Abstract
Severe dengue (SD) is a major cause of morbidity and mortality. To define dengue virus (DENV) target cells and immunological hallmarks of SD progression in children’s blood, we integrated two single-cell approaches capturing cellular and viral elements: virus-inclusive single-cell RNA sequencing (viscRNA-Seq 2) and targeted proteomics with secretome analysis and functional assays. Beyond myeloid cells, in natural infection, B cells harbor replicating DENV capable of infecting permissive cells. Alterations in cell type abundance, gene and protein expression and secretion as well as cell–cell communications point towards increased immune cell migration and inflammation in SD progressors. Concurrently, antigen-presenting cells from SD progressors demonstrate intact uptake yet impaired interferon response and antigen processing and presentation signatures, which are partly modulated by DENV. Increased activation, regulation and exhaustion of effector responses and expansion of HLA-DR-expressing adaptive-like NK cells also characterize SD progressors. These findings reveal DENV target cells in human blood and provide insight into SD pathogenesis beyond antibody-mediated enhancement.
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Data availability
Sequencing data generated in this study have been deposited to the NCBI Gene Expression Omnibus (GEO) and are accessible through GEO accession number GSE220969. The FCS files used for CyTOF data reanalysis are available at FlowRepository under the accession number FR-FCM-Z5MQ. Source data are provided with this paper.
Code availability
The code used in the present study is publicly available at https://github.com/echosun77/dengue.
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Acknowledgements
This work was supported by an Investigator Initiated Award (award number W81XWH1910235) from the Department of Defense (DoD) office of the Congressionally Directed Medical Research Programs (CDMRP)/Peer Reviewed Medical Research Program (PRMRP), Catalyst and Transformational Awards from the Dr. Ralph and Marian Falk Medical Research Trust, a National Institute of Allergy and Infectious Diseases (NIAID) grant U19 AI057229 supplement to S.E. and funds from the Chan Zuckerberg Biohub–San Francisco to S.Q. and S.E. S.E. is a Chan Zuckerberg Biohub–San Francisco investigator, who is also supported by NIAID grant RO1AI158569, an Investigator Initiated Award (award number W81XWH2210283) and an Expansion Award (award number W81XWH2110456) from the DoD office of the CDMRP/PRMRP, and a Defense Threat Reduction Fundamental Research to Counter Weapons of Mass Destruction grant (grant number HDTRA11810039). L.G. was supported by an EMBO Postdoctoral Fellowship (grant number ALTF 584–2021). Z.Y. was supported by a Thrasher Research Fund Early Career Award Program grant and a postdoctoral fellowship from the Maternal and Child Health Research Institute, Lucile Packard Foundation for Children’s Health. V.D. was supported by a Chan Zuckerberg Biohub Collaborative Postdoctoral Fellowship. M.L.R. was supported by a A. P. Giannini Foundation Postdoctoral Fellowship and the Harold Amos Medical Faculty Development Program. I.O. was supported by a Sue Merigan Student Scholar Fund in Infectious Diseases and Geographic Medicine. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank C. Blish for her guidance in the interpretation of NK cell data. Figs. 1 and 6 were created with BioRender.com. Co-first authors L.G., Z.Y., Y.X. and V.D. contributed equally to this manuscript and each reserves the right to list themselves as first authors in public outlets.
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Conceptualization: Z.Y., L.G., Y.X., V.D., S.E. and F.Z. Validation: L.G., V.D. and S.E. Methodology: Z.Y., L.G., Y.X., V.D., I.O., M.L.R., M.K.S., B.A.P., J.S., F.L., F.Z. and S.E. Software: Z.Y., Y.X., L.G., V.D., H.B.C., J.S., F.L. and F.Z. Formal analysis: Z.Y., Y.X., L.G., V.D., H.B.C., F.Z. and S.E. Investigation: L.G., Z.Y., Y.X., V.D., S.E. and F.Z. Resources: D.E.R.-S., O.L.A.-R., A.M.S., R.M.G.-R., N.B., M.I.E.-C., L.A.V.-C., E.M.R.-G., F.R., M.L.R. and J.G.M. Writing (original draft): L.G., S.E., Z.Y., V.D., Y.X. and F.Z. Writing (review and editing): L.G., S.E., Y.X., V.D. and F.Z. Supervision: S.E. and F.Z. Funding acquisition: S.E., F.Z. and S.R.Q. S.E. and F.Z. contributed equally.
