Tao Huan: Associate Professor at Department of Chemistry, UBC Faculty of Science

Integration of metabolomics with other ‘omics’ (epigenomics, genomics, transcriptomics, and proteomics) data for the systems-level interrogation of biological problems
Application of state-of-art metabolomics technologies in various biological challenges, such as mechanistic understanding of cancer metabolism and disease biomarker discovery
Synergetic development of analytical and bioinformatic techniques to enhance metabolomic coverage and improve the confidence of metabolite identification

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ADVICE AND INSIGHTS FROM UBC FACULTY ON REACHING OUT TO SUPERVISORS

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Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Development of analytical methods and bioinformatic programs for data acquisition and data processing in LC-MS-based untargeted metabolomics (2023)

Untargeted metabolomics studies the complete set of small metabolic molecules in a given biological system. Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is currently the most prominent analytical platform for untargeted metabolomics owing to its high sensitivity, specificity, and metabolic coverage. However, the current LC-MS-based untargeted metabolomics workflow has limited performance in detecting and quantifying trace-level metabolites of bad chromatographic peak shapes. It is also hard to differentiate signals of real metabolites from noise and background. During my Ph.D., I have developed a suite of analytical and bioinformatic tools to address the critical challenges in metabolomics data acquisition and data processing. In this thesis, Chapters 2 to 4 focus on the development of data acquisition methods. Specifically, Chapters 2 to 3 describe the detailed comparison of the existing data acquisition modes in two different aspects. Chapter 4 describes the development of a novel data acquisition strategy, DaDIA, to increase metabolomic coverage. On the other hand, Chapters 5 to 9 describe the development of data processing methods. In Chapter 5, a data processing parameter optimization tool, Paramounter is introduced to rapidly and accurately determine the best peak-picking parameters for five commonly used data processing programs. In Chapter 6, the five most commonly used metabolomics data processing programs were compared to mechanistically explain the difference regarding the performance in metabolic feature extraction. In Chapter 7, a novel data processing program, JPA, was developed to efficiently extract the metabolic features by combining multiple peak-picking algorithms. In Chapter 8, a deep learning-based software, EVA, was created to automatically remove the false positive features generated from the background noise. In Chapter 9, I developed a bioinformatic workflow, ISFrag, to automatically recognize and remove the false positive metabolic features originating from in-source fragmentation.

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Development of analytical workflows and bioinformatic programs for mass spectrometry-based metabolomics (2023)

Quantitative determination of metabolite concentrations in biological samples is fundamental to biological and clinical research. Metabolomics analyzes the entire set of metabolites in a given biological system. It is an emerging technology in the post-genomic era to interrogate cellular biochemistry, perform diagnostic testing, stratify patient populations, and characterize biochemical mechanisms of disease. Recent successes in metabolomics demonstrate the central role of mass spectrometry (MS) in small molecule quantification, owing to its high sensitivity, high throughput, and broad metabolic coverage. Even though diverse MS instruments have been developed for metabolite quantification, it is still challenging to quantify the entire metabolome accurately and precisely. Besides MS hardware advances, quantitative metabolomics also requires extensive efforts in other analytical and bioinformatic methodology development. For a given MS platform, analytical method development focuses on laboratory practice, including sample handling, metabolome extraction, and data acquisition. In comparison, bioinformatic method development emphasizes computational data processing, such as data calibration, data curation, and statistical analysis. The subsequent chapters detail the development of analytical and bioinformatic solutions for quantitative metabolomics from improving metabolic coverage, analytical accuracy, analytical precision, and statistical analysis. Lastly, this thesis describes a metabolomics study of mouse brain regional differences in metabolism between males and females. Collectively my studies of quantitative metabolomics improve quantitative performance, deepen our knowledge of the MS-based quantification process, and facilitate the generation of confident biological conclusions.

