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Rischke S, Gurke R, Bennett A, Behrens F, Geisslinger G, Hahnefeld L. ALISTER - Application for lipid stability evaluation and research. Clin Chim Acta 2024; 557:117858. [PMID: 38492658 DOI: 10.1016/j.cca.2024.117858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/30/2024] [Accepted: 03/03/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND AND AIMS In lipidomic and metabolomic studies, pre-analytical pitfalls enhance the risk of misusing resources such as time and money, as samples that are analyzed may not yield accurate or reliable data due to poor sample handling. Guidance and pre-analytic know-how are necessary for translation of omics technologies into routine clinical testing. The present work aims to enable decision making regarding sample stability in every phase of lipidomics- and metabolomics-centered studies. MATERIALS AND METHODS Data of multiple pre-analytic studies were aggregated into a database. Flexible approaches for evaluating these data were implemented in an RShiny-based web-application, tailored towards broad applicability in clinical and bioanalytic research. RESULTS Our "Application for lipid stability evaluation & research" - ALISTER facilitates decision making on blood sample stability during lipidomic and metabolomic studies, such as biomarker research, analysis of biobank samples and clinical testing. The interactive tool gives sampling recommendations when planning sample collection or aids in the assessment of sample quality of experiments retrospectively. CONCLUSION ALISTER is available for use under https://itmp.shinyapps.io/alister/. The application enables and simplifies data-driven decision making concerning pre-analytic blood sample handling and fits the needs of clinical investigations from multiple perspectives.
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Affiliation(s)
- Samuel Rischke
- Goethe University Frankfurt, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Robert Gurke
- Goethe University Frankfurt, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - Alexandre Bennett
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - Frank Behrens
- Goethe University Frankfurt, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany; Goethe University Frankfurt, University Hospital, Department of Rheumatology, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Gerd Geisslinger
- Goethe University Frankfurt, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - Lisa Hahnefeld
- Goethe University Frankfurt, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.
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Wang Q, Hoene M, Hu C, Fritsche L, Ahrends R, Liebisch G, Ekroos K, Fritsche A, Birkenfeld AL, Liu X, Zhao X, Li Q, Su B, Peter A, Xu G, Lehmann R. Ex vivo instability of lipids in whole blood: preanalytical recommendations for clinical lipidomics studies. J Lipid Res 2023; 64:100378. [PMID: 37087100 PMCID: PMC10208886 DOI: 10.1016/j.jlr.2023.100378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 04/24/2023] Open
Abstract
Reliability, robustness, and interlaboratory comparability of quantitative measurements is critical for clinical lipidomics studies. Lipids' different ex vivo stability in blood bears the risk of misinterpretation of data. Clear recommendations for the process of blood sample collection are required. We studied by UHPLC-high resolution mass spectrometry, as part of the "Preanalytics interest group" of the International Lipidomics Society, the stability of 417 lipid species in EDTA whole blood after exposure to either 4°C, 21°C, or 30°C at six different time points (0.5 h-24 h) to cover common daily routine conditions in clinical settings. In total, >800 samples were analyzed. 325 and 288 robust lipid species resisted 24 h exposure of EDTA whole blood to 21°C or 30°C, respectively. Most significant instabilities were detected for FA, LPE, and LPC. Based on our data, we recommend cooling whole blood at once and permanent. Plasma should be separated within 4 h, unless the focus is solely on robust lipids. Lists are provided to check the ex vivo (in)stability of distinct lipids and potential biomarkers of interest in whole blood. To conclude, our results contribute to the international efforts towards reliable and comparable clinical lipidomics data paving the way to the proper diagnostic application of distinct lipid patterns or lipid profiles in the future.
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Affiliation(s)
- Qingqing Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian, China; University of Chinese Academy of Sciences, Beijing, China
| | - Miriam Hoene
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
| | - Chunxiu Hu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian, China
| | - Louise Fritsche
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany; German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Robert Ahrends
- Department of Analytical Chemistry, University of Vienna, Vienna, Austria
| | - Gerhard Liebisch
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Kim Ekroos
- Lipidomics Consulting Ltd., Espoo, Finland
| | - Andreas Fritsche
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany; German Center for Diabetes Research (DZD), Tübingen, Germany; Internal Medicine 4, University Hospital Tuebingen, Tuebingen, Germany
| | - Andreas L Birkenfeld
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany; German Center for Diabetes Research (DZD), Tübingen, Germany; Internal Medicine 4, University Hospital Tuebingen, Tuebingen, Germany
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian, China
| | - Xinjie Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian, China
| | - Qi Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian, China
| | - Benzhe Su
- School of Computer Science & Technology, Dalian University of Technology, Dalian, China
| | - Andreas Peter
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany; German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian, China.
| | - Rainer Lehmann
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany; German Center for Diabetes Research (DZD), Tübingen, Germany.
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Safari F, Kehelpannala C, Safarchi A, Batarseh AM, Vafaee F. Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis. Cancers (Basel) 2023; 15:2780. [PMID: 37345117 DOI: 10.3390/cancers15102780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/02/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023] Open
Abstract
Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.
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Affiliation(s)
- Fatemeh Safari
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
| | - Cheka Kehelpannala
- BCAL Diagnostics Ltd., Suite 506, 50 Clarence St, Sydney, NSW 2000, Australia
- BCAL Dx, The University of Sydney, Sydney Knowledge Hub, Merewether Building, Sydney, NSW 2006, Australia
| | - Azadeh Safarchi
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- Microbiomes for One Systems Health, Health and Biosecurity, CSIRO, Westmead, NSW 2145, Australia
| | - Amani M Batarseh
- BCAL Diagnostics Ltd., Suite 506, 50 Clarence St, Sydney, NSW 2000, Australia
- BCAL Dx, The University of Sydney, Sydney Knowledge Hub, Merewether Building, Sydney, NSW 2006, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- UNSW Data Science Hub (uDASH), University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- OmniOmics.ai Pty Ltd., Sydney, NSW 2035, Australia
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DeBalsi KL, Newman JH, Sommerville LJ, Phillips JA, Hamid R, Cogan J, Fessel JP, Evans AM, Network UD, Kennedy AD. A Case Study of Dysfunctional Nicotinamide Metabolism in a 20-Year-Old Male. Metabolites 2023; 13:399. [PMID: 36984839 PMCID: PMC10055858 DOI: 10.3390/metabo13030399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/24/2023] [Accepted: 03/04/2023] [Indexed: 03/10/2023] Open
Abstract
We present a case study of a 20-year-old male with an unknown neurodegenerative disease who was referred to the Undiagnosed Diseases Network Vanderbilt Medical Center site. A previous metabolic panel showed that the patient had a critical deficiency in nicotinamide intermediates that are generated during the biosynthesis of NAD(H). We followed up on these findings by evaluating the patient's ability to metabolize nicotinamide. We performed a global metabolic profiling analysis of plasma samples that were collected: (1) under normal fed conditions (baseline), (2) after the patient had fasted, and (3) after he was challenged with a 500 mg nasogastric tube bolus of nicotinamide following the fast. Our findings showed that the patient's nicotinamide N-methyltransferase (NNMT), a key enzyme in NAD(H) biosynthesis and methionine metabolism, was not functional under normal fed or fasting conditions but was restored in response to the nicotinamide challenge. Altered levels of metabolites situated downstream of NNMT and in neighboring biochemical pathways provided further evidence of a baseline defect in NNMT activity. To date, this is the only report of a critical defect in NNMT activity manifesting in adulthood and leading to neurodegenerative disease. Altogether, this study serves as an important reference in the rare disease literature and also demonstrates the utility of metabolomics as a diagnostic tool for uncharacterized metabolic diseases.
