Decoding phenotypes using single cell genomics
The Papatheodorou group develops computational approaches to unravel the molecular basis of phenotypes in health and disease at the cell type level, by integrating bulk and single-cell “omics” data of different modalities.
With recent advances in technologies for tissue dissociation and high-throughput sequencing at the single cell level, it is now possible to describe phenotypes and their molecular basis at a greater resolution. As technologies enabling transcriptomic profiling of single cells have matured, so have the tools required for the computational analysis of the resulting data. This has enabled the generation of complete pipelines that align the reads to the genome, perform quality control, calculate gene expression levels and perform downstream analyses such as clustering, trajectory inference, and dimensionality reduction. Building on these efforts, the Papatheodorou group and Expression Atlas team have developed standardised, scalable, workflows for analysis of scRNA-Seq data that underpin the development of resources that integrate and add value to diverse scRNA-Seq datasets.
This development now enables linking in a consistent manner transcriptomic profiles (scRNA-Seq or spatial) from different studies to assemble larger cohorts and investigate phenotypes at greater depth, at the level of cell types. Moreover, in is now possible to compare cellular mechanisms across species in a very consistent manner. Understanding how gene expression relates across model organisms to humans at the cellular level can have major implications for medical research and help accelerate drug development.
Our team leverages the availability of big data sets accumulating from single cell RNA-Seq studies, to understand the relationship between gene expression and phenotype at the cellular level within and across species. Scientists in the team combine single cell datasets with bulk “omics”, such as RNA-Seq, GWAS, proteomics or biological pathways to perform integrative analyses that combining data sets from different studies to disentangle complex phenotypes. We collaborate with several groups working on the Human Cell Atlas, contributing to the Gut Cell Atlas, Crohn’s Disease Atlas with the University of Edinburgh, as well as to the European project, Human Lung Cell Atlas (discovAIR). In addition, we contribute to the Fly Cell Atlas, where we are embarking on a project that will generate a catalogue of similarities and differences in cell types and their gene expression signatures across different species.
Future projects and goals
- Compare cell atlases across different species, such as human, mouse, fruitfly to investigate within each tissue, which cell types are common or differ significantly between those three key species and as a result produce gene expression signatures per cell type.
- Development of approaches for the inference of disease progression trajectories by combining molecular profiles of different modalities with clinical information.
- Development of approaches that will enable the identification of pathogenic cell types and pathways in different disease phenotypes, as well as potential therapeutic targets.
Selected publications Papatheodorou group
Goldman M, et al. Online resources for PCAWG data exploration, visualization and discovery. Nature Communications. 2020
Mial Z, et al. Putative cell type discovery from single-cell gene expression data. Nature Methods. 2020 18 May
Moreno P, et al. User-friendly, scalable tools and workflows for single-cell analysis. bioRxiv. 2020 09 Apr
Papatheodorou I, et al. Expression Atlas update: from tissues to single cells. Nucleic Acid Research. 2019 30 Oct