MSACL 2026 Abstract

The Human Metabolome Atlas: A Metabolomics-Based Discovery Platform

Jeremy K. Chan (1,2), Nicholas S. Ly (1,2), Olivia Taverniti (1,2), William D. Gwynne (1), Brandon Y. Lieng (1,2), Vanessa Affe (3), Verne T. Urquhart-Cox (1,2), Sophia M. Alonzi (1,4), Mathula Muhundan (1), Alexandra J. Denhart (1), Landon J. Edgar (3,4,5), Andrew T. Quaile (1), J. Rafael Montenegro-Burke (1,2,4)
(1) Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, (2) Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, (3) Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada, (4) Department of Chemistry, University of Toronto, Toronto, ON, Canada, (5) Department of Immunology, University of Toronto, Toronto, ON, Canada

Jeremy Chan (Presenter) University of Toronto

Presenter Bio: I am a senior PhD student from the University of Toronto. My work focuses on generating a collection of metabolomic and lipidomic profiles from human cell lines to establish one of the first 'Human Metabolome Atlas' available.

Relevant Financial Disclosures (within past 24 months, reported on Jun 09, 2026) No relevant financial relationship(s) to disclose. Conflict mitigation NOT REQUIRED.

INTRODUCTION:
Cellular atlases like the Human Protein Atlas (HPA) and the Cancer Dependency Map have established themselves as foundational resources for their respective fields. In contrast, few ‘atlas-like’ resources are available for the human metabolome. The metabolomics resources that are available are often limited by chemical coverage or have samples restricted to non-human organisms. As a result, there is a need for a resource that is equivalent to the HPA for the human metabolome. Here, we present the Human Metabolome Atlas (HMA)—a comprehensive metabolomics resource and discovery platform. Using state-of-the-art liquid chromatography-mass spectrometry methodologies, we collected the metabolomic and lipidomic profiles of 70 human cell lines across 22 tissue types. We also demonstrate how the HMA can be used as an interactive tool to understand cellular processes like sialylation and ferroptosis.

METHODS:
From 70 human cell lines, metabolites and lipids (n = 5) were simultaneously extracted using a methyl tert-butyl ether biphasic method and spiked with isotopically labeled internal standards (UltimateSplashTM ONE and IROA TruQuant Yeast Extract). After extraction, samples were analyzed using an Orbitrap IQ-X Tribrid MS coupled to a Vanquish Horizon UHPLC (Thermo Fisher Scientific). Three complementary LC-MS methods were used to analyze cell line extracts. Metabolites were separately analyzed using either a ZIC-pHILIC column (SeQuant; 2.1 x 150 mm, 5 µm) or a BEH amide column (WATERS; 2.1 x 100 mm, 1.7 µm) in positive or negative-ionization mode, respectively. Lipids were analyzed using a CSH C18 column (WATERS; 2.1 x 100 mm, 1.7 µm) in a polarity switching method. Metabolites and lipids were identified by matching MS/MS spectra and retention time to authentic reference standards or available databases (e.g., mzCloud, LipidBlast). Peak areas were manually integrated and curated using Skyline-daily.

RESULTS:
The HMA is comprised of the metabolomic and lipidomic profiles of 70 human cell lines across 22 tissue types. In total, we identified 1768 metabolites and lipids at high confidence (minimum Metabolomics Standards Initiative level 2). The metabolome coverage consisted of 200 metabolites from well-characterized essential metabolic pathways such as central carbon metabolism and nucleotide metabolism. The lipidome coverage consisted of 1568 lipids from >50 lipid classes. The HMA constitutes the most extensive human-based metabolomics resource currently available in terms of chemical coverage and identification confidence. Principal component analysis of the metabolomic and lipidomic profiles revealed two major clusters separated by cell line tissue of origin (hematological vs non-hematological). Closer examination of the specific metabolites driving these differences revealed increased levels of nucleotide intermediates in hematological cell lines. Cell lines enriched with nucleotide intermediates were found to be associated with lower levels of sialylation precursors and increased cell surface sialylation in hematological cell lines. Similarly, for the lipidome, we observed a significant enrichment of unsaturated triglycerides (TGs) in hematological cell lines. We also found that this enrichment of unsaturated TGs was associated with ferroptosis susceptibility. Lastly, we developed an interactive web portal with custom data analysis tools (hma.ccbr.utoronto.ca) to provide an accessible method of probing the HMA dataset. The HMA web portal features flexible data analysis options including volcano plots, clustered heatmaps, and fatty acid composition heatmaps (FACHs) that can be tailored to cell lines and metabolites of interest. In particular, the HMA contains many human cancer cell lines, which could be leveraged to identify disease-associated metabolomic signatures and biomarkers. Ultimately, the continuous exploration of the HMA dataset has potential to drive the development of metabolomics-based clinical diagnostics and applications.

CONCLUSION:
We developed a robust atlas and interactive discovery platform for the human metabolome. Our work highlights how the HMA can serve as a starting point for metabolomics-based biomedical research.