Ville Koistinen, Topi Meuronen, Pekka Keski-Rahkonen, Reza Salek, Otto Savolainen, Hany Ahmed, Carl Brunius, Rikard Landberg, Marko Lehtonen, Seppo Auriola, Augustin Scalbert, Kati Hanhineva
{"title":"与血浆基准相协调的人血浆代谢物注释","authors":"Ville Koistinen, Topi Meuronen, Pekka Keski-Rahkonen, Reza Salek, Otto Savolainen, Hany Ahmed, Carl Brunius, Rikard Landberg, Marko Lehtonen, Seppo Auriola, Augustin Scalbert, Kati Hanhineva","doi":"10.1038/s42255-025-01376-w","DOIUrl":null,"url":null,"abstract":"<p>The human plasma metabolome has been extensively characterized: version 5.0 of the Human Metabolome Database (HMDB)<sup>1</sup> currently encompasses 37,229 entries for metabolites reported in human blood. Although the analytical coverage of modern LC–MS platforms enables the detection and identification of 1,000–2,000 plasma metabolites, the number of well-known and regularly detected plasma metabolites is considerably smaller: reference values of 144 plasma metabolites in a healthy population<sup>2</sup> and 588 lipids in the National Institute of Standards and Technology’s (NIST) reference plasma sample<sup>3</sup> have been reported previously. The plasma metabolome also exhibits vast interindividual and intraindividual variability, which is explained by variation in the microbiome, dietary habits and genetics<sup>4</sup>. Challenges in consistently reporting the human plasma metabolome arise from interlaboratory variation in non-targeted LC–MS methodologies and varying practices and capabilities in the annotation of the metabolites themselves<sup>5</sup>.</p><p>To construct Plasma Benchmark, three participating laboratories analysed the same in-house pooled plasma and the NIST1950 human reference plasma<sup>6</sup> in four analytical modes, including reversed-phase and hydrophilic interaction chromatography in the positive and negative ionization modes followed by data matrix generation in MS-DIAL<sup>7</sup>. We applied a set of inclusion criteria based on signal-to-noise ratio, relative standard deviation, sample-to-blank ratio, and acquisition of MS/MS data and predicted molecular formula to narrow the number of detected molecular features down to 639 robust molecular features that most probably represent actual plasma metabolites. Nearly 88% of the detected molecular features did not fulfil the signal-to-noise ratio and sample-to-blank ratio criteria, probably due to inherent noise and contaminants in LC–MS data but also because of high variability in the detection of many molecular features across laboratories<sup>8</sup> (Fig. 1b). Manual curation of the robust molecular features resulted in 288 unique metabolites, whereas the rest of the molecular features were classified as redundant molecular features and in-source fragments. The inclusion of four analytical modes was crucial for an extensive coverage of metabolites because most were detected in only one mode (Fig. 1b).</p>","PeriodicalId":19038,"journal":{"name":"Nature metabolism","volume":"42 1","pages":""},"PeriodicalIF":20.8000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harmonizing human plasma metabolite annotation with Plasma Benchmark\",\"authors\":\"Ville Koistinen, Topi Meuronen, Pekka Keski-Rahkonen, Reza Salek, Otto Savolainen, Hany Ahmed, Carl Brunius, Rikard Landberg, Marko Lehtonen, Seppo Auriola, Augustin Scalbert, Kati Hanhineva\",\"doi\":\"10.1038/s42255-025-01376-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The human plasma metabolome has been extensively characterized: version 5.0 of the Human Metabolome Database (HMDB)<sup>1</sup> currently encompasses 37,229 entries for metabolites reported in human blood. Although the analytical coverage of modern LC–MS platforms enables the detection and identification of 1,000–2,000 plasma metabolites, the number of well-known and regularly detected plasma metabolites is considerably smaller: reference values of 144 plasma metabolites in a healthy population<sup>2</sup> and 588 lipids in the National Institute of Standards and Technology’s (NIST) reference plasma sample<sup>3</sup> have been reported previously. The plasma metabolome also exhibits vast interindividual and intraindividual variability, which is explained by variation in the microbiome, dietary habits and genetics<sup>4</sup>. Challenges in consistently reporting the human plasma metabolome arise from interlaboratory variation in non-targeted LC–MS methodologies and varying practices and capabilities in the annotation of the metabolites themselves<sup>5</sup>.