A Standardized Nontargeted Metabolomics Analytical Method for Qualitatively Comparing Apples to Apples

A Standardized Nontargeted Metabolomics Analytical Method for Qualitatively Comparing Apples to Apples | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Standardized Nontargeted Metabolomics Analytical Method for Qualitatively Comparing Apples to Apples Steve Watkins, Melanie Odenkirk, Cole Michel, Katrina Doenges, and 22 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5851757/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Food composition has been traditionally defined by 35-160 chemical components with established nutritional significance for human health. Modern omics technologies have revealed that the chemical complexity of food is far greater, offering the potential to deepen our understanding of food composition to more precisely inform data-driven solutions across food systems. However, challenges in generating comparable omics data have limited the utility of omics technologies at the scale required to expand food composition databases. Herein, we present a standardized nontargeted LC-MS metabolomics method, supported by a novel internal retention time standard (IRTS) mixture of compounds non-endogenous to food that enables robust chromatographic alignment of data across laboratories. Our results demonstrate qualitative consensus of features across laboratories and/or instrumentation. This approach establishes the foundation for comparable, nontargeted omics analysis to support the next generation of food composition data. Scientific community and society/Scientific community Biological sciences/Biological techniques/Metabolomics Biological sciences/Biological techniques/Mass spectrometry Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Food exists at the forefront of our greatest global challenges: degradation of ecosystems, climate change, and diet-related morbidity. 1 – 3 Food composition databases serve as essential resources for policymakers, healthcare professionals, and consumers, helping guide both short- and long-term decisions about food. For nearly a century, our knowledge of food composition has focused on roughly 35 to 160 molecular components with established nutritional value — a small fraction of the tens of thousands of molecules that make up food. 4 This limited scope not only overlooks the vast chemical diversity in food, but also often lacks biological diversity, with data from discrete food specimens of various cultivars and unique geographical origins often generalized to a single composite value. 5 Furthermore, due to resource limitations, food composition databases typically include only the most commonly consumed foods, rather than the over 35,000 species that comprise the planet’s edible biodiversity. 6 – 11 Our planet and food systems are facing an unprecedented loss of biodiversity, ecosystem degradation, rising diet-related morbidity, and climate change, making it crucial to develop a more comprehensive understanding of the biological and chemical diversity of food to address these complex challenges and their direct implications on both human and planetary health. 4 , 12 – 14 Mass spectrometry-based omics technologies have the potential to significantly expand the breadth and depth of the observable molecular space which is used to define food composition. 15 Specifically, metabolomics provides a powerful approach for analyzing the diverse array of small molecules (50-2000 Da) present in and on food. This includes molecules associated with positive health attributes, such as vitamins, amino acids, and flavonoids, as well as those linked to negative health attributes, such as environmental contaminants including pesticides, microplastics, and per- and poly-fluoroalkyl substances (PFAS). 16 – 19 However, despite the potential for using metabolomics to characterize the molecular inventory contained within food, omics techniques have not yet been integrated into routine food composition analyses. This is largely due to the vast chemical diversity of small molecules, which cannot be captured by a single analytical method. Consequently, researchers must make methodological choices throughout their experiment, often optimizing for specific food matrices or compound classes. 20 In practice, this means that two laboratories rarely perform identical analyses, limiting data comparability and impeding the generation of foodomics data at scale. 21 Even minor changes in a single experimental variable (e.g., choice of analytical platform or data analysis approach) can significantly alter the observed small molecule profiles and the observed relative amounts of individual chemical species. 22 , 23 Thus, the lack of standardization hinders the application of metabolomics for reproducible, comprehensive characterization of food composition, posing a barrier to the production of evidence at the scale needed to inform data-driven solutions for impact. Achieving a comprehensive understanding of global food composition is beyond the scope of any single laboratory. Therefore, there is a critical need for strategies to align analytical metabolomics approaches to create a globally relevant, accessible, and comprehensive knowledgebase of food composition. Uniform methodological practices and resources that enable the global community to generate data that is both publicly accessible and comparable — similar to genomic data resources such as those from the National Center for Biotechnology Information (NCBI) — are essential to meet this goal. 24 Within metabolomics, significant efforts are being made to standardize current practices. 