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Cellular and transcriptional dynamics of human neutrophils at steady state and upon stress

Nature Immunology volume 23, pages 1470–1483 (2022)Cite this article

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Abstract

Traditionally viewed as poorly plastic, neutrophils are now recognized as functionally diverse; however, the extent and determinants of neutrophil heterogeneity in humans remain unclear. We performed a comprehensive immunophenotypic and transcriptome analysis, at a bulk and single-cell level, of neutrophils from healthy donors and patients undergoing stress myelopoiesis upon exposure to growth factors, transplantation of hematopoietic stem cells (HSC-T), development of pancreatic cancer and viral infection. We uncover an extreme diversity of human neutrophils in vivo, reflecting the rates of cell mobilization, differentiation and exposure to environmental signals. Integrated control of developmental and inducible transcriptional programs linked flexible granulopoietic outputs with elicitation of stimulus-specific functional responses. In this context, we detected an acute interferon (IFN) response in the blood of patients receiving HSC-T that was mirrored by marked upregulation of IFN-stimulated genes in neutrophils but not in monocytes. Systematic characterization of human neutrophil plasticity may uncover clinically relevant biomarkers and support the development of diagnostic and therapeutic tools.

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Fig. 1: Dynamics and phenotype of neutrophils elicited by G-CSF.
Fig. 2: Dynamics and phenotype of neutrophils during HSC-T or PDAC.
Fig. 3: Functional analysis of G-CSF-elicited neutrophils.
Fig. 4: Bulk RNA-seq of neutrophils and monocytes upon G-CSF, HSC-T, IPMN or PDAC.
Fig. 5: Plasma factors underlying stress myelopoiesis upon G-CSF, HSC-T, IPMN or PDAC.
Fig. 6: Single-cell RNA-seq analysis of human neutrophils at steady state and upon stress.
Fig. 7: Transcriptome reprogramming of human neutrophils upon stress.
Fig. 8: Maturation stage-dependent transcriptome dynamics in neutrophils stimulated with IFNs.

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Data availability

Bulk and scRNA-seq data generated in this study have been deposited in ArrayExpress under accession nos. E-MTAB-11190 and E-MTAB-11188. scRNA-seq data of patients with COVID-19 27 were downloaded from FASTGenomics database at https://beta.fastgenomics.org/datasets/detail-dataset-7656cfe94fb14a01b787f4774e555036. Source data are provided with this paper.

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Acknowledgements

We thank S. Gregori and G. Amodio for help with neutrophil isolation and culture experiments; F. Di Salvo, F. Porzio and M. Tassara for patient recruitment and data management; the Center for Omics Sciences, the Flow cytometry Resource, Advanced Cytometry Technical Applications Laboratory, Centro Risorse Biologiche at Ospedale San Raffaele; and the Centro Universitario di Statistica per le Scienze Biomediche at Vita-Salute San Raffaele University. Figures were created with Adobe Illustrator and BioRender.com. V.B. and F.V.M. conducted this study as partial fulfillment of a PhD in Molecular Medicine (Basic and Applied Immunology and Oncology program) at Vita-Salute San Raffaele University. R.D.M. is a New York Stem Cell Foundation – Robertson Investigator. M.A.C. and P.S. are supported by grants from the Italian Association for Cancer Research (AIRC) (IG 20339) and the Italian Ministry of University and Research (PRIN 20177J4E75_004). A.A. is supported by the Italian Telethon Foundation (SR-Tiget grant award B02). E.M. and N.C. are supported by fellowships from Fondazione Umberto Veronesi. This study was supported by grants from the Italian Telethon Foundation (SR-Tiget grant award F04 to R.O.) and the Italian Ministry of Health (GR-201602362156 to R.O. and S.C.). Research in the R.O. laboratory is supported by the European Research Council (starting grant 759532, X-TAM) and by AIRC (MFAG 20247 and AIRC 5×1000 special program 22737).

