Longitudinal Single Cell Atlas of COVID-19 Identifies an Early, Transient Prognostic Signature
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ABSTRACT:
PROVIDER: EGAC00001002271 | EGA |
REPOSITORIES: EGA
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Project description:Although the majority of COVID-19 patients are asymptomatic or mildly symptomatic at diagnosis, some will subsequently progress to pneumonia, oxygen desaturation and other symptoms requiring active clinical management. However, molecular markers that identify high-risk patients early in the disease course (Days 1-8 from symptom onset) are scarce, due to the paucity of relevant longitudinal studies. We performed longitudinal single cell RNA-seq on 286 peripheral blood samples from 108 COVID-19 patients, 73 of which had at least one early sample. By examining discrete cell subtypes as well as continuous single cell states, we identified upregulation of type I IFN response genes as the predominant cellular prognostic signature of future disease severity. Type I IFN response was dynamic and complex, spiking early in progressors and then regressing to the cohort mean at the very next sampling, with the cohort mean itself receding to asymptomatic levels by Day 14 regardless of severity. Moreover, in severe and critical cases, IFN response was impaired during Days 5-8, partially due to upregulation of SOCS3 and other negative regulators. We have identified an early prognostic signature for predicting COVID-19 severity as well as potential mechanisms underlying the dysfunctional host immune response in severe disease.
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Project description:Due to the paucity of longitudinal molecular studies of COVID-19, particularly those covering the early stages of infection (Days 1-8 symptom onset), our understanding of host response over the disease course is limited. We perform longitudinal single cell RNA-seq on 286 blood samples from 108 age- and sex-matched COVID-19 patients, including 73 with early samples. We examine discrete cell subtypes and continuous cell states longitudinally, and we identify upregulation of type I IFN-stimulated genes (ISGs) as the predominant early signature of subsequent worsening of symptoms, which we validate in an independent cohort and corroborate by plasma markers. However, ISG expression is dynamic in progressors, spiking early and then rapidly receding to the level of severity-matched non-progressors. In contrast, cross-sectional analysis shows that ISG expression is deficient and IFN suppressors such as SOCS3 are upregulated in severe and critical COVID-19. We validate the latter in four independent cohorts, and SOCS3 inhibition reduces SARS-CoV-2 replication in vitro. In summary, we identify complexity in type I IFN response to COVID-19, as well as a potential avenue for host-directed therapy.
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Project description:In managing patients with coronavirus disease 2019 (COVID-19), early identification of those at high risk and real-time monitoring of disease progression to severe COVID-19 is a major challenge. We aimed to identify early prognostic protein markers and to discover surrogate protein markers that effectively reflect the clinical progression of the disease. We performed in-depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID-19 (non-severe patients, n = 13; patients who progressed to severe COVID-19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3-19 and BNC2, as early prognostic indicators of severe COVID-19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID-19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. This study supports the development of protein biomarkers, which might enable better predicting and monitoring progression to severe COVID-19.
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Project description:A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.
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Project description:Colorectal cancer (CRC) is a major contributor to global morbidity and mortality, necessitating more effective therapeutic approaches. T cells, prominent in the tumor microenvironment, exert a crucial role in modulating immunotherapeutic responses and clinical outcomes in CRC. This study introduces a pioneering method for characterizing the CRC immune microenvironment using single-cell sequencing data. Unlike previous approaches, which focused on individual T-cell signature genes, we utilized overall infiltration levels of colorectal cancer signature T-cells. Through weighted gene co-expression network analysis, Lasso regression, and StepCox analysis, we developed a prognostic risk model, TRGS (T-cell related genes signatures), based on six T cell-related genes. Multivariate Cox analysis identified TRGS as an independent prognostic factor for CRC, showcasing its superior predictive efficacy compared to existing immune-related prognostic models. Immunoreactivity analysis revealed higher Immunophenoscore and lower Tumor Immune Dysfunction and Exclusion scores in the low-risk group, indicating potential responsiveness to immune checkpoint inhibitor therapy. Additionally, patients in the low-risk group demonstrated heightened sensitivity to 5-fluorouracil-based chemotherapy regimens. In summary, TRGS emerges as a standalone prognostic biomarker for CRC, offering insights to optimize patient responses to immunotherapy and chemotherapy, thereby laying the groundwork for personalized tumor management strategies.
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Project description:There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2 which has infected more than 3 million people worldwide. Approximately 20% of patients with COVID-19 develop severe disease and 5% require intensive care. Severe disease has been associated with changes in peripheral immune activity, including increased levels of pro-inflammatory cytokines that may be produced by a subset of inflammatory monocytes, lymphopenia, and T cell exhaustion. To elucidate pathways in peripheral immune cells that might lead to immunopathology or protective immunity in severe COVID-19, we applied single-cell RNA sequencing (scRNA-seq) to profile peripheral blood mononuclear cells (PBMCs) from 7 patients hospitalized for COVID-19 and 6 healthy controls. We identify reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene (ISG) signature, HLA class II downregulation, and a developing neutrophil population that appears closely related to plasmablasts appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, we found that peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines. Collectively, we provide a cell atlas of the peripheral immune response to severe COVID-19. Sample IDs used in the manuscript were shortened for clarity. They relate to the titles of deposited files as follows: covid_555_1: C1 A covid_555_2: C1 B covid_556: C2 covid_557: C3 covid_558: C4 covid_559: C5 covid_561: C7 HIP002: H1 HIP015: H2 HIP023: H3 HIP043: H4 HIP044: H5 HIP045: H6
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