Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles
Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles
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DOI：
10.1073/pnas.1311647111
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发表时间：
2014-02-04
影响因子：
11.1
通讯作者：
Janes, Kevin A.
中科院分区：
文献类型：
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作者：
Bajikar, Sameer S.;Fuchs, Christiane;Janes, Kevin A.
基因表达的调控变化是许多生物过程的基础，但在测量单细胞时，全面分析细胞间转录调控的变化是有问题的。通过随机收集少量细胞并进行统计分析，可以稳健地实现转录组范围内调控异质性的识别。然而，这种随机分析方法模糊了每个合并样本中单个细胞的表达状态。在这里，我们表明单细胞调控状态的基本分布可以通过最大似然推理从随机分析数据中解卷积。在转录调控机制的指导下，我们为细胞间调控异质性制定了合理的混合模型，并最大化所得似然函数以推断模型参数。对不同混合模型的推论经过计算和实验验证，其中包括群体中少至四分之一的细胞占据的多细胞功能的调节状态。重要的是，当该方法扩展到异质共表达转录本的程序时，我们发现，当采样范围有限时，合并样本的群体水平推断比单细胞样本的推断准确得多。我们的反卷积方法提供了一种有效量化分子状态异质调节的方法，并更深入地了解细胞决策的异质执行。
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.