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Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis

Nature Immunology volume 24, pages 767–779 (2023)Cite this article

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Abstract

Sepsis arises from diverse and incompletely understood dysregulated host response processes following infection that leads to life-threatening organ dysfunction. Here we showed that neutrophils and emergency granulopoiesis drove a maladaptive response during sepsis. We generated a whole-blood single-cell multiomic atlas (272,993 cells, n = 39 individuals) of the sepsis immune response that identified populations of immunosuppressive mature and immature neutrophils. In co-culture, CD66b+ sepsis neutrophils inhibited proliferation and activation of CD4+ T cells. Single-cell multiomic mapping of circulating hematopoietic stem and progenitor cells (HSPCs) (29,366 cells, n = 27) indicated altered granulopoiesis in patients with sepsis. These features were enriched in a patient subset with poor outcome and a specific sepsis response signature that displayed higher frequencies of IL1R2+ immature neutrophils, epigenetic and transcriptomic signatures of emergency granulopoiesis in HSPCs and STAT3-mediated gene regulation across different infectious etiologies and syndromes. Our findings offer potential therapeutic targets and opportunities for stratified medicine in severe infection.

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Fig. 1: Whole-blood single-cell census in sepsis.
Fig. 2: Neutrophil function in sepsis and convalescence.
Fig. 3: Circulating HSC atlas confirms heightened granulopoiesis in sepsis.
Fig. 4: Immature and immunosuppressive neutrophils drive the SRS1 subphenotype.
Fig. 5: GEPs in SRS1 neutrophils and expression of plasma granulopoiesis mediators.
Fig. 6: Heightened STAT3-mediated EG in SRS1.
Fig. 7: SRS1 signatures are consistent across differing clinical contexts of infectious disease.

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Data availability

Raw data for whole-blood single-cell sequencing and HSC single-cell sequencing datasets are deposited on the European Genome–phenome Archive (EGAS00001006283) and derived data are at Zenodo (https://doi.org/10.5281/zenodo.7723202). For sequence-level raw datasets deposited at the European Genome–phenome Archive, access is managed by a Data Access Committee.

Code availability

Code used for every algorithm followed in data processing and analysis is fully referenced within the specific Methods sections.

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Acknowledgements

We thank the patients and healthy donors who participated for their generous contribution to this study and the Emergency Medicine Research Oxford team and wider clinical staff, in particular A. Brent, A. Novak, A. Kingwill and R. Sayeed and Oxford University NHS Hospital Trusts recruiting hospitals involved in patient recruitment and sample collection. We thank the Oxford Genomics Centre for sequencing support, in particular A. Lee for project management and I. Nassiri for bioinformatic support. We thank the Wellcome Centre for Human Genetics and Jenner Institute flow facilities for assistance in sorting experiments. We are grateful to M. Attar, F. Curion, K. Kedzierska, P. Maclean, G. Scozzafava and members of the Knight laboratory for support and critical discussion. We also thank the COMBAT consortium for access to data for re-analysis.

We acknowledge the support of the National Institute for Health Research (NIHR) through the Comprehensive Clinical Research Network for patient recruitment. This research was funded by the Medical Research Council (MR/V002503/1) (to J.C.K. and E.E.D.), Wellcome Trust Investigator Award (204969/Z/16/Z, to J.C.K.; 209422/Z/17/Z, to I.A.U.), Chinese Academy of Medical Sciences Innovation 537 Fund for Medical Science (2018-I2M-2-002; to J.C.K.), Croucher Foundation (to A.J.K.), Wellcome Trust Grants (090532/Z/09/Z and 203141/Z/16/Z) to core facilities Wellcome Centre for Human Genetics, Wellcome Trust core funding to the Wellcome Sanger Institute (grant numbers WT206194 and 108413/A/15/D) and NIHR Oxford Biomedical Research Centre (to J.C.K.). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the UK Department of Health. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

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Authors and Affiliations

  1. Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK

    Andrew J. Kwok, Alice Allcock, Ricardo C. Ferreira, Eddie Cano-Gamez, Madeleine Smee, Alexander J. Mentzer, John A. Todd & Julian C. Knight

