Generation of single cell multi-omics computational methods for biological insights and novel deconvolution techniques for bulk omics data

Studentship strategic priority area:Bioenergy and Industrial Biotechnology
Keywords: Machine learning; Single cell biology; deconvolution; irritation; Skin

In recent years, many bulk RNA-Seq and epigenetic data has been generated, but we lack the resolution of cell type and tissue positional information to draw strong conclusions.

Advances in single-cell transcriptomics sequencing (scRNA-Seq) have been rapid and is revolutionizing the ability to understand, for example, cellular heterogeneity and cell-cell communication . To date, however, there are no efficient methods to combine scRNA-Seq, single-cell open chromatin capture (scATAC) and spatial transcriptomics (histological location of gene expression) and use them to better understand existing bulk 'omics data. For instance, an increase in chromatin opening in specific cells should link to changes in methylation and expression in the same genomic region found in bulk data. We propose to generate a multi-omics single-cell dataset from skin "cutaneous irritation", as an example biological domain, to build and test models for integration and interpretation of these multi-omic single-cell technologies that can be used to better interpret existing bulk omics data.

Objectives:
1) Generate novel computational models to integrate gene expression (scRNA-Seq), epigenetic (scATACseq) and mRNA tissue location data (spatial transcriptomics) to generate cell-specific tissue-localised signatures and multi-omic networks.
2) In-silico 'bulkify' cell signatures (2a) and validate with the actual bulk RNAseq data (2b).
3) Deconvolute existing RNAseq and methylation data using models from 1 and signatures from 2, to map differential signatures onto cell signatures and multi-omic networks.
4) Identify irritation signatures at a single cell level in skin and its relationship with skin conditions.