SymBioSys: Computationally disentangling cellular heterogeneity

Cells are the fundamental units of life, and their characterization via single-cell sequencing (sc-Seq) approaches is revolutionizing our fundamental understanding of normal organismal development, aging, and disease. We here propose to develop innovative computational and experimental methods aimed at understanding the extent and biology of cellular heterogeneity in health and disease, while overcoming the technological challenges of exploiting large-scale sc-Seq data by developing novel analysis algorithms, setting up an efficient IT infrastructure, and constructing effective analysis pipelines. These resources will be shared between project partners and allow us to build a computational (single-cell) omics hub for the broader KU Leuven research community. These methods will be used to replace current single-cell mono-omic approaches with novel single-cell multi-omics methods whereby the genome, epigenome, transcriptome, and proteins of the same single cell are characterized in parallel. They will also enable the reconstruction and annotation of cell lineage trees annotated with phenotypic data of the same cells, the mapping of developmental trajectories from normal and diseased tissues or organs and of the function of (epi)genetic variants acquired in health and disease, and the discovery of single-cell multi-omic biomarkers for diagnosis and drug discovery. Application of these technologies on hundreds of thousands to millions of single cells will provide novel understanding of development and physiology, and how these are perturbed in aging and in disease processes, such as congenital malformations, cancer, and neurodegeneration.

Keywords:single cell, genomics, transcriptomics, epigenomics, computational

Disciplines:Genetics, Systems biology, Molecular and cell biology, Medical imaging and therapy, Other paramedical sciences