Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908.
Proc Natl Acad Sci U S A. 2014 Feb 4;111(5):E626-35. doi: 10.1073/pnas.1311647111. Epub 2014 Jan 21.
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.
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，全面分析转录调控的细胞间变化是有问题的。通过随机收集少量细胞并进行统计分析，可以稳健地实现全转录组识别调控异质性。然而，这种随机分析方法模糊了每个混合样本中单个细胞的表达状态。在这里，我们表明，可以通过最大似然推断从随机分析数据中解卷积单细胞调节状态的基础分布。受转录调控机制的指导，我们为细胞间调节异质性制定了合理的混合模型，并最大化了由此产生的似然函数来推断模型参数。对于不同的混合模型，我们从计算和实验两方面验证了推断，其中包括由群体中多达 1/40 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，当采样程度有限时，与单细胞样本相比，混合样本的群体水平推断更为准确。我们的解卷积方法提供了一种有效量化分子状态异质性调节的方法，并深入了解细胞决策的异质性执行。
