Enhancing untargeted metabolomics using metadata-based source annotation.

Gauglitz JM, West KA, Bittremieux W, Williams CL, Weldon KC, Panitchpakdi M, Di Ottavio F, Aceves CM, Brown E, Sikora NC, Jarmusch AK, Martino C, Tripathi A, Meehan MJ, Dorrestein K, Shaffer JP, Coras R, Vargas F, Goldasich LDR, Schwartz T, Bryant MK, Humphrey G, Johnson AJ, Spengler K, Belda-Ferre P, Diaz E, McDonald D, Zhu Q, Elijah EO, Wang M, Marotz C, Sprecher KE, Vargas-Robles D, Withrow D, Ackermann G, Herrera L, Bradford BJ, Marques LMM, Amaral JG, Silva RM, Veras FP, Cunha TM, Oliveira RDR, Louzada-Junior P, Mills RH, Piotrowski PK, Servetas SL, Da Silva SM, Jones CM, Lin NJ, Lippa KA, Jackson SA, Daouk RK, Galasko D, Dulai PS, Kalashnikova TI, Wittenberg C, Terkeltaub R, Doty MM, Kim JH, Rhee KE, Beauchamp-Walters J, Wright KP, Dominguez-Bello MG, Manary M, Oliveira MF, Boland BS, Lopes NP, Guma M, Swafford AD, Dutton RJ, Knight R, Dorrestein PC (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 40

Pages Range: 1774-1779

Journal Issue: 12

DOI: 10.1038/s41587-022-01368-1

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.

Involved external institutions

University of California, San Diego United States (USA) (US) San Diego Zoo Wildlife Alliance United States (USA) (US) University of Colorado at Boulder United States (USA) (US) University of Cincinnati United States (USA) (US) University of São Paulo / Universidade de São Paulo (USP) Brazil (BR) The State University of New Jersey (Rutgers) United States (USA) (US) Washington University United States (USA) (US) Billings Clinic United States (USA) (US) National Institute of Standards and Technology (NIST) United States (USA) (US) Duke University United States (USA) (US) Universidade Federal da Bahia Brazil (BR) Michigan State University United States (USA) (US) Centro Amazónico de Investigación y Control de Enfermedades Tropicales "Simón Bolívar " (CAICET) Venezuela, Bolivarian Republic of (VE) Scripps Research Institute (TSRI) United States (USA) (US)

How to cite

APA:

Gauglitz, J.M., West, K.A., Bittremieux, W., Williams, C.L., Weldon, K.C., Panitchpakdi, M.,... Dorrestein, P.C. (2022). Enhancing untargeted metabolomics using metadata-based source annotation. Nature Biotechnology, 40(12), 1774-1779. https://doi.org/10.1038/s41587-022-01368-1

MLA:

Gauglitz, Julia M., et al. "Enhancing untargeted metabolomics using metadata-based source annotation." Nature Biotechnology 40.12 (2022): 1774-1779.

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