Enhancing untargeted metabolomics using metadata-based source annotation

Published

July 7, 2022

Author(s)

Scott Jackson, Paulina Piotrowski, Nancy Lin, Sandra M. Da Silva, Katrice Lippa, Christina Jones, Stephanie Servetas, Julia Gauglitz, Kiana West, Wout Bittremieux, Candace Williams, Kelly Weldon, Morgan Panitchpakdi, Francesca Ottavio, Christine Aceves, Elizabeth Brown, Nicole Sikora, Alan Jarmusch, Cameron Martino, Pieter Dorrestein, Rob Knight, Rachel Dutton, Austin Swafford, Monica Guma, Norberto Peporine Lopes, Brigid Boland, Michelli Oliveira, Mark Manary, Maria Gloria Dominguez-Bello, Kenneth Wright, Julia Beauchamp-Walters, Kyung Rhee, Jae Kim, Megan Doty, Robert Terkeltaub, David Gonzalez, Curt Wittenberg, Tatyana Kalashnikova, Parambir Dulai, Douglas Galasko, Rima Kaddurah Daouk, Robert Mills, Paulo Louzada-Junior, Rene Donizeti Ribeiro Oliveira, Thaigo Mattar Cunha, Flavio Protaso Veras, Rodrigo Moreira Silva, Juliano Geraldo Amaral, Lucas Maciel Mauriz Marques, Barry Bradford, Lourdes Herrera, Gail Ackermann, Dana Withrow, Daniela Vargas Robles, Kate Sprecher, Clarisse Marotz, Mingxun Wang, Emmanuel Elijah, Dominic Nguyen, Qiyun Zhu, Daniel McDonald, Edgar Diaz, Pedro Belda-Ferre, Katharina Spengler, Abigail Johnson, Gregory Humphrey, MacKenzie Bryant, Tara Schwartz, Lindsay Goldasich, Fernando Vargas, Roxana Coras, Justin Schaffer, Erfan Sayyari, Kathleen Dorrestein, Michael Meehan, Anupriya Tripathi

Abstract

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.

Citation

Nature Biotechnology

Pub Type

Journals

Download Paper

https://doi.org/10.1038/s41587-022-01368-1

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Keywords

metabolomics, fecal material, reference data, ms/ms, microbiome, diet, reference data-driven analysis

Citation

Jackson, S. , Piotrowski, P. , Lin, N. , Da Silva, S. , Lippa, K. , Jones, C. , Servetas, S. , Gauglitz, J. , West, K. , Bittremieux, W. , Williams, C. , Weldon, K. , Panitchpakdi, M. , Ottavio, F. , Aceves, C. , Brown, E. , Sikora, N. , Jarmusch, A. , Martino, C. , Dorrestein, P. , Knight, R. , Dutton, R. , Swafford, A. , Guma, M. , Peporine Lopes, N. , Boland, B. , Oliveira, M. , Manary, M. , Dominguez-Bello, M. , Wright, K. , Beauchamp-Walters, J. , Rhee, K. , Kim, J. , Doty, M. , Terkeltaub, R. , Gonzalez, D. , Wittenberg, C. , Kalashnikova, T. , Dulai, P. , Galasko, D. , Kaddurah Daouk, R. , Mills, R. , Louzada-Junior, P. , Ribeiro Oliveira, R. , Mattar Cunha, T. , Protaso Veras, F. , Moreira Silva, R. , Geraldo Amaral, J. , Mauriz Marques, L. , Bradford, B. , Herrera, L. , Ackermann, G. , Withrow, D. , Vargas Robles, D. , Sprecher, K. , Marotz, C. , Wang, M. , Elijah, E. , Nguyen, D. , Zhu, Q. , McDonald, D. , Diaz, E. , Belda-Ferre, P. , Spengler, K. , Johnson, A. , Humphrey, G. , Bryant, M. , Schwartz, T. , Goldasich, L. , Vargas, F. , Coras, R. , Schaffer, J. , Sayyari, E. , Dorrestein, K. , Meehan, M. and Tripathi, A. (2022), Enhancing untargeted metabolomics using metadata-based source annotation, Nature Biotechnology, [online], https://doi.org/10.1038/s41587-022-01368-1, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932728 (Accessed November 20, 2024)