Ordering molecular diversity in untargeted metabolomics via molecular community networking
11 affiliations
- 1Department of Chemistry, University of Connecticut, Storrs, CT, USA
- 2Department of Chemistry, University of Connecticut, Storrs, CT, USA; Arome Science Inc., Farmington, CT, USA; BileOmix Inc, Farmington, CT, USA
- 3Arome Science Inc., Farmington, CT, USA
- 4BileOmix Inc, Farmington, CT, USA
- 5Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, San Diego, CA, USA
- 6Department of Pharmacology and Toxicology, University of Alabama at Birmingham, Birmingham, AL, 3233, USA
- 7Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Alabama at Birmingham BBRB 711, 845 19(th) St. S, Birmingham, AL 35244, USA
- 8Department of Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL, USA
- 9Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA
- 10Department of Industrial & Management Systems Engineering, University of South Florida, Tampa, FL, USA
- 11Department of Chemistry, University of Connecticut, Storrs, CT, USA; Arome Science Inc., Farmington, CT, USA; BileOmix Inc, Farmington, CT, USA. Electronic address: [email protected]
Abstract
Molecules in living systems are not random but are shaped by biological necessity. Mass spectrometry (MS) is a powerful tool for exploring these complex molecular landscapes. Molecular networking links metabolites by spectral similarity, but conventional methods leave many nodes disconnected. We introduce molecular community networking (MCN), which identifies natural molecular clusters and prunes them to keep the strongest links. The approach increases connectivity to about 95% of molecules and better captures structurally related compounds, including distinct ion forms and in-source fragmentation ions. MCN also improves the mapping of molecular space, helping distinguish true novel molecules from artifacts. Using MCN, we discovered dipeptide-conjugated bile acids associated with Bifidobacterium breve and proposed structures for previously unexplored N-acyl amides that interact with G protein-coupled receptors. We also built a global metabolome map from public GNPS/MassIVE data, covering about 8.4 million molecular features, creating a "roadmap" for molecular diversity.
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BibTeX
@article{coler2026ordering,
title = {{Ordering molecular diversity in untargeted metabolomics via molecular community networking}},
author = {Elizabeth Coler and Alexey V. Melnik and Ali Lotfi and Dana Moradi and Ben Ahiadu and Paulo Wender Portal Gomes and Abubaker Patan and Vincent Charron‐Lamoureux and Pieter C. Dorrestein and Stephen Barnes and Vladimir L. Boginski and Alexander Semenov and Alexander A. Aksenov},
journal = {Cell Reports Methods},
year = {2026},
month = jun,
doi = {10.1016/j.crmeth.2026.101468},
url = {https://doi.org/10.1016/j.crmeth.2026.101468},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Ordering molecular diversity in untargeted metabolomics via molecular community networking
AU - Elizabeth Coler
AU - Alexey V. Melnik
AU - Ali Lotfi
AU - Dana Moradi
AU - Ben Ahiadu
AU - Paulo Wender Portal Gomes
AU - Abubaker Patan
AU - Vincent Charron‐Lamoureux
AU - Pieter C. Dorrestein
AU - Stephen Barnes
AU - Vladimir L. Boginski
AU - Alexander Semenov
AU - Alexander A. Aksenov
PY - 2026
DA - 2026/06/02
JO - Cell Reports Methods
DO - 10.1016/j.crmeth.2026.101468
UR - https://doi.org/10.1016/j.crmeth.2026.101468
AB - Molecules in living systems are not random but are shaped by biological necessity. Mass spectrometry (MS) is a powerful tool for exploring these complex molecular landscapes. Molecular networking links metabolites by spectral similarity, but conventional methods leave many nodes disconnected. We introduce molecular community networking (MCN), which identifies natural molecular clusters and prunes them to keep the strongest links. The approach increases connectivity to about 95% of molecules and better captures structurally related compounds, including distinct ion forms and in-source fragmentation ions. MCN also improves the mapping of molecular space, helping distinguish true novel molecules from artifacts. Using MCN, we discovered dipeptide-conjugated bile acids associated with Bifidobacterium breve and proposed structures for previously unexplored N-acyl amides that interact with G protein-coupled receptors. We also built a global metabolome map from public GNPS/MassIVE data, covering about 8.4 million molecular features, creating a "roadmap" for molecular diversity.
SN - 2667-2375
ER -