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Affiliation(s)
| | - John H. Newman
- Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | | | | | - Rizwan Hamid
- Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Joy Cogan
- Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Joshua P. Fessel
- National Institutes of Health, National Center for Advancing Translational Sciences, Bethesda, MD 20892, USA
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Hertzog A, Selvanathan A, Devanapalli B, Ho G, Bhattacharya K, Tolun AA. A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism. Transl Pediatr 2022; 11:1704-1716. [PMID: 36345452 PMCID: PMC9636448 DOI: 10.21037/tp-22-105] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 08/23/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs. METHODS Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique. KEY CONTENT AND FINDINGS Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible. CONCLUSIONS When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.
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Affiliation(s)
- Ashley Hertzog
- NSW Biochemical Genetics Service, The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Arthavan Selvanathan
- Genetic Metabolic Disorders Service, The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Beena Devanapalli
- NSW Biochemical Genetics Service, The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Gladys Ho
- Sydney Genome Diagnostics, The Children's Hospital at Westmead, Westmead, NSW, Australia.,Specialty of Genomic Medicine, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Kaustuv Bhattacharya
- Genetic Metabolic Disorders Service, The Children's Hospital at Westmead, Westmead, NSW, Australia.,Specialty of Genomic Medicine, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Adviye Ayper Tolun
- NSW Biochemical Genetics Service, The Children's Hospital at Westmead, Westmead, NSW, Australia.,Specialty of Genomic Medicine, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
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Bliziotis NG, Kluijtmans LAJ, Tinnevelt GH, Reel P, Reel S, Langton K, Robledo M, Pamporaki C, Pecori A, Van Kralingen J, Tetti M, Engelke UFH, Erlic Z, Engel J, Deutschbein T, Nölting S, Prejbisz A, Richter S, Adamski J, Januszewicz A, Ceccato F, Scaroni C, Dennedy MC, Williams TA, Lenzini L, Gimenez-Roqueplo AP, Davies E, Fassnacht M, Remde H, Eisenhofer G, Beuschlein F, Kroiss M, Jefferson E, Zennaro MC, Wevers RA, Jansen JJ, Deinum J, Timmers HJLM. Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension. Metabolites 2022; 12:679. [PMID: 35893246 PMCID: PMC9394285 DOI: 10.3390/metabo12080679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/17/2022] [Accepted: 07/11/2022] [Indexed: 11/24/2022] Open
Abstract
Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.
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Affiliation(s)
- Nikolaos G. Bliziotis
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Leo A. J. Kluijtmans
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Gerjen H. Tinnevelt
- Department of Analytical Chemistry, Institute for Molecules and Materials, Radboud University, 6500 HB Nijmegen, The Netherlands; (G.H.T.); (J.J.J.)
| | - Parminder Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, UK; (P.R.); (S.R.); (E.J.)
| | - Smarti Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, UK; (P.R.); (S.R.); (E.J.)
| | - Katharina Langton
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (K.L.); (C.P.); (G.E.)
| | - Mercedes Robledo
- Hereditary Endocrine Cancer Group, Spanish National Cancer Research Centre (CNIO), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 28029 Madrid, Spain;
| | - Christina Pamporaki
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (K.L.); (C.P.); (G.E.)
| | - Alessio Pecori
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, 10124 Torino, Italy; (A.P.); (M.T.); (T.A.W.)
| | - Josie Van Kralingen
- British Heart Foundation Glasgow Cardiovascular Research Centre (BHF GCRC), Institute of Cardiovascular & Medical Sciences (ICAMS), University of Glasgow, Glasgow G12 8TA, UK; (J.V.K.); (E.D.)
| | - Martina Tetti
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, 10124 Torino, Italy; (A.P.); (M.T.); (T.A.W.)
| | - Udo F. H. Engelke
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Zoran Erlic
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ), University of Zurich (UZH), 8006 Zurich, Switzerland; (Z.E.); (F.B.)
| | - Jasper Engel
- Biometris, Wageningen University & Research, 6708 PB Wageningen, The Netherlands;
| | - Timo Deutschbein
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany; (T.D.); (M.F.); (H.R.); (M.K.)
- Medicover Oldenburg MVZ, 26122 Oldenburg, Germany
| | - Svenja Nölting
- Department of Medicine IV, University Hospital, LMU Munich, 80336 Munich, Germany;
| | - Aleksander Prejbisz
- Department of Hypertension, Institute of Cardiology, 04-628 Warsaw, Poland; (A.P.); (A.J.)
| | - Susan Richter
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, 01307 Dresden, Germany;
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Genome Analysis Center, Helmholtz Center München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
- Institute of Experimental Genetics, Technical University München, 85350 Freising-Weihenstephan, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 119077 Singapore, Singapore
| | - Andrzej Januszewicz
- Department of Hypertension, Institute of Cardiology, 04-628 Warsaw, Poland; (A.P.); (A.J.)
| | - Filippo Ceccato
- Endocrinology Unit, Department of Medicine DIMED, University-Hospital of Padova, 35128 Padova, Italy; (F.C.); (C.S.)
| | - Carla Scaroni
- Endocrinology Unit, Department of Medicine DIMED, University-Hospital of Padova, 35128 Padova, Italy; (F.C.); (C.S.)
| | - Michael C. Dennedy
- The Discipline of Pharmacology and Therapeutics, School of Medicine, National University of Ireland, H91 CF50 Galway, Ireland;
| | - Tracy A. Williams
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, 10124 Torino, Italy; (A.P.); (M.T.); (T.A.W.)
| | - Livia Lenzini
- Department of Medicine-DIMED, Emergency and Hypertension Unit, University of Padova, University Hospital, 35126 Padova, Italy;
| | - Anne-Paule Gimenez-Roqueplo
- INSERM, PARCC, Université de Paris, 75015 Paris, France; (A.-P.G.-R.); (M.-C.Z.)
- Service de Genétique, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 75015 Paris, France
| | - Eleanor Davies
- British Heart Foundation Glasgow Cardiovascular Research Centre (BHF GCRC), Institute of Cardiovascular & Medical Sciences (ICAMS), University of Glasgow, Glasgow G12 8TA, UK; (J.V.K.); (E.D.)
| | - Martin Fassnacht
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany; (T.D.); (M.F.); (H.R.); (M.K.)
- Core Unit Clinical Mass Spectrometry, University Hospital Würzburg, 97080 Würzburg, Germany
- Comprehensive Cancer Center Mainfranken, Würzburg University, 97070 Würzburg, Germany
| | - Hanna Remde
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany; (T.D.); (M.F.); (H.R.); (M.K.)
| | - Graeme Eisenhofer
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (K.L.); (C.P.); (G.E.)
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, 01307 Dresden, Germany;
| | - Felix Beuschlein
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ), University of Zurich (UZH), 8006 Zurich, Switzerland; (Z.E.); (F.B.)
- Department of Medicine IV, University Hospital, LMU Munich, 80336 Munich, Germany;
| | - Matthias Kroiss
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany; (T.D.); (M.F.); (H.R.); (M.K.)
- Department of Medicine IV, University Hospital, LMU Munich, 80336 Munich, Germany;
- Core Unit Clinical Mass Spectrometry, University Hospital Würzburg, 97080 Würzburg, Germany
- Comprehensive Cancer Center Mainfranken, Würzburg University, 97070 Würzburg, Germany
| | - Emily Jefferson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, UK; (P.R.); (S.R.); (E.J.)