25 However, the biological and chemical diversity within food matrices offer additional challenges to achieve standardization. Here we present a standardized nontargeted metabolomics analytical protocol developed through coordination among expert labs along with a rationally-designed internal retention time standard (IRTS) mixture to enable reproducible characterization of small molecules across laboratories and instrumentation. The effectiveness of this approach is demonstrated through the analysis of five diverse food types (green pepper, strawberry, beef ribeye, wheat, and carrot) by three laboratories. Results Development of a Standardized Protocol for Nontargeted Metabolomics As a first step towards addressing challenges in comparing nontargeted metabolomics data, we focused on the development of a standardized protocol for sample preparation and data acquisition (Fig. 1 ). To evaluate this method, a set of five foods were procured from local markets and fields by Lab A, lyophilized, and then homogenized into a fine powder to normalize variation in water content and create a uniform sample mixture with increased surface area to enhance extraction efficiency. 26 The lyophilized and homogenized samples were then provided to Labs B and C for all three labs to perform sample extraction and data acquisition using a common protocol adapted from a method originally developed by Lab B. 27 Briefly, 50 mg of each food was extracted using 80% methanol in water, a commonly used extraction solvent for nontargeted metabolomics methods, followed by lipid removal by solid phase extraction. 28 , 29 The eluent was dried under nitrogen and resuspended for LC-MS1 analysis. Reverse-phase chromatography was performed using identical gradients, buffers, and columns. Eluent from separations was detected by high-resolution mass spectrometry using positive mode electrospray ionization. Labs A, B, and C all operated various commercially available quadrupole time-of-flight (qTOF) instruments at a scan rate of 2–3 scans per second. All other acquisition settings were optimized independently for each instrument (Online Methods). Data from each lab was processed individually using a consistent workflow across all datasets. Namely, basic feature finding of mzML-converted file types was completed using XCMS prior to retention time alignment to enable qualitative comparisons (see Online Methods for full details). 30 , 31 To evaluate the within-laboratory agreement of small molecule profiles, each laboratory analyzed process replicates of an identical carrot sample. Correlation plots were used to evaluate the consistency of duplicate analysis. For these carrot samples, correlation scores were 0.97, 0.99, and 0.99 for Labs A, B, and C; respectively (Fig. 2 ) , demonstrating high intra-laboratory reproducibility. Retention Time Alignment Across Laboratories Expanding the comparison of LC-MS data from within-laboratory to between-laboratories is complex, as variables that are easily controlled within a laboratory may vary across laboratories, leading to notable differences in data. Chromatography is a powerful separation technique often used in nontargeted analyses to simplify matrices and enable more comprehensive molecular coverage. However, even with identical chromatographic conditions, small variations in mobile phases (e.g., pH, solvent lot number) or instrument configuration (e.g., tubing length, diameter, or material) can introduce appreciable differences in chromatographic separations. 32 Notably, with the application of our standardized method, we observed retention time variations of over 20 seconds on different instruments, indicating that merely using the same protocol across laboratories does not ensure comparable data (Fig. 3 A). A common approach for achieving retention time alignment is the use of internal standards. 33 – 36 While this method has proven effective within a laboratory for similar sample types, enabling analysis of globally distributed, highly variable food samples presents unique challenges. Specifically, to be suitable for the desired application, internal standard molecules must (1) be unlikely to occur naturally in food, (2) span the full range of the chromatographic separation (Fig. 3 B), (3) ionize readily for mass spectrometry detection, (4) exhibit measurable abundances in mass spectrometry data, and (5) be available at scale and accessible to researchers worldwide for the foreseeable future. Based on these criteria, a set of 33 molecules at varying concentrations were selected to establish our internal retention time standard (IRTS) mixture. Approximately 32 of these IRTS molecules were detected in positive ionization mode and 25 in negative ionization mode, spanning the full chromatographic separation. To ensure peak intensities remained within the linear detection range across different instruments and sensitivities, each laboratory optimized the resuspension volume after extraction and/or the injection volume. After feature finding was performed on the raw mass spectrometry data files, the IRTS molecules and their observed retention times (RT) were used to define injection-specific coefficients for fitting quadratic regressions (Online Methods). This allowed for the conversion of observed RT values to retention index (RI) values, significantly reducing chromatographic differences between laboratories from over 20 seconds to less than 5 seconds (Fig. 3 C). Inter-Laboratory Feature Consensus in LC-MS Standardized Data The application of a standardized analytical method combined with a robust internal standard reagent enables the successful alignment of chromatographic data across laboratories. However, not every experimental variable can be reasonably modulated. For example, even two instruments of the same model can perform