Evidence map›Paper›PMID 37341527›Full record

ArticleJournal of medical virology2023

COVID-19db linkage maps of cell surface proteins and transcription factors in immune cells.

Koushul Ramjattun, Xiaojun Ma, Shou-Jiang Gao, Harinder Singh, Hatice Ulku Osmanbeyoglu

Open access · hybridAbstract read
In one paragraph

Article in Journal of medical virology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 74% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Koushul RamjattunDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Xiaojun MaDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Shou-Jiang GaoDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.ORCID 0000-0001-6194-1742
Harinder SinghDepartment of Immunology, Center for Systems Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Hatice Ulku OsmanbeyogluDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.ORCID 0000-0002-3175-1777
University of Pittsburgh · US

Funding

Computational methods for delineating cell context-specific regulatory programsR35GM146989 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Hatice Ulku Osmanbeyoglu · 2022 to 2026
$1.9M
NIGMS NIH HHS R35 GM146989
6 · The paper itself

Abstract

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.

Indexed as

COVID-19Transcription FactorsGene Expression RegulationHumansProteomicsSARS-CoV-2Transcription FactorsCOVID-19immune cellssurface proteinstranscription factors

Identifiers

PMID37341527
PMCPMC10478683
OpenAlexW4381469360

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.