Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals

• DOI: 10.3389/fimmu.2022.1034159
• Publication Date: 2022-12-02T06:45:22Z
• Authors: Partha Chattopadhyay; Kriti Khare; Manish Kumar; Pallavi Mishra; Alok Anand; Ranjeet Maurya; Rohit Gupta; Shweta Sahni; Ayushi Gupta; Saruchi Wadhwa; Aanchal Yadav; Priti Devi; Kishore Tardalkar; Meghnad Joshi; Tavpritesh Sethi; Rajesh Pandey
• Keywords: 0301 basic medicine; Immunology; Infectious disease (medical specialty); Coronavirus Disease 2019 Research; FOS: Health sciences; Presentation (obstetrics); immune response; recovered COVID-19 individuals; Coronavirus Disease 2019; 03 medical and health sciences; Cd4 t cell; Biochemistry, Genetics and Molecular Biology; Virology; T-cell activation; Health Sciences; Pathology; Genetics; Humans; Disease; Transcriptomics; Antigen presentation; Molecular Biology; Biology; Antigen Presentation; SARS-CoV-2; FOS: Clinical medicine; COVID-19; Life Sciences; T cell; Bayes Theorem; RC581-607; Comprehensive Integration of Single-Cell Transcriptomic Data; Multiomics; 3. Good health; Coronavirus disease 2019 (COVID-19); Infectious Diseases; Immune system; Antigen; FOS: Biological sciences; single cell multi-omics; Medicine; Cell; Immunologic diseases. Allergy; Radiology; bayesian network model

IntroductionDespite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome.MethodsBy combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals.ResultsBased on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1β, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors.DiscussionIn conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.