  2. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK

    Eddie Cano-Gamez, Katie L. Burnham & Emma E. Davenport

  3. Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

    Yasemin-Xiomara Zurke, Claudia Monaco & Irina A. Udalova

  4. John Radcliffe Hospital, Oxford Universities Hospitals NHS Foundation Trust, Oxford, UK

    Alex Novak, Melanie Darwent, Tanya Baron, Charlotte Brown, Sally Beer, Alexis Espinosa, Tine Panduro, Dominique Georgiou, Jose Martinez, Hannah Thraves, Elena Perez, Rocio Fernandez, Alberto Sobrino, Veronica Sanchez, Rufino Magallano, Karen Dineen, Jean Wilson, Stuart McKechnie, Alexander J. Mentzer & Julian C. Knight

  5. NIHR Oxford Biomedical Research Centre, Oxford, UK

    Alexander J. Mentzer, John A. Todd & Julian C. Knight

  6. William Harvey Research Institute, Faculty of Medicine and Dentistry, Queen Mary University, London, UK

    Charles J. Hinds

  7. Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK

    Julian C. Knight

Authors
  1. Andrew J. Kwok
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  2. Alice Allcock
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  3. Ricardo C. Ferreira
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  11. Irina A. Udalova
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Consortia

Emergency Medicine Research Oxford (EMROx)

  • Alex Novak
  • , Melanie Darwent
  • , Tanya Baron
  • , Charlotte Brown
  • , Sally Beer
  • , Alexis Espinosa
  • , Tine Panduro
  • , Dominique Georgiou
  • , Jose Martinez
  • , Hannah Thraves
  • , Elena Perez
  • , Rocio Fernandez
  • , Alberto Sobrino
  • , Veronica Sanchez
  • , Rufino Magallano
  • , Karen Dineen
  •  & Jean Wilson

Contributions

Conceptualization was the responsibility of A.J.K. and J.C.K. Data curation was conducted by A.J.K. Formal analysis was conducted by A.J.K., E.C.-G., A.A., M.S. and Y.-X.Z. Funding was acquired by J.C.K. and E.E.D. Investigation was carried out by A.J.K., A.A., M.S. and R.C.F. Methodology was the responsibility of A.J.K., E.C.-G. and K.L.B. Project administration was carried out by J.C.K., C.M., I.A.U., J.A.T. and E.E.D. Resources were the responsibility of A.J.K., E.C.-G., J.C.K., A.J.M., S.M. and C.J.H. Software was the responsibility of A.J.K. and E.C.-G. Supervision was carried out by J.C.K., I.A.U. and C.M. Visualization was carried out by A.J.K. and J.C.K. Writing of the original draft was conducted by A.J.K. and J.C.K. Review and editing was carried out by all authors who read, provided input on and approved the paper.

Corresponding author

Correspondence to Julian C. Knight.

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Nature Immunology thanks John Marshall and Renato Ostuni for their contribution to the peer review of this work. Primary Handling Editor: Ioana Visan, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 scWB single-cell RNA and cell surface profiling annotation, clustering and neutrophil RNA velocity analysis.

a, UMAP of 272,993 cells with TotalVI denoised surface protein marker expression overlaid for selected lineage defining markers. b, Cell surface expression of proteins denoting neutrophil maturation stages for annotated neutrophil subsets. Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. c, Comparison of broad annotations (Methods ‘Whole blood single cell multi-omic analysis (scWB)’) with data driven algorithmic labeling of cell identity by SingleR based on reference bulk RNA-seq profiles of pure cell populations from the Blueprint Consortium. d, Proportions of neutrophils and lymphocytes and HLA-DRA and HLA-DRB1 gene expression in classical monocytes in healthy controls (HC) (n = 6), sepsis (n = 26) and post-cardiac surgery (CS) (n = 7) samples. Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. e, Clustering results at varying resolutions (0.8–1.1) for fine annotation strategy. f–i, Heat maps of gene markers for (f) PADI4+ immature neutrophils (g) IL1R2+ immature neutrophils (h) MPO+ immature neutrophils/progenitors and (i) cycling neutrophil progenitors. j, UMAP of neutrophils (excluding degranulating neutrophils) with RNA velocity stream directions plotted. k, Partition-based graph abstraction analysis of RNA velocity predicted cellular trajectory transitions. Neu, neutrophils.

