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

细胞中的信息流是如何组织的？外部信号是如何传递到细胞核来开启或关闭转录程序的？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开创的既定计算方法相结合来解决这些问题。新的实验技术使用一种称为CRISPR的基因编辑方法来干扰细胞中的基因，然后使用单细胞RNA测序来测量其中的基因表达反应。通过使用许多细胞中的许多扰动，数据给出了基因扰动的影响的全面画面，从而给出了基因在细胞中的功能。数据完全符合申请人开发的计算方法，以从基因扰动的表达效应推断基因相互作用和途径。该方法称为嵌套效应模型。在过去的12年里，该方法已经得到了很好的发展，许多关键思想已经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 seq CRISPR筛选的新数据。该项目的目标是，首先，更好地了解新类型数据的特征，并确保可以稳健地估计扰动效应。第二，根据这些新数据的具体情况定制NEM。第三，了解不同实验参数的影响，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，利用方法学的进步来获得对生物学的新见解。两个案例研究将在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.