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Liang K, Li Q, Song Z, Zhao K, Su R, Huang S, Guo X, Li Y. Endogenous Plasma Peptides Modulated by Protease in a Time-Dependent Manner as Effective Biomarkers for Preanalytical Quality Control. J Proteome Res 2023; 22:3029-3039. [PMID: 37530177 DOI: 10.1021/acs.jproteome.3c00335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Non-cryopreservation temperature exposure (NCE) is a vital preanalytical factor for assessing plasma quality. NCE can introduce undesirable errors in clinical diagnosis or when developing biomarkers of diseases. Biomarkers that can effectively indicate the changes in sample quality caused by long-term NCE (0-several days) are limited. Low-molecular-weight (LMW) peptides in the plasma are modulated by endogenous proteases. These protease activities are significantly correlated with NCE temperatures and duration, indicating a potential link of these protease reactions with the preanalytical quality of plasma samples. In this study, two groups of plasma samples were aged at room temperature (RT, 57 samples) and 4 °C (69 samples) for different durations (0, 1, 2, 5, and 10 days), and LMW peptidomics were analyzed through nanopore-assisted matrix-assisted laser desorption ionization time-of-flight mass spectrometry. The analysis revealed 10 peptides that consistently exhibited time-dependent changes, which were used to develop multiple-variable models for predicting the changes in sample quality resulting from extended NCE. These biomarker models exhibited outstanding performance in distinguishing poor-quality samples aged at both RT and 4 °C. To validate the findings, tests on samples from validation sets were conducted by analysts who were blinded to the detailed conditions, which revealed a high specificity (94.3-96.9%) and sensitivity (90.5-99.3%). These results indicate the potential of these peptides as novel biomarkers of quality control.
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Affiliation(s)
- Kai Liang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Western Institute of Health Data Science, Chongqin 400050, China
| | - Qianqian Li
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhijing Song
- Western Institute of Health Data Science, Chongqin 400050, China
| | - Keli Zhao
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Rong Su
- Foshan Hospital of Traditional Chinese Medicine, Foshan 528000, China
| | - Shengchun Huang
- Foshan Hospital of Traditional Chinese Medicine, Foshan 528000, China
| | - Xueyan Guo
- Foshan Hospital of Traditional Chinese Medicine, Foshan 528000, China
| | - Yan Li
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Western Institute of Health Data Science, Chongqin 400050, China
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2
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Gowda GAN, Pascua V, Raftery D. Anomalous Dynamics of Labile Metabolites in Cold Human Blood Detected Using 1H NMR Spectroscopy. Anal Chem 2023; 95:12923-12930. [PMID: 37582233 PMCID: PMC10528060 DOI: 10.1021/acs.analchem.3c02478] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Recent efforts in our laboratory have enabled access to an unprecedented number (∼90) of quantifiable metabolites in human blood by a simple nuclear magnetic resonance (NMR) spectroscopy method, which includes energy coenzymes, redox coenzymes, and antioxidants that are fundamental to cellular functions [ J. Magn. Reson. Open 2022, 12-13, 100082]. The coenzymes and antioxidants, however, are notoriously labile and are extremely sensitive to specimen harvesting, extraction, and measurement conditions. This problem is largely underappreciated and carries the risk of grossly inaccurate measurements and incorrect study outcomes. As a part of addressing this challenge, in this study, human blood specimens were comprehensively and quantitatively investigated using 1H NMR spectroscopy. Freshly drawn human blood specimens were treated or not treated with methanol, ethanol, or a mixture of methanol and chloroform, and stored on ice or on bench, at room temperature for different time periods from 0 to 24 h, prior to storing at -80 °C. Interestingly, the labile metabolite levels were stable in blood treated with an organic solvent. However, their levels in blood in untreated samples increased or decreased by factors of up to 5 or more within 3 h. Further, surprisingly, and contrary to the current knowledge about metabolite stability, the variation of coenzyme levels was more dramatic in blood stored on ice than on bench, at room temperature. In addition, unlike the generally observed phenomenon of oxidation of redox coenzymes, reduction was observed in untreated blood. Such preanalytical dynamics of the labile metabolites potentially arises from the active cellular metabolism. From the metabolomics perspective, the massive variation of the labile metabolite levels even in blood stored on ice is alarming and stresses the critical need to immediately quench the cellular metabolism for reliable analyses. Overall, the results provide compelling evidence that warrants a paradigm shift in the sample collection protocol for blood metabolomics involving labile metabolites.