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Towards accurate compound annotation in mass spectrometry-based global metabolomics (2023)

Metabolomics is an emerging omics study that aims to characterize the entire metabolome in a biological system. Mass spectrometry (MS) is a preferred analytical technique for metabolomics research owing to its high sensitivity and highly specific structural information content. However, it remains a longstanding challenge to accurately translate MS signals into chemical language, thus hindering the downstream biological interpretation.This dissertation presents computational strategies contributing to tandem mass (MS/MS) spectral interpretations with the aid of machine learning and statistical approaches. Chapter 1 provides a holistic introduction to MS-based metabolomics and the developed bioinformatic tools for uncovering the unidentified metabolic features in untargeted metabolomics. Chapter 2 describes a novel MS/MS spectral comparison algorithm, Core Structure-based Search (CSS), which searches for structural analogs of unknown MS/MS spectra within the existing MS/MS reference libraries. CSS shows improved correlations with structural similarity in large-scale benchmarking. In Chapter 3, a deep learning-based tool is developed for automated extraction of steroid-like metabolic features from the untargeted metabolomics data by classifying MS/MS fragmentation patterns. This biology-driven metabolomics pipeline enables metabolite characterization and discovery on the compound class level. Chapter 4 depicts the purification of chimeric MS/MS spectra using a random forest model. Purified MS/MS spectra are demonstrated to yield better spectral matching results against MS/MS reference libraries. Chapter 5 describes the systematic analysis of radical fragment ions in MS/MS through MS/MS database mining. Larger than expected percentages of radical ions are present in collision- induced dissociation-based MS/MS; relationships between radical ion percentages and compound classes, chemical substructures and collision energies are also investigated. Chapter 6 discusses a standalone platform, BUDDY, for molecular formula discovery via bottom-up MS/MS interrogation and experiment-specific global peak annotation. BUDDY further integrates machine-learned ranking and significance control, showing improved formula annotation accuracy and lower computational cost than other benchmarking tools. Applying BUDDY on repository- scale recurrent unidentified MS/MS spectra, we discovered >5,000 chemical database-unarchived molecular formulae with high confidence. Overall, this dissertation demonstrates computational contributions to enriching structural insights into MS-based untargeted metabolomics data, thus paving the way for understanding biological mechanisms behind various health disorders and diseases from the perspective of small molecules.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Development of bioinformatic solution to enhance metabolomics data quality and its application in plant research (2023)

This thesis delves into the development and application of metabolomics, a discipline focused on the comprehensive study of metabolites within biological systems. The research is segmented into two interconnected parts: methodology development for metabolomics data processing and its subsequent application in plant stress physiology. The first project tackles the challenge of computational variation in untargeted metabolomics, which arises due to the incapability of data processing for complex LC-MS data. An in-depth exploration led to the identification of sources and causes of computational variation, followed by the development of novel methodologies to mitigate these challenges. These methodologies, including data processing parameter optimization and a machine learning program, successfully reduced computational variation, thereby enhancing the quantitative precision of untargeted metabolomics. The second segment of the thesis applies these methodologies to study the salinity stress response in Alfalfa (Medicago sativa L.). Comprehensive analysis of the plant's metabolic alterations, coupled with transcriptomics data, revealed significant pathways and mechanisms of salinity response. The integration of multi-omics data provided a deeper understanding of the complex interplay between genes and metabolites. The research advances the field of metabolomics, providing improved data processing methodology and valuable insights into plant stress physiology. Future work may expand these findings towards personalized medicine, disease diagnosis, and precision treatment.

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Development of bioinformatics solutions to enable hair-based exposome research (2023)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

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Development of mass spectrometry-based untargeted metabolomics for precision health (2022)