- Institute of Health & Wellbeing, Glasgow University, Glasgow G12 8RZ, UK
| | - Maria-Christina Zennaro
- INSERM, PARCC, Université de Paris, 75015 Paris, France; (A.-P.G.-R.); (M.-C.Z.)
- Service de Genétique, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 75015 Paris, France
| | - Ron A. Wevers
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Jeroen J. Jansen
- Department of Analytical Chemistry, Institute for Molecules and Materials, Radboud University, 6500 HB Nijmegen, The Netherlands; (G.H.T.); (J.J.J.)
| | - Jaap Deinum
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Henri J. L. M. Timmers
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
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Plasma Oxylipins and Their Precursors Are Strongly Associated with COVID-19 Severity and with Immune Response Markers. Metabolites 2022; 12:metabo12070619. [PMID: 35888743 PMCID: PMC9319897 DOI: 10.3390/metabo12070619] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 12/13/2022] Open
Abstract
COVID-19 is characterised by a dysregulated immune response, that involves signalling lipids acting as mediators of the inflammatory process along the innate and adaptive phases. To promote understanding of the disease biochemistry and provide targets for intervention, we applied a range of LC-MS platforms to analyse over 100 plasma samples from patients with varying COVID-19 severity and with detailed clinical information on inflammatory responses (>30 immune markers). The second publication in a series reports the results of quantitative LC-MS/MS profiling of 63 small lipids including oxylipins, free fatty acids, and endocannabinoids. Compared to samples taken from ward patients, intensive care unit (ICU) patients had 2−4-fold lower levels of arachidonic acid (AA) and its cyclooxygenase-derived prostanoids, as well as lipoxygenase derivatives, exhibiting negative correlations with inflammation markers. The same derivatives showed 2−5-fold increases in recovering ward patients, in paired comparison to early hospitalisation. In contrast, ICU patients showed elevated levels of oxylipins derived from poly-unsaturated fatty acids (PUFA) by non-enzymatic peroxidation or activity of soluble epoxide hydrolase (sEH), and these oxylipins positively correlated with markers of macrophage activation. The deficiency in AA enzymatic products and the lack of elevated intermediates of pro-resolving mediating lipids may result from the preference of alternative metabolic conversions rather than diminished stores of PUFA precursors. Supporting this, ICU patients showed 2-to-11-fold higher levels of linoleic acid (LA) and the corresponding fatty acyl glycerols of AA and LA, all strongly correlated with multiple markers of excessive immune response. Our results suggest that the altered oxylipin metabolism disrupts the expected shift from innate immune response to resolution of inflammation.
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Knutti N, Neugebauer S, Scherr F, Mathay C, Marchese M, Henry E, Palm J, Betsou F, Kiehntopf M. Introduction of BD Vacutainer ® Barricor™ tubes in clinical biobanking and application of amino acid and cytokine quality indicators to Barricor plasma. Clin Chem Lab Med 2022; 60:689-700. [PMID: 35073617 DOI: 10.1515/cclm-2021-0899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/10/2022] [Indexed: 01/04/2024]
Abstract
OBJECTIVES The use of BD Vacutainer® Barricor™ tubes (BAR) can reduce turnaround time (TAT) and improve separation of plasma from cellular components using a specific mechanical separator. Concentrations of amino acids (AAs) and cytokines, known to be labile during pre-analytical time delays, were compared in heparin (BAR, BD Heparin standard tube [PST]), EDTA and serum gel tubes (SER) to validate previously identified quality indicators (QIs) in BAR. METHODS Samples of healthy individuals (n=10) were collected in heparin, EDTA and SER tubes and exposed to varying pre- and post-centrifugation delays at room temperature (RT). Cytokines (interleukin [IL]-8, IL-16 and sCD40L) were analyzed by enzyme-linked immunosorbent assay (ELISA) and AAs were characterized by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). RESULTS All QIs, AAs/AA ratio and cytokines increased during prolonged blood storage in heparin plasma (PST, BAR) and SER tubes. Comparison of 53 h/1 h pre-centrifugation delay resulted in an increase in taurine (Tau) and glutamic acid (Glu) concentrations by more than three times, soluble CD40L increased by 13.6, 9.2 and 4.3 fold in PST, BAR-CTRL and BAR-FAST, and IL-8 increased even more by 112.8 (PST), 266.1 (BAR-CTRL), 268.1 (BAR-FAST) and 70.0 (SER) fold, respectively. Overall, compared to prolonged blood storage, effects of post-centrifugation delays were less pronounced in all tested materials. CONCLUSIONS BAR tubes are compatible with the use of several established QIs and can therefore be used in clinical biobanking to reduce pre-analytical TAT without compromising QIs and thus pre-analytical sample quality analysis.
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Affiliation(s)
- Nadine Knutti
- Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Jena, Germany
| | - Sophie Neugebauer
- Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Jena, Germany
| | - Franziska Scherr
- Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Jena, Germany
| | - Conny Mathay
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - Monica Marchese
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - Estelle Henry
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - Julia Palm
- Institute of Medical Statistics, Computer and Data Science, Jena University Hospital, Jena, Germany
| | - Fay Betsou
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
- Laboratoire National de Santé (LNS), Dudelange, Luxembourg
| | - Michael Kiehntopf
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, Jena, Germany
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9
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Ford L, Mitchell M, Wulff J, Evans A, Kennedy A, Elsea S, Wittmann B, Toal D. Clinical metabolomics for inborn errors of metabolism. Adv Clin Chem 2022; 107:79-138. [PMID: 35337606 DOI: 10.1016/bs.acc.2021.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.
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Affiliation(s)
- Lisa Ford
- Metabolon, Inc., Morrisville, NC, United States
| | | | - Jacob Wulff
- Metabolon, Inc., Morrisville, NC, United States
| | - Annie Evans
- Metabolon, Inc., Morrisville, NC, United States
| | | | - Sarah Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | | | - Douglas Toal
- Metabolon, Inc., Morrisville, NC, United States.
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10
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Zheng R, Brunius C, Shi L, Zafar H, Paulson L, Landberg R, Naluai ÅT. Prediction and evaluation of the effect of pre-centrifugation sample management on the measurable untargeted LC-MS plasma metabolome. Anal Chim Acta 2021; 1182:338968. [PMID: 34602206 DOI: 10.1016/j.aca.2021.338968] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 12/16/2022]
Abstract
Optimal handling is the most important means to ensure adequate sample quality. We aimed to investigate whether pre-centrifugation delay time and temperature could be accurately predicted and to what extent variability induced by pre-centrifugation management can be adjusted for. We used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to predict and evaluate the influence of pre-centrifugation temperature and delayed time on plasma samples. Pre-centrifugation temperature (4, 25 and 37 °C; classification rate 87%) and time (5-210 min; Q2 = 0.82) were accurately predicted using Random Forest (RF). Metabolites uniquely reflecting temperature and temperature-time interactions were discovered using a combination of RF and generalized linear models. Time-related metabolite profiles suggested a perturbed stability of the metabolome at all temperatures in the investigated time period (5-210 min), and the variation at 4 °C was observed in particular before 90 min. Fourteen and eight metabolites were selected and validated for accurate prediction of pre-centrifugation temperature (classification rate 94%) and delay time (Q2 = 0.90), respectively. In summary, the metabolite profile was rapidly affected by pre-centrifugation delay at all temperatures and thus the pre-centrifugation delay should be as short as possible for metabolomics analysis. The metabolite panels provided accurate predictions of pre-centrifugation delay time and temperature in healthy individuals in a separate validation sample. Such predictions could potentially be useful for assessing legacy samples where relevant metadata is lacking. However, validation in larger populations and different phenotypes, including disease states, is needed.