Author information

Author notes

  1. These authors contributed equally. Elisa Montaldo, Eleonora Lusito, Valentina Bianchessi, Nicoletta Caronni.

Authors and Affiliations

  1. San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy

    Elisa Montaldo, Eleonora Lusito, Valentina Bianchessi, Nicoletta Caronni, Serena Scala, Luca Basso-Ricci, Carla Cantaffa, Alice Masserdotti, Mattia Barilaro, Simona Barresi, Marco Genua, Francesco Maria Vittoria, Giulia Barbiera, Raffaella Di Micco, Andrea Ditadi, Matteo Maria Naldini, Bernhard Gentner, Luigi Naldini, Fabio Ciceri, Alessandro Aiuti & Renato Ostuni

  2. Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan, Italy

    Dejan Lazarevic

  3. Hematology and Bone Marrow Transplantation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Carlo Messina, Elisabetta Xue, Sarah Marktel & Fabio Ciceri

  4. Molecular Hematology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Cristina Tresoldi

  5. Immunohematology and Transfusion Medicine Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Raffaella Milani, Paola Ronchi, Salvatore Gattillo & Luca Santoleri

  6. Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Giulio Belfiori, Francesca Aleotti, Stefano Crippa & Massimo Falconi

  7. Section of General Pathology, Department of Medicine, University of Verona, Verona, Italy

    Elisa Gardiman, Nicola Tamassia, Marco Antonio Cassatella, Francesca Pettinella & Patrizia Scapini

  8. Area of Cell and Developmental Biology, Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain

    Andrés Hidalgo

  9. Singapore Immunology Network (SIgN), A*STAR, Biopolis, Singapore, Singapore

    Immanuel Kwok & Lai Guan Ng

  10. Vita-Salute San Raffaele University, Milan, Italy

    Stefano Crippa, Massimo Falconi, Luigi Naldini, Fabio Ciceri, Alessandro Aiuti & Renato Ostuni

  11. Pediatric Immunohematology and Bone Marrow Transplantation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Alessandro Aiuti

Authors
  1. Elisa Montaldo
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Contributions

E.M. and E.L. contributed to the design of the study, performed experiments, analyzed data, prepared figures and edited the manuscript; E.M. performed immunophenotype analyses together with S.S. and L.B.R. E.M., V.B. and N.C. generated transcriptome and functional data together with C.C., A.M., M.B., S.B., M.G., F.M.V., D.L., E.G., N.T. and F.P. E.L. performed computational and statistical analyses with help from G. Barbiera. C.M., E.X., S.M., C.T., R.M., P.R., S.G., L.S., G. Belfiori and F.A. selected and recruited study participants, collected biological samples and acquired clinical information under the supervision of S.C., M.F. and F.C. R.D.M., A.D., M.M.N., B.G., A.H., I.K., L.G.N. and L.N. provided key scientific inputs. M.A.C. and P.S. supervised E.G., N.T. and F.P. and contributed to data interpretation. A.A. supervised S.S. and L.B.R. and contributed to data interpretation. R.O. conceptualized and coordinated the study, acquired funding, analyzed the data and wrote the paper. All authors read and edited the manuscript.

Corresponding authors

Correspondence to Elisa Montaldo or Renato Ostuni.

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The authors declare no competing interests

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Peer review information

Nature Immunology thanks Zvi Fridlender, Hongbo Luo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Leukocyte dynamics in G-CSF-treated donors.

a. Full gating strategy used to identify leukocyte subsets in whole PB or BM samples (blue: myeloid cells; green: lymphoid cells; red: HSPC; pink: CD45+ Lineage−CD34−cells; brown: CD45− cells). Neutrophil subsets are numbered from 1 to 4 (1: SSChi CD38+ CD11c− CD10− neutrophil precursors; 2: SSChi CD38− CD11c− CD10− immature neutrophils; 3: SSChi CD38− CD11c+ CD10− immature neutrophils; 4: SSChi CD38− CD11c− CD10+ mature neutrophils). b. Gating strategy used to identify low-density neutrophils (LDNs) within PBMCs. c. Gating strategy used to identify normal-density neutrophils (NDNs) within granulocytes. d. Representative May-Grunwald Giemsa staining of NDNs isolated by bead sorting (lower row) and LDN (upper row) isolated by FACS sorting from PB of healthy donors (steady state, n = 3) and from G-CSF mobilized PB (n = 2). e,f. Absolute HSPC counts in whole PB (e) and frequency of HSPC subsets (gated on Lin− CD45+ CD34+ cells) in whole BM or PB (f) of controls or G-CSF-treated donors (PB n = 15; BM n = 14). g-j. Absolute counts of phenotypically defined hematopoietic stem cells (HSC), multipotent progenitors (MPP) and multi-lymphoid progenitors (MLP) (g), of committed common myeloid progenitors (CMP) or granulocyte-monocyte progenitors (GMP) (h) and of differentiated myeloid and lymphoid cells (i,j) in whole PB of controls or G-CSF-treated donors (n = 15). k. Representative contour plot showing immature and mature neutrophils in the whole PB of a representative G-CSF-treated donor. Gating strategies for the indicated cell types are also reported in Supplementary Table 7. Bar plot report data as mean ± SD. Numbers in red represent fold increases in the indicated conditions. Statistical analyses. e, g-j: two-sided Mann-Whitney test.