As the most recently emerged “omics”, metabolomics grabbed attention in human health studies by measuring thousands of small-molecule metabolites in a wide range of biological samples. As the downstream products in the biological pathway, metabolites are regarded as the closest link to the phenotypes. Small stimuli in the human body will cause relatively huge changes in the level of metabolites. Liquid chromatography-mass spectrometry (LC-MS) is the mainstay in metabolomics research due to its high throughput, sensitivity, and reliable analysis of metabolites. Nevertheless, two of the main challenges in LC-MS based metabolomics are 1) how to apply metabolomics in studying human health and 2) apart from commonly used biological samples, including serum, plasma, and urine, how to develop a methodology of new biological samples that can be adapted to specific human health research. To address those challenges, in Chapter 2, I integrated metabolomics with metagenomics to examine human gut health. 13-species metagenomic signature was selected by random forest machine learning and achieved high diagnostic accuracy in differentiating hepatic decompensation in NAFLD-related cirrhosis. The signature was cross-validated by metabolomics. 32 metabolites and 15 metabolites from serum and feces, respectively, were found to be significantly linked to 13-discriminatory species, suggesting that the identified discriminatory species may play important roles in the progression from compensated to decompensated cirrhosis. This multi-omics study yields new avenues for identifying novel targets for therapy and microbial biomarkers of hepatic decompensation, a worldwide human disease. In Chapter 3, I integrated plasma metabolomics and proteomics to examine the health conditions of highly trained females and males following acute, severe-intensity exercise. Metabolomic and proteomic homeostasis were substantially perturbed. Through statistical analysis, some metabolites and proteins were found to be closely linked to high-intensity exercise. This multi-omics study was a powerful tool to study molecular responses to acute exercise and provided a new insight to exercise-bolstered human health. In Chapter 4, I developed a new methodology to track skin secretion. Our high-performance workflow was readily applied to a wide range of skin metabolomics research to gain a better understanding of the molecular signatures on skin that link to human health and disease.

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The comprehensive analysis of different MS/MS acquisition modes and mass spectrometry related applications (2022)

For this work, two areas of metabolomics were investigated relating to the fundamentals of the field and application to different experiments. The first chapter was an assessment and comparison of the MSMS spectra generated from different acquisition modes. The chosen acquisition modes were data-dependant acquisition (DDA), data-independent acquisition (DIA), and enhanced insource-fragmentation (eISF) at a range of collision energies. The data was obtained by performing untargeted metabolomics on a urine sample and a standard mixture solution through a LC-MS platform while also covering multiple ionization modes. The spectra from the three modes were compared against each other through several factors that relate to the various ways MSMS spectra are used in a metabolomics workflow. These comparisons involved investigating the spectral purity, quality of reference matching results, structural similarity, and de novo annotation performance. It was found that DDA performed the best with eISF and DIA following. It was seen that eISF performed on-par or slightly better than DIA at higher collision energies. This indicates that the collision energy used will have a notable impact on the performance of the mode. The second chapter involves the metabolomics of pancreatic cell samples. The purpose of this was to determine the metabolic profile between the control and treated groups. The control was regular cancer cells from the MiaPaCa2 cell line while the treated groups had specific genes knocked out. The investigation was performed to gain insight into which metabolic pathways the knocked-out genes were involved in. Using a LC-MS platform it was found that 12 metabolites showed significant intensity differences between the groups. A literature review of these compounds highlighted possible metabolomic pathways affected such as polyamine metabolism. The last chapter focuses on a lipidomics experiment that was performed on the bacteria Thermotoga maritima to investigate the lipid content of the bacterial membranes. The samples relating to each fraction were run through the same LC-MS platform as above. It was seen that there were three significantly different lipids apart from the fatty acid, phosphatidylethanolamine, and phosphatidylinositol lipid classes. These classes have all been shown to be involved in membrane stability and transport.

