Spatial transcriptomics of post-mortem lung tissue from COVID-19 donors
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA788282 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Visualization of gene expression in lung tissue was performed using Visium spatial gene expression kits (10x Genomics) following the manufacturer`s protocol. The four capture areas in a 10x Genomics Visium Gene Expression slide consist of 5000 spots with DNA oligos for mRNA capture that have a unique spatial barcode and a unique Molecular Identifier (UMI). Each spot has 55 µm diameter and can therefore capture mRNA from 1 to 10 cells. We report the spatially resolved transcriptome of 3 control lung samples from non-COVID-19-related pneumonia donors and 9 COVID-19 lung samples analyzed with the 10x Visium platform.
2022-12-21 | GSE190732 | GEO
Project description:Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.
2022-02-01 | E-MTAB-10129 | biostudies-arrayexpress
Project description:To find inflammatory signatures in post mortem vagus nerves of COVID-19 patients in comparisin to non-infected controls.
2023-08-21 | GSE233732 | GEO
Project description:RNAseq analysis of human CD4+ regulatory Tregs and Tconvs in COVID-19 and healthy donors isolated from peripheral blood.
2021-07-07 | GSE179448 | GEO
Project description:Although most SARS-CoV-2-infected individuals experience mild COVID-19, some patients suffer from severe COVID-19, which is accompanied by acute respiratory distress syndrome and systemic inflammation. To identify factors driving severe progression of COVID-19, we performed single-cell RNA-seq using peripheral blood mononuclear cells (PBMCs) obtained from healthy donors, patients with mild or severe COVID-19, and patients with severe influenza. Patients with COVID-19 exhibited hyper-inflammatory signatures across all types of cells among PBMCs, particularly upregulation of the TNF/IL-1beta-driven inflammatory response as compared to severe influenza. In classical monocytes from patients with severe COVID-19, type I IFN response co-existed with the TNF/IL-1beta-driven inflammation, and this was not seen in patients with milder COVID-19 infection. Based on this, we propose that the type I IFN response exacerbates inflammation in patients with severe COVID-19 infection.
2020-07-06 | GSE149689 | GEO
Project description:This study utilizes multi-omic biological data to perform deep immunophenotyping on the major immune cell classes in COVID-19 patients. 10X Genomics Chromium Single Cell Kits were used with Biolegend TotalSeq-C human antibodies to gather single-cell transcriptomic, surface protein, and TCR/BCR sequence information from 254 COVID-19 blood draws (a draw near diagnosis (-BL) and a draw a few days later (-AC)) and 16 healthy donors.
2020-10-29 | E-MTAB-9357 | biostudies-arrayexpress
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leads to coronavirus disease 2019 (Covid-19) which has caused worldwide pandemic infection. Yet due to unknown reason, certain COVID-19 patients exhibit severe inflammatory reactions associated with cytokine storm and neutrophil infiltration and neutrophil extracellular traps (NETs) in the lung, leading to further complications of SARS-CoV-2 infection. To find out whether the cause of lung injury in COVID-19 patients is due to increased reactive oxygen species and subsequently NET formation we have compared the post-mortem lung biopsies of deceased COVID-19 patients to normal lung tissues using RNA-Seq analysis.
2023-01-31 | GSE208076 | GEO
Project description:Although a substantial proportion of severe COVID-19 pneumonia survivors exhibit long-term pulmonary sequalae, the underlying mechanisms or associated local and systemic immune correlates are not known. Here, we have performed high dimensional characterization of the pathophysiological and immune traits of aged COVID-19 convalescents, and correlated the local and systemic immune profiles with pulmonary function and lung imaging. In this cohort of aged COVID-19 convalescents, chronic lung impairment was accompanied by persistent systemic inflammation and respiratory immune alterations. Detailed evaluation of the lung immune compartment revealed dysregulated respiratory CD8+ T cell responses that likely underlie the impaired lung function following acute COVID-19 during aging. Single cell transcriptomic analysis identified the potential pathogenic subsets of respiratory CD8+ T cells causing persistent tissue conditions following COVID-19. Our results have revealed key pathophysiological and immune traits that support the development of lung sequelae following SARS-CoV2 pneumonia during aging, with implications for the treatment of chronic COVID-19 symptoms.
2021-09-17 | GSE176201 | GEO
Project description:Immune responses in lungs of Coronavirus Disease 2019 (COVID-19) are poorly characterized. We conducted transcriptomic, histologic and cellular profiling of post mortem COVID-19 and normal lung tissues. Two distinct immunopathological reaction patterns were identified. One pattern showed high expression of interferon stimulated genes (ISGs) and cytokines, high viral loads and limited pulmonary damage, the other pattern showed severely damaged lungs, low ISGs, low viral loads and abundant immune infiltrates. Distinct patterns of pulmonary COVID-19 immune responses correlated to hospitalization time and may guide treatment and vaccination approaches.
2020-09-02 | GSE151764 | GEO
Project description:Severe lung damage in COVID-19 is known to involve complex interactions between diverse populations of immune and stromal cells. The pneumonitis manifesting in COVID-19 and acute respiratory distress syndrome results in spatially heterogenous manifestations of injury, such as infiltrates, loss of epithelial integrity and fibrosis. In this study, we applied a spatial transcriptomics approach to better delineate the cells, pathways and genes responsible for promoting and perpetuating severe tissue pathology in COVID-19 pneumonitis. Guided by tissue histology and multiplex immunofluorescence, we performed a targeted sampling of dozens of regions representing a spectrum of diffuse alveolar damage (mild to severe) from the post-mortem lung of three COVID-19 patients. These microscopic sites of injury had varying known compositions of CD3+ lymphocytes, CD68+ myeloid cells and panCK+ epithelial cells. DCC files are the processed sequencing files using the NanoString DND pipeline. The "Initial Dataset.xlsx" is represents raw gene counts for each probe replicate (n=47). "Post Biological Probe QC.xlsx" removes a sample (n=46) with failed sequencing (no rawReads) and conducts biological probe quality controls to collapse probe replicates into a single count per target gene using the GeoMx Analysis suite (version 2.1.0.102). "qn.exprs.tsv" is the matrix of quantile normalised gene expression by segment (n=46) and "qn.exprs.corrected.tsv" is the matrix of quantile normalised and batch corrected matrix of gene expression by segment (n=46). Rendered multichannel immunofluorescent microscopy png images corresponding to each area of interest (AOI with the acquistion borders outlined in white) are included. Further images can be made available upon request.
2023-01-27 | GSE186213 | GEO