differently if the source settings vary or if detector sensitivities differ. Thus, while data alignment is achievable, a key question remains: how comparable are the individual features detected across labs? To address this, we assessed the qualitative consensus of features observed across the three laboratories (Fig. 4 ). To account for differences in instrument sensitivity, we evaluated consensus features—defined as features that were reproducibly detected in at least two laboratories—in relation to the total feature count from each lab (see Online Methods). Consistently, the number of consensus features decreased with the addition of more laboratories (i.e., consensus in two laboratories (dark blue) vs. all three laboratories (blue), Fig. 4 A). For Labs A and C, consensus features across two or more laboratories comprised an average of 68 and 63% of the total detected features per food in these laboratories. For Lab B, which yielded almost two times the number of total features compared to Labs A and C, the average consensus across two or more laboratories was significantly lower at 54% (p-value = 0.002). Notably, features in consensus in only two labs were most often shared between Labs B and C, both of which used Agilent instruments, followed by Labs A and B, both of which used instruments without ion mobility separations. This trend, represented by wheat flour in Fig. 4 B, was consistent across foods, demonstrating the expected impact of different instrumentation on feature observations. Regardless of food type, the proportion of consensus features observed across laboratories remained consistent. Discussion The results presented here lay the foundational framework for generating high-quality, reproducible non-targeted metabolomics data on food, ushering in a new era of food composition databases. Notably, we have demonstrated that, regardless of food type, alignment of small molecule data across different laboratories is feasible. However, the path of standardization is not without limitations. Specifically, the broad nature of this method means that optimized performance for specific foods or compound classes is lacking; for example, it does not support the absolute quantitative measurement of a specific subset of molecules as is common in targeted omic assays. Additionally, differences in the features detected from individual labs are observed due to variations in source configuration and settings, laboratory-specific contaminants, instrument types, instrument acquisition settings, and additional factors. The current study did not attempt to account for these differences as they are difficult, if not impossible, to control for across laboratories and instruments. Nonetheless, the benefits of adopting a standardized approach are substantial. Specifically, the use of standardized sample preparation and LC methods, along with the inclusion of IRTS, across laboratories and food matrices provides an unprecedented opportunity to build a robust and high-quality resource on food chemical composition focused on confident consensus detection of compounds across laboratories and instruments. Further, capturing both known and uncharacterized metabolites can drive discovery and fuel data mining efforts. To achieve this long-term goal, the next steps will focus on other critical components of the metabolomics workflow such as data processing and compound annotation—two major sources of variation in metabolomics data 37 —as well as the integration of more complex mass spectrometry data including MS/MS fragmentation and ion mobility spectrometry (IMS) data. Collectively, these resources will pave the way for a new frontier of food composition data. Declarations Acknowledgements. The authors would like to acknowledge The Rockefeller Foundation and the Foundation for Food & Agriculture Research which funded this work as part of The Periodic Table of Food Initiative (PTFI) that is managed by the American Heart Association and the Alliance of Bioversity CIAT. The content, findings and conclusions presented are those of the authors and does not necessarily reflect the official views, positions or policies of the funders or the institutions with which the funders or the authors are affiliated. The authors would also like to acknowledge Andres Jaramillo-Botero, Juliana Chaura, and Gabriel E. Velez Mejia for their adoption and evaluation of the nontargeted metabolomics method. This work was supported in part by the resources of the Center for Innovative Technology at Vanderbilt University. Author Contributions. JAM, NR, OF, TS, SA, JP, JEP and SW conceptualized the manuscript. MLR, JMC, SBM, and JEP procured, prepared, and processed foods for analysis with the metabolomics method. MLR, JMC, SBM, and TS coordinated the distribution of samples to all laboratories. CM, KAD, RR, and NR developed an initial method to standardize reverse phase nontargeted metabolomics. CM, KAD, KLL, SDS, JCM, NM, CDB, AV, RR, JAM, NR, OF, TS, JEP and SW all assisted in optimizing the standardized, nontargeted metabolomics method. MTO, CM, KAD, KLL, SDS, JCM, NM, CDB, RR, NR, JAM, and JEP contributed metabolomics data with the PTFI method. BY, JKE, LH and CMC performed feature extraction and RI alignment of data. MTO and SW analyzed datasets. MLR, SB, JKE, BY, and CMC contributed text. MTO wrote the manuscript and generated the figures. Ethics Declaration. Competing Interests. Authors CMC, TS, and SW are employed by Verso Biosciences, Inc. Data availability. Un-aligned raw mass spectrometry data for the standardized nontargeted metabolomics analytical method developed by the Periodic Table of Food Initiative are presented on MassIVE (ftp://massive.ucsd.edu/v06/MSV000096789/). RI-aligned feature outputs are presented in the Supplemental Information (Table S1-S5). Code availability. All code for data processing and figure production is available on GitHub at https://github.com/ThePrenniLab/Apples-to-Apples_Standardized-Metabolomics. References Ritchie, H., Rosado, P. & Roser, M. Environmental Impacts of Food Production. Our World in Data (2022). James, S. L. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392 , 1789–1858 (2018). Campbell, B. M. et al. Agriculture production as a major driver of the Earth system exceeding planetary boundaries. 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Authors CMC, TS, and SW are employed by Verso Biosciences, Inc. Supplementary Files OdenkirkSUPPApplestoApples250117.pdf Supplemental Data for A Standardized Nontargeted Metabolomics Analytical Method for Qualitatively Comparing Apples to Apples AtoAGraphicAbstract250115.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5851757","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":410897672,"identity":"a75992a2-82e0-4b72-a68a-78c3f0392066","order_by":0,"name":"Steve 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Colorado","correspondingAuthor":false,"prefix":"","firstName":"Nichole","middleName":"","lastName":"Reisdorph","suffix":""},{"id":410897692,"identity":"56b4ad41-3610-4ccb-b3d5-96b475b8172c","order_by":20,"name":"John McLean","email":"","orcid":"https://orcid.org/0000-0001-8918-6419","institution":"Department of Chemistry and Center for Innovative Technology, Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"McLean","suffix":""},{"id":410897693,"identity":"3801451f-f561-4d82-ab11-07fb02abdd68","order_by":21,"name":"Chi-Ming Chien","email":"","orcid":"https://orcid.org/0009-0001-8172-9113","institution":"Verso Biosciences, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Chi-Ming","middleName":"","lastName":"Chien","suffix":""},{"id":410897694,"identity":"81ecaa43-62ea-49f7-b76e-f1499c207423","order_by":22,"name":"Tracy Shafizadeh","email":"","orcid":"","institution":"Verso Biosciences","correspondingAuthor":false,"prefix":"","firstName":"Tracy","middleName":"","lastName":"Shafizadeh","suffix":""},{"id":410897695,"identity":"dc9681e2-7301-4d43-a860-547439e3abb4","order_by":23,"name":"John de la Parra","email":"","orcid":"https://orcid.org/0000-0003-1425-6288","institution":"The Rockefeller Foundation","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"de la","lastName":"Parra","suffix":""},{"id":410897696,"identity":"37bac2a9-f055-476d-b340-1953559752cd","order_by":24,"name":"Selena Ahmed","email":"","orcid":"","institution":"American Heart Association, Inc., Dallas, TX","correspondingAuthor":false,"prefix":"","firstName":"Selena","middleName":"","lastName":"Ahmed","suffix":""},{"id":410897697,"identity":"ab2a444e-3cd8-48cc-a4d5-8e72a70e115f","order_by":25,"name":"Jessica Prenni","email":"","orcid":"https://orcid.org/0000-0002-0337-8450","institution":"Colorado State University","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Prenni","suffix":""}],"badges":[],"createdAt":"2025-01-17 21:10:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5851757/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5851757/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75483890,"identity":"5ce4551a-e4d8-4c54-b8a1-e3f825c219d9","added_by":"auto","created_at":"2025-02-05 06:03:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140040,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized, nontargeted metabolomics workflow for food. Initially, food was procured and processed with lyophilization and homogenization. Small molecules were then extracted using solid-phase extraction (SPE). LC-MS was collected with a high-resolution mass spectrometer in MS1 mode across three time-of-flight (TOF) instruments from two vendors. Data processing was then completed using feature finding in XCMS prior to retention time alignment of data using internal retention time standards (IRTS).\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5851757/v1/b0d4d36627e4e87560952c96.png"},{"id":75484925,"identity":"09568347-0879-41f3-ac80-f415ae7ed629","added_by":"auto","created_at":"2025-02-05 06:11:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60773,"visible":true,"origin":"","legend":"\u003cp\u003eIntra-lab reproducibility using a standardized, nontargeted metabolomics method of two process replicates of carrot run by all three laboratories.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5851757/v1/5dcaa6d57ccfd319a4f40018.png"},{"id":75483892,"identity":"f6162284-b7fb-4fd5-a441-e15c627adf8a","added_by":"auto","created_at":"2025-02-05 06:03:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":217996,"visible":true,"origin":"","legend":"\u003cp\u003eRetention time agreement across laboratories. A) Retention time (RT) differences between 2500 features found in strawberries across all three laboratories. Dashed lines represent +/-5 seconds of retention time differences between labs. B) Chromatogram of IRTS mix following positive mode electrospray ionization. Individual compounds are as follows: (1) Cordycepin, (2) 2-amino-5-[2-[[(3S)-2,3-dihydroxy-2-[(1S)-1 hydroxyethyl]butanoyl]oxymethyl]anilino]-5-oxopentanoic acid, (3) Pyranonigrin, (4) Chrysogine, (5) Ouabain, (6) Zinndiol, (7) Lignicol, (8) Grayanoside B, (9) Methylharmine, (10) Obscurolide A1, (11) Kipukasin D, (12) Acobioside A, (13) Coagulin L, (14) Diphysin, (15) 5-[3-hydroxy-5-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxydecanoyl]oxy-3-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxydecanoic acid, (16) 2-methyl-N-[2-oxo-1-[(E)-2-phenylethenyl]piperidin-3-yl]propanamide, (17) Koninginin E, (18) N-Phenethylisovaleramide, (19) Dechlorogriseofulvin, (20) Destruxin