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Publications

Adaptive Box–Cox Transformation: A Highly Flexible Feature-Specific Data Transformation to Improve Metabolomic Data Normality for Better Statistical Analysis (2022)
Analytical Chemistry,
Addressing big data challenges in mass spectrometry-based metabolomics (2022)
Chemical Communications,
Comprehensive assessment of the diminished statistical power caused by nonlinear electrospray ionization responses in mass spectrometry-based metabolomics (2022)
Analytica Chimica Acta,
JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics (2022)
Metabolites,
MAFFIN: metabolomics sample normalization using maximal density fold change with high-quality metabolic features and corrected signal intensities (2022)
Bioinformatics,
Molecular formula discovery via bottom-up MS/MS interrogation (2022)
Paramounter: Direct Measurement of Universal Parameters To Process Metabolomics Data in a “White Box” (2022)
Analytical Chemistry,
Radical fragment ions in collision-induced dissociation-based tandem mass spectrometry (2022)
Analytica Chimica Acta,
Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics (2021)
Analytical Chemistry, 93 (25), 8719--8728
DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data (2021)
Analytical Chemistry, 93 (4), 2669--2677
EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms (2021)
Analytical Chemistry,
ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography–Mass Spectrometry Data (2021)
Analytical Chemistry, 93 (29), 10243--10250
Patterned Signal Ratio Biases in Mass Spectrometry-Based Quantitative Metabolomics (2021)
Analytical Chemistry, 93 (4), 2254--2262
Recognizing Contamination Fragment Ions in Liquid Chromatography–Tandem Mass Spectrometry Data (2021)
Journal of the American Society for Mass Spectrometry,
RTP: One Effective Platform to Probe Reactive Compound Transformation Products and Its Applications for a Reactive Plasticizer BADGE (2021)
Environmental Science & Technology,
SteroidXtract: Deep Learning-Based Pattern Recognition Enables Comprehensive and Rapid Extraction of Steroid-Like Metabolic Features for Automated Biology-Driven Metabolomics (2021)
Analytical Chemistry, 93 (14), 5735--5743
System Biology-Guided Chemical Proteomics to Discover Protein Targets of Monoethylhexyl Phthalate in Regulating Cell Cycle (2021)
Environmental Science & Technology, 55 (3), 1842--1851
Cariogenic Streptococcus mutans Produces Tetramic Acid Strain-Specific Antibiotics That Impair Commensal Colonization (2020)
ACS Infectious Diseases, 6 (4), 563--571
Comparison of Full-Scan, Data-Dependent, and Data-Independent Acquisition Modes in Liquid Chromatography–Mass Spectrometry Based Untargeted Metabolomics (2020)
Analytical Chemistry, 92 (12), 8072--8080
Evaluation of significant features discovered from different data acquisition modes in mass spectrometry-based untargeted metabolomics (2020)
Analytica Chimica Acta, 1137, 37--46
Fold-Change Compression: An Unexplored But Correctable Quantitative Bias Caused by Nonlinear Electrospray Ionization Responses in Untargeted Metabolomics (2020)
Analytical Chemistry, 92 (10), 7011--7019
Metabolomics-Based Discovery of Molecular Signatures for Triple Negative Breast Cancer in Asian Female Population (2020)
Scientific Reports, 10 (1)
Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns (2020)
Analytica Chimica Acta,
Pass-back chain extension expands multimodular assembly line biosynthesis (2020)
Nature Chemical Biology, 16 (1), 42--49
Avant-garde assembly-line biosynthesis expands diversity of cyclic lipodepsipeptide products (2019)
Cariogenic Streptococcus mutans produces strain-specific antibiotics that impair commensal colonization (2019)
Chemical Isotope Labeling Exposome (CIL-EXPOSOME): One High-Throughput Platform for Human Urinary Global Exposome Characterization (2019)
Environmental Science & Technology,
Enhancing Metabolome Coverage in Data-Dependent LC–MS/MS Analysis through an Integrated Feature Extraction Strategy (2019)
Analytical Chemistry, 91 (22), 14433--14441
Alzheimer’s Biomarkers from Multiple Modalities Selectively Discriminate Clinical Status: Relative Importance of Salivary Metabolomics Panels, Genetic, Lifestyle, Cognitive, Functional Health, and Demographic Risk Markers (2018)
Frontiers in aging neuroscience, 10, 296
Autonomous Multi-Modal Metabolomics Data Integration for Comprehensive Pathway Analysis (2018)
Analytical chemistry,
Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology (2018)
Analytical Chemistry, 90 (14), 8396--8403
Bretschneider solution-induced alterations in the urine metabolome in cardiac surgery patients (2018)
Scientific reports, 8 (1), 17774
Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online (2018)
Nature protocols, 13 (4), 633
Dietary xenoestrogens significantly alter cellular metabolism induced by palbociclib/letrozole combination cancer therapy (2018)
Cell Chem. Biol.,
Metabolomics Analyses of Saliva Detect Novel Biomarkers of Alzheimer’s Disease (2018)
Journal of Alzheimer's Disease, (Prepr), 1--16
Metabolomics reveals that dietary xenoestrogens alter cellular metabolism induced by palbociclib/letrozole combination cancer therapy (2018)
Cell chemical biology, 25 (3), 291--300
METLIN: A Technology Platform for Identifying Knowns and Unknowns (2018)
Analytical Chemistry, 90 (5), 3156--3164
Data streaming for metabolomics: accelerating data processing and analysis from days to minutes (2017)
Analytical chemistry, 89 (2), 1254--1259
Exposing the Exposome with Global Metabolomics and Cognitive Computing (2017)
bioRxiv, , 145722
Exposome-Scale Investigations Guided by Global Metabolomics, Pathway Analysis, and Cognitive Computing (2017)
Analytical Chemistry, 89 (21), 11505--11513
Systems biology guided by XCMS Online metabolomics (2017)
Nature Methods, 14 (5), 461--462
Cerebrospinal fluid metabolomics after natural product treatment in an experimental model of cerebral ischemia (2016)
Omics: a journal of integrative biology, 20 (11), 670--680
Dansylation isotope labeling liquid chromatography mass spectrometry for parallel profiling of human urinary and fecal submetabolomes (2016)
Analytica Chimica Acta, 903, 100--109
Fatty acid and sterol composition reveal food selectivity of juvenile ark shell Tegillarca granosa Linnaeus after feeding with mixed microalgae (2016)
Aquaculture, 455, 109--117
High-performance chemical isotope labeling liquid chromatography--mass spectrometry for profiling the metabolomic reprogramming elicited by ammonium limitation in yeast (2016)
Journal of proteome research, 15 (5), 1602--1612
Metabolite Analysis and Histology on the Exact Same Tissue: Comprehensive Metabolomic Profiling and Metabolic Classification of Prostate Cancer (2016)
Scientific reports, 6, 32272
Smartphone analytics: mobilizing the lab into the cloud for omic-scale analyses (2016)
Analytical chemistry, 88 (19), 9753--9758
Development and Application of Chemical Isotope Labeling Methods for Metabolomics Data Processing and Liquid Chromatography-Mass Spectrometry-Based Metabolomics (2015)
Development of versatile isotopic labeling reagents for profiling the amine submetabolome by liquid chromatography--mass spectrometry (2015)
Analytica Chimica Acta, 881, 107--116
DnsID in MyCompoundID for Rapid Identification of Dansylated Amine-and Phenol-Containing Metabolites in LC-MS-Based Metabolomics (2015)
Analytical chemistry, 87 (19), 9838--9845
Effects of Gastrodia elata and Uncaria rhynchophylla water extract on cerebral ischemic rat revealed by LC-FTICR-MS metabolomics approach (2015)
中国药理学与毒理学杂志, 1
Metabolomics analyses of salivary samples discriminate normal aging, mild cognitive impairment, and Alzheimer’s disease groups and produce biomarkers predictive of neurocognitive performance (2015)
Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 11 (7), P654
MyCompoundID MS/MS Search: Metabolite Identification Using a Library of Predicted Fragment-Ion-Spectra of 383,830 Possible Human Metabolites (2015)
Analytical chemistry, 87 (20), 10619--10626
Quantitative Metabolome Analysis Based on Chromatographic Peak Reconstruction in Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry (2015)
Analytical chemistry, 87 (14), 7011--7016
Application of stable isotope-assisted metabolomics in neovascular (wet) age-related macular degeneration (AMD) (2014)
Investigative Ophthalmology & Visual Science, 55 (13), 1336--1336
Counting Missing Values in a Metabolite-Intensity Data Set for Measuring the Analytical Performance of a Metabolomics Platform (2014)
Analytical chemistry, 87 (2), 1306--1313
Development of High-Performance Chemical Isotope Labeling LC--MS for Profiling the Human Fecal Metabolome (2014)
Analytical chemistry, 87 (2), 829--836
IsoMS: automated processing of LC-MS data generated by a chemical isotope labeling metabolomics platform (2014)
Analytical chemistry, 86 (10), 4675--4679
Rewiring AMPK and mitochondrial retrograde signaling for metabolic control of aging and histone acetylation in respiratory-defective cells (2014)
Cell reports, 7 (2), 565--574
MyCompoundID: using an evidence-based metabolome library for metabolite identification (2013)
Analytical chemistry, 85 (6), 3401--3408
Proanthocyanidin-Rich Pea Seed Coats Improved Glucose Intolerance by Affecting Insulin Production in Rats (2013)
Canadian journal of diabetes, (37), S60