ArticleDisease models & mechanisms2020
Quantitative single-cell interactomes in normal and virus-infected mouse lungs.
Article in Disease models & mechanisms, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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.
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Who cites it
17 citing papers in PubMed.
- Mouse-Specific Single cell cytokine activity prediction and Estimation (MouSSE).PLoS computational biology · 2025Article
- Advances in Single-Cell Sequencing for Infectious Diseases: Progress and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Truncated NTRK2 is induced in CAP1 endothelial cells during mouse lung injury-repair.iScience · 2025Article
- Morphogenic, molecular and cellular adaptations for unidirectional airflow in the chicken lung.Development (Cambridge, England) · 2025Article
- The dynamics of tubulogenesis in development and disease.Development (Cambridge, England) · 2025Review
- Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.Genome biology · 2024Article
- Lung repair and regeneration: Advanced models and insights into human disease.Cell stem cell · 2024Review
- DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.Bioinformatics (Oxford, England) · 2023Article
- Antimicrobial mitochondrial reactive oxygen species induction by lung epithelial immunometabolic modulation.PLoS pathogens · 2023Article
- Use of intercellular proximity labeling to quantify and decipher cell-cell interactions directed by diversified molecular pairs.Science advances · 2022Article
- Cellular Heterogeneity and Molecular Reprogramming of the Host Response during Influenza Acute Lung Injury.Journal of virology · 2022Article
- A systematic evaluation of the computational tools for ligand-receptor-based cell-cell interaction inference.Briefings in functional genomics · 2022Article
- Intermediary Role of Lung Alveolar Type 1 Cells in Epithelial Repair upon Sendai Virus Infection.American journal of respiratory cell and molecular biology · 2022Article
- Three-axis classification of mouse lung mesenchymal cells reveals two populations of myofibroblasts.Development (Cambridge, England) · 2022Article
- Differential chromatin binding of the lung lineage transcription factor NKX2-1 resolves opposing murine alveolar cell fates in vivo.Nature communications · 2021Article
- Deciphering cell-cell interactions and communication from gene expression.Nature reviews. Genetics · 2021Review
- Breathing fresh air into respiratory research with single-cell RNA sequencing.European respiratory review : an official journal of the European Respiratory Society · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Mammalian organs consist of diverse, intermixed cell types that signal to each other via ligand-receptor interactions - an interactome - to ensure development, homeostasis and injury-repair. Dissecting such intercellular interactions is facilitated by rapidly growing single-cell RNA sequencing (scRNA-seq) data; however, existing computational methods are often not readily adaptable by bench scientists without advanced programming skills. Here, we describe a quantitative intuitive algorithm, coupled with an optimized experimental protocol, to construct and compare interactomes in control and Sendai virus-infected mouse lungs. A minimum of 90 cells per cell type compensates for the known gene dropout issue in scRNA-seq and achieves comparable sensitivity to bulk RNA sequencing. Cell lineage normalization after cell sorting allows cost-efficient representation of cell types of interest. A numeric representation of ligand-receptor interactions identifies, as outliers, known and potentially new interactions as well as changes upon viral infection. Our experimental and computational approaches can be generalized to other organs and human samples.
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Registered trials
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 Socratic graph.