A, (21) Spirodihydrobenzofuranlactam IV, (22) Griseofulvin, (23) (2E,6E)-10,11,15,19,23,27,31-Heptahydroxy-3,7,11,15,19,23,27,31,35-nonamethyl-2,6,34-hexatriacontatrien-1-yl β-D-glucopyranoside, (24) Unguinol, (25) Bionectriol A, (26) Andrastin A, (27) Pongachin, (28) Purpactin A, (29) Isopongaflavone, (30) Euphorbia Factor L3, (31) Deschlorothricin, (32) Lasalocid A. Additional information on IRTS compounds can be found in Online Methods. C) Retention index (RI) differences following IRTS-alignment between 2500 features found in strawberries across all three laboratories. Dashed lines represent +/-5 RI differences across labs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5851757/v1/3f5de5d0d7d35900c1113b5f.png"},{"id":75483906,"identity":"b0aa054f-f107-42d5-9bbf-bc6a1c3c1cba","added_by":"auto","created_at":"2025-02-05 06:03:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92598,"visible":true,"origin":"","legend":"\u003cp\u003eInter-laboratory consensus of features using the PTFI standardized metabolomics method. A) Percentage of overlapping features for two labs (dark blue), and all three labs (blue) relative to total feature count of each lab. B) Venn diagram of feature overlaps for wheat flour.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5851757/v1/b31649055cca8b125d662ce5.png"},{"id":79183374,"identity":"a57725f8-e3d7-4cf2-9191-d97adc4dc780","added_by":"auto","created_at":"2025-03-25 10:59:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1037561,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5851757/v1/79b5aa4b-3b07-4099-a071-222ee70d5299.pdf"},{"id":75483909,"identity":"2c4a9b2d-53ef-456b-82db-c0873c09ae93","added_by":"auto","created_at":"2025-02-05 06:03:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41084814,"visible":true,"origin":"","legend":"Supplemental Data for A Standardized Nontargeted Metabolomics Analytical Method for Qualitatively Comparing Apples to Apples","description":"","filename":"OdenkirkSUPPApplestoApples250117.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5851757/v1/bc1a54067a0ab55e4939fd0b.pdf"},{"id":75483891,"identity":"4ab71fd0-8159-4d21-9f56-e25c793dc496","added_by":"auto","created_at":"2025-02-05 06:03:54","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21831,"visible":true,"origin":"","legend":"","description":"","filename":"AtoAGraphicAbstract250115.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5851757/v1/f039337312ee48a8e39046ff.jpg"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nAuthors CMC, TS, and SW are employed by Verso Biosciences, Inc.","formattedTitle":"A Standardized Nontargeted Metabolomics Analytical Method for Qualitatively Comparing Apples to Apples","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFood exists at the forefront of our greatest global challenges: degradation of ecosystems, climate change, and diet-related morbidity.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Food composition databases serve as essential resources for policymakers, healthcare professionals, and consumers, helping guide both short- and long-term decisions about food. For nearly a century, our knowledge of food composition has focused on roughly 35 to 160 molecular components with established nutritional value \u0026mdash; a small fraction of the tens of thousands of molecules that make up food.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e This limited scope not only overlooks the vast chemical diversity in food, but also often lacks biological diversity, with data from discrete food specimens of various cultivars and unique geographical origins often generalized to a single composite value.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Furthermore, due to resource limitations, food composition databases typically include only the most commonly consumed foods, rather than the over 35,000 species that comprise the planet\u0026rsquo;s edible biodiversity.\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Our planet and food systems are facing an unprecedented loss of biodiversity, ecosystem degradation, rising diet-related morbidity, and climate change, making it crucial to develop a more comprehensive understanding of the biological and chemical diversity of food to address these complex challenges and their direct implications on both human and planetary health.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMass spectrometry-based omics technologies have the potential to significantly expand the breadth and depth of the observable molecular space which is used to define food composition.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Specifically, metabolomics provides a powerful approach for analyzing the diverse array of small molecules (50-2000 Da) present in and on food. This includes molecules associated with positive health attributes, such as vitamins, amino acids, and flavonoids, as well as those linked to negative health attributes, such as environmental contaminants including pesticides, microplastics, and per- and poly-fluoroalkyl substances (PFAS).\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e However, despite the potential for using metabolomics to characterize the molecular inventory contained within food, omics techniques have not yet been integrated into routine food composition analyses. This is largely due to the vast chemical diversity of small molecules, which cannot be captured by a single analytical method. Consequently, researchers must make methodological choices throughout their experiment, often optimizing for specific food matrices or compound classes.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e In practice, this means that two laboratories rarely perform identical analyses, limiting data comparability and impeding the generation of foodomics data at scale.