Bioinformatics Seminar
Time: 11AM
Venue: Davis Auditorium and Online
30 April 2024
Single-cell and spatial analysis of diseased and healthy lungs with statistics and deep learning
Davis McCarthySt. Vincent's Institute of Medical Research
Understanding molecular and structural heterogeneity in tissues is a key component of studying health and disease. Indeed, making progress towards new treatments for a deadly, progressive disease like idiopathic pulmonary fibrosis requires genetic and molecular analysis at high cellular and spatial resolution. Happily, modern omics technologies provide the ability to characterise genetic and other high-dimensional molecular states at single-cell resolution, now also with spatial context. Rich, complex datasets are exciting, but bring with them deep challenges for winnowing the wheat from the chaff to answer biological questions of interest. In this talk, I will cover two related projects from my lab using traditional statistical and recently developed deep learning approaches to study single-cell gene expression and spatial transcriptomic data in diseased and healthy lungs. First, I will discuss a project in which we undertook single-cell expression quantitative trait locus mapping on 500,000 cells from 114 human donors with and without interstitial lung disease. We present a cell-type-level examination of the genetic control of gene regulation across the major cell types in the human lung and find disease-specific eQTLs that colocalise with GWAS loci for pulmonary fibrosis. Second, I will discuss our use of graph neural network models (among other approaches) to characterise the molecular basis for tissue niche structure in lung fibrosis using 10x Xenium data on 28 lung samples. This analysis offers new insights into the spatial heterogeneity of gene expression in healthy and fibrotic regions of the lung and identifies early transition regions from healthy to disease states as the most promising area for clinical intervention.