Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Robert-Rössle-Str. 10, 13125 Berlin, Germany; Luxembourg Institute of Health, 1A-B rue Thomas Edison, L-1445 Strassen, Luxembourg.
MicroDiscovery GmbH, Marienburgerstr. 1, 10405 Berlin, Germany.
Cell Syst. 2019 Dec 18;9(6):569-579.e7. doi: 10.1016/j.cels.2019.07.009. Epub 2019 Sep 11.
Estimating fold changes of average mRNA and protein molecule counts per cell is the most common way to perform differential expression analysis. However, these gene expression data may be affected by cell division, an often-neglected phenomenon. Here, we develop a quantitative framework that links population-based mRNA and protein measurements to rates of gene expression in single cells undergoing cell division. The equations we derive are easy-to-use and widely robust against biological variability. They integrate multiple "omics" data into a coherent, quantitative description of single-cell gene expression and improve analysis when comparing systems or states with different cell division times. We explore these ideas in the context of resting versus activated B cells. Analyzing differences in protein synthesis rates enables to account for differences in cell division times. We demonstrate that this improves the resolution and hit rate of differential gene expression analysis when compared to analyzing population protein abundances alone.
估计细胞内平均 mRNA 和蛋白质分子数的倍数变化是进行差异表达分析最常用的方法。然而，这些基因表达数据可能受到细胞分裂的影响，而这是一个经常被忽视的现象。在这里，我们开发了一种定量框架，将基于群体的 mRNA 和蛋白质测量与正在进行细胞分裂的单细胞中的基因表达速率联系起来。我们推导出的方程易于使用，并且对生物变异性具有广泛的稳健性。它们将多种“组学”数据整合到单细胞基因表达的连贯、定量描述中，并在比较具有不同细胞分裂时间的系统或状态时改善分析。我们在静息和激活的 B 细胞的背景下探讨了这些想法。分析蛋白质合成速率的差异可以解释细胞分裂时间的差异。我们证明，与单独分析群体蛋白丰度相比，这提高了差异基因表达分析的分辨率和命中率。
