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An immune-cell signature of bacterial sepsis

Abstract

Dysregulation of the immune response to bacterial infection can lead to sepsis, a condition with high mortality. Multiple whole-blood gene-expression studies have defined sepsis-associated molecular signatures, but have not resolved changes in transcriptional states of specific cell types. Here, we used single-cell RNA-sequencing to profile the blood of people with sepsis (n = 29) across three clinical cohorts with corresponding controls (n= 36). We profiled total peripheral blood mononuclear cells (PBMCs, 106,545 cells) and dendritic cells (19,806 cells) across all subjects and, on the basis of clustering of their gene-expression profiles, defined 16 immune-cell states. We identified a unique CD14+ monocyte state that is expanded in people with sepsis and validated its power in distinguishing these individuals from controls using public transcriptomic data from subjects with different disease etiologies and from multiple geographic locations (18 cohorts, n= 1,467 subjects). We identified a panel of surface markers for isolation and quantification of the monocyte state and characterized its epigenomic and functional phenotypes, and propose a model for its induction from human bone marrow. This study demonstrates the utility of single-cell genomics in discovering disease-associated cytologic signatures and provides insight into the cellular basis of immune dysregulation in bacterial sepsis.

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Fig. 1: Cohort definition and analysis strategy.
Fig. 2: scRNA-seq identifies sepsis-specific immune-cell states and gene signatures.
Fig. 3: Analysis of the MS1 cell state as a sepsis marker.
Fig. 4: Induction and characterization of MS1 monocytes.

Data availability

scRNA-seq data is available for visualization and secondary analysis through the Broad Institute Single Cell Portal (https://singlecell.broadinstitute.org/single_cell): SCP548 (subject PBMCs) and SCP550 (bone-marrow stimulation). Expression matrices are also deposited in SCP. Raw sequencing data are available for download from the controlled-access data repository Broad DUOS (https://duos.broadinstitute.org/).

Code availability

Code used for data analysis are available at https://github.com/reyes-m/sepsis_signature.

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Acknowledgements

We thank the subjects in our clinical cohorts for participating and donating blood for our study. We are also grateful to the clinical research coordinators and fellows who conducted patient enrollment and performed blood collections: B. Parry, K. Gohres, J. Lee, J. Hwang and K. Richards (MGH); K. Cummins, A. Higuera-Moreno, M. Pinilla-Vera and M. Benson (BWH). We thank A.-C. Villani, A. Arazi, J. Wenger and other members of the Blainey and Hacohen Labs (Broad Institute) for helpful discussions. We thank the Broad Flow Cytometry core for assistance in cell-sorting experiments, and the Broad Genomics Platform for assistance in sequencing. We also thank P. Khatri, S. Schaffert and M. Donato (Stanford University) for advice on bulk gene expression deconvolution and meta-analysis of published datasets. This work was supported by a SPARC award from the Broad Institute (M.B.G.), awards from the Ellison Foundation and the Allergan Foundation (M.B.G.), BARDA 1DSEP160030-01-00 (D.T.H.) and NIH NIAID U24 AI118668 (N.H. and P.C.B.). N.H. was supported by the David P. Ryan, MD Endowed Chair in Cancer Research, R.P.B. was supported by a Mentored Clinical Scientist Research Career Development Award from the NIH (1K08AI119157-04) and P.C.B. was supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.

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Affiliations

Authors

Contributions

M.R. designed and performed experiments, and analyzed the data with clinical input from M.R.F., R.P.B., R.M.B. and M.B.G.. K.B. and T.E. assisted in sorting and sample processing for single-cell RNA-seq. M.R.F., R.P.B. and M.B.G. designed the MGH clinical cohorts and supervised enrollment and specimen collection, and performed clinical adjudications. D.T.H., B.D.L. and R.M.B. supervised enrollment and specimen collection at the BWH. M.B.G., M.R.F., N.H., P.C.B., D.T.H. and R.P.B. conceived the study. M.B.G., N.H. and P.C.B. supervised the study. M.R., P.C.B. and N.H. prepared the manuscript; all authors reviewed and edited the final manuscript.

Corresponding authors

Correspondence to Paul C. Blainey or Marcia B. Goldberg or Nir Hacohen.

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Competing interests

The Broad Institute and MIT may seek to commercialize aspects of this work, and related applications for intellectual property have been filed. In addition, P.C.B. is a consultant to and equity holder in a company, 10X Genomics, whose products were used in this study.

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Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 scRNA-seq demultiplexing and quality assessment.

