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. 2024 Nov 4;20(6):125.
doi: 10.1007/s11306-024-02185-0.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors

Abraham Kuri Cruz  1 , Marina Amaral Alves  2   3 , Thorkell Andresson  4 , Amanda L Bayless  5 , Kent J Bloodsworth  6 , John A Bowden  7 , Kevin Bullock  8 , Meagan C Burnet  6 , Fausto Carnevale Neto  9 , Angelina Choy  8 , Clary B Clish  8 , Sneha P Couvillion  6 , Raquel Cumeras  10   11 , Lucas Dailey  8 , Guido Dallmann  12 , W Clay Davis  5 , Amy A Deik  8 , Alex M Dickens  2   13 , Danijel Djukovic  9 , Pieter C Dorrestein  14 , Josie G Eder  6 , Oliver Fiehn  10 , Roberto Flores  15 , Helen Gika  16   17 , Kehau A Hagiwara  5 , Tuan Hai Pham  12 , James J Harynuk  18 , Juan J Aristizabal-Henao  7   19 , David W Hoyt  6 , Focant Jean-François  20 , Matilda Kråkström  2 , Amit Kumar  4 , Jennifer E Kyle  6 , Santosh Lamichhane  2 , Yuan Li  21 , Seo Lin Nam  18 , Rupasri Mandal  22 , A Paulina de la Mata  18 , Michael J Meehan  14 , Thomas Meikopoulos  15   17 , Thomas O Metz  6 , Thomai Mouskeftara  16   17 , Nathalie Munoz  6 , G A Nagana Gowda  9 , Matej Orešic  2   23 , Morgan Panitchpakdi  14 , Stefanuto Pierre-Hugues  20 , Daniel Raftery  9 , Blake Rushing  21 , Tracey Schock  5 , Harold Seifried  4 , Stephanie Servetas  24 , Tong Shen  10 , Susan Sumner  21 , Kieran S Tarazona Carrillo  18 , Dejong Thibaut  20 , Jesse B Trejo  6 , Lieven Van Meulebroek  25 , Lynn Vanhaecke  25 , Christina Virgiliou  16   17 , Kelly C Weldon  14 , David S Wishart  22 , Lu Zhang  22 , Jiamin Zheng  22 , Sandra Da Silva  26
Affiliations

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors

Abraham Kuri Cruz et al. Metabolomics. .

Abstract

Introduction: Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications.

Objectives: This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements.

Methods: An interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR).

Results: Different laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques.

Conclusion: This collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.

Keywords: Fecal matter; Gut metabolomics; Human stool; Lipidomics; Metabolomics; Multiplatform analysis; Reference materials.

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Conflict of interest statement

Conflict of interest

PCD consulted for DSM animal Health in 2023, is an advisor and holds equity in Cybele and scientific co-founder and holds equity in Enveda, Arome and Ometa with prior approval by UC-San Diego. All other authors declared no financial or proprietary interests in any material discussed in this article.

Figures

Fig. 1
Fig. 1
Data Analysis Workflow
Fig. 2
Fig. 2
Frequency of metabolites (full list) reported by participants using LC-MS. The table denotes how often a metabolite was reported across participants. For instance, four metabolites were similarly reported by 12 participants while no common metabolites (zero) were reported across all 14 participants.
Fig. 3
Fig. 3
Frequency (%) of annotated metabolites reported before (A) and after (B) nomenclature standardization by RefMet. The colors indicate the frequency of metabolites reported, ranging from one to ten times. For reference, before standardization 47.4%, 42.2%, 47% and 46% of metabolites were reported one time across all samples (blue). After nomenclature standardization these values were 60.5%, 58.7%, 62.4% and 62.8% respectively, showing a higher frequency of multiple reported metabolites.
Fig. 4
Fig. 4
Frequency of annotated metabolites (full list, 875 total) reported by participants using GC-MS. The table denotes the number of times a metabolite was reported across participants. For instance, 16 metabolites were reported by five participants, while no single metabolite was consistently reported by all six participants. For example, 21% of metabolites (92 unique metabolites) were reported by two datasets.
Fig. 5
Fig. 5
Frequency of annotated metabolites (full list, 246 total) reported by participants using NMR. The table shows how often metabolites were recorded across participants. For instance, nine unique metabolites were reported by all five participants and 16% of metabolites were reported by three datasets.
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