Extended Data Fig. 2 ScWB healthy control (HC)-post-cardiac surgery (CS) and CS-sepsis cell type/state differential abundance (DA) analysis and sepsis neutrophil-CD4+ T cell immunosuppression.

a, DA analysis using graph neighborhood-based method to detect enrichment of neighborhoods between CS and HC samples, with UMAP of sampled neighborhoods and statistically significant enrichment colored (spatial FDR < 0.05 with generalized linear modeling). b, Beeswarm plot of CS-HC DA analysis with cluster labels of neighborhoods depicted. c, UMAP of DA analysis between CS and sepsis. Nhood, neighborhood; logFC, log fold change. d, Proportions of immature neutrophil populations as a percentage of total neutrophils across comparator groups (n = 26 sepsis, n = 6 HC, n = 7 CS). Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. e, Percentage of live, non-apoptotic CD4+ T cells after 72–96 hours of co-culture with either HC or sepsis neutrophils. Error bars denote standard error of the mean. f, Correlations (Spearman’s Rho) of proportions of each neutrophil subset as measured from single-cell RNA and cell surface protein profiling from sepsis patients with neutrophil-CD4+ T cell co-culture suppression readouts (n = 11). g, Percentage of CD4+ T cells proliferating, expressing PD-1 or expressing CD69 in sepsis neutrophil-allogeneic T cell co-cultures with various treatments compared to untreated (UT) control (n = 6). Untreated (UT), 15 μM TG6−10-1 (PG EP2 receptor antagonist), 15 μM GW-627368 (PG EP4 receptor antagonist), 10 μM indomethacin (COX inhibitor), 10 ng/mL anti-PD-L1/2, 1 mM L-arginine + 1 μM arginase-1 inhibitor. Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. h, Gene set enrichment analysis of MsigDB C2 prostaglandin related pathways for sepsis vs. HC neutrophil differential gene expression. i, Violin plots of gene set scoring of mature and immature neutrophils in sepsis and healthy control (HC) samples by gene signatures of granulocytic myeloid-derived suppressor cells (G-MDSC)22,23. P-values for gene set scoring were calculated with two-sided Wilcoxon rank-sum tests while P-values for co-culture inhibition reversal were calculated with two-sided Wilcoxon signed-rank tests. Neu, neutrophils; Cycling progen, cycling neutrophil progenitors; IL1R2+/IL1R2+ immature, IL1R2+ immature neutrophils; MPO+, MPO+ immature neutrophils or progenitors; PADI4+, PADI4+ immature neutrophils; PG, prostaglandin; COX, cyclo-oxygenase; NES, normalized enrichment score; FDR, false discovery rate. *P < 0.05, ****P < 0.0001.

Extended Data Fig. 3 scWB convalescent sepsis neutrophil analysis and scHSPC hematopoietic stem and progenitor cell (HSPC) RNA based identity mapping.

a, Boxplots of proportions of neutrophil states in HC vs. convalescent sepsis samples from scWB. Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. b, Proliferative fraction of anti-CD3/28 bead-stimulated CD4+ T cells after co-culture with neutrophils at a 4 neutrophil:1 T cell ratio, compared to positive controls of CD4+ T cells cultured with anti-CD3/28 beads alone (n = 10 HC, n = 9 convalescent sepsis). Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. c,d, HSPC (right) mapping to (c) Seurat bone marrow mononuclear cell (BMMC) dataset24 (left) and (d) healthy donor BMMC dataset from Granja et al.25 (left). e, UMAP of HSPCs clustered on Harmony batch corrected RNA and chromatin reduced dimensions after non-HSPCs were filtered out (46,156 cells; Methods). f, UMAP colored by RNA prediction identities from HSPC mapping to Seurat BMMC dataset. g, UMAP of progenitor cells alone clustered on Harmony batch corrected RNA and chromatin reduced dimensions (16,820 cells; Methods). h, Boxplots of progenitor cell clusters as a proportion of total progenitor cells (n = 15 acute sepsis, n = 7 HC). Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. i, Heat map of genes defining progenitor clusters with known lineage defining genes highlighted. j, Boxplots of HSC clusters as a proportion of total HSCs (n = 15 acute sepsis, n = 7 HC). Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. k, UMAP of CD34+ HSPCs from annotated Human Cell Atlas bone marrow mononuclear cell (BMMC) scRNA-seq data26 for deriving granulopoiesis gene set (Methods). Neu, neutrophils; c/pDC, conventional/plasmacytoid dendritic cell; GMP, granulocyte monocyte progenitor; LMPP, lymphoid-primed multipotential progenitors; NK, natural killer; Prog, progenitor; Mk, megakaryocyte; RBC, red blood cell; Eryth, erythrocyte; CMP, common myeloid progenitor; Mono, monocyte; CD4 N1/2, CD4+ naïve T cell 1/2; CD4 M, memory CD4+ T cell; CD8 CM, central memory CD8+ T cell; PC, plasma cell; MDP, monocyte/dendritic cell progenitor; MultiLin, multilineage progenitor; MEP, megakaryocyte/erythroid progenitor; MKP megakaryocyte progenitor; ERP, erythrocyte progenitor; Eo, eosinophil; Mast, mast cell.