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Extended Data Fig. 2 Leukocyte dynamics in HSC-T and PDAC patients.

a. Expression of the indicated markers in NDNs and LDNs from controls (n > 10) or G-CSF-treated donors (n > 12). b, c. Histogram (b) and cumulative histogram (c) plot showing the expression levels of CD49d in neutrophil precursors, immature and mature neutrophils. d, e. Contour (d) and cumulative histogram (e) plots showing percentages of EdU+ cells within NDNs and LDNs from BM, CB and PB samples of the indicated patients (BM n = 3, CB n = 6, G-CSF n = 8, HSC-T 2° f.u. n = 3). f, g. Representative histogram (f) and cumulative histogram (g) plot showing expression of the indicated markers in in NDNs and LDNs from controls (n > 11) or G-CSF-treated donors (n > 11). h. Representation of leukocytes dynamics in HSC-T patients. i. Quantification of white blood cells (WBCs), neutrophil, monocyte and lymphocyte count in HSC-T patients. Gray intervals highlight normal ranges. Arrows indicate the beginning of myeloablative conditioning; day 0 indicates the day of HSC-T. j. Counts of monocytes or lymphocytes in PB of controls (n = 8) or HSC-T patients (1° f.u. n = 8, 2° f.u. n = 9, 3° f.u. n = 3). k,l. Percentage of neutrophil precursors, immature and mature neutrophils within PBMCs (k) or LDNs (l) in controls (n = 8) and HSC-T patients (1° f.u. n = 7, 2° f.u. n = 8). m-o. Absolute counts (m) and frequencies of HSPC subsets (n, o) in whole PB (n = 15) or BM (n = 14) of controls and PB of PDAC patients (n = 8). p. Leukocyte counts in whole PB of controls (n = 15) and PDAC patients (n = 8). q. Neutrophil-to-lymphocyte ratio (NLR) in whole PB of controls (n = 15) and PDAC (n = 8) patients. NLR is calculated as the ratio between absolute counts (FACS) of neutrophils and total lymphocytes. r. Leukocyte counts (hemocytometer) and corresponding NLR values in whole PB of controls (n = 10), IPMN (n = 12) and PDAC (n = 15) patients. s. Contour plots showing CD16 and CD11b expression in LDNs of three PDAC patients. t, u. Percentage of neutrophil precursors, immature and mature neutrophils within LDNs (t) and PBMCs (u) of controls (n = 12), IPMN (n = 12) and PDAC (n = 18) patients. v. Percentage of EdU+ cells within neutrophil precursors, immature and mature neutrophils in PB of PDAC patients (n = 6). Gating strategies for the indicated cell types are reported in Supplementary Table 7. Bar plots report data as mean ± SD. Statistical analyses. a, c, g, j, k and r: Kruskal-Wallis test plus two-sided Dunn’s multiple comparison. m, o-q: two-sided Mann-Whitney test.

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Extended Data Fig. 3 Purity of isolated cell populations.

a-d. Representative contour plots showing cell purity before and after magnetic bead selection of LDNs (a, b), monocytes (c) and NDNs (d) from PB samples. e. Representative contour plots showing the gating strategy used to isolate LDNs and monocytes (left panel) and post-sort purity analysis of sorted cells (right panel). f. Representative contour plots showing the gating strategy used to isolate BM neutrophil subsets and post-sort purity analysis of sorted cells.

