COVID‐19db linkage maps of cell surface proteins and transcription factors in immune cells
The highly contagious SARS‐CoV‐2 and its associated disease (COVID‐19) are a threat to global public health and economies. To develop effective treatments for COVID‐19, we must understand the host cell types, cell states and regulators associated with infection and pathogenesis such as dysregulated...
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Published in: | Journal of medical virology Vol. 95; no. 6; pp. e28887 - n/a |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
United States
Wiley Subscription Services, Inc
01-06-2023
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Subjects: | |
Online Access: | Get full text |
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Summary: | The highly contagious SARS‐CoV‐2 and its associated disease (COVID‐19) are a threat to global public health and economies. To develop effective treatments for COVID‐19, we must understand the host cell types, cell states and regulators associated with infection and pathogenesis such as dysregulated transcription factors (TFs) and surface proteins, including signaling receptors. To link cell surface proteins with TFs, we recently developed SPaRTAN (Single‐cell Proteomic and RNA‐based Transcription factor Activity Network) by integrating parallel single‐cell proteomic and transcriptomic data based on Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE‐seq) and gene cis‐regulatory information. We apply SPaRTAN to CITE‐seq data sets from patients with varying degrees of COVID‐19 severity and healthy controls to identify the associations between surface proteins and TFs in host immune cells. Here, we present COVID‐19db of Immune Cell States (https://covid19db.streamlit.app/), a web server containing cell surface protein expression, SPaRTAN‐inferred TF activities, and their associations with major host immune cell types. The data include four high‐quality COVID‐19 CITE‐seq data sets with a toolset for user‐friendly data analysis and visualization. We provide interactive surface protein and TF visualizations across major immune cell types for each data set, allowing comparison between various patient severity groups for the discovery of potential therapeutic targets and diagnostic biomarkers. |
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Bibliography: | Koushul Ramjattun and Xiaojun Ma contributed equally to this study. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0146-6615 1096-9071 |
DOI: | 10.1002/jmv.28887 |