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

从 CRISPR 扰动的单细胞 RNA 谱中学习信号通路

基本信息

批准号：
BB/R006563/1
负责人：
Florian Markowetz
金额：
$ 64.64万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FR006563%2F1
关键词：
Learning signalling pathways single cell

项目摘要

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.

细胞内的信息流是如何组织的？外部信号如何转移到细胞核以打开或关闭转录程序？拟议的研究通过将最近几个月才发表的新颖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。这项新颖的实验技术使用一种称为 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 测序来测量细胞中的基因表达反应。通过在许多细胞中使用许多扰动，数据可以全面了解基因扰动的影响，从而全面了解基因在细胞中的功能。这些数据完全适合申请人开发的计算方法，该方法用于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，该方法得到了很好的发展，许多关键思想已被引入不同的应用中（其中基因受到不同的扰动或效应的测量也不同）。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，更好地了解新型数据的特征，并确保扰动效应可以进行稳健估计。其次，根据这些新数据的具体情况定制 NEM。第三，了解不同的实验参数会产生哪些影响，从而能够在将来设计更好的实验。最后，与领先的实验科学家合作，利用方法论的进步来获得对生物学的新见解。两个案例研究将涉及 T 辅助细胞的调控网络以及 JAK-STAT 通路如何塑造表观遗传景观。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The Genomic Landscape of Early-Stage Ovarian High-Grade Serous Carcinoma.

DOI：
10.1158/1078-0432.ccr-21-1643
发表时间：
2022-07-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
0
作者：
Cheng Z;Mirza H;Ennis DP;Smith P;Morrill Gavarró L;Sokota C;Giannone G;Goranova T;Bradley T;Piskorz A;Lockley M;BriTROC-1 Investigators;Kaur B;Singh N;Tookman LA;Krell J;McDermott J;Macintyre G;Markowetz F;Brenton JD;McNeish IA
通讯作者：
McNeish IA

Data generation and network reconstruction strategies for single cell transcriptomic profiles of CRISPR-mediated gene perturbations