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. 2020 Jun 25;181(7):1475-1488.e12.
doi: 10.1016/j.cell.2020.05.006. Epub 2020 May 8.

Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients

Pierre Bost  1 , Amir Giladi  2 , Yang Liu  3 , Yanis Bendjelal  4 , Gang Xu  3 , Eyal David  2 , Ronnie Blecher-Gonen  2 , Merav Cohen  2 , Chiara Medaglia  2 , Hanjie Li  2 , Aleksandra Deczkowska  2 , Shuye Zhang  5 , Benno Schwikowski  6 , Zheng Zhang  7 , Ido Amit  8
Affiliations

Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients

Pierre Bost et al. Cell. .

Abstract

Viruses are a constant threat to global health as highlighted by the current COVID-19 pandemic. Currently, lack of data underlying how the human host interacts with viruses, including the SARS-CoV-2 virus, limits effective therapeutic intervention. We introduce Viral-Track, a computational method that globally scans unmapped single-cell RNA sequencing (scRNA-seq) data for the presence of viral RNA, enabling transcriptional cell sorting of infected versus bystander cells. We demonstrate the sensitivity and specificity of Viral-Track to systematically detect viruses from multiple models of infection, including hepatitis B virus, in an unsupervised manner. Applying Viral-Track to bronchoalveloar-lavage samples from severe and mild COVID-19 patients reveals a dramatic impact of the virus on the immune system of severe patients compared to mild cases. Viral-Track detects an unexpected co-infection of the human metapneumovirus, present mainly in monocytes perturbed in type-I interferon (IFN)-signaling. Viral-Track provides a robust technology for dissecting the mechanisms of viral-infection and pathology.

Keywords: COVID-19; Viral-Track; single-cell RNA-seq; virus host interactions.

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Conflict of interest statement