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Affiliation(s)
- Rui Zheng
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Carl Brunius
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Chalmers Mass Spectrometry Infrastructure, Chalmers University of Technology, Gothenburg, Sweden
| | - Lin Shi
- Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi' an, China.
| | - Huma Zafar
- Biobank West, Sahlgrenska University Hospital, Region Västra Götaland, Sweden
| | - Linda Paulson
- Biobank West, Sahlgrenska University Hospital, Region Västra Götaland, Sweden
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Åsa Torinsson Naluai
- Biobank West, Sahlgrenska University Hospital, Region Västra Götaland, Sweden; Institute of Biomedicine, Biobank Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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11
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Heiling S, Knutti N, Scherr F, Geiger J, Weikert J, Rose M, Jahns R, Ceglarek U, Scherag A, Kiehntopf M. Metabolite Ratios as Quality Indicators for Pre-Analytical Variation in Serum and EDTA Plasma. Metabolites 2021; 11:638. [PMID: 34564454 PMCID: PMC8465943 DOI: 10.3390/metabo11090638] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/18/2022] Open
Abstract
In clinical diagnostics and research, blood samples are one of the most frequently used materials. Nevertheless, exploring the chemical composition of human plasma and serum is challenging due to the highly dynamic influence of pre-analytical variation. A prominent example is the variability in pre-centrifugation delay (time-to-centrifugation; TTC). Quality indicators (QI) reflecting sample TTC are of utmost importance in assessing sample history and resulting sample quality, which is essential for accurate diagnostics and conclusive, reproducible research. In the present study, we subjected human blood to varying TTCs at room temperature prior to processing for plasma or serum preparation. Potential sample QIs were identified by Ultra high pressure liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) based metabolite profiling in samples from healthy volunteers (n = 10). Selected QIs were validated by a targeted MS/MS approach in two independent sets of samples from patients (n = 40 and n = 70). In serum, the hypoxanthine/guanosine (HG) and hypoxanthine/inosine (HI) ratios demonstrated high diagnostic performance (Sensitivity/Specificity > 80%) for the discrimination of samples with a TTC > 1 h. We identified several eicosanoids, such as 12-HETE, 15-(S)-HETE, 8-(S)-HETE, 12-oxo-HETE, (±)13-HODE and 12-(S)-HEPE as QIs for a pre-centrifugation delay > 2 h. 12-HETE, 12-oxo-HETE, 8-(S)-HETE, and 12-(S)-HEPE, and the HI- and HG-ratios could be validated in patient samples.
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Affiliation(s)
- Sven Heiling
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
| | - Nadine Knutti
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
| | - Franziska Scherr
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
| | - Jörg Geiger
- Interdisciplinary Bank of Biological Material and Data Würzburg (IBDW), Straubmühlweg 2a, Haus A9, 97078 Würzburg, Germany; (J.G.); (R.J.)
| | - Juliane Weikert
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.W.); (U.C.)
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, 04103 Leipzig, Germany
| | - Michael Rose
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
| | - Roland Jahns
- Interdisciplinary Bank of Biological Material and Data Würzburg (IBDW), Straubmühlweg 2a, Haus A9, 97078 Würzburg, Germany; (J.G.); (R.J.)
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.W.); (U.C.)
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, 04103 Leipzig, Germany
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Bachstrasse 18, 07743 Jena, Germany;
| | - Michael Kiehntopf
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
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12
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Li X, Milosavljevic A, Elsea SH, Wang CC, Scaglia F, Syngelaki A, Nicolaides KH, Poon LC. Effective Aspirin Treatment of Women at Risk for Preeclampsia Delays the Metabolic Clock of Gestation. Hypertension 2021; 78:1398-1410. [PMID: 34225470 DOI: 10.1161/hypertensionaha.121.17448] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Preeclampsia, characterized by the onset of hypertension with significant proteinuria after 20 weeks' gestation, is one of the leading causes of maternal and perinatal morbidity and mortality. Prophylactic low-dose aspirin treatment reduces the rate of preterm preeclampsia in high-risk women, but a significant proportion still develops preeclampsia. The mechanism of the prophylactic response is unknown. Here, the untargeted metabolomics analysis of 144 plasma samples from high-risk pregnant women before (11-13 weeks) and after (20-23 weeks) aspirin/placebo treatment elucidated metabolic effects of aspirin and metabolic differences potentially associated with the variation of the treatment response. We demonstrated that aspirin treatment resulted in a strong drug-associated metabolomics signature and that the preeclamptic or nonpreeclamptic outcome in response to treatment was significantly associated with the level of internal aspirin exposure ascertained from metabolomics data (t test, P=0.0083). Comparing women with and without preeclampsia after aspirin treatment, differences in 73 metabolites were detected, some of which involve pathways whose regulation is of importance in pregnancy and placental functions, such as glycerophospholipids metabolism, polyunsaturated fatty acid metabolism, and steroid hormone biosynthesis. To further examine the hypothesis that aspirin delays gestational age advancement and thus the onset of preeclampsia, we constructed a metabolic clock on pretreatment and placebo-treated samples that estimated gestational age with high accuracy and found that aspirin significantly decelerated metabolic gestational age by 1.27 weeks (95% CI, 0.66-1.88 weeks), and partially reversed one-fourth of the metabolites changed over gestational age advancement, suggesting that aspirin treatment slowed down the metabolic clock of gestation.
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Affiliation(s)
- Xiqi Li
- Molecular and Human Genetics Department, Baylor College of Medicine, Houston, TX (X.L., A.M., S.H.E., F.S.)
| | - Aleksandar Milosavljevic
- Molecular and Human Genetics Department, Baylor College of Medicine, Houston, TX (X.L., A.M., S.H.E., F.S.)
| | - Sarah H Elsea
- Molecular and Human Genetics Department, Baylor College of Medicine, Houston, TX (X.L., A.M., S.H.E., F.S.)
| | - Chi Chiu Wang
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong. (C.C.W., L.C.P.).,Li Ka Shing Institute of Health Sciences, School of Biomedical Sciences, Chinese University of Hong Kong-Sichuan University Joint Laboratory in Reproductive Medicine, The Chinese University of Hong Kong. (C.C.W., K.H.N.)
| | - Fernando Scaglia
- Molecular and Human Genetics Department, Baylor College of Medicine, Houston, TX (X.L., A.M., S.H.E., F.S.).,Texas Children's Hospital, Houston (F.S.).,Joint BCM-CUHK Center of Medical Genetics, Prince of Wales Hospital, Shatin, Hong Kong (F.S.)
| | - Argyro Syngelaki
- Fetal Medicine Research Institute, Harris Birthright Centre, King's College Hospital, London, United Kingdom (A.S.)
| | - Kypros H Nicolaides
- Li Ka Shing Institute of Health Sciences, School of Biomedical Sciences, Chinese University of Hong Kong-Sichuan University Joint Laboratory in Reproductive Medicine, The Chinese University of Hong Kong. (C.C.W., K.H.N.)
| | - Liona C Poon
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong. (C.C.W., L.C.P.)