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Extended data
Extended Data Fig. 1 Differences in cell subtype abundance in the viscRNA-seq 2 and CyTOF datasets are comparable and do not correlate with serum viral load.
a, Dot plot depicting examples of marker genes used to annotate the indicated 21 immune cell subpopulations in the viscRNAseq 2 dataset (Fig. 1). Dot size indicates the fraction of cells expressing the marker gene; color indicates expression level of the marker gene in cpm; and identification numbers of distinct cell populations refer to the UMAP in Fig. 1a. b, Cumulative distribution of Treg fractions within T cells from all patients across disease severity in the viscRNAseq 2 dataset. c, Box plots showing the fractions of cell subtypes within T cells, monocytes, and NK cell populations computed from the (publicly available) CyTOF dataset of samples obtained from the same Colombia dengue cohort. Notably, in this reanalysis, samples from patients who presented with SD upon enrollment were excluded from the dataset, leaving only those from SD patients who progressed to SD within several days following enrollment. Each dot represents a participant, color-coded by disease severity: D (orange, n = 47); SDp (pink, n = 21). The Box horizontal lines indicate the first, second (median) and third quartiles. Whiskers extend to ±1.5 × IQR. p values by two-tailed Mann–Whitney-Wilcoxon test followed by Bonferroni correction are shown. d, Cumulative distribution of Treg fractions within T cells from all patients across disease severity measured via CyTOF. e, Scatter plots showing correlations between fractions of classical monocytes, signaling NK cells, proliferating plasmablasts, and Tregs and serum viral load measured by RT–qPCR. Each dot represents a participant, color-coded by disease severity: H (green, n = 4), D/DWS (orange, n = 11), SDp (pink, n = 7). Two-tailed Spearman correlation coefficients and p values are shown below each panel.
Extended Data Fig. 2 Alterations in cell type abundance are independent of age and pregnancy status and are partially associated with prior DENV exposure.
a, UMAP of the entire dataset (left panel) and box plots showing the fractions (%) of cell subtypes within each major immune cell type (right panel) color-coded by patients’ age (in years): <10 (red, n = 7); 10-14 (pink, n = 11); 14-17 (blue, n = 5). b, UMAP (left panel) and box plots (right panel) showing distribution and fraction of cells (%) color-coded by pregnancy status: pregnant (red, n = 1); non-pregnant (gray, n = 14). c, Box plots showing the fractions (%) of cell subtypes within each major immune cell type by disease severity (SDp and D/DWS) and DENV exposure (primary and secondary). Each dot represents a participant, color-coded by disease severity and DENV exposure: D/DWS-primary (light blue, n = 5); SDp-primary (dark blue, n = 2); D/DWS-secondary (light orange, n = 5); SDp-secondary (dark orange, n = 7). d,e Two (c)- and three (d) dimensional Support Vector Machine (SVM) classifiers for SDp versus D/DWS using the fraction of cells indicated on the axes. Accuracy is evaluated using leave-one-out cross-validation. For this prediction, we trained a support vector machine (SVM) regression model with a third-degree polynomial kernel using the class NuSVC in scikit-learn. We chose SVMs partly because they have a straightforward geometrical interpretation as one can directly plot the hypersurface with the nullcline of the decision function (black dashed curve in d and gray surface in e). Each dot represents a participant, color-coded by disease severity: D/DWS (orange, n = 11); SDp (pink, n = 7). The horizontal lines of boxes in panels a-c indicate the first, second (median) and third quartiles. Whiskers extend to ±1.5 × IQR.
Extended Data Fig. 3 APCs from SDp show signatures of increased activation but decreased antigen presentation.
