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.
Project description:As a primary target of SARS-CoV-2, lung exhibits heterogeneous histopathological changes following infection. However, comprehensive insight into their protein basis with spatial resolution remains deficient, which hinders further understanding of COVID-19-related pulmonary injury. Here, we generated a region-resolved proteomic atlas of hallmark pathological pulmonary structures by integrating histological examination, laser microdissection, and ultrasensitive proteomics. Over 10,000 proteins were quantified across 71 post-mortem specimens. We identified a spectrum of pathway dysregulations in alveolar epithelium, bronchial epithelium, and blood vessels comparing with non-COVID-19 controls, providing evidence for transitional-state pneumocyte hyperplasia. Additionally, our data revealed the region-specific enrichment of functional markers in bronchiole mucus plug, pulmonary fibrosis, airspace inflammation, and alveolar type 2 cells, uncovering their distinctive features. Furthermore, we detected increased protein expression associated with viral entry and inflammatory response across multiple regions, suggesting potential therapeutic targets. Collectively, this study provides a unique perspective for deciphering COVID-19-caused pulmonary dysfunction by spatial proteomics.
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.
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.
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.
Project description:Our work illustrates how high-resolution molecular and spatial profiling of COVID-19 patient tissues collected during rapid autopsies can serve as a hypothesisgenerating tool to identify key mediators driving the pathophysiology of COVID-19 for diagnostic and therapeutic target testing. Here we employ bulk RNA sequencing to identify key regulators of COVID-19 and list specific mediators for further study as potential diagnostic and therapeutic targets. We use single-nuclei RNA sequencing to highlight the diversity and heterogeneity of coronavirus receptors within the brain, suggesting that it will be critical to expand the focus from ACE2 to include other receptors, such as BSG and ANPEP, and we perform digital spatial profiling of lung and lymph node tissue to compare two patients with different clinical courses and symptomatology.
Project description:To determine definitively whether lung myeloid cells exhibit a pro- or anti-inflammatory signature in COVID-19 disease, we performed digital spatial profiling using the nanoString GeoMx ImmuneOncology plus COVID-19 platform on CD68+ macrophages, myeloperoxidase+ granulocytes and cytokeratin+ epithelium in normal and COVID-19 lung tissue specimens, collecting RNA expression data for each type within 6-8 regions of 5mM tissue sections. One COVID-19 lung tissue yielded minimal sequence data and was excluded from analysis. A volcano plot and heat map of differentially expressed genes within macrophages demonstrate that COVID-19 lung macrophages when compared with normal lung macrophages exhibit a largely alternatively activated, wound-healing signature characterized by expression of the alternatively active macrophage marker CD163, complement/coagulation genes (C1QA, C1QB, THBS1, C1S, C1R), IL6 signaling (STAT2, STAT1) and wound healing (COL3A1, COL6A3), but also interferon response signatures (ISG15, OAS3, IFITM2, IFI6, HLA-A, HLA-B, HLA-C) (Fig. 3H-J). As one of the tissues used for macrophage spatial profiling was from a patient was positive for the virus at the time of death, we compared the expression profiles of virus+ and virus- specimens and found that macrophages in virus+ tissues predominantly expressed an interferon-associated signature
Project description:The identification of COVID-19 patients with high-risk of severe disease is a challenge in routine care. We performed blood RNA-seq gene expression analyses in severe hospitalized patients compared to healthy donors. Supervised and unsupervised analyses revealed a high abundance of CD177, a specific neutrophil activation marker, contributing to the clustering of severe patients. Gene abundance correlated with high serum levels of CD177 in severe patients. These results highlight neutrophil activation as a hallmark of severe disease and CD177 assessment as a reliable prognostic marker for routine care.