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From bedside to bench-practical considerations to avoid pre-analytical pitfalls and assess sample quality for high-resolution metabolomics and lipidomics analyses of body fluids. Anal Bioanal Chem 2021; 413:5567-5585. [PMID: 34159398 PMCID: PMC8410705 DOI: 10.1007/s00216-021-03450-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/24/2021] [Accepted: 05/31/2021] [Indexed: 11/22/2022]
Abstract
The stability of lipids and other metabolites in human body fluids ranges from very stable over several days to very unstable within minutes after sample collection. Since the high-resolution analytics of metabolomics and lipidomics approaches comprise all these compounds, the handling of body fluid samples, and thus the pre-analytical phase, is of utmost importance to obtain valid profiling data. This phase consists of two parts, sample collection in the hospital (“bedside”) and sample processing in the laboratory (“bench”). For sample quality, the apparently simple steps in the hospital are much more critical than the “bench” side handling, where (bio)analytical chemists focus on highly standardized processing for high-resolution analysis under well-controlled conditions. This review discusses the most critical pre-analytical steps for sample quality from patient preparation; collection of body fluids (blood, urine, cerebrospinal fluid) to sample handling, transport, and storage in freezers; and subsequent thawing using current literature, as well as own investigations and practical experiences in the hospital. Furthermore, it provides guidance for (bio)analytical chemists to detect and prevent potential pre-analytical pitfalls at the “bedside,” and how to assess the quality of already collected body fluid samples. A knowledge base is provided allowing one to decide whether or not the sample quality is acceptable for its intended use in distinct profiling approaches and to select the most suitable samples for high-resolution metabolomics and lipidomics investigations.
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14
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Ford L, Kennedy AD, Goodman KD, Pappan KL, Evans AM, Miller LAD, Wulff JE, Wiggs BR, Lennon JJ, Elsea S, Toal DR. Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism. J Appl Lab Med 2021; 5:342-356. [PMID: 32445384 DOI: 10.1093/jalm/jfz026] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 09/09/2019] [Indexed: 01/29/2023]
Abstract
BACKGROUND The application of whole-exome sequencing for the diagnosis of genetic disease has paved the way for systems-based approaches in the clinical laboratory. Here, we describe a clinical metabolomics method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. By simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways. METHODS We conducted a single- and multi-day precision study on hundreds of metabolites in human plasma on 4, multi-arm, high-throughput metabolomics platforms. RESULTS The average laboratory coefficient of variation (CV) on the 4 platforms was between 9.3 and 11.5% (median, 6.5-8.4%), average inter-assay CV on the 4 platforms ranged from 9.9 to 12.6% (median, 7.0-8.3%) and average intra-assay CV on the 4 platforms ranged from 5.7 to 6.9% (median, 3.5-4.4%). In relation to patient sample testing, the precision of multiple biomarkers associated with IEM disorders showed CVs that ranged from 0.2 to 11.0% across 4 analytical batches. CONCLUSIONS This evaluation describes single and multi-day precision across 4 identical metabolomics platforms, comprised each of 4 independent method arms, and reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, providing evidence that the method is robust and reproducible for the screening of patients with inborn errors of metabolism.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Sarah Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
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15
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Kennedy AD, Ford L, Wittmann B, Conner J, Wulff J, Mitchell M, Evans AM, Toal DR. Global biochemical analysis of plasma, serum and whole blood collected using various anticoagulant additives. PLoS One 2021; 16:e0249797. [PMID: 33831088 PMCID: PMC8031419 DOI: 10.1371/journal.pone.0249797] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 03/25/2021] [Indexed: 01/23/2023] Open
Abstract
Introduction Analysis of blood for the evaluation of clinically relevant biomarkers requires precise collection and sample handling by phlebotomists and laboratory staff. An important consideration for the clinical application of metabolomics are the different anticoagulants utilized for sample collection. Most studies that have characterized differences in metabolite levels in various blood collection tubes have focused on single analytes. We define analyte levels on a global metabolomics platform following blood sampling using five different, but commonly used, clinical laboratory blood collection tubes (i.e., plasma anticoagulated with either EDTA, lithium heparin or sodium citrate, along with no additive (serum), and EDTA anticoagulated whole blood). Methods Using an untargeted metabolomics platform we analyzed five sample types after all had been collected and stored at -80°C. The biochemical composition was determined and differences between the samples established using matched-pair t-tests. Results We identified 1,117 biochemicals across all samples and detected a mean of 1,036 in the sample groups. Compared to the levels of metabolites in EDTA plasma, the number of biochemicals present at statistically significant different levels (p<0.05) ranged from 452 (serum) to 917 (whole blood). Several metabolites linked to screening assays for rare diseases including acylcarnitines, bilirubin and heme metabolites, nucleosides, and redox balance metabolites varied significantly across the sample collection types. Conclusions Our study highlights the widespread effects and importance of using consistent additives for assessing small molecule levels in clinical metabolomics. The biochemistry that occurs during the blood collection process creates a reproducible signal that can identify specimens collected with different anticoagulants in metabolomic studies. Impact statement In this manuscript, normal/healthy donors had peripheral blood collected using multiple anticoagulants as well as serum during a fasted blood draw. Global metabolomics is a new technology being utilized to draw clinical conclusions and we interrogated the effects of different anticoagulants on the levels of biochemicals from each of the donors. Characterizing the effects of the anticoagulants on biochemical levels will help researchers leverage the information using global metabolomics in order to make conclusions regarding important disease biomarkers.
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Affiliation(s)
- Adam D. Kennedy
- Metabolon, Morrisville, North Carolina, United States of America
- * E-mail:
| | - Lisa Ford
- Metabolon, Morrisville, North Carolina, United States of America
| | - Bryan Wittmann
- Metabolon, Morrisville, North Carolina, United States of America
| | - Jesse Conner
- Metabolon, Morrisville, North Carolina, United States of America
| | - Jacob Wulff
- Metabolon, Morrisville, North Carolina, United States of America
| | - Matthew Mitchell
- Metabolon, Morrisville, North Carolina, United States of America
| | - Anne M. Evans
- Metabolon, Morrisville, North Carolina, United States of America
| | - Douglas R. Toal
- Metabolon, Morrisville, North Carolina, United States of America
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16
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Goodman K, Mitchell M, Evans AM, Miller LAD, Ford L, Wittmann B, Kennedy AD, Toal D. Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics. Metabolomics 2021; 17:31. [PMID: 33704583 DOI: 10.1007/s11306-021-01782-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/26/2021] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Clinical metabolomics has utility as a screen for inborn errors of metabolism (IEM) and variant classification in patients with rare disease. It is important to understand and characterize preanalytical factors that influence assay performance during patient sample testing. OBJECTIVES To evaluate the impact of extended thawing of human EDTA plasma samples on ice prior to extraction as well as repeated freeze-thaw cycling of samples to identify compounds that are unstable prior to metabolomic analysis. METHODS Twenty-four (24) donor EDTA plasma samples were collected and immediately frozen at - 80 °C. Twelve samples were thawed on ice and extracted for analysis at time 0, 2, 4, and 6 h. Twelve other donor samples were repeatedly thawed and frozen up to four times and analyzed at each cycle. Compound levels at each time point/freeze-thaw cycle were compared to the control samples using matched-paired t tests to identify analytes affected by each condition. RESULTS We identified 1026 biochemicals across all samples. Incubation of thawed EDTA plasma samples on ice for up to 6 h resulted in < 1% of biochemicals changing significantly. Freeze-thaw cycles affected a greater percentage of the metabolome; ~ 2% of biochemicals changed after 3 freeze-thaw cycles. CONCLUSIONS Our study highlights that the number and magnitude of these changes are not as widespread as other aspects of improper sample handling. In total, < 3% of the metabolome detected on our clinical metabolomics platform should be disqualified when multiple freeze-thaw cycles or extended thawing at 4 °C are performed on a given sample.
