Bioinformatically mapping the transcriptome 3’-end dynamics that specify cell identity

Single-cell analyses are at the leading edge of technological development for cell biology. And, transcriptomics analyses at single-cell resolution are redefining our thinking about gene expression. Unlike the population averages assessed by traditional transcriptomics, analysis of single cells builds a fingerprint of gene expression in individual cells within a population, thereby deconvoluting the complex signatures of different cell-groups within populations. This can reveal the different cell-cycle stages within steady-state cultures and/or identify cells of different cell lineages in mixed populations such as the immune cells of peripheral blood. Here the supervisor team combine unique expertise in RNA metabolism and bioinformatics to ask how mRNA 3’-end formation is controlled in cell populations to specify normal cell identity and how 3’-ends switch in response to perturbation. In this project, the applicant will take advantage of the molecular underpinnings of single-cell RNA-seq (scRNA-seq) to tease out a new dimension from public and new, single cell transcriptomic data. Typically, scRNA-seq is used to ‘count’ cellular mRNA content by digital gene expression (DGE). However, the approaches are generally, 3’-focussed and thus, where mRNA have more than one 3’-end generated by alternative end-processing, these can be detected. At least ¾ of all eukaryotic mRNA are subject to alternative polyadenylation. This has important cellular consequence because the loss/gain of regulatory regions in the 3’-UTR can alter mRNA fate. Yet how this processing links to cell identity, how, mechanistically, it is regulated and ultimately, what 3’-end switches means for cell biology is still unclear. The overarching aim here is to unlock this rich orthogonal source of information in scRNA-seq for biological interrogation. NOTE: A fluency in computational programming languages is a pre-requisite