(a) Sample strategy for gating for hashtag oligo (HTO) positive cells based on UMI tag counts of each barcode. (b) Histogram of cells per 10X gel beads in emulsion (GEM) barcode for one representative channel. Data are shown for one channel with n = 15,304 detected GEMs. (c) tSNE plots of all cells (n = 126,351 cells total from 65 individuals) in the study colored by institution of origin of the cohort, hashtag barcode, and processing batch. Adjusted Rand index is shown for each when compared with cell state assignments. (d) Violin plots (n = 126,351 cells total from 65 individuals) of various quality control metrics for the full scRNA-seq dataset generated in this study. (e) Violin plots of genes detected across different cell-types (n = 32,341, 7,970, 9,390, 58,557, 14,299, 3,794 cells for T, B, NK, Mono, DC, and MK, respectively). Violin plots show a kernel density estimate of the data, using Scott’s rule to calculate the appropriate kernel bandwidth. The violin extends to 2x the bandwidth in both directions.

Extended Data Fig. 2 Robust identification of cell states with two-step clustering.

(a-b) Identification of immune cell types based on marker genes of low-resolution clusters. Color scale in (b) corresponds to z-scored, log2-transformed mean gene expression counts across all cells (n = 126,351 cells total from 65 individuals). (c-d) Assessment of cluster robustness for T-cells (T) (c) and monocytes (Mono) (d) (n = 32,341 and 58,557 cells for T and Mono, respectively). Boxplots show distributions of Rand indices when comparing clustering solutions with subsampled data (20 iterations). Boxes show the median and IQR for each resolution, with whiskers extending to 1.5 IQR in either direction from the top or bottom quartile. tSNE plots show final assigned states for each cell type. (e-f) Barplots showing the fraction of each patient (e) and batch (f) in each of the 16 cell states (number of patients or batches with each state is indicated).

Extended Data Fig. 3 Flow cytometry abundances of classical myeloid cell states.

(a) Gating strategy for determination of CD14 + mono, CD16 + mono, and dendritic cell abundance. (b) Correlation of fractional abundances defined by flow cytometry and scRNA-seq for each patient (n = 65 individuals). (c) Fractional abundance of the three cell types based on flow cytometry, grouped by disease state. Sample size (n) for each cohort is indicated in Fig. 1b. Boxes show the median and IQR for each patient cohort, with whiskers extending to 1.5 IQR in either direction from the top or bottom quartile.

Extended Data Fig. 4 Differentially expressed genes in immune cell states.

Top 10 differentially expressed genes (FDR < 0.05, two-tailed Wilcoxon rank-sum test) for each cell state when compared with other cells within the same cell type. Heatmaps are grouped according to the parent cell type of the different states: (a) T cells, (b) B cells, (c) NK cells, (d) monocytes, and (e) dendritic cells. n = 32,341, 7,970, 9,390, 58,557, 14,299, 3,794 cells for T, B, NK, Mono, DC, and MK, respectively. cDC, conventional dendritic cells; pDC, plasmacytoid dendritic cells; AS DC, AXL-SIGLEC6 dendritic cells.

Extended Data Fig. 5 Fractional abundance of states defined by scRNA-seq.

(a) Cell type and state composition for each patient in each cohort. (b) Pearson correlation matrix of cell states across all patients (n = 65 patients).

Extended Data Fig. 6 Disease-specific abundance of cell types and states.

Boxplots showing fractional abundances of (a) cell types and (b) states among patients grouped by patient cohort. FDR values are shown when comparing each disease state with healthy controls (two-tailed Wilcoxon rank-sum test, corrected for multiple testing of states). Sample size (n) for each cohort is indicated in Fig. 1b. (c) Boxplots showing absolute abundances of cell states among patients (for which leukocyte counts were available), grouped by patient cohort. Boxes show the median and IQR for each patient cohort, with whiskers extending to 1.5 IQR in either direction from the top or bottom quartile. n = 10, 6, 10, 3, 6, and 4 patients, for Leuk-UTI, Int-URO, URO, BAC-SEP, ICU-SEP and ICU-NoSEP, respectively.

Extended Data Fig. 7 In-depth analysis of the gene expression profile of MS1.

(a) Top 30 differentially expressed genes (among highly variable genes) when comparing MS1 against other CD14 + monocyte states (MS4 and MS2). (b) Dotplot showing enrichment of pathways (KEGG database) for upregulated genes in MS1 vs. MS2 (FDR < 0.1, edgeR exact test). n = 15,021 and 11,439 cells for MS1 and MS2, respectively. Sizes of circles are proportional to the number of gene hits in a set, whereas color represents the enrichment score of each gene set. (c) Heatmap showing the average expression of genes that are differentially expressed (FDR < 0.1, two-sided Wilcoxon rank-sum test) between all MS1 cells from each patient in the ICU-SEP cohort and all MS1 cells from each patient in the ICU-NoSEP cohort (n = 2,153 and 1,442 cells from 8 and 7 ICU-SEP and ICU-NoSEP patients, respectively). To specifically identify genes that discriminate the two patient populations, genes are filtered for expression in-group fraction > 0.4 and out-group fraction < 0.6. (d) k-selection plot to determine the number of components for non-negative matrix factorization (NMF). Dotted line indicates selected number of components for further analysis. (e) tSNE plot of MS1 cells (n= 15,021 cells) colored by patient cohort of origin. (f) Scaled TPM usage values of each gene module derived from NMF analysis. The top 20 genes in each module are shown. (g) Scatterplots showing correlation between mean gene module usage in MS1 cells and sequential organ failure assessment (SOFA) scores for Int-URO and URO patients (top row), and Bac-SEP and ICU-SEP patients (bottom row). Sample size (n) for each cohort is indicated in Fig. 1b. Significance of the correlation (Pearson r) was calculated with a two-sided permutation test. Line and shadow indicate linear regression fit and 95% confidence interval, respectively.