Extended Data Fig. 4 scWB scRNA-seq data substructure and concordance with transcriptomic sepsis response signature (SRS) endotypes.

a, First two principal components from principal components analysis of pseudobulked single-cell expression profiles of sepsis samples colored by SRS assignment. b, Pearson’s correlation of fold change in gene expression between SRS1 vs. non-SRS1 (sample level pseudobulks) in scWB and cohort in which SRS was originally derived5. c, Consensus clustering of pseudobulked single-cell gene expression profiles of sepsis samples (all cells per sample) at the level of two clusters. d, Overlaps of consensus clustering classification vs. SRS classification. e, Unsupervised hierarchical clustering of pseudobulked single-cell expression profiles of sepsis samples. SRS, sepsis response signature.

Extended Data Fig. 5 scWB scRNA-seq SRS gene expression analysis and SRS neutrophil functional interrogation.

a, b, Volcano plots of DGE analysis between SRS groups for pseudobulked (a) neutrophil states and (b) mononuclear cells (red denoting genes with fold change > 1.2 and FDR < 0.1) (positive fold change denoting upregulation in SRS1). c, Correlation (Spearman’s Rho 95% confidence interval) of SRS1 sample median neutrophil IL1R2 gene expression as measured by scRNA-seq with median neutrophil IL1R2 cell surface protein expression as measured by flow cytometry (n = 14). d, Proliferative fraction of anti-CD3/28 bead-stimulated CD4+ T cells after co-culture with neutrophils at a 4 neutrophil:1 T cell ratio, compared to positive controls of CD4+ T cells cultured with anti-CD3/28 beads alone (n = 6 SRS1, n = 13 non-SRS1). Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. e, Neutrophil phagocytosis with and without GM-CSF stimulation (n = 15). Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. P-values for neutrophil functional assays were calculated with two-sided Wilcoxon rank-sum tests. SRS, sepsis response signature; FDR, false discovery rate; ns, not significant. Neu, neutrophils.

Extended Data Fig. 6 MmV validation of neutrophil immaturity underlying SRS1.

a, First two principal components from principal components analysis of n = 42 sepsis patient samples (36 individuals) for peripheral blood bulk RNA-seq (23,063 genes). b, Kaplan-Meier survival by SRS (log-rank test P-value, 95% confidence intervals). c, Heat map of 15 immune cell clusters (with neutrophil clusters merged; Methods) from clustering on 41 protein markers for n = 41 sepsis (36 individuals) and n = 11 healthy control (HC) samples. d, UMAP of nine broad immune cell types (2,287,410 cells) from CyTOF immunophenotyping. e, Marker enrichment modeling (MEM) score heat map of eight neutrophil clusters (1,921,471 cells), identified from neutrophil subclustering on 17 selected protein markers. f, g, UMAP of (f) neutrophil subsets showing (g) density-based distribution by SRS/healthy control (HC) status. h, Neutrophil cluster frequency as a proportion of all cells with proportional differences across SRS analyzed by generalized linear mixed models (n = 41 samples, 36 individuals). i, Diffusion map dimensionality reduction of neutrophils. Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. j, Diffusion components 1 and 2 with black line depicting trajectory identified by principal curve fitting. Color reflects pseudotime value of cell with red indicating early pseudotime and blue late pseudotime. k, Dotplot of neutrophil subsets according to pseudotemporal ordering from principal curve trajectory with median and interquartile range. l, Density distribution of neutrophils over pseudotime with Kolmogorov-Smirnov test for SRS1 vs. non-SRS1 distributions. m, CyTOF PCA using proportions of all cells (left), only mononuclear cells (MNC, middle) and only neutrophils (right) with 95% data ellipses (assuming a multivariate t-distribution). SRS, sepsis response signature; cMono, classical monocyte; ncMono, non-classical monocyte; NK cell, natural killer cell; Neu, neutrophil; pro-neu, pro-neutrophil; pre-neu, pre-neutrophil; imm neu, immature neutrophil; int neu, intermediate neutrophil; int inf neu, intermediate inflammatory neutrophil; mature neu, mature neutrophil; MNP, mononuclear phagocytes; DC, diffusion component; FDR, false discovery rate. * FDR < 0.05, ** FDR < 0.01, *** FDR < 0.001.

