Xia Lab @ McGill

Multi-Omics & Systems Biology

Our genetic blueprint charts the course of our life, yet genetics can explain less than 25% of most major disorders. Many external forces play importance roles in health and disease. My general research interests are to understand the complex interplays of genetics and environmental factors by profiling transcriptome, metabolome, microbiome and exposome.

Metabolomics is the systematic study of all small molecules in a biological system. The metabolome consists of both endogenous metabolites and exogenous compounds derived from diet, gut microbes and environmental exposures. The increased application and availability of high-resolution (HR) MS systems have significantly advanced progress in metabolomics. The microbiome, the microbes living in and on us, interact extensively with their host through metabolic exchanges and co-metabolism of foods, drugs, and chemical pollutants. Growing number of studies have indicated the importance of microbiome in immune and metabolic diseases such as obesity, diabetes, etc. Finally, the exposome is a systematic, unbiased and omics-scale examination of external factors contributing to disease and health status. HR-MS is a key instrument for measuring exposome and host responses.

Web & Cloud-based Visual Analytics

The current biomedical big data challenges are characterized by both size and complexity. A long-term interest in my laboratory is to develop new-generation computational frameworks integrating high-performance computing, statistics and data visualization techniques, coupled with comprehensive domain knowledge to facilitate novel discovery, hypothesis generation, and systems understanding. We use both local supercomputers and public cloud to enable high-performance data analysis. To date, we have developed multiple popular software tools for metabolomics, transcriptomics and microbiomics. These tools are used by 1000s of researchers worldwide in their omics data analysis and interpretation.

We are currently developing two complementary approaches (biological networks and multivariate statistics) for multi-omics integration - the former is based mainly on known molecular interactions (suitable for model organisms), while the latter can be used to identify coherent patterns for any organisms.

C. elegans model for Gene-Environment-Microbiome (GEM) Interactions

Recent studies have shown the that natural populations of C. elegans harbor distinct microbiome, which can be easily established and maintained in the laboratory. We are developing this model to study the effects of different microbiome compositions on worm fitness as well as response to chemical exposures. Distinct phenotypes are further investigated using deep sequencing and metabolomics for mechanistic understandings.

We are developing a high-throughput platform consisting of a powerful microscope, a worm ScreenChip system, and a cutting-edge Orbitrap LC-MS for investigating GEM interactions.