背景:在过去的十年中,用于确定转录组范围内的RNA转换率的代谢标记等实验程序已被广泛采用,现在正转向单细胞测量。几种估算RNA合成的计算方法,这些实验的处理和降解率已经被提出,但是它们都需要几个RNA测序样本。在这里,我们提出了一种可以从单个样本中估计这三种速率的方法。
方法:我们的方法依赖于RNA动力学Zeisel模型的分析解决方案。在小鼠胚胎干细胞上进行的代谢标记实验中对其进行了验证。将所得降解速率与先前在同一系统上公布的速率以及应用于相同数据的现有技术方法进行比较。
结果:我们的方法计算效率高,输出速率与以前发布的数据集很好地相关。在单个样本上使用它,我们能够重现动态生物过程倾向于涉及具有较高代谢率的基因的观察结果,而稳定的过程涉及速率较低的基因。这支持以下假设:细胞不仅控制mRNA的稳态丰度,而且它的反应能力,即,达到稳态的速度有多快。此外,用我们的方法获得的降解率与其他试验方法相比是有利的。
结论:除了节省实验工作和计算时间外,估计单个样本的速率有几个优点。它不需要对样品进行易错的标准化,并且可以使用重复来估计不确定性和评估样品质量。最后,本文描述的方法和理论结果足够通用,可用于其他情况,例如核苷酸转化方法和单细胞代谢标记实验。
BACKGROUND: Over the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted and are now turning to single cell measurements. Several computational methods to estimate RNA synthesis, processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate those three rates from a single sample.
METHODS: Our method relies on the analytical solution to the Zeisel model of RNA dynamics. It was validated on metabolic labeling experiments performed on mouse embryonic stem cells. Resulting degradation rates were compared both to previously published rates on the same system and to a state-of-the-art method applied to the same data.
RESULTS: Our method is computationally efficient and outputs rates that correlate well with previously published data sets. Using it on a single sample, we were able to reproduce the observation that dynamic biological processes tend to involve genes with higher metabolic rates, while stable processes involve genes with lower rates. This supports the hypothesis that cells control not only the mRNA steady-state abundance, but also its responsiveness, i.e., how fast steady state is reached. Moreover, degradation rates obtained with our method compare favourably with the other tested method.
CONCLUSIONS: In addition to saving experimental work and computational time, estimating rates for a single sample has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and assess sample quality. Finally the method and theoretical results described here are general enough to be useful in other contexts such as nucleotide conversion methods and single cell metabolic labeling experiments.