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Affiliation(s)
- Kelli Goodman
- Metabolon, 617 Davis Drive, Suite 100, Morrisville, NC, 27560, USA
| | - Matthew Mitchell
- Metabolon, 617 Davis Drive, Suite 100, Morrisville, NC, 27560, USA
| | - Anne M Evans
- Metabolon, 617 Davis Drive, Suite 100, Morrisville, NC, 27560, USA
| | - Luke A D Miller
- Metabolon, 617 Davis Drive, Suite 100, Morrisville, NC, 27560, USA
| | - Lisa Ford
- Metabolon, 617 Davis Drive, Suite 100, Morrisville, NC, 27560, USA
| | - Bryan Wittmann
- Metabolon, 617 Davis Drive, Suite 100, Morrisville, NC, 27560, USA
| | - Adam D Kennedy
- Metabolon, 617 Davis Drive, Suite 100, Morrisville, NC, 27560, USA
| | - Douglas Toal
- Metabolon, 617 Davis Drive, Suite 100, Morrisville, NC, 27560, USA.
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Effects of processing conditions on stability of immune analytes in human blood. Sci Rep 2020; 10:17328. [PMID: 33060628 PMCID: PMC7566484 DOI: 10.1038/s41598-020-74274-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 09/09/2020] [Indexed: 11/30/2022] Open
Abstract
Minimizing variability in collection and processing of human blood samples for research remains a challenge. Delaying plasma or serum isolation after phlebotomy (processing delay) can cause perturbations of numerous analytes. Thus, a comprehensive understanding of how processing delay affects major endpoints used in human immunology research is necessary. Therefore, we studied how processing delay affects commonly measured cytokines and immune cell populations. We hypothesized that short-term time delays inherent to human research in serum and plasma processing impact commonly studied immunological analytes. Blood from healthy donors was subjected to processing delays commonly encountered in sample collection, and then assayed by 62-plex Luminex panel, 40-parameter mass cytometry panel, and 540,000 transcript expression microarray. Variance for immunological analytes was estimated using each individual’s baseline as a control. In general, short-term processing delay led to small changes in plasma and serum cytokines (range − 10.8 to 43.5%), markers and frequencies of peripheral blood mononuclear cell phenotypes (range 0.19 to 3.54 fold), and whole blood gene expression (stable for > 20 K genes)—with several exceptions described herein. Importantly, we built an open-access web application allowing investigators to estimate the degree of variance expected from processing delay for measurements of interest based on the data reported here.
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18
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González-Domínguez R, González-Domínguez Á, Sayago A, Fernández-Recamales Á. Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics. Metabolites 2020; 10:metabo10060229. [PMID: 32503183 PMCID: PMC7344701 DOI: 10.3390/metabo10060229] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 05/29/2020] [Accepted: 05/31/2020] [Indexed: 12/11/2022] Open
Abstract
Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these technical issues, as well as for maintaining the metabolic integrity of biological samples to ensure that metabolomic profiles are a direct expression of the in vivo biochemical status. This review article provides an updated literature revision of the most important factors related to sample handling and pre-processing that may affect metabolomics results, particularly focusing on the most commonly investigated biofluids in metabolomics, namely blood plasma/serum and urine. Finally, we also provide some general recommendations and best practices aimed to standardize and accurately report all these pre-analytical aspects in metabolomics research.
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Affiliation(s)
- Raúl González-Domínguez
- AgriFood Laboratory, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain; (A.S.); (Á.F.-R.)
- International Campus of Excellence CeiA3, University of Huelva, 21007 Huelva, Spain
- Correspondence: ; Tel.: +34-959219975
| | - Álvaro González-Domínguez
- Department of Pediatrics, Hospital Universitario Puerta del Mar, 11009 Cádiz, Spain;
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cádiz (INiBICA), 11009 Cádiz, Spain
| | - Ana Sayago
- AgriFood Laboratory, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain; (A.S.); (Á.F.-R.)
- International Campus of Excellence CeiA3, University of Huelva, 21007 Huelva, Spain
| | - Ángeles Fernández-Recamales
- AgriFood Laboratory, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain; (A.S.); (Á.F.-R.)
- International Campus of Excellence CeiA3, University of Huelva, 21007 Huelva, Spain
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19
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Bi H, Guo Z, Jia X, Liu H, Ma L, Xue L. The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies. Metabolomics 2020; 16:68. [PMID: 32451742 DOI: 10.1007/s11306-020-01666-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 03/14/2020] [Indexed: 10/25/2022]
Abstract
BACKGROUND Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results. AIM OF REVIEW In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing. KEY SCIENTIFIC CONCEPTS OF REVIEW Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.
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Affiliation(s)
- Hai Bi
- Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China
| | - Zhengyang Guo
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China
| | - Xiao Jia
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China
- Biobank, Peking University Third Hospital, Beijing, People's Republic of China
| | - Huiying Liu
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, People's Republic of China
| | - Lulin Ma
- Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China.
| | - Lixiang Xue
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China.
- Biobank, Peking University Third Hospital, Beijing, People's Republic of China.
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, People's Republic of China.
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20
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Mordaunt D, Cox D, Fuller M. Metabolomics to Improve the Diagnostic Efficiency of Inborn Errors of Metabolism. Int J Mol Sci 2020; 21:ijms21041195. [PMID: 32054038 PMCID: PMC7072749 DOI: 10.3390/ijms21041195] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 02/08/2020] [Accepted: 02/09/2020] [Indexed: 12/21/2022] Open
Abstract
Early diagnosis of inborn errors of metabolism (IEM)—a large group of congenital disorders—is critical, given that many respond well to targeted therapy. Newborn screening programs successfully capture a proportion of patients enabling early recognition and prompt initiation of therapy. For others, the heterogeneity in clinical presentation often confuses diagnosis with more common conditions. In the absence of family history and following clinical suspicion, the laboratory diagnosis typically begins with broad screening tests to circumscribe specialised metabolite and/or enzyme assays to identify the specific IEM. Confirmation of the biochemical diagnosis is usually achieved by identifying pathogenic genetic variants that will also enable cascade testing for family members. Unsurprisingly, this diagnostic trajectory is too often a protracted and lengthy process resulting in delays in diagnosis and, importantly, therapeutic intervention for these rare conditions is also postponed. Implementation of mass spectrometry technologies coupled with the expanding field of metabolomics is changing the landscape of diagnosing IEM as numerous metabolites, as well as enzymes, can now be measured collectively on a single mass spectrometry-based platform. As the biochemical consequences of impaired metabolism continue to be elucidated, the measurement of secondary metabolites common across groups of IEM will facilitate algorithms to further increase the efficiency of diagnosis.
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Affiliation(s)
- Dylan Mordaunt
- Genetics and Molecular Pathology, SA Pathology at Women’s and Children’s Hospital, 72 King William Road, North Adelaide, SA 5006, Australia; (D.M.); (D.C.)
- School of Medicine, University of Adelaide, Adelaide, SA 5000, Australia
| | - David Cox
- Genetics and Molecular Pathology, SA Pathology at Women’s and Children’s Hospital, 72 King William Road, North Adelaide, SA 5006, Australia; (D.M.); (D.C.)
| | - Maria Fuller
- Genetics and Molecular Pathology, SA Pathology at Women’s and Children’s Hospital, 72 King William Road, North Adelaide, SA 5006, Australia; (D.M.); (D.C.)
