当前位置： SCI文献检索 > PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA期刊下所有文献 > Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.

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.

journal_name

Proc Natl Acad Sci U S A

journal_title

Proceedings of the National Academy of Sciences of the United States of America

authors

Bajikar SS,Fuchs C,Roller A,Theis FJ,Janes KA

doi

10.1073/pnas.1311647111

subject

Has Abstract

pub_date

2014-02-04 00:00:00

pages

E626-35

issue

5

eissn

0027-8424

issn

1091-6490

pii

1311647111

journal_volume

111

pub_type

杂志文章

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