Declaration of Interests The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Viral-Track Retrieves Viral Reads in a Variety of Tissues, Viral Strains, and Sequencing Platforms (A) Schematics of the Viral-Track approach. Single-cell sequencing data of cells from an infected tissue, containing infected and bystander cells are analyzed by Viral-Track. Viral-Track maps the sequenced reads to both the host reference genome and a database of viral genomes, overlaying infection status on top of the host transcriptional landscape. (B) Results of Viral-Track analysis on scRNA-seq data from influenza A PR8-infected mouse lungs. For each viral segment, represented by a dot, the complexity of the sequences (measured by entropy, i.e., how repetitive are the mapped sequences) and the percentage of the segment that is mapped are plotted. Dark red dots correspond to viral segments of the influenza A PR8 strain and yellow dots to segments belonging to other H1N1 influenza strains. Viral segments with more than 50 mapped reads are plotted. (C) Coverage plot of the influenza A segment NC_002016 (influenza A PR8 segment 7), M2 transcript location estimated using StringTie is shown below with the splicing site position. (D) Quantification of the number of reads assigned to influenza viral segments across experimental settings. Each dot corresponds to a technical replicate (384-well plate). Two-tailed Welch’s t test was used to compare viral load betwen CD45− and CD45+ cells (p = 0.039). (E) Quantification of the number of reads assigned to LCMV viral segments in the different zones of the spleen. Each dot corresponds to a technical replicate (384-well plate). Two-tailed Welch’s t test was used to compare viral load between cells from the infected marginal zone to cells from the B zone or the whole speen (p = 0.0067 and 0.0083 respectively). (F) Result of Viral-Track analysis on scRNA-seq data from a HBV patient. For each viral segment, represented by a dot, the entropy of the sequence and the percentage of the segment that is mapped is plotted. Green dots correspond to viral segments that passed quality control. Viral segments with more than 50 mapped reads are plotted. (G) Coverage plot of the HBV genome. Locations of the different viral genes from NCBI database are depicted at the bottom. (H) Enrichment of infected cells across hepatic cell subsets (left panel); red line corresponds to an enrichment of one. Distribution of the number of HBV UMIs per cell in each cell subset (right panel). See also Figure S1.
Figure S1
Figure S1
Benchmarking of Viral-Track on Diverse Infection Models, Related to Figure 1 A. Graph chart representing the different steps of the Viral-Track pipeline. B-D. Results of Viral-Track analysis performed on LCMV spleen, LCMV lymph node and VSV lymph node datasets, respectively. Viral segments with more than 50 mapped reads are plotted. (E). Number of detected LCMV (left panel) and VSV (right panel) reads in the different samples from the lymph node experiment. F. Results of Viral-Track analysis performed on the in-vitro HSV-1 data. G. Quantification of the number of HSV-1 reads in HSV-1 infected and control samples. (H). Results of Viral-Track analysis performed on the in-vitro HIV data. I. Quantification of the number of HIV reads in HIV infected and control samples. J. UMAP plot of the liver HBV data, dots are colored by cell subset assignment based on Louvain clustering. K. UMAP plot of the liver HBV data. infected cells are colored in orange and bystander cells in gray.
Figure S2
Figure S2
Comparison of Viral-Track Performance to Fluorescence Tagging Techniques, Related to Figure 2 A. Proportion of vUMI+ cells from total spleen and the LCMV-GFP+ population B. UMAP plot of the spleen LCMV data, spots are colored based on Louvain clustering. C. UMAP plot of the spleen LCMV data, bystander cells are colored in gray, vUMI+ cells are colored in red and GFP+ cells in green. D. Mean gene expression in bystander and infected MZB cells. Genes with a log2FC bigger than 1 or lower than −1 and a corrected p value lower than 0.01 are colored in orange.
Figure 2
Figure 2
Viral-Track Identifies Virus-Modified Transcription in Infected Cell Subsets (A) Distribution of vUMI+ and GFP+ cells across cells types found in the spleen. (B) Distribution of the Pearson Correlation between GFP+ cells, vUMI+, and bystander (GFP−vUMI−) cells. Two-tailed Kruskal-Wallis test. (C) Number of differentially expressed genes between bystander and infected cells in MZB cells, monocytes, and macrophages. (D) Top 10 enriched terms identified by Gene Ontology enrichment analysis. (E) Mean expression of four top differentially expressed genes in bystander and infected MZB cells. See also Figure S2.
Figure 3
Figure 3