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Even minor changes in a single experimental variable (e.g., choice of analytical platform or data analysis approach) can significantly alter the observed small molecule profiles and the observed relative amounts of individual chemical species.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Thus, the lack of standardization hinders the application of metabolomics for reproducible, comprehensive characterization of food composition, posing a barrier to the production of evidence at the scale needed to inform data-driven solutions for impact.\u003c/p\u003e \u003cp\u003eAchieving a comprehensive understanding of global food composition is beyond the scope of any single laboratory. Therefore, there is a critical need for strategies to align analytical metabolomics approaches to create a globally relevant, accessible, and comprehensive knowledgebase of food composition. Uniform methodological practices and resources that enable the global community to generate data that is both publicly accessible and comparable \u0026mdash; similar to genomic data resources such as those from the National Center for Biotechnology Information (NCBI) \u0026mdash; are essential to meet this goal.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Within metabolomics, significant efforts are being made to standardize current practices.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e However, the biological and chemical diversity within food matrices offer additional challenges to achieve standardization. Here we present a standardized nontargeted metabolomics analytical protocol developed through coordination among expert labs along with a rationally-designed internal retention time standard (IRTS) mixture to enable reproducible characterization of small molecules across laboratories and instrumentation. The effectiveness of this approach is demonstrated through the analysis of five diverse food types (green pepper, strawberry, beef ribeye, wheat, and carrot) by three laboratories.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of a Standardized Protocol for Nontargeted Metabolomics\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs a first step towards addressing challenges in comparing nontargeted metabolomics data, we focused on the development of a standardized protocol for sample preparation and data acquisition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To evaluate this method, a set of five foods were procured from local markets and fields by Lab A, lyophilized, and then homogenized into a fine powder to normalize variation in water content and create a uniform sample mixture with increased surface area to enhance extraction efficiency.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e The lyophilized and homogenized samples were then provided to Labs B and C for all three labs to perform sample extraction and data acquisition using a common protocol adapted from a method originally developed by Lab B.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Briefly, 50 mg of each food was extracted using 80% methanol in water, a commonly used extraction solvent for nontargeted metabolomics methods, followed by lipid removal by solid phase extraction.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e The eluent was dried under nitrogen and resuspended for LC-MS1 analysis. Reverse-phase chromatography was performed using identical gradients, buffers, and columns. Eluent from separations was detected by high-resolution mass spectrometry using positive mode electrospray ionization. Labs A, B, and C all operated various commercially available quadrupole time-of-flight (qTOF) instruments at a scan rate of 2\u0026ndash;3 scans per second. All other acquisition settings were optimized independently for each instrument (Online Methods). Data from each lab was processed individually using a consistent workflow across all datasets. Namely, basic feature finding of mzML-converted file types was completed using XCMS prior to retention time alignment to enable qualitative comparisons (see Online Methods for full details).\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e To evaluate the within-laboratory agreement of small molecule profiles, each laboratory analyzed process replicates of an identical carrot sample. Correlation plots were used to evaluate the consistency of duplicate analysis. For these carrot samples, correlation scores were 0.97, 0.99, and 0.99 for Labs A, B, and C; respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, demonstrating high intra-laboratory reproducibility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRetention Time Alignment Across Laboratories\u003c/h3\u003e\n\u003cp\u003eExpanding the comparison of LC-MS data from within-laboratory to between-laboratories is complex, as variables that are easily controlled within a laboratory may vary across laboratories, leading to notable differences in data. Chromatography is a powerful separation technique often used in nontargeted analyses to simplify matrices and enable more comprehensive molecular coverage. However, even with identical chromatographic conditions, small variations in mobile phases (e.g., pH, solvent lot number) or instrument configuration (e.g., tubing length, diameter, or material) can introduce appreciable differences in chromatographic separations.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Notably, with the application of our standardized method, we observed retention time variations of over 20 seconds on different instruments, indicating that merely using the same protocol across laboratories does not ensure comparable data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA common approach for achieving retention time alignment is the use of internal standards.