WEHI Special Bioinformatics Seminar hosted by Professor Gordon Smyth
 

Dr Davis McCarthy

Head – Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research
 

Single-cell and spatial analysis of diseased and healthy lungs with statistics and deep learning

 

Davis Auditorium

Join via ZOOM

Including Q&A session
 

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.

Dr McCarthy has formal training in Statistics and Machine Learning (BSc (Hons), Melbourne; DPhil, Oxford; Post-doc, EMBL-EBI) and more than 15 years of experience in genomics and bioinformatics. He has been a core developer of methods for the analysis of bulk RNA-sequencing data, including the edgeR package, and single-cell RNA-seq data, such as the scater package. As a post-doc at EMBL-EBI with Dr Oliver Stegle he pioneered expression QTL mapping using single-cell RNA-seq data and integrating genomic DNA and single-cell RNA-seq data to better understand clonal structure using somatic mutations. He leads the Bioinformatics and Cellular Genomics unit at St Vincent’s Institute of Medical Research in Melbourne. Studying the ways in which DNA variation contributes to variation in gene expression and other molecular phenotypes at the level of individual cells is a major theme of his lab’s current work, along with ongoing efforts to develop statistical and machine learning methods and software tools for the analysis of large-scale biomedical data, especially spatial transcriptomic and clinical image data. 

All welcome!