Single-cell analysis of transcription kinetics across the cell cycle.
Single-cell analysis of transcription kinetics across the cell cycle.
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DOI：
10.7554/elife.12175
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发表时间：
2016-01-29
期刊：
影响因子：
7.7
通讯作者：
Golding I
中科院分区：
文献类型：
--
作者：
Skinner SO;Xu H;Nagarkar-Jaiswal S;Freire PR;Zwaka TP;Golding I
转录是一个高度随机的过程。为了推断感兴趣基因的转录动力学，研究人员通常将mRNA拷贝数的分布与理论模型的预测进行比较。然而，这种方法的可靠性是有限的，因为测量的mRNA数量代表了mRNA寿命内的整合，来自多个基因拷贝的贡献，以及来自不同细胞周期阶段的细胞的混合。我们通过同时定量单个细胞中的新生和成熟mRNA，并将细胞周期效应纳入mRNA统计分析来解决这些限制。我们在小鼠胚胎干细胞中展示了我们对Oct 4和Nanog的方法。这两个基因都遵循类似的两态动力学。然而，Nanog表现出较慢的开/关切换，导致mRNA水平的细胞间变异性增加。在细胞周期的早期，每个基因的两个拷贝表现出独立的活性。在基因复制后，每个基因拷贝被激活的概率降低，导致剂量补偿。DOI：http://dx.doi.org/10.7554/eLife.12175.001科学调查要求研究人员使用实验观察来理解导致这些观察的生物过程。一个例子是称为转录的细胞过程，其中基因的DNA被多次复制以产生信使RNA（mRNA）分子，这些分子后来被用作制造蛋白质的指令。科学家们通过计算单个细胞中有多少mRNA分子来间接测量转录的动态，即基因产生mRNA的频率。然后将这些数字与转录的数学模型所做的预测进行比较，如果模型和实验吻合得很好，这就意味着模型正确地描述了该基因转录的频率。不幸的是，这个过程并不简单，因为许多因素使转录动力学与在任何一个时间点在每个细胞中检测到的mRNA数量之间的关系复杂化。例如，不可能判断检测到的mRNA是刚刚转录的，还是几小时前产生的。细胞的年龄和模板DNA的拷贝数也会影响转录的动力学。因此，mRNA测量可能会被误解，导致关于高度特定基因转录的错误结论。为了解决这个问题，Skinner等人首先通过区分成熟mRNA和仍在转录的新mRNA来改进实验测量。实验还测量了每个细胞含有多少DNA，这表明细胞的年龄。这些测量结果被整合到一个新的数学模型中，该模型能够预测特定基因转录的动态。Skinner等人将他们的方法应用于两种名为Oct 4和Nanog的小鼠基因，它们调节胚胎干细胞向其他类型细胞的转化。实验表明，这两种基因都可以在“开启”状态和“关闭”状态之间切换，在“开启”状态下，它们被积极转录，而在“关闭”状态下，很少或根本没有mRNA被产生。然而，Nanog在这两种状态之间切换的频率低于Oct 4，这导致不同细胞之间mRNA数量的变化更大。实验还表明，在细胞的生命过程中，每个DNA拷贝的转录水平都会降低。Skinner等人的方法可以用来完善我们对其他基因转录的知识。然而，为了进一步提高我们对转录的理解，需要将其他因素的测量纳入数学模型。DOI：http://dx.doi.org/10.7554/eLife.12175.002网站
Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation. DOI: http://dx.doi.org/10.7554/eLife.12175.001 Scientific investigation requires researchers to use experimental observations to understand the biological process that resulted in these observations. One example is a cellular process called transcription, where the DNA of a gene is copied many times to make molecules of messenger RNA (mRNA), which are later used as instructions to make proteins. Scientists indirectly measure the dynamics of transcription, that is, how often the gene produces mRNA, by counting how many mRNA molecules there are in many individual cells. These numbers are then compared to the predictions made by a mathematical model of transcription, and if the model and experiment agree well, this is interpreted to mean that the model properly describes how often this gene is transcribed. Unfortunately, this procedure is not straightforward because many factors complicate the relationship between the dynamics of transcription and the number of mRNAs that will be detected in each cell at any one point in time. For example, it is not possible to tell whether a detected mRNA has just been transcribed, or whether it was made hours ago. The age of the cell and how many copies of the template DNA are present also affect the dynamics of transcription. As a result, mRNA measurements may be misinterpreted, leading to wrong conclusions about how highly particular genes are transcribed. To address this problem, Skinner et al. first improved the experimental measurements by discriminating between mature mRNA and the new mRNA that is still being transcribed. The experiments also measured how much DNA each cell contains, which indicates how old the cell is. These measurements were incorporated into a new mathematical model that is able to predict the dynamics of transcription of particular genes. Skinner et al. applied their method to two mouse genes called Oct4 and Nanog, which regulate the transformation of embryonic stem cells into other types of cells. The experiments show that both genes can switch between an “on” state where they are being actively transcribed and an “off” state where little or no mRNA is being produced. However, Nanog switches between these two states less often than Oct4, which results in larger variations between the numbers of mRNAs between different cells. The experiments also show that over the life of the cell, the level of transcription from each copy of the DNA decreases. Skinner et al.’s approach can be used to refine our knowledge of the transcription of other genes. However, to further improve our understanding of transcription, measurements of other factors will need to be incorporated into the mathematical models. DOI: http://dx.doi.org/10.7554/eLife.12175.002