Enhancing untargeted metabolomics using metadata-based source annotation.

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 Goldasich,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-Robles,Dana Withrow,Gail Ackermann,Lourdes Herrera,Barry J Bradford,Lucas Maciel Mauriz Marques,Juliano Geraldo Amaral,Rodrigo Moreira Silva,Flavio Protasio Veras,Thiago Mattar Cunha,Rene Donizeti Ribeiro Oliveira,Paulo Louzada-Junior,Robert H Mills,Paulina K Piotrowski,Stephanie L Servetas,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 Kalashnikova,Curt Wittenberg, Robert Terkeltaub,Megan M Doty,Jae H Kim,Kyung E Rhee,Julia Beauchamp-Walters,Kenneth P Wright,Maria Gloria Dominguez-Bello,Mark Manary,Michelli F Oliveira,Brigid S Boland,Norberto Peporine Lopes,Monica Guma,Austin D Swafford,Rachel J Dutton,Rob Knight,Pieter C Dorrestein

Nature biotechnology（2022）

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摘要

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.

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