Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
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.
Pubmed ID: 33657410
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Software R package as single cell regulatory network inference and clustering. Used for simultaneous gene regulatory network reconstruction and cell state identification from single cell RNA-seq data.
View all literature mentionsSoftware tool to detect doublets in single-cell RNA-seq data. Software algorithm to remove doublets. Python code for identifying doublets in single-cell RNA-seq data. Framework for predicting impact of multiplets in given analysis and identifying problematic multiplets.
View all literature mentionsSoftware tool for manipulating BUS files for single cell RNA-Seq datasets. Used to error correct barcodes, collapse UMIs, produce gene count or transcript compatibility count matrices, and is useful for many other tasks.
View all literature mentionsSoftware Python package that wraps kallisto and bustools single-cell RNA-seq workflow. Used for single-cell RNA-seq pre-processing. Simplifies downloading and running of kallisto and bustools programs. Consists of kb ref and kb count commands. kb ref builds or downloads species specific index for pseudo alignment of reads and must be run prior to kb count and it runs kallisto index. kb count runs kallisto and bustools programs and is used for pre-processing of data from variety of single-cell RNA-seq technologies, and for number of different workflows (e.g. production of gene count matrices, RNA velocity analyses, etc.).
View all literature mentionsWelcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
