Project description
Computational platform for characterisation of gene regulation at the single-cell level
The development of single-cell technologies has enabled the characterisation of cell types and underlying patterns of the developmental processes at higher resolution, while the integration of data from different omics analyses yields a more differentiated picture of mechanistic connections. Funded by the Marie Skłodowska-Curie Actions programme, the GReCS project aims to develop a computational method that generates insights into gene regulation at the single-cell level. The approach will integrate transcriptomics and open chromatin data to filter prior information about candidate interactions and predict cell-specific gene regulatory networks using machine learning. The developed computational toolkit will be available to the community to expand the characterisation of gene regulation by combining different types of data.
Objective
The advent of single cell technologies has enabled the characterization of cell types and developmental processes. Observations from different cells allow one to identify underlying patterns at higher resolution than convoluted bulk data, and integration of different omics data can yield a more differentiated picture of mechanistic connections. In this proposal, Gene REgulatory Cell States (GReCS) from multi-modal data, I plan to develop a computational method that combines these aspects to generate insights into gene regulation at the level of single cells.
Measurements of chromatin accessibility in single cells are becoming increasingly common. The method I propose to develop combines sc/sn-ATAC- and scRNA-sequencing data to characterize gene regulation. My approach will integrate and use transcriptomics and open chromatin data to filter comprehensive prior information about candidate interactions and predict cell-specific gene regulatory network versions using machine learning, while sparse single cell measurements are imputed using local cell similarities. In this way, rare measurements across cell types and a larger condition space for network inference can be exploited, using the natural potential of chromatin accessibility data as a filter to map interactions into a cell-specific context.
A distinguishing feature of the proposed method is the characterization of local gene regulatory states, which allows the observation of continuous changes throughout a cell-cell similarity embedding. This will be useful to examine changes during cell differentiation and along gradients in spatial reconstructions, for example of embryonic development. The developed methods will be made available to the community as a computational toolkit to improve the characterization of gene regulation by combining different types of data.
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Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
CB10 1SA SAFFRON WALDEN
United Kingdom