Extended Data Fig. 7 MmV factor analysis (MOFA+) and brWB-CID validation of SRS1 IL1R2+ immature neutrophil expansion.

a-j MmV. a, Differentially expressed modules in whole blood bulk RNA-seq (FDR < 0.01, two-sided Wilcoxon rank-sum test) (n = 26 SRS1, n = 16 non-SRS1). Violin plots with median, 95% confidence interval and interquartile range. b, Enrichment of neutrophil related gene sets of weighted gene co-expression network analysis (WGCNA) modules upregulated in SRS1. c, Correlation (and 95% confidence interval) of module 10 eigengene expression with percentage of immature neutrophils from mass cytometry (CyTOF) per sample of mmV. d, Leading edge analysis for enrichment of scWB derived IL1R2+ immature neutrophil defining signature in module 10. e, IL1R2+ immature neutrophil (top) and cycling neutrophil progenitor (bottom) proportions following total leukocyte bulk transcriptomics deconvolution of n = 542 sepsis patients (brWB-CID microarray data)5,18. Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. f, Input data into MOFA+77 model consisting of three modalities (105 plasma proteins, 33 module eigengenes (eigen) from WGCNA and 22 cell clusters from CyTOF) for n = 36 sepsis samples with one and six missing samples for CyTOF and plasma proteomics respectively. g, Scatter-plots of latent factor values for sepsis patients. h, Factor values by SRS groups (two-sided Wilcoxon rank-sum test) (n = 26 SRS1, n = 16 non-SRS1). Violin plots with median, 95% confidence interval and interquartile range. i, Variance decomposition showing percentage variance explained for individual modalities by each latent factor. j, Latent factor two weights per cell cluster (top) and gene module (bottom). k, Latent factor one weights per cell cluster (top) and gene module (bottom). SRS, sepsis response signature; Neu, neutrophil; imm neu, immature neutrophil; int inf neu, intermediate inflammatory neutrophil; pro-neu, pro-neutrophil; pre neu, pre-neutrophil; cMono, classical monocyte; ncMono non-classical monocyte; intMono, intermediate monocyte; cMono_1; Ki67+, cMono_2: CD163+CD354+Ki67+, cMono_3: CD163+, cMono_4: CD354+Ki67+. NES, normalized enrichment score; FDR, false discovery rate. * FDR < 0.05, ** FDR < 0.01, **** FDR < 0.0001, NS, not significant.

Extended Data Fig. 8 Hematopoietic stem cell (HSC) SRS differential abundance (DA) and clusters C5 and C7 peak set analysis.

a, HSC DA across SRS with UMAP showing sampled neighborhoods colored by statistically significant enrichment (spatial FDR < 0.05). b, c, Volcano plots of differentially accessible (DA) peaks (fold change > 1.5, FDR < 0.05) for HSC (b) cluster C5 vs. all other clusters and (c) cluster C7 s. all other clusters (Wilcoxon rank-sum test). d, e, Enrichment of bulk ATAC-seq profiles in differentially open peaks for clusters (d) C5 (184 peaks) and (e) C7 (718 peaks). f, g, Enrichment of transcription factor (TF) motifs of differentially open peaks for clusters (f) C5 and (g) C7. h, ChIP-seq overlap analysis for cluster C7 differentially open peaks. The Giggle score denotes a composite significance and effect size scoring (doi:10.1038/nmeth.4556). Nhood, neighborhood; SRS, sepsis response signature; FDR, false discovery rate; ChIP, chromatin immunoprecipitation.

