Research | Huan Lab

Research Vision

Metabolomics profiles the complete set of small molecules in biological and environmental systems. These molecules provide a direct readout of metabolism, exposure, physiology, and disease state, making metabolomics one of the most powerful omics approaches in the post-genome era.

The Huan Lab integrates chemistry, biology, computer science, and environmental science to advance technologies for the comprehensive, sensitive, and high-throughput detection, quantification, and characterization of small-molecule chemicals in complex biological and environmental systems.

Research aims

Building the next generation of small-molecule omics

01Systematically understand, evaluate, and improve mass spectrometry for omics-scale analysis of small molecules.

02Develop computational solutions that automate and improve mass spectrometry data processing and interpretation.

03Adapt cutting-edge metabolomics to reveal biology at the metabolite level, including cell metabolism and protein-metabolite interactions.

Platform 01

Next-Generation Mass Spectrometry

We improve LC-MS/MS acquisition, sample preparation, sensitivity, throughput, and quality control for omics-scale measurement of metabolites, lipids, and exposure-related chemicals.

Platform 02

AI-Assisted Data Processing

We develop computational approaches for feature detection, alignment, normalization, batch correction, annotation, and confidence scoring in high-dimensional mass spectrometry datasets.

Platform 03

Molecular Annotation and Discovery

We combine spectral evidence, chemical knowledge, databases, and machine learning to prioritize unknown metabolites and environmental chemicals for downstream validation.

Platform 04

Biological and Environmental Applications

We apply metabolomics and exposomics to collaborative studies in disease biology, microbiome research, environmental health, and chemical exposure.

Analytical Chemistry

Global detection and quantification from limited samples

The first challenge in metabolomics is to globally detect and quantify metabolites across broad concentration ranges and chemical structures in complex biological systems. LC-MS is a powerful platform for this work, and the lab develops integrated workflows that can perform both metabolomics and lipidomics from limited biological sample quantities.

We also create new data acquisition modes and quality-control strategies to improve sensitivity, throughput, and metabolomics data quality.

Computational Biology

Extracting biologically-meaningful features from high-dimensional LC-MS data

Raw LC-MS data contain a large number of low-abundance and chromatographically complex metabolic features. Conventional peak-picking algorithms can miss or distort these signals, creating a major big-data challenge for metabolomics.

The Huan Lab develops bioinformatic strategies to extract, recognize, normalize, and evaluate metabolic features more comprehensively. We also address overlooked quantitative issues that affect downstream statistical analysis, biomarker discovery, and biological interpretation.

Machine Learning

AI-assisted annotation of unknown metabolites and chemicals

Metabolite identification remains one of the central challenges in mass spectrometry-based metabolomics. Accurate-mass searching alone can return many possible chemical matches, while reference MS/MS spectra remain limited because many authentic standards are unavailable.

To address this gap, we develop computational and machine-learning tools that integrate MS and MS/MS evidence, chemical knowledge, and predicted spectral libraries to improve metabolite identification and unknown chemical prioritization.

Health and Environmental Applications

Applying metabolomics and exposomics to biological and environmental questions

Our analytical and bioinformatic developments are driven by biological and environmental problems raised through collaboration. The lab works with researchers across disease biology, microbiome science, environmental health, exposure science, and chemical risk assessment.

Recent work connects method development to high-impact applications in Nature Chemical Biology, Cancer Discovery, Cell Metabolism, Environmental Health Perspectives, and Environmental Science & Technology.

Approach

Integrated chemistry, computation, and systems biology

Our research is designed to move iteratively between instrument performance, data science, and application-driven validation. This full-stack approach helps ensure that computational discoveries remain grounded in analytical evidence.

MeasureGenerate high-quality LC-MS/MS data from complex samples.

ExtractProcess large datasets into reliable molecular features.

AnnotateUse AI and spectral evidence to prioritize chemical identities.

InterpretConnect molecules to biological and environmental questions.

Sponsors and support

Supported by competitive funding and collaborative research programs

The Huan Lab's research program is supported by sponsors and partners that advance analytical chemistry, metabolomics, exposomics, environmental science, and human health research.