scRNA-Seq of 6 COVID-19 Samples Reveals Myeloid Remodeling in Severe Patients (A) A 2-dimensional visualization of 50,615 single cells from three mild and six severe COVID-19 patients, generated by the MetaCell algorithm. Colors indicate grouping of cells into 27 subsets, based on transcriptional similarity (Figure S3A). (B) Quantification of the three main compartments, myeloid, lymphoid, and epithelial, across the three mild (M1–M3) and six severe (S1–S6) patients. (C) Density plots depicting projection of cells from the mild (left) and severe (right) patients on the 2D map shown in (A). (D–F) Quantification of the frequency of specific cell subsets in the myeloid (D), lymphoid (E), and epithelial (F) compartments, across the nine patients. Diamond marks patient S1, co-infected with the human metapneumovirus (Figures 4D–4H). Horizontal lines indicate mean frequency. (G) Percentage of proliferating cells (determined by thresholding over a cell-cycle-related gene module, detailed in Table S3) in each of 455 metacells, projected on the 2D map shown in (A). (H) Quantification of the type I interferon response gene module across 455 metacells, projected on the 2D map shown in (A). Color scale represents log2 fold change over the median expression of the module across all metacells. (I) Differential gene expression analysis. Each panel compares pooled gene expression between naive and non-naive CD4+ T cells (left) and effector and cytotoxic CD8+ T cells (right) cell subsets. (J) Differential gene expression analysis between cells belonging to AM (left) and SPP1hiC1Qhi macrophages (right) from mild (x axis) and severe (y axis) patients. (I and J) Values represent log2 size-normalized expression (transcripts per 1,000 UMI). See also Figure S3.
Figure S3
Figure S3
Detailed Molecular and Cellular Profiling of COVID-19 BAL Samples, Related to Figure 3 A. The confusion matrix of the MetaCell model shown in Figure 3A. Entries denote for each pair of metacells the propensity of cells from both metacells to be clustered together in a bootstrap analysis. B-D. Gene expression profiles of cells belonging to the epithelial (B), lymphoid (C), and myeloid (D). In A-D, color bars indicate association to 27 cell subsets depicted in Figure 3A. E-G. Quantification of the frequency of specific cell subsets in the myeloid (E), lymphoid (F), and epithelial (G) compartments, across the nine patients. Diamond marks patient S1, co-infected with the human Metapneumovirus (Figures 4D-4H). Horizontal lines indicate mean frequency. (H). Projection of IL6 and IL8 (CXCL8) expression on the 2D map shown in Figure 4A. Colors represent expression quantiles.
Figure 4
Figure 4
Viral-Track Reveals Infection Specificity and a Co-infection in Severe COVID-19 (A) Total number of viral reads mapped to the SARS-CoV-2 viral genome in the profiled COVID-19 patients. (B) Coverage plot of the SARS-CoV-2 viral genome. (C) Enrichment of viral UMIs over expected values across 361 metacells, projected on the 2D map shown in Figure 4A. Color scale indicates log2 observed/expected vUMIs. Only metacells with more than one expected UMI are plotted. (D) Result of Viral-Track analysis on patient S1. For each viral segment, represented by a dot, the entropy of the sequence (how repetitive are the mapped sequences) and the percentage of the segment that is mapped is plotted. Green dots correspond to viral segments that have passed quality control. Viral segments with more than 50 mapped reads are plotted. (E) Coverage plot of the human metapneumovirus (hMPV) genome. (F) Distribution of hMPV UMIs across patient S1 sequenced cells. Red dashed line indicates automatic thresholding of vUMI+ cells. (G) Enrichment of vUMI+ cells over expected values across 297 metacells, projected on the 2D map shown in Figure 4A. Color scale indicates log2 observed/expected. Only metacells with more than one expected vUMI+ cell are plotted. (H) Volcano plot showing the relative expression between infected and bystander monocytes of patient S1. Differentially expressed (>1 log2 fold change) and statistically significant (p value <0.01) are colored in orange. See also Figure S4.
Figure S4
Figure S4
Viral-Track Performance on COVID-19 BAL Samples, Related to Figure 4 A. Results of Viral-Track analysis performed on samples with highest viral load (patients S2 and S3). B. Mean normalized expression of ACE2, TMPRSS2 and BSG across the 27 cell subsets C. Log2 fold change between vUMI+ and vUMI- SPP1+ monocyte-derived macrophages in patient S2 (x axis) and patient S3 (y axis). D. Relation between total human and viral UMIs in cells from patient S1. E. Projection of cells from patient S1, co-infected with hMPV, on the metacell map from Figure 3A. F. Enrichment analysis of the downregulated genes in hMPV infected monocytes. G. Number of hMPV UMIs in cells producing type I IFN or not. P value was computed by fitting a logistic regression predicting if a cell would produce type I IFN using total host and viral UMIs.
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References

    1. Bailey J.R., Barnes E., Cox A.L. Approaches, Progress, and Challenges to Hepatitis C Vaccine Development. Gastroenterology. 2019;156:418–430. - PMC - PubMed
    1. Baran Y., Bercovich A., Sebe-Pedros A., Lubling Y., Giladi A., Chomsky E., Meir Z., Hoichman M., Lifshitz A., Tanay A. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol. 2019;20:206. - PMC - PubMed
    1. Beier J.I., Jokinen J.D., Holz G.E., Whang P.S., Martin A.M., Warner N.L., Arteel G.E., Lukashevich I.S. Novel mechanism of arenavirus-induced liver pathology. PLoS ONE. 2015;10:e0122839. - PMC - PubMed
    1. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B. 1995;57:289–300.
    1. Blecher-Gonen R., Bost P., Hilligan K.L., David E., Salame T.M., Roussel E., Connor L.M., Mayer J.U., Bahar Halpern K., Tóth B. Single-Cell Analysis of Diverse Pathogen Responses Defines a Molecular Roadmap for Generating Antigen-Specific Immunity. Cell Syst. 2019;8:109–121. - PubMed

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