ArticleProceedings of the National Academy of Sciences of the United States of America2014
Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 57 citations in OpenAlex.
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- MEMO: multi-experiment mixture model analysis of censored data.Bioinformatics (Oxford, England) · 2016Article
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- Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.Methods (San Diego, Calif.) · 2015Review
- Variability of Gene Expression Identifies Transcriptional Regulators of Early Human Embryonic Development.PLoS genetics · 2015Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Here, we show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, we formulated plausible mixture models for cell-to-cell regulatory heterogeneity and maximized the resulting likelihood functions to infer model parameters. Inferences were validated both computationally and experimentally for different mixture models, which included regulatory states for multicellular function that were occupied by as few as 1 in 40 cells of the population. Importantly, when the method was extended to programs of heterogeneously coexpressed transcripts, we found that population-level inferences were much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. Our deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.
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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 OpenQuestion graph.