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

• 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

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.

细胞中的信息流如何组织？外部信号如何转移到细胞核以打开或关闭转录程序？拟议的研究通过结合新型实验技术的数据来解决这些问题，这些技术仅在过去几个月中才发表，并通过申请人开创了一种既定的计算方法。新型的实验技术使用一种称为CRISPR的基因编辑方法来扰动细胞中的基因，然后使用单细胞RNA测序测量其中基因表达反应。通过在许多细胞中使用许多扰动，数据可以全面地了解基因扰动的影响，从而对基因在细胞中的功能进行了什么功能。数据完全符合申请人开发的计算方法，从而从基因扰动的表达效果推断出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年中，该方法发展得很好，并且在不同的应用中引入了许多关键思想（在不同的应用中，基因受干扰不同或对效果的测量不同）。但是，关键的想法存在，现在可以将单个单元RNA SEQ CRISPR屏幕的新数据转换为新数据。该项目的目标首先是更好地了解新类型数据的功能，并确保可以牢固地估算扰动效应。其次，为这些新数据的细节量身定制了NEM。第三，要了解哪些影响不同的实验参数具有，因此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，利用方法学进步获得了对生物学的新见解。两个案例研究将在T辅助细胞中的调节网络以及JAK-STAT途径如何塑造表观遗传景观上。

项目成果

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
• 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

• DOI: 10.1016/j.bbagrm.2019.194441
• 发表时间: 2020-06-01
• 期刊: BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS
• 影响因子: 4.7
• 作者: Holding, Andrew N.;Cook, Helen, V;Markowetz, Florian
• 通讯作者: Markowetz, Florian