATAC-Seq kit10× GenomicsPN-1000111
Commercial assay or kitLineage depletion kitMiltenyi Biotech130-090-858
Chemical compound, drugPoly(I:C) HMWInvivogentlrl-pic-5
Chemical compound, drugDmPGE2Cayman14750
Chemical compound, drugG-CSFThermo FisherPHC2031
Chemical compound, drugIndomethacinSigmaPHR1247-500MG
Software, algorithmGraphPad PrismGraphPad(Version 6.05)RRID:SCR_002798https://www.graphpad.com/
Software, algorithmFlowJo (Tree Star)FlowJo(Version 10.5.3)RRID:SCR_008520https://www.flowjo.com/
Software, algorithmCellranger10× Genomicsv3.0.1v2.1.0 (Replicate 1) v1.2.0 (scATAC-Seq)https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome
Software, algorithmCITE-Seq counthttps://hoohm.github.io/CITE-seq-Count/(version 1.4.3)RRID:SCR_019239https://github.com/Hoohm/CITE-seq-Count, Roelli, 2021
Software, algorithmScanpy(Wolf et al., 2018)Various versions, see jupyter notebooks + dockerhub for documentationRRID:SCR_018139https://scanpy.readthedocs.io/en/stable/
Software, algorithmpegasuspyGaublomme et al., 2019Version 0.17.1https://github.com/klarman-cell-observatory/pegasus/tree/0.17.1, Yang, 2021
Software, algorithmSignac(Stuart et al., 2019)Version 0.2.5RRID:SCR_021158https://satijalab.org/signac/
Software, algorithmGitHubThis paperhttps://github.com/evafast/scrnaseq_paper, copy archived at swh:1:rev:231286dc1447516f938bed8191839edb554a4fd3 (Fast, 2021)Code for all analyses + description
Software, algorithmDockerhubThis paperhttps://hub.docker.com/u/evafast1Docker images for analysis
Software, algorithmUCSC cell browserSpeir et al., 2021https://cells.ucsc.edu/Interactive app

Additional files

Supplementary file 1

Table of sequencing metrics.

Sequencing metric output from cellranger.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp1-v1.xlsx
Supplementary file 2

Table with overlap of differentially regulated genes in male and female hematopoietic stem cells (HSCs) and Lin-, c-Kit+, Sca1+ (LSKs).

Table summarizing number of differentially regulated genes within male and female HSCs and LSKs. Over-representation analysis odds ratio and p-value were calculated using a Fisher’s exact test.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp2-v1.csv
Supplementary file 3

Table of differential expression result (model-based analysis of single-cell transcriptomics [MAST]) by sex.

Each tab contains a treatment vs. control comparison (16,16-dimethyl prostaglandin E2 [dmPGE2], Indo, poly(I:C), granulocyte colony-stimulating factor [G-CSF]). Each cluster was compared to its respective control cluster separated by sex. Log fold change and adjusted p-value from the Hurdle model are listed for genes with p-values < 0.01.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp3-v1.xlsx
Supplementary file 4

Table with marker gene enrichments in single-cell RNA sequencing (scRNA-Seq) clusters.

Marker gene enrichment was calculated using a Wilcoxon rank-sum test. Score (suffix ‘_s’) indicates the z-score of each gene on which p-value computation is based. Other fields are log fold change = suffix ‘_l’ and false discovery adjusted p-value – suffix ‘_p’.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp4-v1.xlsx
Supplementary file 5

Table with curated pathways used for over-representation analysis.

Gene lists curated from literature search and MsigDB.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp5-v1.xlsx
Supplementary file 6

Table of pathway enrichment for hematopoietic stem cell (HSC) and Lin-, c-Kit+, Sca1+ (LSK) clusters and treatments.

Over-representation analysis for genes induced by external stimulants or enriched in single-cell RNA sequencing (scRNA-Seq) clusters (top 3–5 pathways shown for each database or curated pathway set with adjusted p-value < 0.05). Granulocyte colony-stimulating factor (G-CSF) has an additional tab listing all enrichments (including adjusted p-value > 0.05) of curated pathways.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp6-v1.xlsx
Supplementary file 7

Table of differential gene expression result (model-based analysis of single-cell transcriptomics [MAST]).

Each tab contains a treatment vs. control comparison (16,16-dimethyl prostaglandin E2 [dmPGE2], Indo, poly(I:C), granulocyte colony-stimulating factor [G-CSF]). Each cluster was compared to its respective control cluster. Log fold change and adjusted p-value from the Hurdle model are listed for genes with p-values < 0.01.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp7-v1.xlsx
Supplementary file 8

Table of average expression per cluster of differentially regulated genes in hematopoietic stem cells (HSCs).

Count normalized and log transformed UMI counts were averaged across cells in HSC clusters for differentially regulated genes from model-based analysis of single-cell transcriptomics (MAST).

https://cdn.elifesciences.org/articles/66512/elife-66512-supp8-v1.xlsx
Supplementary file 9

Table of coocurrence of top 100 genes across Lin-, c-Kit+, Sca1+ (LSK) and hematopoietic stem cell (HSC) treatments and clusters.

Comparison of top 100 genes between LSK or HSC treatments and clusters (no duplicates) and comparison of top 100 genes between LSK and HSC clusters (with duplicates) and in HSC clusters Replicate 1 vs. Replicate 2.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp9-v1.xlsx
Supplementary file 10

Table of average expression per cluster of differentially regulated genes in Lin-, c-Kit+, Sca1+ (LSKs).

Count normalized and log transformed UMI counts were averaged across cells in LSK clusters for differentially regulated genes from model-based analysis of single-cell transcriptomics (MAST).

https://cdn.elifesciences.org/articles/66512/elife-66512-supp10-v1.xlsx
Supplementary file 11

Table of ChromVar transcription factor (TF) motif activity score enrichment in Lin-, c-Kit+, Sca1+ (LSK) and hematopoietic stem cell (HSC) single-cell chromatin accessibility sequencing (scATAC) clusters.

ChromVar motif activity score enrichment for HSC and LSK scATAC clusters.

https://cdn.elifesciences.org/articles/66512/elife-66512-supp11-v1.xlsx
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