Analytical Instrument: We maintain an in-house, high-resolution Orbitrap LC-MS system dedicated to global metabolomics and lipidomics of complex biological matrices (such as blood, urine, and fecal samples). This direct access to instrumentation inspires our team to develop both computational and analytical innovations to address critical challenges in data acquisition and processing.

Computational Modeling: We are actively developing genome-scale metabolic models (GEMs) and GEM-derived pathways for cells, tissues, and organisms. These models are viewable, computable, and customizable based on biological context. While our current implementation focuses on metabolic interactions, future versions will incorporate cellular boundaries and receptor/ligand interactions to better accommodate single-cell multi-omics and cell-cell communication. Our long-term goal is to develop fully functional virtual cells and tissues.

Animal Model: C. elegans harbor distinct microbiomes that can be easily established and maintained in the laboratory. We utilize this model to study how microbiome composition impacts host fitness, specifically within the context of aging and neurodegenerative diseases (such as Alzheimer's and Parkinson's). Distinct phenotypes are further investigated using deep sequencing and metabolomics to gain mechanistic understanding. We use LC-MS coupled with stable isotope labeling, high-resolution microscopy, and various phenotypic assays to gain insights into these complex host-microbiome interactions.

Genetics accounts for less than half of the risk for most chronic complex diseases, with environmental factors playing a crucial role. Our research focuses on understanding the complex interplay between genetics and the environment using 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 (microbes living in and on us) interacts extensively with the host through metabolic exchanges and co-metabolism of foods and drugs, contributing to immune and metabolic diseases such as obesity and diabetes. Finally, the exposome is a systematic, unbiased, and omics-scale examination of external factors contributing to health and disease.

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).

Data provides little value unless it can be interpreted to increase our knowledge or inform our decisions. A long-term interest in the Xia Lab is to make omics data analytics accessible to broad researcher communities by leveraging cloud, web, statistics, AI, and data visualization techniques, coupled with comprehensive domain knowledge to facilitate data analysis and understanding. We use both local supercomputers and public cloud infrastructure to enable high-performance data analysis. To date, we have developed a series of omics tool suites that are accessed by ~1,000,000 researchers worldwide for their omics data analysis and interpretation.

We are actively exploring new technologies to better educate, engage, and empower users. We are interested in integrating AI agents with our validated analysis modules to enable fully automated or semi-supervised omics data analysis through conversation (conversational analytics). Results are presented as interactive dashboards and PDF reports, linked to fully featured web interfaces. This allows users to toggle between high-level overviews and detailed results with granular controls. We are also exploring virtual or augmented reality (VR/AR) to enhance the user experience.