Learning signalling pathways from single-cell RNA profiles of CRISPR perturbations

单元格中的信息流是如何组织的？外界信号是如何传递到细胞核来开启或关闭转录程序的？这项拟议的研究通过将最近几个月才发表的新实验技术的数据与申请人首创的既定计算方法相结合来解决这些问题。这些新的实验技术使用一种名为CRISPR的基因编辑方法来扰乱细胞中的基因，然后使用单细胞RNA测序来测量其中的基因表达反应。通过在许多细胞中使用许多扰动，这些数据给出了基因扰动的影响以及基因在细胞中的功能的综合图景。这些数据与申请人开发的一种计算方法非常吻合，该方法可以从基因扰动的表达效应中推断出基因相互作用和途径。这种方法被称为嵌套效果模型。在过去的12年里，该方法得到了很好的发展，许多关键的想法被引入到不同的应用中(其中基因被不同地干扰或影响被不同地测量)。但关键的想法已经存在，现在可以转化为来自单细胞RNA序列CRISPR筛查的新数据。该项目的目标是，首先，更好地了解新型数据的特征，并确保扰动效应可以得到强有力的估计。其次，根据这些新数据的具体情况调整新的货币政策。第三，了解不同的实验参数有哪些影响，从而能够在未来设计更好的实验。最后，与领先的实验科学家合作，利用方法学上的进步来获得对生物学的新见解。两个案例研究将是关于T辅助细胞的调控网络和JAK-STAT途径如何塑造表观遗传景观。

How is information flow in the cell organised? How are outside signals transferred to the cell nucleus to turn transcriptional programs on or off? The proposed research addresses these questions by combining data from novel experimental techniques, which have only been published in the last few months, with an established computational approach pioneered by the applicant. The novel experimental techniques use a gene editing method called CRISPR to perturb genes in a cell and then measure the gene expression response in it using single cell RNA sequencing. By using many perturbations in many cells the data give a comprehensive picture of the effects of gene perturbations and thus of what function the genes have in the cell. The data fit perfectly to a computational method the applicant has developed to infer gene interactions and pathways from the expression effects of gene perturbations. The method is called Nested Effects Models. Over the last 12 years the method has been very well developed and many key ideas have been introduced in different applications (where genes were perturbed differently or effects were measured differently). But the key ideas are there and can now be translated to the new data from single cell RNA seq CRISPR screens.The goals of the project are, first, to understand the features of the new type of data better and make sure that perturbation effects can be estimated robustly. Second, to tailor NEMs to the specifics of these new data. Third, to understand which effect different experimental parameters have and thus be able to design better experiments in the future. And finally, in collaboration with leading experimental scientists, to use the methodological advances to gain new insights into biology. Two case studies will be on regulatory networks in T helper cells and on how the JAK-STAT pathway shapes epigenetic landscapes.