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 De Right Goldasich; Tara Schwartz; MacKenzie K. 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

doi:10.1038/s41587-022-01368-1

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 De Right Goldasich
, Tara Schwartz

MacKenzie K. 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

Research output: Contribution to journal › Article › peer-review

53 Scopus citations

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.

Original language	English
Pages (from-to)	1774-1779
Number of pages	6
Journal	Nature Biotechnology
Volume	40
Issue number	12
DOIs	https://doi.org/10.1038/s41587-022-01368-1
State	Published - Dec 2022

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10.1038/s41587-022-01368-1

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Gauglitz, J. M., West, K. A., Bittremieux, W., Williams, C. L., Weldon, K. C., Panitchpakdi, M., Di Ottavio, F., Aceves, C. M., Brown, E., Sikora, N. C., Jarmusch, A. K., Martino, C., Tripathi, A., Meehan, M. J., Dorrestein, K., Shaffer, J. P., Coras, R., Vargas, F., Goldasich, L. D. R., ... 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

@article{1a50f701fe2d43afbb707948950a7cb1,

title = "Enhancing untargeted metabolomics using metadata-based source annotation",

abstract = "Human untargeted metabolomics studies annotate only \textasciitilde{}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.",

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

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.",

year = "2022",

month = dec,

doi = "10.1038/s41587-022-01368-1",

language = "English",

volume = "40",

pages = "1774--1779",

journal = "Nature Biotechnology",

issn = "1087-0156",

number = "12",

}

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, 'Enhancing untargeted metabolomics using metadata-based source annotation', Nature Biotechnology, vol. 40, no. 12, pp. 1774-1779. https://doi.org/10.1038/s41587-022-01368-1

TY - JOUR

T1 - Enhancing untargeted metabolomics using metadata-based source annotation

AU - Gauglitz, Julia M.

AU - West, Kiana A.

AU - Bittremieux, Wout

AU - Williams, Candace L.

AU - Weldon, Kelly C.

AU - Panitchpakdi, Morgan

AU - Di Ottavio, Francesca

AU - Aceves, Christine M.

AU - Brown, Elizabeth

AU - Sikora, Nicole C.

AU - Jarmusch, Alan K.

AU - Martino, Cameron

AU - Tripathi, Anupriya

AU - Meehan, Michael J.

AU - Dorrestein, Kathleen

AU - Shaffer, Justin P.

AU - Coras, Roxana

AU - Vargas, Fernando

AU - Goldasich, Lindsay De Right

AU - Schwartz, Tara

AU - Bryant, MacKenzie K.

AU - Humphrey, Gregory

AU - Johnson, Abigail J.

AU - Spengler, Katharina

AU - Belda-Ferre, Pedro

AU - Diaz, Edgar

AU - McDonald, Daniel

AU - Zhu, Qiyun

AU - Elijah, Emmanuel O.

AU - Wang, Mingxun

AU - Marotz, Clarisse

AU - Sprecher, Kate E.

AU - Vargas-Robles, Daniela

AU - Withrow, Dana

AU - Ackermann, Gail

AU - Herrera, Lourdes

AU - Bradford, Barry J.

AU - Marques, Lucas Maciel Mauriz

AU - Amaral, Juliano Geraldo

AU - Silva, Rodrigo Moreira

AU - Veras, Flavio Protasio

AU - Cunha, Thiago Mattar

AU - Oliveira, Rene Donizeti Ribeiro

AU - Louzada-Junior, Paulo

AU - Mills, Robert H.

AU - Piotrowski, Paulina K.

AU - Servetas, Stephanie L.

AU - Da Silva, Sandra M.

AU - Jones, Christina M.

AU - Lin, Nancy J.

AU - Lippa, Katrice A.

AU - Jackson, Scott A.

AU - Daouk, Rima Kaddurah

AU - Galasko, Douglas

AU - Dulai, Parambir S.

AU - Kalashnikova, Tatyana I.

AU - Wittenberg, Curt

AU - Terkeltaub, Robert

AU - Doty, Megan M.

AU - Kim, Jae H.

AU - Rhee, Kyung E.

AU - Beauchamp-Walters, Julia

AU - Wright, Kenneth P.

AU - Dominguez-Bello, Maria Gloria

AU - Manary, Mark

AU - Oliveira, Michelli F.

AU - Boland, Brigid S.

AU - Lopes, Norberto Peporine

AU - Guma, Monica

AU - Swafford, Austin D.

AU - Dutton, Rachel J.

AU - Knight, Rob

AU - Dorrestein, Pieter C.

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85133629409

U2 - 10.1038/s41587-022-01368-1

DO - 10.1038/s41587-022-01368-1

M3 - Article

C2 - 35798960

AN - SCOPUS:85133629409

SN - 1087-0156

VL - 40

SP - 1774

EP - 1779

JO - Nature Biotechnology

JF - Nature Biotechnology

IS - 12

ER -

Enhancing untargeted metabolomics using metadata-based source annotation

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