- School of Medicine, University of Adelaide, Adelaide, SA 5000, Australia
- Correspondence: ; Tel.: +61-8-8161-6741
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21
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Stevens VL, Hoover E, Wang Y, Zanetti KA. Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review. Metabolites 2019; 9:metabo9080156. [PMID: 31349624 PMCID: PMC6724180 DOI: 10.3390/metabo9080156] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 07/18/2019] [Accepted: 07/19/2019] [Indexed: 01/01/2023] Open
Abstract
Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.
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Affiliation(s)
- Victoria L Stevens
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA.
| | - Elise Hoover
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20850, USA
- PKD Foundation, Kansas City, MO 64131, USA
| | - Ying Wang
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
| | - Krista A Zanetti
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20850, USA.
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22
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Schwarz N, Knutti N, Rose M, Neugebauer S, Geiger J, Jahns R, Klopp N, Illig T, Mathay C, Betsou F, Scherag A, Kiehntopf M. Quality Assessment of the Preanalytical Workflow in Liquid Biobanking: Taurine as a Serum-Specific Quality Indicator for Preanalytical Process Variations. Biopreserv Biobank 2019; 17:458-467. [PMID: 31339743 DOI: 10.1089/bio.2019.0004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The scientific impact of translational biomedical research largely depends on the availability of high-quality biomaterials. However, evidence-based and robust quality indicators (QIs) covering the most relevant preanalytical variations are still lacking. The aim of this study was to identify and validate a QI suitable for assessing time-to-centrifugation (TTC) delays in human liquid biospecimens originating from both healthy and diseased individuals. Serum and plasma samples with varying TTCs were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) in a pilot cohort of healthy individuals to identify a suitable QI candidate. Taurine (TAU), as a TTC QI candidate, was validated in healthy individuals and patients with rheumatologic and cardiologic diseases, considering the (1) preanalytical handling temperature, (2) platelet count, and (3) postcentrifugation delay. For discrimination of high TTC (TTC >60 minutes) from low TTC serum specimens, a probability calculation tool was developed (Triple-T-cutoff-model). TTC-dependent changes in healthy individuals were observed for amino acids, particularly TAU. Validation of the TAU levels in an independent cohort of healthy individuals revealed a time-dependent increase in serum, but not in plasma, for a TTC delay of 30-240 minutes. TAU increases were dependent on the handling temperature and platelet count and volume. By contrast, no changes in TAU concentrations were observed for additional postcentrifugation delays. Validation of TAU and the Triple-T-cutoff-model, in rheumatologic/cardiologic patient collectives, allowed the discrimination of samples with TTC ≤60 min/>60 min with estimated AUROC (area under the receiver operating characteristic curve) values of 89% [78%-100%]/86% [71%-100%] and 91% [79%-100%]/84% [68%-100%], respectively. Considering the preanalytical handling temperature and platelet count and volume, TAU and the Triple-T-cutoff-model represent reliable QIs for TTC >60 minutes in serum samples from healthy individuals and selected rheumatologic/cardiologic patients. However, further studies in larger patient collectives with various diseases are needed to assess the robustness and potential of the QIs presented in this article as biobanking quality assurance/quality control tools to support high-quality biomedical research.
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Affiliation(s)
- Nicolle Schwarz
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, Jena, Germany
| | - Nadine Knutti
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, Jena, Germany
| | - Michael Rose
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, Jena, Germany
| | - Sophie Neugebauer
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, Jena, Germany
| | - Jörg Geiger
- Interdisciplinary Bank of Biomaterials and Data Würzburg (ibdw), Würzburg, Germany
| | - Roland Jahns
- Interdisciplinary Bank of Biomaterials and Data Würzburg (ibdw), Würzburg, Germany
| | - Norman Klopp
- Hannover Unified Biobank (HUB), Hannover, Germany
| | - Thomas Illig
- Hannover Unified Biobank (HUB), Hannover, Germany
| | - Conny Mathay
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - Fay Betsou
- Integrated BioBank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - André Scherag
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany.,Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Michael Kiehntopf
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, Jena, Germany
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23
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Kennedy AD, Wittmann BM, Evans AM, Miller LAD, Toal DR, Lonergan S, Elsea SH, Pappan KL. Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing. JOURNAL OF MASS SPECTROMETRY : JMS 2018; 53:1143-1154. [PMID: 30242936 DOI: 10.1002/jms.4292] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/10/2018] [Accepted: 09/17/2018] [Indexed: 06/08/2023]
Abstract
Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.
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Affiliation(s)
| | | | | | | | | | | | - Sarah H Elsea
- Department of Molecular and Human Genetics and Baylor Genetics, Baylor College of Medicine, Houston, TX, USA
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24
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Ulaszewska MM, Weinert CH, Trimigno A, Portmann R, Andres Lacueva C, Badertscher R, Brennan L, Brunius C, Bub A, Capozzi F, Cialiè Rosso M, Cordero CE, Daniel H, Durand S, Egert B, Ferrario PG, Feskens EJM, Franceschi P, Garcia-Aloy M, Giacomoni F, Giesbertz P, González-Domínguez R, Hanhineva K, Hemeryck LY, Kopka J, Kulling SE, Llorach R, Manach C, Mattivi F, Migné C, Münger LH, Ott B, Picone G, Pimentel G, Pujos-Guillot E, Riccadonna S, Rist MJ, Rombouts C, Rubert J, Skurk T, Sri Harsha PSC, Van Meulebroek L, Vanhaecke L, Vázquez-Fresno R, Wishart D, Vergères G. Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies. Mol Nutr Food Res 2018; 63:e1800384. [PMID: 30176196 DOI: 10.1002/mnfr.201800384] [Citation(s) in RCA: 143] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 07/10/2018] [Indexed: 12/13/2022]
Abstract
The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.