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Affiliation(s)
- G. A. Nagana Gowda
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA 98109
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109
| | - Vadim Pascua
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA 98109
| | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA 98109
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109
- Fred Hutchinson Cancer Center, Seattle, WA 98109
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Lee EB, Sayem SAJ, Lee GY, Kim TW, Hossain MA, Park SC. Assessment of Plasma Tylosin Concentrations: A Comparative Study of Immunoassay, Microbiological Assay, and Liquid Chromatography/Mass Spectrometry. Antibiotics (Basel) 2023; 12:1023. [PMID: 37370342 DOI: 10.3390/antibiotics12061023] [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: 05/09/2023] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Employing affordable and uncomplicated sample preparation techniques to recommend the most efficient antibacterial therapy could help reduce antibiotic-resistant bacteria. This study evaluated the suitability of immunoassays and microbiological assays as alternatives for liquid chromatography/mass spectrometry (LC/MS) in determining plasma tylosin concentrations after intramuscular administration at a dose of 20 mg/kg to both healthy and diseased pigs in clinical veterinary practice. The diseased pigs were confirmed using the target genes Actinobacillus pleuropneumoniae (apxIVA) and Pasteurella multocida (kmt1). The methods showed good linearity, precision, and accuracy. In both healthy and diseased pigs, a significant correlation was observed between LC/MS and the microbiological assay (Pearson correlation coefficient: 0.930, p < 0.001 vs. Pearson correlation coefficient: 0.950, p < 0.001) and between LC/MS and the enzyme-linked immunosorbent assay (ELISA) (Pearson correlation coefficient: 0.933; p < 0.001 vs. Pearson correlation coefficient: 0.976, p < 0.001). A strong correlation was observed between the microbiological assay and the ELISA in both healthy and diseased pigs (Pearson correlation coefficient: 0.911; p < 0.001 vs. Pearson correlation coefficient: 0.908, p < 0.001). A Bland-Altman analysis revealed good agreement between the methods, i.e., 95% of the differences were within the limits of agreement. Therefore, the microbiological assay and the ELISA, which demonstrated sufficient precision and accuracy, can be viable alternatives to LC/MS when it is unavailable.
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Affiliation(s)
- Eon-Bee Lee
- Laboratory of Veterinary Pharmacokinetics and Pharmacodynamics, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Syed Al Jawad Sayem
- Laboratory of Veterinary Pharmacokinetics and Pharmacodynamics, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Ga-Yeong Lee
- Laboratory of Veterinary Pharmacokinetics and Pharmacodynamics, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Tae-Won Kim
- College of Veterinary Medicine, Institute of Veterinary Science, Chungnam National University, 99 Daehak-Ro, Yuseong-Gu, Daejeon 34134, Republic of Korea
| | - Md Akil Hossain
- Department of Oral Biology, College of Dentistry, University of Illinois Chicago, 801 S., Chicago, IL 60612, USA
| | - Seung-Chun Park
- Laboratory of Veterinary Pharmacokinetics and Pharmacodynamics, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Republic of Korea
- Cardiovascular Research Institute, Kyungpook National University, Daegu 41566, Republic of Korea
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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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Märtens A, Holle J, Mollenhauer B, Wegner A, Kirwan J, Hiller K. Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples. Metabolites 2023; 13:metabo13050665. [PMID: 37233706 DOI: 10.3390/metabo13050665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/08/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.
