scRNA-seq characterization of immune cell types among individuals with or without Long COVID

Long COVID (LC), a type of post-acute sequelae of SARS-CoV-2 infection (PASC), occurs after at least 10% of SARS-CoV-2 infections, yet its etiology remains poorly understood. Here, we used multiple “omics” assays (CyTOF, RNAseq/scRNAseq, Olink) and serology to deeply characterize both global and SARS-CoV-2-specific immunity from blood of individuals with clear LC and non-LC clinical trajectories, 8 months following infection and prior to receipt of any SARS-CoV-2 vaccine. Our analysis focused on deep phenotyping of T cells, which play important roles in immunity against SARS-CoV-2 yet may also contribute to COVID-19 pathogenesis. Our findings demonstrate that individuals with LC exhibit systemic inflammation and immune dysregulation. This is evidenced by global differences in T cell subset distribution in ways that imply ongoing immune responses, as well as by sex-specific perturbations in cytolytic subsets. Individuals with LC harbored increased frequencies of CD4+ T cells poised to migrate to inflamed tissues, and exhausted SARS-CoV-2-specific CD8+ T cells. They also harbored significantly higher levels of SARS-CoV-2 antibodies, and in contrast to non-LC individuals, exhibited a mis-coordination between their SARS-CoV-2-specific T and B cell responses. RNAseq and Olink analyses similarly revealed immune dysregulatory mechanisms, along with non-immune associated perturbations, in individuals with LC. Collectively, our data suggest that proper crosstalk between the humoral and cellular arms of adaptive immunity has broken down in LC, and that this, perhaps in the context of persistent virus, leads to the immune dysregulation, inflammation, and clinical symptoms associated with this debilitating condition. SARS-CoV-2 serological analysis and four types of “omics” assays were performed on the same blood specimens from our cohort of LC and non-LC individuals (Fig. 1). Plasma/sera were analyzed for RBD-specific antibody levels, and for the levels of 394 analytes using the Olink platform. PBMCs from the same specimens were subjected to bulk RNAseq, as well as in-depth CD4+ and CD8+ T cell phenotyping using a 39-parameter CyTOF panel designed to simultaneously interrogate the differentiation states, activation states, effector functions, and homing properties of T cells (Table S2). Cells were phenotyped by CyTOF at baseline and following a 6-hour stimulation with SARS-CoV-2 peptides to identify and characterize SARS-CoV-2-specific T cells at the single-cell level through intracellular cytokine staining. The RNAseq/scRNAseq and Olink datasets, as well as the CyTOF datasets corresponding to total and SARS-CoV-2-specific T cells, were visualized and analyzed using a variety of integrative high-dimensional analysis approaches (Fig. 1). In total, we obtained 5 distinct datasets, enabling us to assess humoral response (serology), plasma analytes (Olink), transcriptional signatures at the bulk (RNAseq) and single-cell (scRNAseq) levels, T cell features (CyTOF), and SARS-CoV-2-specific T cell phenotypes and effector functions (CyTOF). sample_sheet.csv: the details of every sample sequenced including grouping information as well as index information. sample_cell_count_data.csv: the relevant information considering clustering information, absolute cell counts information as well as cell type information. Methods_Kailin_et_al_PASC_scRNAseq.txt: the details of every step involved in data processing sessionInfo.text: the platforms as well as packages used in the whole analysis