Grant: Canada Research Chairs

Metabolomics is an emerging biotechnology that comprehensively characterizes the entire set of metabolites (molecular weight < 1500 Da) in a given biological system. It provides snapshots of metabolic fingerprints in a biological system to better understand the underlying biological mechanisms. Mass spectrometry (MS) is the most prominent technology among various analytical platforms for metabolomics due to its high throughput, sensitivity, specificity, and coverage. In particular, liquid chromatography coupled to MS detection (LC-MS) can quantitatively and rapidly measure hundreds to thousands of metabolites from a wide range of environmental and biological sample types in an unbiased manner. Since 2018, my lab has independently developed a suite of analytical and bioinformatic tools for LC-MS-based metabolomics. The potential of metabolomics shines brightly in its capacity to comprehensively detect environmental exposures, commonly referred to as the exposome. This concept encapsulates the lifelong summation of an organism's chemical, microbial, and environmental encounters, intricately linked to its well-being, reproductive health, and overall viability. Environmental exposures wield a significant influence in the genesis of human ailments, with an estimated 80–85% of diseases attributed to the intricate interplay between hundreds of thousands of environmental chemicals. Nevertheless, the intricate nexus involving environmental exposures, genetic susceptibility, and potential health ramifications remains shrouded in mystery. To better study environmental exposomes and understand how they affect human health and disease, my lab will develop novel analytical and bioinformatic strategies to empower LC-MS-based metabolomics. In particular, we will develop an artificial intelligence-assisted metabolomics data processing pipeline. We will also construct a cloud-based platform for data visualization and quantitative data quality checking. Finally, we will develop a compound annotation platform to recognize unknown-unknown environmental exposures. The goal is to create a next-generation metabolomics analytical pipeline that provides comprehensive metabolome coverage, precise quantitative comparison, and accurate metabolite annotation within the environmental context, thus helping elucidate how environmental risk factors contribute to human health and diseases.