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Affiliation(s)
- Marynka M Ulaszewska
- Department of Food Quality and Nutrition, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Christoph H Weinert
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Alessia Trimigno
- Department of Agricultural and Food Science, University of Bologna, Italy
| | - Reto Portmann
- Method Development and Analytics Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
| | - Cristina Andres Lacueva
- Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Barcelona, Spain. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - René Badertscher
- Method Development and Analytics Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
| | - Lorraine Brennan
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin, Ireland
| | - Carl Brunius
- Department of Biology and Biological Engineering, Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Achim Bub
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Francesco Capozzi
- Department of Agricultural and Food Science, University of Bologna, Italy
| | - Marta Cialiè Rosso
- Dipartimento di Scienza e Tecnologia del Farmaco Università degli Studi di Torino, Turin, Italy
| | - Chiara E Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco Università degli Studi di Torino, Turin, Italy
| | - Hannelore Daniel
- Nutritional Physiology, Technische Universität München, Freising, Germany
| | - Stéphanie Durand
- Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, INRA, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Bjoern Egert
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Paola G Ferrario
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Edith J M Feskens
- Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands
| | - Pietro Franceschi
- Computational Biology Unit, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Mar Garcia-Aloy
- Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Barcelona, Spain. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Franck Giacomoni
- Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, INRA, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Pieter Giesbertz
- Molecular Nutrition Unit, Technische Universität München, Freising, Germany
| | - Raúl González-Domínguez
- Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Barcelona, Spain. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Kati Hanhineva
- Institute of Public Health and Clinical Nutrition, Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Lieselot Y Hemeryck
- Laboratory of Chemical Analysis, Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Joachim Kopka
- Department of Molecular Physiology, Applied Metabolome Analysis, Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Sabine E Kulling
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Rafael Llorach
- Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Barcelona, Spain. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Claudine Manach
- INRA, UMR 1019, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy.,Center Agriculture Food Environment, University of Trento, San Michele all'Adige, Italy
| | - Carole Migné
- Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, INRA, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Linda H Münger
- Food Microbial Systems Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
| | - Beate Ott
- Else Kröner Fresenius Center for Nutritional Medicine, Technical University of Munich, Munich, Germany.,ZIEL Institute for Food and Health, Core Facility Human Studies, Technical University of Munich, Freising, Germany
| | - Gianfranco Picone
- Department of Agricultural and Food Science, University of Bologna, Italy
| | - Grégory Pimentel
- Food Microbial Systems Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
| | - Estelle Pujos-Guillot
- Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, INRA, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Samantha Riccadonna
- Computational Biology Unit, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Manuela J Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Caroline Rombouts
- Laboratory of Chemical Analysis, Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Josep Rubert
- Department of Food Quality and Nutrition, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Thomas Skurk
- Else Kröner Fresenius Center for Nutritional Medicine, Technical University of Munich, Munich, Germany.,ZIEL Institute for Food and Health, Core Facility Human Studies, Technical University of Munich, Freising, Germany
| | - Pedapati S C Sri Harsha
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin, Ireland
| | - Lieven Van Meulebroek
- Laboratory of Chemical Analysis, Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Lynn Vanhaecke
- Laboratory of Chemical Analysis, Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Rosa Vázquez-Fresno
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Canada
| | - David Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Canada
| | - Guy Vergères
- Food Microbial Systems Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
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25
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Burla B, Arita M, Arita M, Bendt AK, Cazenave-Gassiot A, Dennis EA, Ekroos K, Han X, Ikeda K, Liebisch G, Lin MK, Loh TP, Meikle PJ, Orešič M, Quehenberger O, Shevchenko A, Torta F, Wakelam MJO, Wheelock CE, Wenk MR. MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines. J Lipid Res 2018; 59:2001-2017. [PMID: 30115755 PMCID: PMC6168311 DOI: 10.1194/jlr.s087163] [Citation(s) in RCA: 197] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 08/11/2018] [Indexed: 12/19/2022] Open
Abstract
Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.
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Affiliation(s)
- Bo Burla
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore
| | - Makoto Arita
- Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Cellular and Molecular Epigenetics Laboratory, Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
- Division of Physiological Chemistry and Metabolism, Keio University Faculty of Pharmacy, Tokyo, Japan
| | - Masanori Arita
- National Institute of Genetics, Shizuoka, Japan and RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Anne K Bendt
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry, YLL School of Medicine, National University of Singapore, Singapore
| | - Edward A Dennis
- Departments of Pharmacology and Chemistry and Biochemistry, School of Medicine, University of California at San Diego, La Jolla, CA
| | - Kim Ekroos
- Lipidomics Consulting Ltd., Esbo, Finland
| | - Xianlin Han
- Barshop Institute for Longevity and Aging Studies and Department of Medicine-Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Kazutaka Ikeda
- Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Cellular and Molecular Epigenetics Laboratory, Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
| | - Gerhard Liebisch
- Institute of Clinical Chemistry and Laboratory Medicine, University of Regensburg, Regensburg, Germany
| | - Michelle K Lin
- Department of Biochemistry, YLL School of Medicine, National University of Singapore, Singapore
| | - Tze Ping Loh
- Department of Laboratory Medicine, National University Hospital, Singapore
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland and School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Oswald Quehenberger
- Departments of Pharmacology and Medicine, School of Medicine, University of California at San Diego, La Jolla, CA
| | - Andrej Shevchenko
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Federico Torta
- Department of Biochemistry, YLL School of Medicine, National University of Singapore, Singapore
| | | | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Markus R Wenk
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore
- Department of Biochemistry, YLL School of Medicine, National University of Singapore, Singapore
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Wang Y, Carter BD, Gapstur SM, McCullough ML, Gaudet MM, Stevens VL. Reproducibility of non-fasting plasma metabolomics measurements across processing delays. Metabolomics 2018; 14:129. [PMID: 30830406 DOI: 10.1007/s11306-018-1429-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 09/14/2018] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Processing delays after blood collection is a common pre-analytical condition in large epidemiologic studies. It is critical to evaluate the suitability of blood samples with processing delays for metabolomics analysis as it is a potential source of variation that could attenuate associations between metabolites and disease outcomes. OBJECTIVES We aimed to evaluate the reproducibility of metabolites over extended processing delays up to 48 h. We also aimed to test the reproducibility of the metabolomics platform. METHODS Blood samples were collected from 18 healthy volunteers. Blood was stored in the refrigerator and processed for plasma at 0, 15, 30, and 48 h after collection. Plasma samples were metabolically profiled using an untargeted, ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform. Reproducibility of 1012 metabolites over processing delays and reproducibility of the platform were determined by intraclass correlation coefficients (ICCs) with variance components estimated from mixed-effects models. RESULTS The majority of metabolites (approximately 70% of 1012) were highly reproducible (ICCs ≥ 0.75) over 15-, 30- or 48-h processing delays. Nucleotides, energy-related metabolites, peptides, and carbohydrates were most affected by processing delays. The platform was highly reproducible with a median technical ICC of 0.84 (interquartile range 0.68-0.93). CONCLUSION Most metabolites measured by the UPLC-MS/MS platform show acceptable reproducibility up to 48-h processing delays. Metabolites of certain pathways need to be interpreted cautiously in relation to outcomes in epidemiologic studies with prolonged processing delays.
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Affiliation(s)
- Ying Wang
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA.
| | - Brian D Carter
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Susan M Gapstur
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Marjorie L McCullough
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Mia M Gaudet
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Victoria L Stevens
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
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Vuckovic D. Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery. Chem Commun (Camb) 2018; 54:6728-6749. [PMID: 29888773 DOI: 10.1039/c8cc02592d] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.
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Affiliation(s)
- Dajana Vuckovic
- Department of Chemistry and Biochemistry, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec H4B 1R6, Canada.
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28
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Kirwan JA, Brennan L, Broadhurst D, Fiehn O, Cascante M, Dunn WB, Schmidt MA, Velagapudi V. Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative). Clin Chem 2018; 64:1158-1182. [PMID: 29921725 DOI: 10.1373/clinchem.2018.287045] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/01/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data. CONTENT This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices. SUMMARY Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, "time to freezer," sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.
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Affiliation(s)
- Jennifer A Kirwan
- Berlin Institute of Health, Berlin, Germany; .,Max Delbrück Center for Molecular Medicine, Berlin-Buch, Germany
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, UCD, Dublin, Ireland
| | | | - Oliver Fiehn
- NIH West Coast Metabolomics Center, UC Davis, Davis, CA
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine and IBUB, Universitat de Barcelona, Barcelona and Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBER-EHD), Madrid, Spain
| | - Warwick B Dunn
- School of Biosciences and Phenome Centre Birmingham, University of Birmingham, Birmingham, UK
| | - Michael A Schmidt
- Advanced Pattern Analysis and Countermeasures Group, Research Innovation Center, Colorado State University, Fort Collins, CO.,Sovaris Aerospace, LLC, Boulder, CO
| | - Vidya Velagapudi
- Metabolomics Unit, Institute for Molecular Medicine FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
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