a, Schematic illustrating the pairwise comparison strategy used to identify DEGs between SDp and D patients. Pairs were generated considering only patients that showed n ≥ 5 cells in a cell type or n ≥ 3 in a cell subtype. For each gene, we calculated the geometric mean expression in various cell subtypes for each patient in the pair and the log2 fold change in expression between the two patients in the pair. A median log2 fold change was then obtained for each gene by analyzing all pair combinations. DEGs were defined as those with a median log2 fold change greater than 1 or smaller than -1. b, Pathway analysis showing top 10 upregulated and downregulated gene ontology (GO) terms between SDp and D in monocytes. DEGs for downstream pathway analysis were identified by a 2-sample Kolmogorov–Smirnov test using anndataks 0.1.3. Genes with >1 log2 fold change between the two groups and a p value <= 0.05 (after FDR correction for multiple hypotheses). Metascape55 and GSEAPY56 open source software were used for pathway analysis of groups of up- or downregulated genes. c, Scatter plots depicting log2 fold change in expression between SDp and D measured at the transcript level via viscRNAseq 2 (x axis) and at the protein level via CyTOF (y axis). Dashed lines depict the log2 fold change cutoffs (|log2 fold change| <1 in the viscRNA-seq dataset; |log2 fold change| < 0.2 in the CyTOF dataset). Shapes represent specific cell types. Each symbol represents a single cellular factor color coded based on the expression pattern (red: upregulated in both datasets; blue: downregulated in both datasets; gray: unaltered in the two datasets). Notably, in this reanalysis of the CyTOF dataset, samples from patients who presented with SD upon enrollment were excluded. d, Violin plots showing expression (Log2 CPM) of CD163, FCGR1A and FCGR2A in monocytes, cDCs and B cell subtypes in healthy (gray) and DENV+ patients including SD and D (red). e, Violin plots showing expression of CD163, CD64 and CD32 proteins on B cells (CD19+), classical monocytes (CD14+CD16−), nonclassical monocytes (CD14−CD16+), intermediate monocytes (CD14+CD16+), cDC1s (CD141+) and cDC2s (CD1c+) measured via spectral flow cytometry in DWS patient-derived PBMCs. Data are shown as fold change (FC) of mean fluorescence intensity (MFI) above healthy control. Dotted lines represent mean values of healthy controls normalized at value 1; horizontal line in each violin plot represents median value. Data in panel e are combined from two independent experiments with biologically independent samples from healthy (n = 6) and DENV+ patients (n = 6).
Extended Data Fig. 4 DEGS between SDp and D in APCs are only partially associated with prior DENV exposure.
Differentially expressed genes (DEGs) via pairwise comparison of patient averages (see Methods) between D and SDp (Fig. 2a–c) were analyzed based on DENV exposure (Secondary versus Primary) in monocyte, cDC, and B cell populations (Box plots, left) and the corresponding distinct cell subtypes (heatmaps, right). Data are color-coded based on the median log2 fold change of pairwise comparisons. The box horizontal lines indicate the first, second (median) and third quartiles. Whiskers extend to ±1.5 × IQR. n = 15 participants: D (n = 8); SDp (n = 7).
Extended Data Fig. 5 NK cells in SDp show activation and adaptive-like signatures that may be linked to a prior DENV exposure.
a, Pathway analysis showing top 10 upregulated and downregulated gene ontology (GO) terms between SDp and D in NK cells (see Extended Data Fig. 3b for technical details). b, Box plot showing the fraction of NK cells expressing HLA-DRA gene out of the total NK cell population in D/DWS (n = 11) and SDp (n = 7) participants. Box plots’ horizontal lines indicate the first, second (median) and third quartiles. Whiskers extend to ±1.5× IQR. p value by two-tailed KS test is shown. c, Violin plots showing CD32 protein expression in CD56bright NK cells and CD56dimCD16+ NK cells measured via spectral flow cytometry in patient-derived PBMCs. Data are shown as fold change (FC) of mean fluorescence intensity (MFI) above healthy control from two independent experiments with n = 6 participants. Dotted line represents mean value of healthy controls normalized to 1; horizontal line in each violin plot represents median value. d, Histograms showing the percentage of pHrodo Bioparticles positive cells in distinct cell subtypes derived from healthy (gray) and DENV-infected patients measured at 4 °C (cyan) or 37 °C (red). Dot plot showing bioparticle uptake quantification as FC of mean MFI above 4 °C control. Each dot represents a participant, color-coded by disease status: healthy (gray, n = 5); DENV-infected (red, n = 5) from two independent experiments. Error bars represent mean values ± SD. e, DEGs between D and SDp detected in regulatory T cells (Tregs). Data are aggregated from all patients and color-coded based on log2 fold change. f, Differentially expressed genes (DEGs) of patient averages (see Methods) between D and SDp (Fig. 3a–c) analyzed via pairwise comparison based on DENV exposure (Secondary versus Primary) for NK cell, plasmablast and T cell populations (Box plots, left) and the corresponding distinct cell subtypes (heatmaps, right). Data are color-coded based on median log2 fold change of pairwise comparisons.