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Affiliation(s)
- Andre Märtens
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
- Physikalisch-Technische Bundesanstalt, 38116 Braunschweig, Germany
| | - Johannes Holle
- Department of Pediatric Gastroenterology, Nephrology and Metabolic Diseases, Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Brit Mollenhauer
- Department of Neurology, University Medical Center Göttingen, 37073 Göttingen, Germany
- Paracelsus-Elena-Klinik, 34128 Kassel, Germany
| | - Andre Wegner
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
| | - Jennifer Kirwan
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Karsten Hiller
- Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany
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Effect of serum sample storage temperature on metabolomic and proteomic biomarkers. Sci Rep 2022; 12:4571. [PMID: 35301383 PMCID: PMC8930974 DOI: 10.1038/s41598-022-08429-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/08/2022] [Indexed: 12/28/2022] Open
Abstract
Prospective biomarker studies can be used to identify biomarkers predictive of disease onset. However, if serum biomarkers are measured years after their collection, the storage conditions might affect analyte concentrations. Few data exists concerning which metabolites and proteins are affected by storage at − 20 °C vs − 80 °C. Our objectives were to document analytes affected by storage of serum samples at − 20 °C vs − 80 °C, and to identify those indicative of the storage temperature. We utilized liquid chromatography tandem mass spectrometry and Luminex to quantify 300 analytes from serum samples of 16 Finnish individuals with type 1 diabetes, with split-aliquot samples stored at − 80 °C and − 20 °C for a median of 4.2 years. Results were validated in 315 Finnish and 916 Scottish individuals with type 1 diabetes, stored at − 20 °C and at − 80 °C, respectively. After quality control, we analysed 193 metabolites and proteins of which 120 were apparently unaffected and 15 clearly susceptible to storage at − 20 °C vs − 80 °C. Further, we identified serum glutamate/glutamine ratio greater than 0.20 as a biomarker of storage at − 20 °C vs − 80 °C. The results provide a catalogue of analytes unaffected and affected by storage at − 20 °C vs − 80 °C and biomarkers indicative of sub-optimal storage.
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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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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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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: 15] [Impact Index Per Article: 5.0] [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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10
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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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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: 61] [Impact Index Per Article: 15.3] [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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12
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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: 99] [Impact Index Per Article: 19.8] [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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13
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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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14
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Findeisen P, Hemanna S, Maharjan RS, Mindt S, Costina V, Hofheinz R, Neumaier M. Mass spectrometry based analytical quality assessment of serum and plasma specimens with patterns of endo- and exogenous peptides. Clin Chem Lab Med 2019; 57:668-678. [DOI: 10.1515/cclm-2018-0811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 11/05/2018] [Indexed: 12/23/2022]
Abstract
Abstract
Background
Inappropriate preanalytical sample handling is a major threat for any biomarker discovery approach. Blood specimens have a genuine proteolytic activity that leads to a time dependent decay of peptidic quality control markers (QCMs). The aim of this study was to identify QCMs for direct assessment of sample quality (DASQ) of serum and plasma specimens.
Methods
Serum and plasma specimens of healthy volunteers and tumor patients were spiked with two synthetic reporter peptides (exogenous QCMs) and aged under controlled conditions for up to 24 h. The proteolytic fragments of endogenous and exogenous QCMs were monitored for each time point by mass spectrometry (MS). The decay pattern of peptides was used for supervised classification of samples according to their respective preanalytical quality.
Results
The classification accuracy for fresh specimens (1 h) was 96% and 99% for serum and plasma specimens, respectively, when endo- and exogenous QCMs were used for the calculations. However, classification of older specimens was more difficult and overall classification accuracy decreased to 79%.
Conclusions
MALDI-TOF MS is a simple and robust method that can be used for DASQ of serum and plasma specimens in a high throughput manner. We propose DASQ as a fast and simple step that can be included in multicentric large-scale projects to ensure the homogeneity of sample quality.
