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

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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.