Extended Data Fig. 9 Hematopoietic stem cell (HSC) trajectory and vector field analysis, and transcription factor (TF) knockout and overexpression analysis.

a, UMAP of HSCs with supervised pseudotime trajectory from cluster C6 (HC enriched) to cluster C7 (sepsis but not SRS1 enriched). b, STAT3 and CEBPB gene expression with pseudotime. c, Genes (left) and TF motifs (right) which change along C6−C7 pseudotime trajectory and correlate with each other (Pearson’s r > 0.5, FDR < 0.05). d, CEBPA gene expression with pseudotime. e, CellOracle68 in silico knockout (top) and overexpression (bottom) of CEBPA and CEBPB and effects on clusters C5/6/7, with arrows displaying predicted changes in cell fate after gene of interest is perturbed (Methods). f–i, HSC clusters C5/6/7 vector field analysis with dynamo70. f, UMAP of HSC clusters C5/6/7 with RNA velocity stream directions plotted. g, RNA velocity based pseudotime of HSC clusters C5/6/7. h, dynamo acceleration and curvature vector fields. i, In silico knockout of CEBPA (left), CEBPB (middle) and STAT3 (right) and effects on clusters C5/6/7 with arrows displaying predicted changes in cell fate after gene of interest is perturbed. j, dynamo Jacobian analysis of gene-gene regulatory relationships. Scatter-plots show normalized expression values of genes of interest along x and y axes. Blue denotes inhibition of the numerator gene by the denominator gene in the Jacobian partial derivative, while red denotes activation of the numerator by the denominator.

Extended Data Fig. 10 SRS1 signatures across differing clinical contexts of infectious disease.

a–e, BrWB-CID contexts 2–7. a, Cycling neutrophil progenitor proportions following whole blood bulk transcriptomics deconvolution in 6 cohorts of infectious disease patients defined by infecting organism (n = 77 COVID-19 and n = 109 influenza, top), source of infection (n = 438 community acquired pneumonia and n = 229 fecal peritonitis, middle) and clinical syndrome (n = 77 adult acute respiratory distress syndrome (ARDS) and n = 106 pediatric septic shock, bottom). Boxplots denote minimum and maximum with whiskers and bottom quartile, median and upper quartile with the box. Two-sided Wilcoxon rank-sum test comparing SRS1 and non-SRS1 groups. b, Gene set enrichment analysis of scWB STAT3 gene expression programs (GEPs) (the union of all three GEPs, STAT3-union-GEP) in SRS1 upregulated genes for each of the 6 cohorts. c, IL1R2+ immature neutrophil proportions in COVID-19 whole blood scRNA-seq cohort (n = 8) after referencing mapping of dataset to scWB reference single cell atlas. d, Violin plots of scWB neutrophil STAT3 GEP expression in COVID-19 neutrophils as defined by scWB neutrophil states. Two-sided Wilcoxon rank-sum test comparing SRS1 and non-SRS1 groups. e, Violin plots of scWB neutrophil STAT3-union-GEP expression in all COVID-19 neutrophils. Two-sided Wilcoxon rank-sum test comparing SRS1 and non-SRS1 groups. SRS, sepsis response signature; Neu, neutrophil. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Supplementary information

Supplementary Information

Supplementary Note 1.

Reporting Summary

Supplementary Table 1

Clinical data for cohorts 1 and 3.

Supplementary Table 2

Antibodies used for cell surface protein profiling for scWB on BD Rhapsody platform.

Supplementary Table 3

Gene markers for each annotated cell type/state in scWB single-cell profiling and top 50 genes of each program for neutrophil states from scWB neutrophil consensus non-negative matrix factorization.

Supplementary Table 4

MsigDB Hallmark pathways and prostaglandin gene sets enriched from scWB neutrophil consensus non-negative matrix factorization.

Supplementary Table 5

Differentially accessible peaks (fold change > 1.5, FDR < 0.05) for HSC clusters C5 and C7.

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Kwok, A.J., Allcock, A., Ferreira, R.C. et al. Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis. Nat Immunol 24, 767–779 (2023). https://doi.org/10.1038/s41590-023-01490-5

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