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15
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Kofanova O, Henry E, Aguilar Quesada R, Bulla A, Navarro Linares H, Lescuyer P, Shea K, Stone M, Tybring G, Bellora C, Betsou F. IL8 and IL16 levels indicate serum and plasma quality. Clin Chem Lab Med 2019; 56:1054-1062. [PMID: 29425105 DOI: 10.1515/cclm-2017-1047] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 12/30/2017] [Indexed: 12/13/2022]
Abstract
BACKGROUND Longer pre-centrifugation times alter the quality of serum and plasma samples. Markers for such delays in sample processing and hence for the sample quality, have been identified. METHODS Twenty cytokines in serum, EDTA plasma and citrate plasma samples were screened for changes in concentration induced by extended blood pre-centrifugation delays at room temperature. The two cytokines that showed the largest changes were further validated for their "diagnostic performance" in identifying serum or plasma samples with extended pre-centrifugation times. RESULTS In this study, using R&D Systems ELISA kits, EDTA plasma samples and serum samples with a pre-centrifugation delay longer than 24 h had an IL16 concentration higher than 313 pg/mL, and an IL8 concentration higher than 125 pg/mL, respectively. EDTA plasma samples with a pre-centrifugation delay longer than 48 h had an IL16 concentration higher than 897 pg/mL, citrate plasma samples had an IL8 concentration higher than 21.5 pg/mL and serum samples had an IL8 concentration higher than 528 pg/mL. CONCLUSIONS These robust and accurate tools, based on simple and commercially available ELISA assays can greatly facilitate qualification of serum and plasma legacy collections with undocumented pre-analytics.
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Affiliation(s)
- Olga Kofanova
- Integrated Biobank of Luxembourg, Strassen, Luxembourg
| | - Estelle Henry
- Integrated Biobank of Luxembourg, Strassen, Luxembourg
| | | | - Alexandre Bulla
- Sérothèque Centrale, Département de Médecine Génétique et de Laboratoire, Hôpitaux Universitaires de Genève, Genève, Switzerland
| | | | - Pierre Lescuyer
- Sérothèque Centrale, Département de Médecine Génétique et de Laboratoire, Hôpitaux Universitaires de Genève, Genève, Switzerland
| | - Kathi Shea
- Precision for Medicine, Frederick, MD, USA
| | - Mars Stone
- Blood Systems Research Institute, San Francisco, CA, USA
| | | | | | - Fay Betsou
- Integrated Biobank of Luxembourg, Strassen, Luxembourg
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16
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Venturella M, Carpi FM, Zocco D. Standardization of Blood Collection and Processing for the Diagnostic Use of Extracellular Vesicles. CURRENT PATHOBIOLOGY REPORTS 2019. [DOI: 10.1007/s40139-019-00189-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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17
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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: 142] [Impact Index Per Article: 23.7] [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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18
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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: 192] [Impact Index Per Article: 32.0] [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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19
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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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20
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Brunius C, Pedersen A, Malmodin D, Karlsson BG, Andersson LI, Tybring G, Landberg R. Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data. Bioinformatics 2018; 33:3567-3574. [PMID: 29036400 PMCID: PMC5870544 DOI: 10.1093/bioinformatics/btx442] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 07/13/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples. Results This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1–36 h pre-centrifugation delay time at 4 °C and 22 °C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 °C. Moreover, pre-centrifugation delay at 4 °C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 °C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS. Availability and implementation The sampleDrift R package is available at https://gitlab.com/CarlBrunius/sampleDrift. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Carl Brunius
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.,Department of Molecular Sciences, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden
| | - Anders Pedersen
- Swedish NMR Centre, University of Gothenburg, SE-405?30 Gothenburg, Sweden
| | - Daniel Malmodin
