Enhancing untargeted metabolomics using metadata-based source annotation

• DOI: 10.1038/s41587-022-01368-1
• Publication Date: 2022-07-07T16:04:15Z
• Authors: Julia M. Gauglitz; Kiana A. West; Wout Bittremieux; Candace L. Williams; Kelly C. Weldon; Morgan Panitchpakdi; Francesca Di Ottavio; Christine M. Aceves; Elizabeth Brown; Nicole C. Sikora; Alan K. Jarmusch; Cameron Martino; Anupriya Tripathi; Michael J. Meehan; Kathleen Dorrestein; Justin P. Shaffer; Roxana Coras; Fernando Vargas; Lindsay DeRight G...; Tara Schwartz; MacKenzie Bryant; Gregory Humphrey; Abigail J. Johnson; Katharina Spengler; Pedro Belda-Ferre; Edgar Diaz; Daniel McDonald; Qiyun Zhu; Emmanuel O. Elijah; Mingxun Wang; Clarisse Marotz; Kate E. Sprecher; Daniela Vargas-Ro...; Dana Withrow; Gail Ackermann; Lourdes Herrera; Barry J. Bradford; Lucas Maciel Maur...; Juliano Geraldo A...; Rodrigo Moreira S...; Flavio Protasio V...; Thiago Mattar Cunha; Rene Donizeti Rib...; Paulo Louzada-Junior; Robert H. Mills; Paulina K. Piotro...; Stephanie L. Serv...; Sandra M. Da Silva; Christina M. Jones; Nancy J. Lin; Katrice A. Lippa; Scott A. Jackson; Rima Kaddurah Daouk; Douglas Galasko; Parambir S. Dulai; Tatyana I. Kalash...; Curt Wittenberg; Robert Terkeltaub; Megan M. Doty; Jae H. Kim; Kyung E. Rhee; Julia Beauchamp-W...; Kenneth P. Wright; Maria Gloria Domi...; Mark Manary; Michelli F. Oliveira; Brigid S. Boland; Norberto Peporine...; Monica Guma; Austin D. Swafford; Rachel J. Dutton; Rob Knight; Pieter C. Dorrestein
• Keywords: 0301 basic medicine; Metadata; 03 medical and health sciences; Biomedical and Clinical Sciences; Tandem Mass Spectrometry; Chemical Sciences; 2.1 Biological and endogenous factors; Humans; Metabolomics; Medical Biochemistry and Metabolomics; 004; Analytical Chemistry

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.