Enhancing untargeted metabolomics using metadata-based source annotation

Julia M Gauglitz^#^{1

2}, Kiana A West^#^{1

2}, Wout Bittremieux^#^{1

2}, Candace L Williams³, Kelly C Weldon^{1

2

4}, Morgan Panitchpakdi^{1

2}, Francesca Di Ottavio¹, Christine M Aceves^{1

2}, Elizabeth Brown^{2

5}, Nicole C Sikora^{1

2}, Alan K Jarmusch^{1

2}, Cameron Martino^{4

6

7}, Anupriya Tripathi^{2

5

6}, Michael J Meehan^{1

2}, Kathleen Dorrestein^{1

2}, Justin P Shaffer⁶, Roxana Coras⁸, Fernando Vargas^{1

2

5}, Lindsay DeRight Goldasich⁶, Tara Schwartz⁶, MacKenzie Bryant⁶, Gregory Humphrey⁶, Abigail J Johnson⁹, Katharina Spengler¹, Pedro Belda-Ferre^{4

6}, Edgar Diaz⁶, Daniel McDonald⁶, Qiyun Zhu⁶, Emmanuel O Elijah^{1

2}, Mingxun Wang^{1

2}, Clarisse Marotz⁶, Kate E Sprecher^{10

11}, Daniela Vargas-Robles¹², Dana Withrow¹⁰, Gail Ackermann⁶, Lourdes Herrera¹³, Barry J Bradford¹⁴, Lucas Maciel Mauriz Marques¹⁵, Juliano Geraldo Amaral¹⁶, Rodrigo Moreira Silva¹⁷, Flavio Protasio Veras¹⁵, Thiago Mattar Cunha¹⁵, Rene Donizeti Ribeiro Oliveira¹⁸, Paulo Louzada-Junior¹⁸, Robert H Mills^{1

2

6

19}, Paulina K Piotrowski²⁰, Stephanie L Servetas²⁰, Sandra M Da Silva²⁰, Christina M Jones²⁰, Nancy J Lin²⁰, Katrice A Lippa²⁰, Scott A Jackson²⁰, Rima Kaddurah Daouk^{21

22

23}, Douglas Galasko²⁴, Parambir S Dulai²⁵, Tatyana I Kalashnikova²⁶, Curt Wittenberg²⁶, Robert Terkeltaub^{8

27}, Megan M Doty^{6

28}, Jae H Kim²⁹, Kyung E Rhee⁶, Julia Beauchamp-Walters³⁰, Kenneth P Wright Jr¹⁰, Maria Gloria Dominguez-Bello³¹, Mark Manary³², Michelli F Oliveira³³, Brigid S Boland²⁵, Norberto Peporine Lopes¹⁷, Monica Guma⁸, Austin D Swafford⁴, Rachel J Dutton⁵, Rob Knight^{34

35

36

37

38}, Pieter C Dorrestein^{39

40

41

42

43}

Affiliations

¹ Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA.
² Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
³ Beckman Center for Conservation Research, San Diego Zoo Wildlife Alliance, Escondido, CA, USA.
⁴ Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
⁵ Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA.
⁶ Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
⁷ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
⁸ Division of Rheumatology, Allergy & Immunology, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
⁹ Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
¹⁰ Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA.
¹¹ Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.
¹² Servicio Autónomo Centro Amazónico de Investigación y Control de Enfermedades Tropicales Simón Bolívar, Puerto Ayacucho, Amazonas, Venezuela.
¹³ Department of Pediatrics, Billings Clinic, Billings, MT, USA.
¹⁴ Department of Animal Science, Michigan State University, East Lansing, MI, USA.
¹⁵ Department of Pharmacology, Ribeirão Preto Medicinal School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil.
¹⁶ Multidisciplinary Health Institute, Federal University of Bahia, Vitória da Conquista, Bahia, Brazil.
¹⁷ NPPNS, Department of Biomolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil.
¹⁸ Department of Internal Medicine, Ribeirão Preto Medical School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil.
¹⁹ Department of Pharmacology, University of California San Diego, La Jolla, CA, USA.
²⁰ Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
²¹ Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, Durham, NC, USA.
²² Department of Medicine, Duke University, Durham, NC, USA.
²³ Duke Institute of Brain Sciences, Duke University, Durham, NC, USA.
²⁴ Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
²⁵ Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
²⁶ Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA.
²⁷ San Diego VA Healthcare System, San Diego, CA, USA.
²⁸ Division of Neonatology, Department of Pediatrics, Kapi'olani Medical Center for Women and Children, John A. Burns School of Medicine, Honolulu, Hawaii, USA.
²⁹ Division of Neonatology, Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
³⁰ Division of Pediatric Hospital Medicine, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
³¹ Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences; Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.
³² Department of Pediatrics, Washington University, St. Louis, MO, USA.
³³ Department of Medicine, University of California San Diego, La Jolla, CA, USA.
³⁴ Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁵ Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁶ Department of Medicine, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁷ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁸ Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁹ Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
⁴⁰ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
⁴¹ Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
⁴² Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
⁴³ Department of Pharmacology, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.

^# Contributed equally.

PMID: 35798960
PMCID: PMC10277029
DOI: 10.1038/s41587-022-01368-1

Enhancing untargeted metabolomics using metadata-based source annotation

Julia M Gauglitz et al. Nat Biotechnol. 2022 Dec.