\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e While this method has proven effective within a laboratory for similar sample types, enabling analysis of globally distributed, highly variable food samples presents unique challenges. Specifically, to be suitable for the desired application, internal standard molecules must (1) be unlikely to occur naturally in food, (2) span the full range of the chromatographic separation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), (3) ionize readily for mass spectrometry detection, (4) exhibit measurable abundances in mass spectrometry data, and (5) be available at scale and accessible to researchers worldwide for the foreseeable future.\u003c/p\u003e \u003cp\u003eBased on these criteria, a set of 33 molecules at varying concentrations were selected to establish our internal retention time standard (IRTS) mixture. Approximately 32 of these IRTS molecules were detected in positive ionization mode and 25 in negative ionization mode, spanning the full chromatographic separation. To ensure peak intensities remained within the linear detection range across different instruments and sensitivities, each laboratory optimized the resuspension volume after extraction and/or the injection volume. After feature finding was performed on the raw mass spectrometry data files, the IRTS molecules and their observed retention times (RT) were used to define injection-specific coefficients for fitting quadratic regressions (Online Methods). This allowed for the conversion of observed RT values to retention index (RI) values, significantly reducing chromatographic differences between laboratories from over 20 seconds to less than 5 seconds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\n\u003ch3\u003eInter-Laboratory Feature Consensus in LC-MS Standardized Data\u003c/h3\u003e\n\u003cp\u003eThe application of a standardized analytical method combined with a robust internal standard reagent enables the successful alignment of chromatographic data across laboratories. However, not every experimental variable can be reasonably modulated. For example, even two instruments of the same model can perform differently if the source settings vary or if detector sensitivities differ. Thus, while data alignment is achievable, a key question remains: how comparable are the individual features detected across labs? To address this, we assessed the qualitative consensus of features observed across the three laboratories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). To account for differences in instrument sensitivity, we evaluated consensus features\u0026mdash;defined as features that were reproducibly detected in at least two laboratories\u0026mdash;in relation to the total feature count from each lab (see Online Methods). Consistently, the number of consensus features decreased with the addition of more laboratories (i.e., consensus in two laboratories (dark blue) vs. all three laboratories (blue), Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). For Labs A and C, consensus features across two or more laboratories comprised an average of 68 and 63% of the total detected features per food in these laboratories. For Lab B, which yielded almost two times the number of total features compared to Labs A and C, the average consensus across two or more laboratories was significantly lower at 54% (p-value\u0026thinsp;=\u0026thinsp;0.002). Notably, features in consensus in only two labs were most often shared between Labs B and C, both of which used Agilent instruments, followed by Labs A and B, both of which used instruments without ion mobility separations. This trend, represented by wheat flour in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, was consistent across foods, demonstrating the expected impact of different instrumentation on feature observations. Regardless of food type, the proportion of consensus features observed across laboratories remained consistent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results presented here lay the foundational framework for generating high-quality, reproducible non-targeted metabolomics data on food, ushering in a new era of food composition databases. Notably, we have demonstrated that, regardless of food type, alignment of small molecule data across different laboratories is feasible. However, the path of standardization is not without limitations. Specifically, the broad nature of this method means that optimized performance for specific foods or compound classes is lacking; for example, it does not support the absolute quantitative measurement of a specific subset of molecules as is common in targeted omic assays. Additionally, differences in the features detected from individual labs are observed due to variations in source configuration and settings, laboratory-specific contaminants, instrument types, instrument acquisition settings, and additional factors. The current study did not attempt to account for these differences as they are difficult, if not impossible, to control for across laboratories and instruments. Nonetheless, the benefits of adopting a standardized approach are substantial. Specifically, the use of standardized sample preparation and LC methods, along with the inclusion of IRTS, across laboratories and food matrices provides an unprecedented opportunity to build a robust and high-quality resource on food chemical composition focused on confident consensus detection of compounds across laboratories and instruments. Further, capturing both known and uncharacterized metabolites can drive discovery and fuel data mining efforts. To achieve this long-term goal, the next steps will focus on other critical components of the metabolomics workflow such as data processing and compound annotation\u0026mdash;two major sources of variation in metabolomics data\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u0026mdash;as well as the integration of more complex mass spectrometry data including MS/MS fragmentation and ion mobility spectrometry (IMS) data. Collectively, these resources will pave the way for a new frontier of food composition data.