Integration of long read genome sequencing and single-cell multi-omics to identify genetic variation underlying Parkinson’s disease

In recent years, there have been major developments in ’omics technologies. Long read sequencing now produces highly accurate reads from single molecules with theoretically unlimited length. Long reads enable us to assemble whole human genomes de novo, study complex genomic regions and large structural variants, all of which are particularly difficult through conventional short read technologies. It also allows us to directly study DNA modifications, such as 5-methylcytosine methylation. Simultaneously, advances in single cell technologies now allow us to obtain multi-modal measurements, such as single cell RNA-sequencing and single cell ATAC-sequencing, from the very same cell. These coupled measurements allow us to integrate gene expression and chromatin accessibility information in an unbiased fashion to gain deeper understanding of gene regulation at single cell level. In this project, we combine long reads and single cell multiome sequencing to shed light onto functional genetic variation in the brains of individuals with Parkinson’s disease and healthy controls. Here, we develop an analysis pipeline to quality check, process, and analyse long read sequencing data. De novo and reference-guided genome assembly approaches are explored to construct personalised diploid genomes. Next, we analyse the single cell multiome data to annotate different cell types to learn more about differential gene expression and chromatin accessibility in potentially cell-type specific manner in donors with different disease status. Next, we will be mapping multiome data to the personalised diploid genomes, allowing us to identify allele-specific chromatin accessibility, and allele-specific expression variants. Finally, we are inferring enhancer-driven gene regulatory networks using SCENIC+ combined with enhancer-based deep learning to prioritise and interpret genomic regulatory variation in a cell-type specific manner. Our identification of allele specific chromatin accessibility and gene expression from these data will form a rich basis to untangle the contributions of both germline and somatic variation to (cell-type specific) gene dysregulation underpinning Parkinson’s disease.