Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue

Abstract

Dengue virus (DENV) infection can result in severe complications. However, the understanding of the molecular correlates of severity is limited, partly due to difficulties in defining the peripheral blood mononuclear cells (PBMCs) that contain DENV RNA in vivo. Accordingly, there are currently no biomarkers predictive of progression to severe dengue (SD). Bulk transcriptomics data are difficult to interpret because blood consists of multiple cell types that may react differently to infection. Here, we applied virus-inclusive single-cell RNA-seq approach (viscRNA-Seq) to profile transcriptomes of thousands of single PBMCs derived early in the course of disease from six dengue patients and four healthy controls and to characterize distinct leukocyte subtypes that harbor viral RNA (vRNA). Multiple IFN response genes, particularly MX2 in naive B cells and CD163 in CD14⁺ CD16⁺ monocytes, were up-regulated in a cell-specific manner before progression to SD. The majority of vRNA-containing cells in the blood of two patients who progressed to SD were naive IgM B cells expressing the CD69 and CXCR4 receptors and various antiviral genes, followed by monocytes. Bystander, non-vRNA-containing B cells also demonstrated immune activation, and IgG1 plasmablasts from two patients exhibited clonal expansions. Lastly, assembly of the DENV genome sequence revealed diversity at unexpected sites. This study presents a multifaceted molecular elucidation of natural dengue infection in humans with implications for any tissue and viral infection and proposes candidate biomarkers for prediction of SD.

Keywords: biomarkers; dengue; single cell; transcriptomics; virus–host interactions.

Fig. 1.

FACS-assisted viscRNA-Seq workflow on PBMCs from DENV-infected and healthy control human subjects. (A and B) Blood samples were collected from human subjects enrolled in the Colombia cohort (healthy, dengue, and SD). (C) PBMCs were isolated via Ficoll centrifugation and stained with three antibody panels to distinguish various cell types: T, B, NK, DC, and monocytes. (D) Single cells from each aliquot were sorted and processed by viscRNA-Seq to simultaneously quantify single-cell virus abundance and host transcriptome changes. (E) The information provided for each single cell includes: cell type, immune activation, infection state, and virus population genomics. N.A., nonapplicable.

Fig. 2.

Overview of the types of PBMCs surveyed. (A) Two-dimensional representation of the cells color coded by the expression level of cell type-specific marker genes or the abundance of virus reads within the cell (>30 virus reads per million reads in samples from two SD patients, p1-026-1 and p1-036-1). (B) Number of cells analyzed for each cell type from each subject, see also SI Appendix, Table S6. (C) tSNE visualizations within T, NK, B cells, and monocytes, highlighting some broad cell subpopulations. The colored lines were drawn manually following inspection of the marker genes for visualization only.

Fig. 3.

Differential expression across disease severity and cell types shows hallmarks of progression to SD. (A) Genes that are overexpressed in subjects before progressing to SD versus all other subjects across cell types and subtypes. Color (white to red) indicates the average log-fold change; size of the dot indicates lower P value in a distribution statistical comparison (two sample Kolmogorov–Smirnov). (B) Many inflammatory genes such as IFITM1 are expressed ubiquitously during both mild and SD infection. (C) Other genes such as IFIT3 are specifically expressed before the development of SD in various types of lymphocytes. (D) A number of genes show double specificity for both SD and a single cell type, for instance CD163 in monocytes. (E) Averaging across cells within specific cell types and subtypes indicates promising candidate predictors of SD as assessed by ROC curves at increasing discriminatory thresholds for gene expression versus disease severity. The numbers after the gene name indicate log-twofold changes of average expression in patients progressing to SD versus other dengue patients, indicating an overexpression of these genes by a 100-fold or more in our cohort. D, dengue; H, healthy subject.

Fig. 4.

Naive B cells are the main cell type containing DENV RNA in two SD patients. (A) Fraction of cells containing vRNA across cell types from the two subjects and relative amount of virus RNA from each cell. (B) vRNA-containing B cells from the same subjects show a higher expression of specific surface receptors (CXCR4, CD69) and immune activation genes (IRF1, FCRL1). (C) tSNE visualization of the B cells from the two subjects. The expression level of DENV RNA and MS4A1 (CD20), TCL1A, JCHAIN, IGHM, and IGHG1 are highlighted. (D) Fractional identity of heavy chain V loci to their germ-line counterparts in vRNA-containing IgM, bystander IgM, and IgG B cells from the subjects 1-026-1 and 1-036-1. (E) Coverage (red) and minor allele frequency (MAF, blue) along the DENV genome in the viral reads from all cells from patient sample 1-026-1 show the genetic diversity of the virus population. (F) Site-specific Shannon entropy of a cross-sectional DENV serotype 3 alignment does not correlate with entropy from the viral reads of patient sample 1-026-1. Only sites with a coverage of 200 or more reads are considered (dashed green line in E). (G) B cells that do not contain DENV (bystanders) but are derived from subjects with vRNA-containing cells (B, blue) show a clear IFN response compared with B cells derived from healthy controls (H, green). (H) Graph of heavy chain CDR3 antibody clonality showing clonal expansion of IgG1 plasmablasts in patients 1-013-1 and 1-020-1 (no viral reads were detected in these two patients). Each dot is a unique antibody sequence; larger size corresponds to more somatic hypermutation. Clonal families CF1 and CF2 referred to in the text are labeled.