. 2022 Dec;40(12):1774-1779.

doi: 10.1038/s41587-022-01368-1. Epub 2022 Jul 7.

Authors

Affiliations

¹ Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA.
² Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
³ Beckman Center for Conservation Research, San Diego Zoo Wildlife Alliance, Escondido, CA, USA.
⁴ Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
⁵ Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA.
⁶ Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
⁷ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
⁸ Division of Rheumatology, Allergy & Immunology, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
⁹ Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
¹⁰ Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA.
¹¹ Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.
¹² Servicio Autónomo Centro Amazónico de Investigación y Control de Enfermedades Tropicales Simón Bolívar, Puerto Ayacucho, Amazonas, Venezuela.
¹³ Department of Pediatrics, Billings Clinic, Billings, MT, USA.
¹⁴ Department of Animal Science, Michigan State University, East Lansing, MI, USA.
¹⁵ Department of Pharmacology, Ribeirão Preto Medicinal School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil.
¹⁶ Multidisciplinary Health Institute, Federal University of Bahia, Vitória da Conquista, Bahia, Brazil.
¹⁷ NPPNS, Department of Biomolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil.
¹⁸ Department of Internal Medicine, Ribeirão Preto Medical School, Center of Research in Inflammatory Diseases, University of São Paulo, Ribeirão Preto, Sao Paolo, Brazil.
¹⁹ Department of Pharmacology, University of California San Diego, La Jolla, CA, USA.
²⁰ Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
²¹ Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, Durham, NC, USA.
²² Department of Medicine, Duke University, Durham, NC, USA.
²³ Duke Institute of Brain Sciences, Duke University, Durham, NC, USA.
²⁴ Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
²⁵ Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
²⁶ Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA.
²⁷ San Diego VA Healthcare System, San Diego, CA, USA.
²⁸ Division of Neonatology, Department of Pediatrics, Kapi'olani Medical Center for Women and Children, John A. Burns School of Medicine, Honolulu, Hawaii, USA.
²⁹ Division of Neonatology, Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
³⁰ Division of Pediatric Hospital Medicine, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
³¹ Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences; Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.
³² Department of Pediatrics, Washington University, St. Louis, MO, USA.
³³ Department of Medicine, University of California San Diego, La Jolla, CA, USA.
³⁴ Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁵ Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁶ Department of Medicine, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁷ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁸ Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.
³⁹ Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
⁴⁰ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
⁴¹ Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
⁴² Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
⁴³ Department of Pharmacology, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.

^# Contributed equally.

PMID: 35798960
PMCID: PMC10277029
DOI: 10.1038/s41587-022-01368-1

Erratum in

Author Correction: 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 LD, Schwartz T, Bryant M, 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 Jr, Dominguez-Bello MG, Manary M, Oliveira MF, Boland BS, Lopes NP, Guma M, Swafford AD, Dutton RJ, Knight R, Dorrestein PC. Gauglitz JM, et al. Nat Biotechnol. 2023 Nov;41(11):1656. doi: 10.1038/s41587-023-02025-x. Nat Biotechnol. 2023. PMID: 37853256 No abstract available.

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.

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Figures

**Figure 1.. The concept of reference data-driven based analysis workflow.**
1 - Perform spectral alignment of the MS/MS based untargeted metabolomics data from human biospecimens with data from reference samples that have controlled vocabularies for metadata. This can, optionally, be combined with MS/MS libraries. 2 - link the spectral matches to the source information from the metadata from the reference samples. Create a data table of source ontology, human biospecimen and counts to enable data science and interpretation.

**Figure 2.. RDD with food reference data.**
a. Food RDD analysis schema. b. Food spectral counts (1% FDR) observed in plasma from a sleep restriction and circadian misalignment study that controlled the diet of the participants (n=371 samples from 20 healthy adults). The size of node represents the relative number of spectral matches at each food level. Blue arrow - foods that could be explained based although they were not provided in the study, orange arrow– source is not known. c. A crossover experiment between centenarian data from Italy and a sleep and circadian study from the US, for both fecal and plasma samples. Study region specific foods consumed by those individuals (yes) vs a different set of study region specific foods (no), (one way Welch’s t-test, thick line is the mean, range within the box is the interquartile range, from the 25 to 75 quartile, min / max are the whiskers). d. PCA of food counts color coded by vegan (brown) vs omnivore data (green). e. Statistical analysis for the food counts at level 3 of the ontology, in relation to omnivore and vegan data (Wilcoxon test, n=36, 19 are vegan and 19 are omnivore). f. Same as e. but level 4 ontology using unique spectral counts (spectral usage is the percentage of MS/MS spectra used in the analysis. Since they are unnamed ontologies as one would find in microorganism phylogeny in microbiome science - e.g. kingdom, genus, species we have denoted these as layers, Table S1). For e-f, The boxes represent the interquartile range (IQR). Lower limit (Q1) is 25th percentile, median (Q2), upper limit (Q3) is 75th percentile. Bars show Q3+1.5xIQR and Q1–1.5xIQR.

See this image and copyright information in PMC

References

1. Knights D, et al., Nat Methods. 2011, 8, 8761. - PMC - PubMed
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Enhancing untargeted metabolomics using metadata-based source annotation

Affiliations

Enhancing untargeted metabolomics using metadata-based source annotation

Authors

Affiliations

Erratum in

Abstract

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Molecular Biology Databases