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Identification of distinct immune cell subsets associated with various clinical presentations, disease severity and viral persistence of SARS-CoV-2 infection

ABSTRACT: The clinical presentations and outcomes of SARS-CoV-2 infection are highly variable, ranging from asymptomatic infections to severe conditions, and the underlying mechanisms remain largely unknown. In this study, we performed the single-cell RNA sequencing to examine the profiles of the peripheral blood mononuclear cells (PBMCs) from the healthy controls (HC), asymptomatic individuals (AS) and symptomatic COVID-19 patients (SM) with various clinical features and disease severity. We first identified nine major cell types, including T cells, B cells, monocytes, natural killer (NK) cells, dendritic cells (DC), platelets, neutrophils, plasma cells and stem cells from a total of 119,799 single cells captured and filtered for the final analysis, and then divided these cell types into separate cell clusters and sub-clusters, respectively. We revealed that SARS-CoV-2 infection resulted in profound alternations in various cellular compartments and identified a number of distinct immune cell subsets that were associated with various clinical presentations, disease severity and viral persistence of COVID-19. (NCAM1+CD160+) NK cells increased significantly in AS and SM individuals compared with the HC. SM patients showed a significant increase in (LAG3+CD160+CD8+) NKT cells and a decrease in (CD4-CD8-) double negative T cells compared with the AS subjects. (CD68-CSF1R-IL1BhiCD14+) classical monocytes were significantly higher in patients with severe disease (SD) than the patients with moderate disease (MD). (CD68-CSF1R-IL1BhiCD14+) classical monocytes and (CLEC10A-S100A9lo)pDC were positively and negatively correlated with the age-related disease severity of COVID-19 patients, respectively. Increases in (CD33-HLA-DMA-CD14+) classical monocytes and (CLEC10A-S100A9lo)pDC were associated with long-term nucleic acid test positive (LTNP) patients compared with the short-term nucleic acid test positive (STNP) individuals. Increases in (LAG3+CD160+CD8+) NKTcells and (FOXP3+IL2RA+IL7R+CD4+) Treg cells were observed in a patient lacking B cells and plasma cells. Dramatic increases in neutrophils, (CD33-HLA-DMA-CD14+) classical monocytes, and (CD68-CSF1R-IL1BhiCD14+) classical monocytes were detected at a later stage of a fatal patient. Our findings may enhance our understanding of the immune cell alternations involved in the pathogenesis of COVID-19 and the protective immune responses against SARS-CoV-2 infection.

ORGANISM(S): Homo sapiens

PROVIDER: GSE165080 | GEO | 2022/02/17

REPOSITORIES: GEO

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Project description:Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of COVID-19 patient data with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and severe COVID-19 patients, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamster’s response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species.

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Identification of distinct immune cell subsets associated with various clinical presentations, disease severity and viral persistence of SARS-CoV-2 infection

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