ArticleBriefings in bioinformatics2021
Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 79 papers, 1 of them a synthesis that pooled it.
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Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it, 131 citations in OpenAlex.
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- Dissecting multilayer cell-cell communications with signaling feedback loops from spatial transcriptomics data.Genome research · 2025Article
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- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
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- Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer.Briefings in bioinformatics · 2025Article
- Multicellular Network-Informed Survival Model for Identification of Drug Targets of Gliomas.IEEE journal of biomedical and health informatics · 2025Article
- SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell-cell communication.BMC biology · 2025Article
- Single-cell characterization of the immune heterogeneity of pulmonary hypertension identifies novel targets for immunotherapy.BMC immunology · 2025Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.
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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.