Extended Data Fig. 8 State-specific expression of sepsis signature genes.

(a) tSNE plots showing scaled gene expression counts across all cells (n = 126,351 total from 65 individuals) for FAIM3-PLAC828 and SeptiCyte Lab22 genes ( + or - indicates that a gene is up- or down-regulated, respectively, in sepsis). (b) Mean expression of PLAC8 in T cell (top row) and monocyte (bottom row) states across patients grouped by cohort. Sample size (n) for each cohort is indicated in Fig. 1b. (c-f) Heatmaps show state-specific expression of Sepsis Metascore12 genes (c), genes previously associated with sepsis mortality (d) or survival10,11 (e), and sepsis-linked GWAS genes (f). Color scale corresponds to z-scored, log2-transformed mean gene expression counts for cell state.

Extended Data Fig. 9 State matrix generation and performance comparison of gene-based signatures.

(a) Optimization of the number of marker genes per cell state in the basis matrix for deconvolution. Mean deconvolution accuracy is shown for pseudo-bulk gene expression data generated for each patient in our study (n = 65 patients). Accuracy is measured as high correlation or low root mean-squared error (RMSE) between predicted and true values. Dotted line indicates number of genes used for downstream analysis. (b) Gene expression correlation of all states using the signature matrix with 100 genes per cell state (1,201 total, union of all genes). (c) Scatterplot showing deconvolution accuracy (measured by Pearson correlation between true and inferred fractions) increases with median fractional abundance of cell states. (d) Summary area under the receiver operating characteristic curve (AUROCs) of the mean expression of PLAC8, CLU, and the indicated number of MS1 marker genes when classifying sepsis patients against sterile inflammation in published datasets. Top and bottom lines indicate the 95% confidence interval of the summary AUROC. Dotted line indicates number of MS1 marker genes used for downstream analysis. (e-f) Individual ROC curves of FAIM3-PLAC8 Ratio, SeptiCyte Lab, and Sepsis MetaScore on published datasets comparing sepsis vs. healthy controls (e; n= 751 total patients from 9 cohorts) or sepsis vs. sterile inflammation (f; n= 696 total patients from 7 cohorts).

Extended Data Fig. 10 scRNA-seq characterization of stimulated bone-marrow mononuclear cells.

BM mononuclear cells incubated in HSC cytokine-rich media with no treatment (NT) or 100 ng/mL LPS or Pam3CSK4 (Pam3) for 4 days. Cells (n = 8,702) are visualized on a UMAP projection and colored by (a) treatment, (b) Leiden clusters, and (c) cell-type annotations. (d) Matrixplot showing the mean log-transformed UMI counts of the top 5 differentially expressed genes (FDR < 0.01, two-tailed Wilcoxon rank-sum test) for each cluster in (b). (e) Heatmap showing differentially expressed genes (FDR < 0.01, two-tailed Wilcoxon rank-sum test) between clusters 3 (CD14 monocytes, n= 786 cells) and 14 (iMS1 cluster, n= 130 cells). (f) UMAP projections of non-stimulated BM myeloid and progenitor cells (HSC/MPP, CMP, GMP, Mono; n= 1,976 cells total) colored by cell type (left) and diffusion pseudotime (right). (g) Violin plots showing pseudotime values for each cell type in each stimulation condition. n= 1,976 and 901 cells for NT and LPS or Pam3 treatments, respectively. Violin plots show a kernel density estimate of the data, using Scott’s rule to calculate the appropriate kernel bandwidth. The violin extends to 2x the bandwidth in both directions. (h) Volcano plots showing differentially expressed genes between LPS or Pam3CSK4 and untreated cells for the HSC/MPP (n= 1,168 cells) and GMP populations (n= 519 cells). Differentially expressed genes (logFC > 0.3, p < 0.05; two-sided Wilcoxon rank-sum test) are shown in red. Known receptors (based on a previously published database51) that are differentially expressed are labelled. HSC/MPP, hematopoietic stem cells and multipotent progenitors; CMP, common myeloid progenitors; GMP, granulocyte-macrophage progenitor; MEP, megakaryocyte-erythroid progenitors; MYL, myeloblasts; RBC, red blood cells.

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Reyes, M., Filbin, M.R., Bhattacharyya, R.P. et al. An immune-cell signature of bacterial sepsis. Nat Med 26, 333–340 (2020). https://doi.org/10.1038/s41591-020-0752-4

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