ArticleProceedings of the National Academy of Sciences of the United States of America2022
Inferring gene regulation from stochastic transcriptional variation across single cells at steady state.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 57 citations in OpenAlex.
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Metadomain and metaloop genome interactions in mammalian T cells.Cell reports · 2026Article
- Article
- CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Efficient approximations of transcriptional bursting effects on the dynamics of a gene regulatory network.Journal of the Royal Society, Interface · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- Method of moments framework for differential expression analysis of single-cell RNA sequencing data.Cell · 2024Article
- Leveraging gene correlations in single cell transcriptomic data.BMC bioinformatics · 2024Article
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells.Genome biology · 2024Article
- Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle.Molecular cell · 2024Article
- Transcriptional determinism and stochasticity contribute to the complexity of autism-associated SHANK family genes.Cell reports · 2024Article
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 2024Article
- A dynamical perspective: moving towards mechanism in single-cell transcriptomics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Quantifying Landscape-Flux via Single-Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Transcriptional Determinism and Stochasticity Contribute to the Complexity of Autism AssociatedbioRxiv : the preprint server for biology · 2024Article
- A robust ultrasensitive transcriptional switch in noisy cellular environments.NPJ systems biology and applications · 2024Article
- Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease.medRxiv : the preprint server for health sciences · 2023Article
- Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells.bioRxiv : the preprint server for biology · 2023Article
- Review
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Authors and funding
19 authors at 2 institutions in 1 country.
Funding
Abstract
Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.
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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.