Extended Data Fig. 4 Bulk RNA-Seq analysis of NDNs, LDNs and monocytes.

a, b. Principal-component analysis (PCA) plots of bulk RNA-Seq datasets of NDNs, LDNs and monocytes isolated from PB of healthy controls (n = 19), G-CSF-treated donors (n = 17), HSC-T (n = 8), PDAC (n = 15) and IPMN (n = 14) patients, as well as of neutrophil differentiation intermediates from BM of healthy donors (n = 3) and HSC-T patients (n = 7). Samples are colored based on cell type (a) or stress condition (b), as indicated by the legends. Filled area plot on the left show the frequency of neutrophil precursors (pre), immature (imm) and mature (mat) neutrophils for the corresponding NDNs and LDNs samples along PC2. c. PCA plots of bulk RNA-Seq datasets of NDNs, LDNs or monocytes. Colors represent stress condition, while shapes reflect the tissue of origin (PB circle; BM triangle), as indicated in the legend.

Extended Data Fig. 5 Validation of RNA-Seq analyses in NDNs.

a, b. Representative contour plots (a) and cumulative bar plot (b) showing the basal expression of IL-1b in NDNs and LDNs isolated from controls (n = 3) and G-CSF-treated donors (n = 3). c-e. Cumulative bar plots showing the expression of the indicated genes in NDNs isolated from controls and G-CSF-treated donors (c), HSC-T patients (d) or IPMN and PDAC patients (e). f,g. Representative histogram plots (f) and cumulative histogram plots (g) showing the expression of the indicated markers in NDNs isolated from PB of controls (n > 5) and G-CSF-treated donors (n > 5). Bar plots report data as mean ± SD. Statistical analyses. c and g: two-sided Mann-Whitney test. b, d and e Kruskal-Wallis test plus two-sided Dunn’s multiple comparison.

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Extended Data Fig. 6 Plasma factors in G-CSF-treated donors, HSC-T or PDAC patients.

a,b. Concentration of selected factors in the plasma of controls (n = 19) and G-CSF-treated donors (n = 13) (a) or controls (n = 19) and IPMN (n = 15) or PDAC (n = 18) patients (b). c. Correlation between plasma concentrations of the indicated factor and frequencies of neutrophils precursors or LDNs in the PMBC fraction. Colors indicate calculated Spearman’s correlation coefficients (p-value < 0.05). Gray, not significant. Data are shown for all experimental conditions (upper heat map) or excluding G-CSF-treated donors (lower heat map) (steady state n = 14; G-CSF n = 9; HSC-T 1° f.u. n = 7; HSC-T 2° f.u. n = 8; IPMN n = 14; PDAC n = 16) d, e. Correlation between plasma concentrations of the indicated factor and frequencies of LDNs in the PMBC fraction combining all samples together (d) or excluding (e) G-CSF-treated donors. Spearman’s correlation and p-values are shown for each plot. Bar plots report data as mean ± SD Statistical analyses. a: two-sided Mann-Whitney test; b: Kruskal-Wallis test plus two-sided Dunn’s multiple comparison; c-e: Spearman’s correlation.

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Extended Data Fig. 7 Single-cell RNA-Seq analyses of human neutrophils.

a. Gating strategy used to isolate CD15+ neutrophils from whole BM (upper panel) or PB (lower panel) samples. Expression of CD16 and CD11b in sorted cells is shown. b. UMAP plot showing donor or patient identities. c-d. UMAP plots showing the expression of gene modules related to neutrophil maturation identified in the indicated studies. e. UMAP plots showing the expression of gene modules identified from bulk RNA-Seq analysis (see Fig. 4a and Supplementary Table 9). f, g. Stacked bar plots showing the frequency of cells from PB or BM samples (f) or from donors and patients (g) for each neutrophil cluster. h. Model depicting divergent developmental trajectories in stress-elicited neutrophils, leading to diverse gene expression programs of mature cells.

