Xia Lab @ McGill

Multi-omics & Systems Biology

Genetics contributes to less than 50% of most chronic complex diseases. 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 based on omics technologies. 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 microbiome, the microbes living in and on us, interact extensively with their host through metabolic exchanges and co-metabolism of foods and drugs, contributing to 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.

We employ knowledge-driven and data-driven approaches for multi-omics integration. The former is based on networks of known interactions (suitable for model organisms), while the latter is based on multivariate statistics to identify coherent patterns (suitable for any organisms).

Democratizing Omics Big Data Analytics

Data is unless it can be understood to increase our knowledge or to inform our decision. 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 accessed by >500,000 users worldwide in their omics data analysis and interpretation.

We share our tools (and knowledge) through user-friendly web-based platforms hosted on cloud. To engage and empower users, we are actively exploring various new technologies such as virtual reality (VR) for better visualization, chatbot for user support.

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.