</p><p>To construct Plasma Benchmark, three participating laboratories analysed the same in-house pooled plasma and the NIST1950 human reference plasma<sup>6</sup> in four analytical modes, including reversed-phase and hydrophilic interaction chromatography in the positive and negative ionization modes followed by data matrix generation in MS-DIAL<sup>7</sup>. We applied a set of inclusion criteria based on signal-to-noise ratio, relative standard deviation, sample-to-blank ratio, and acquisition of MS/MS data and predicted molecular formula to narrow the number of detected molecular features down to 639 robust molecular features that most probably represent actual plasma metabolites. Nearly 88% of the detected molecular features did not fulfil the signal-to-noise ratio and sample-to-blank ratio criteria, probably due to inherent noise and contaminants in LC–MS data but also because of high variability in the detection of many molecular features across laboratories<sup>8</sup> (Fig. 1b). Manual curation of the robust molecular features resulted in 288 unique metabolites, whereas the rest of the molecular features were classified as redundant molecular features and in-source fragments. The inclusion of four analytical modes was crucial for an extensive coverage of metabolites because most were detected in only one mode (Fig. 1b).</p>\",\"PeriodicalId\":19038,\"journal\":{\"name\":\"Nature metabolism\",\"volume\":\"42 1\",\"pages\":\"\"},\"PeriodicalIF\":20.8000,\"publicationDate\":\"2025-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature metabolism\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1038/s42255-025-01376-w\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s42255-025-01376-w","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Harmonizing human plasma metabolite annotation with Plasma Benchmark
The human plasma metabolome has been extensively characterized: version 5.0 of the Human Metabolome Database (HMDB)1 currently encompasses 37,229 entries for metabolites reported in human blood. Although the analytical coverage of modern LC–MS platforms enables the detection and identification of 1,000–2,000 plasma metabolites, the number of well-known and regularly detected plasma metabolites is considerably smaller: reference values of 144 plasma metabolites in a healthy population2 and 588 lipids in the National Institute of Standards and Technology’s (NIST) reference plasma sample3 have been reported previously. The plasma metabolome also exhibits vast interindividual and intraindividual variability, which is explained by variation in the microbiome, dietary habits and genetics4. Challenges in consistently reporting the human plasma metabolome arise from interlaboratory variation in non-targeted LC–MS methodologies and varying practices and capabilities in the annotation of the metabolites themselves5.
To construct Plasma Benchmark, three participating laboratories analysed the same in-house pooled plasma and the NIST1950 human reference plasma6 in four analytical modes, including reversed-phase and hydrophilic interaction chromatography in the positive and negative ionization modes followed by data matrix generation in MS-DIAL7. We applied a set of inclusion criteria based on signal-to-noise ratio, relative standard deviation, sample-to-blank ratio, and acquisition of MS/MS data and predicted molecular formula to narrow the number of detected molecular features down to 639 robust molecular features that most probably represent actual plasma metabolites. Nearly 88% of the detected molecular features did not fulfil the signal-to-noise ratio and sample-to-blank ratio criteria, probably due to inherent noise and contaminants in LC–MS data but also because of high variability in the detection of many molecular features across laboratories8 (Fig. 1b). Manual curation of the robust molecular features resulted in 288 unique metabolites, whereas the rest of the molecular features were classified as redundant molecular features and in-source fragments. The inclusion of four analytical modes was crucial for an extensive coverage of metabolites because most were detected in only one mode (Fig. 1b).
期刊介绍:
Nature Metabolism is a peer-reviewed scientific journal that covers a broad range of topics in metabolism research. It aims to advance the understanding of metabolic and homeostatic processes at a cellular and physiological level. The journal publishes research from various fields, including fundamental cell biology, basic biomedical and translational research, and integrative physiology. It focuses on how cellular metabolism affects cellular function, the physiology and homeostasis of organs and tissues, and the regulation of organismal energy homeostasis. It also investigates the molecular pathophysiology of metabolic diseases such as diabetes and obesity, as well as their treatment. Nature Metabolism follows the standards of other Nature-branded journals, with a dedicated team of professional editors, rigorous peer-review process, high standards of copy-editing and production, swift publication, and editorial independence. The journal has a high impact factor, has a certain influence in the international area, and is deeply concerned and cited by the majority of scholars.