Dysregulated ligand-receptor interactions from single-cell transcriptomics.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
13 06 2022
Historique:
received: 16 06 2021
revised: 29 03 2022
accepted: 21 04 2022
pubmed: 29 4 2022
medline: 15 11 2022
entrez: 28 4 2022
Statut: ppublish

Résumé

Intracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand-receptor interactions. Although computational methods have been developed to infer cell type-specific ligand-receptor interactions from one single-cell transcriptomics profile, there is lack of approaches considering ligand and receptor simultaneously to identifying dysregulated interactions across conditions from multiple single-cell profiles. We developed scLR, a statistical method for examining dysregulated ligand-receptor interactions between two conditions. scLR models the distribution of the product of ligands and receptors expressions and accounts for inter-sample variances and small sample sizes. scLR achieved high sensitivity and specificity in simulation studies. scLR revealed important cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection, and activated TGF-β signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis. scLR is freely available at https://github.com/cyhsuTN/scLR. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 35482476
pii: 6575434
doi: 10.1093/bioinformatics/btac294
pmc: PMC9191214
doi:

Substances chimiques

Ligands 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

3216-3221

Subventions

Organisme : NCI NIH HHS
ID : U54 CA217450
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA095103
Pays : United States
Organisme : NCI NIH HHS
ID : U19 CA179514
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA098131
Pays : United States
Organisme : NCI NIH HHS
ID : P01 CA229123
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA091842
Pays : United States
Organisme : NIAID NIH HHS
ID : P01 AI139449
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA068485
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA236733
Pays : United States

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press.

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Auteurs

Qi Liu (Q)

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.

Chih-Yuan Hsu (CY)

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.

Jia Li (J)

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.

Yu Shyr (Y)

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.

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