Extended Data Fig. 6 B cells from DENV-infected patients harbor replicating virus.
a, Stack bar plots showing the absolute numbers and distribution of VHCs across cell types in each of the 10 patients with detectable vRNA. b, Cumulative distributions of DENV reads/million reads per cell in B cell subtypes. p values by KS test followed by Bonferroni correction between distinct B cell subtypes are indicated. c, Flow cytometry gating strategy used to define B cells (CD19+), T cells (CD3+), classical monocytes (CD14+CD16−), nonclassical monocytes (CD14−CD16+), intermediate monocytes (CD14+CD16+), CD56bright NK cells, CD56dimCD16+ NK cells, pDCs (CD123+CD303+), cDC1s (CD141+) and cDC2s (CD1c+). d, Distributions of intracellular expression of DENV envelope (E) protein measured via spectral flow cytometry in B cells (CD19+), classical monocytes (CD14+CD16−), nonclassical monocytes (CD14−CD16+), intermediate monocytes (CD14+CD16+), cDC1s (CD141+), cDC2s (CD1c+), pDCs (CD123+CD303+), CD56bright NK cells, CD56dimCD16+ NK cells, and T cells from the indicated DENV-infected patients (n = 6 participants from two independent experiments) with viremia ranging from 105 to 109 viral copies/mL. e, Number of negative strand RNA-harboring cells (VHCs) over total number of VHCs across immune cell types in three DWS patients with highest vRNA reads shown in Fig. 4a. f, Dot plot showing the average ratio of negative strand over total strand of housekeeping genes (gray) and DENV RNA (orange) reads within the 20 single cells with the largest DENV read counts in three DWS patients with highest vRNA reads shown in Fig. 4a.
Extended Data Fig. 7 Gating strategy and DEGs in VHCs versus bystander cells.
a, FACS gating strategy used to sort populations of B cells (CD19+, CD20+) and CD19−CD20− CD3−HLA-DR+ cells for co-culture experiments. b, Scatter plots showing correlations between viral load in serum and viral load in Huh7 cell lysates (DENV copies /mL) following 5-day co-culture either with PBMCs or with CD19−CD20− CD3−HLA-DR+ and B cell (CD19+, CD20+) fractions from the same patients (n = 5). Two-tailed Spearman correlation coefficients and p values are shown below each panel. c, Violin plots showing log2 fold change of DEGs between VHCs and corresponding bystander monocytes and NK cells in three DWS patients with highest vRNA reads shown in Fig. 4a. DEGs were identified by the median log2 fold change of 100 bootstrapped comparisons between VHCs and equal numbers of subsampled bystander cells. Note large noise driven by the small number of viral reads in these cell subtypes.
Extended Data Fig. 8 Cell-cell communications are altered in SDp.
a, Bar plot showing the number of candidate interactions in each cell type in SDp (top) and D (bottom) based on different expression thresholds (2%, 4%, and 6%). b, Heatmap showing the number of candidate interactions between the indicated cell types in SDp (top rectangle) and D (bottom rectangle). c, d, Heatmaps showing the number of upregulated and inversely regulated DEIs shown in Fig. 6c and d, respectively, across cell types. e, Bar plot showing the number of up- and downregulated interacting DEGs across cell types in SDp versus D. f, Split dot plots showing gene expression in representative DEIs. Top half of each spilt dot plot depicts the expression level in SDp; bottom half depicts the expression level in D; spilt dot plot size depicts the percentage of gene expressing cells in the cell types indicated; and color depicts gene expression level in counts per million (cpm). Red outline indicates the condition with the higher gene expression. g, Example of label randomization for the S100A8-CD36 interaction. The red dot indicates the log2 fold change of the expression of either gene in the corresponding cell type in SDp vs D. The 1000 gray dots indicate the log2 fold changes after randomly picking SDp and D cells following cell shuffling. p value < 0.001 was calculated using a nonparametric method: 1,000 randomizations of the data were computed and for each of them it was assessed if the fold change was larger than in the real data. In none of the randomization this condition was satisfied. h, Violin plots depicting ligand-receptor expression (S100A8-CD36 interaction) in each patient.
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Cell fraction source data.
Source Data Fig. 2
Flow cytometry source data.
Source Data Fig. 3
Flow cytometry source data.
Source Data Fig. 4
Viral RNA-harboring cells source data.
Source Data Fig. 5
Cytokines source data.
Source Data Extended Data
Statistical source data for Extended Data Figs.
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Ghita, L., Yao, Z., Xie, Y. et al. Global and cell type-specific immunological hallmarks of severe dengue progression identified via a systems immunology approach. Nat Immunol 24, 2150–2163 (2023). https://doi.org/10.1038/s41590-023-01654-3
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DOI: https://doi.org/10.1038/s41590-023-01654-3
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