Extended Data Fig. 8 CellHarmony analyses of neutrophils from G-CSF-treated donors, HSC-T or PDAC patients.

a. Heat map showing standardized average expression (computed on normalized expression levels) of developmental marker genes identified by CellHarmony and expressed in at least 20% of cells from reference datasets for the indicated neutrophil subsets in controls (reference, white bars) and PDAC patients (query, black bars). Color bars represent stages of neutrophil development after alignment of scRNA-Seq data with CellHarmony. The number of cells from reference and query datasets for each cluster is shown at the top; the number of developmental marker genes for each cluster is shown on the left. Selected representative genes are highlighted on the right. b, c. Filled area plots showing mean expression in scRNA-Seq data of selected developmental marker genes in neutrophil subsets from controls (gray) and PDAC (green) (b) or controls and G-CSF-treated donors (dark blue) or HSC-T patients (light blue) (c). Numbers on the x axis indicate the stages of neutrophil development identified by CellHarmony. d. Box plots showing standardized average expression of genes upregulated (see Methods) in the indicated neutrophil subsets from PDAC patients versus controls. Each plot refers to induced genes in query versus reference scRNA-Seq datasets for neutrophils at each stage of development defined by CellHarmony. Box plots represent the median, interquartile range (IQR), minimum (25th percentile, 1.5 × IQR) and maximum (75th percentile, 1.5 × IQR). Sample size corresponds to the number of cells indicated in the heat map (a). e. Venn diagram showing the overlap between upregulated genes in G-CSF-treated donors and HSC-T and PDAC patients in the indicated stages of neutrophil development. A selection of genes upregulated in all conditions and of stress-specific genes are shown (see Supplementary Table 37).

Extended Data Fig. 9 CellHarmony analyses of neutrophils from COVID-19 patients.

a. Scheme depicting the experimental and computational strategies used to isolate and process for scRNA-seq analysis cells from controls and COVID-19 patients. b. Heat map showing standardized average expression (computed on normalized expression levels) of developmental marker genes identified by CellHarmony and expressed in at least 20% of cells from reference datasets for the indicated neutrophil subsets in controls (reference, white bars) and COVID-19 patients (query, black bars). Color bars represent stages of neutrophil development after alignment of scRNA-Seq data with CellHarmony. The number of cells from reference and query datasets for each cluster is shown at the top; the number of developmental marker genes for each cluster is shown on the left. Selected representative genes are highlighted on the right. c. Box plots showing standardized average expression of genes up regulated (see Methods) in the indicated neutrophil subsets from COVID-19 patients versus controls. Each plot refers to induced genes in query versus reference scRNA-Seq datasets for neutrophils at each stage of development defined by CellHarmony. Box plots represent the median, interquartile range (IQR), minimum (25th percentile, 1.5 × IQR) and maximum (75th percentile, 1.5 × IQR). Sample size corresponds to the number of cells indicated in the heat map (b). d. Bar plots showing NES of selected GO categories enriched within genes expressed at higher levels in neutrophil subsets from COVID-19 patients as compared to controls. Colors represent stages of neutrophil development defined by CellHarmony. e. Violin plots showing normalized expression levels of selected genes induced in mature neutrophils from COVID-19 patients as compared to controls. Colors represent stages of neutrophil development defined by CellHarmony.

Extended Data Fig. 10 Single-cell RNA-Seq analysis of IFN-stimulated neutrophils.

a. Experimental strategy used to enrich and mix LDNs and NDNs from CB samples, ensuring a sufficient representation of all neutrophil subsets (see Methods). b, c. Representative contour plots (b) and stacked bar plot (c) showing the percentage of neutrophil precursors (pre), immature (imm) and mature (mat) neutrophils in LDN, NDN and LDN-NDN mix (1:3). d. Schematic representation of CB neutrophil stimulation and processing for scRNA-Seq analysis. e. tSNE plots showing the expression of selected developmental marker genes. f. tSNE plots showing expression of cluster 3 marker genes (corresponding to CB neutrophils) in PB and BM neutrophils from steady-state controls.

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Montaldo, E., Lusito, E., Bianchessi, V. et al. Cellular and transcriptional dynamics of human neutrophils at steady state and upon stress. Nat Immunol 23, 1470–1483 (2022). https://doi.org/10.1038/s41590-022-01311-1

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