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge The Rockefeller Foundation and the Foundation for Food \u0026amp; Agriculture Research which funded this work as part of The Periodic Table of Food Initiative (PTFI) that is managed by the American Heart Association and the Alliance of Bioversity CIAT. The content, findings and conclusions presented are those of the authors and does not necessarily reflect the official views, positions or policies of the funders or the institutions with which the funders or the authors are affiliated. The authors would also like to acknowledge Andres Jaramillo-Botero, Juliana Chaura, and Gabriel E. Velez Mejia for their adoption and evaluation of the nontargeted metabolomics method. This work was supported in part by the resources of the Center for Innovative Technology at Vanderbilt University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJAM, NR, OF, TS, SA, JP, JEP and SW conceptualized the manuscript. MLR, JMC, SBM, and JEP procured, prepared, and processed foods for analysis with the metabolomics method. MLR, JMC, SBM, and TS coordinated the distribution of samples to all laboratories. CM, KAD, RR, and NR developed an initial method to standardize reverse phase nontargeted metabolomics. CM, KAD, KLL, SDS, JCM, NM, CDB, AV, RR, JAM, NR, OF, TS, JEP and SW all assisted in optimizing the standardized, nontargeted metabolomics method. MTO, CM, KAD, KLL, SDS, JCM, NM, CDB, RR, NR, JAM, and JEP contributed metabolomics data with the PTFI method. BY, JKE, LH and CMC performed feature extraction and RI alignment of data. MTO and SW analyzed datasets. MLR, SB, JKE, BY, and CMC contributed text. MTO wrote the manuscript and generated the figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests.\u0026nbsp;\u003c/em\u003eAuthors CMC, TS, and SW are employed by Verso Biosciences, Inc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUn-aligned raw mass spectrometry data for the standardized nontargeted metabolomics analytical method developed by the Periodic Table of Food Initiative are presented on MassIVE (ftp://massive.ucsd.edu/v06/MSV000096789/). RI-aligned feature outputs are presented in the Supplemental Information (Table S1-S5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll code for data processing and figure production is available on GitHub at https://github.com/ThePrenniLab/Apples-to-Apples_Standardized-Metabolomics.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRitchie, H., Rosado, P. \u0026amp; Roser, M. Environmental Impacts of Food Production. \u003cem\u003eOur World in Data\u003c/em\u003e (2022).\u003c/li\u003e\n \u003cli\u003eJames, S. 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Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 375 (2008).\u003c/li\u003e\n \u003cli\u003eBlaženović, I. \u003cem\u003eet al.\u003c/em\u003e Structure annotation of all mass spectra in untargeted metabolomics. \u003cem\u003eAnal Chem\u003c/em\u003e acs.analchem.8b04698 (2019) doi:10.1021/acs.analchem.8b04698.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5851757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5851757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFood composition has been traditionally defined by 35-160 chemical components with established nutritional significance for human health. Modern omics technologies have revealed that the chemical complexity of food is far greater, offering the potential to deepen our understanding of food composition to more precisely inform data-driven solutions across food systems. However, challenges in generating comparable omics data have limited the utility of omics technologies at the scale required to expand food composition databases. Herein, we present a standardized nontargeted LC-MS metabolomics method, supported by a novel internal retention time standard (IRTS) mixture of compounds non-endogenous to food that enables robust chromatographic alignment of data across laboratories. Our results demonstrate qualitative consensus of features across laboratories and/or instrumentation. This approach establishes the foundation for comparable, nontargeted omics analysis to support the next generation of food composition data.\u003c/p\u003e","manuscriptTitle":"A Standardized Nontargeted Metabolomics Analytical Method for Qualitatively Comparing Apples to Apples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-05 06:03:49","doi":"10.21203/rs.3.rs-5851757/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86468a9b-e383-4d80-96b2-a62a26d2358d","owner":[],"postedDate":"February 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43807489,"name":"Scientific community and society/Scientific community"},{"id":43807490,"name":"Biological sciences/Biological techniques/Metabolomics"},{"id":43807491,"name":"Biological sciences/Biological techniques/Mass spectrometry"}],"tags":[],"updatedAt":"2025-03-25T10:51:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-05 06:03:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5851757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5851757","identity":"rs-5851757","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}