- Swedish NMR Centre, University of Gothenburg, SE-405?30 Gothenburg, Sweden
| | - B Göran Karlsson
- Swedish NMR Centre, University of Gothenburg, SE-405?30 Gothenburg, Sweden
| | | | | | - Rikard Landberg
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.,Department of Molecular Sciences, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden.,Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
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21
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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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22
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Liu X, Hoene M, Yin P, Fritsche L, Plomgaard P, Hansen JS, Nakas CT, Niess AM, Hudemann J, Haap M, Mendy M, Weigert C, Wang X, Fritsche A, Peter A, Häring HU, Xu G, Lehmann R. Quality Control of Serum and Plasma by Quantification of (4E,14Z)-Sphingadienine-C18-1-Phosphate Uncovers Common Preanalytical Errors During Handling of Whole Blood. Clin Chem 2018; 64:810-819. [PMID: 29567661 DOI: 10.1373/clinchem.2017.277905] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 02/05/2018] [Indexed: 01/08/2023]
Abstract
BACKGROUND Nonadherence to standard operating procedures (SOPs) during handling and processing of whole blood is one of the most frequent causes affecting the quality of serum and plasma. Yet, the quality of blood samples is of the utmost importance for reliable, conclusive research findings, valid diagnostics, and appropriate therapeutic decisions. METHODS UHPLC-MS-driven nontargeted metabolomics was applied to identify biomarkers that reflected time to processing of blood samples, and a targeted UHPLC-MS analysis was used to quantify and validate these biomarkers. RESULTS We found that (4E,14Z)-sphingadienine-C18-1-phosphate (S1P-d18:2) was suitable for the reliable assessment of the pronounced changes in the quality of serum and plasma caused by errors in the phase between collection and centrifugation of whole blood samples. We rigorously validated S1P-d18:2, which included the use of practicality tests on >1400 randomly selected serum and plasma samples that were originally collected during single- and multicenter trials and then stored in 11 biobanks in 3 countries. Neither life-threatening disease states nor strenuous metabolic challenges (i.e., high-intensity exercise) affected the concentration of S1P-d18:2. Cutoff values for sample assessment were defined (plasma, ≤0.085 μg/mL; serum, ≤0.154 μg/mL). CONCLUSIONS Unbiased valid monitoring to check for adherence to SOP-dictated time for processing to plasma or serum and/or time to storage of whole blood at 4 °C is now feasible. This novel quality assessment step could enable scientists to uncover common preanalytical errors, allowing for identification of serum and plasma samples that should be excluded from certain investigations. It should also allow control of samples before long-term storage in biobanks.
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Affiliation(s)
- Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Miriam Hoene
- Division of Clinical Chemistry and Pathobiochemistry (Central Laboratory), University Hospital Tübingen, Tübingen, Germany
| | - Peiyuan Yin
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Louise Fritsche
- Core Facility German Center for Diabetes Research (DZD) Clinical Chemistry Laboratory, Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany.,German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Peter Plomgaard
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark.,Center of Inflammation and Metabolism and Center for Physical Activity Research, Department of Infectious Diseases and Copenhagen Muscle Research Center (CMRC), Rigshospitalet, Copenhagen, Denmark
| | - Jakob S Hansen
- Center of Inflammation and Metabolism and Center for Physical Activity Research, Department of Infectious Diseases and Copenhagen Muscle Research Center (CMRC), Rigshospitalet, Copenhagen, Denmark
| | - Christos T Nakas
- University Institute of Clinical Chemistry, Center of Laboratory Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Laboratory of Biometry, University of Thessaly, Volos, Greece
| | - Andreas M Niess
- Department of Sports Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Jens Hudemann
- Department of Sports Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Michael Haap
- Department of Internal Medicine, Medical Intensive Care Unit, University of Tübingen, Tübingen, Germany
| | - Maimuna Mendy
- Laboratory Services and Biobank Group, International Agency for Research on Cancer (IARC) of the World Health Organization (WHO), Lyon, France
| | - Cora Weigert
- Division of Clinical Chemistry and Pathobiochemistry (Central Laboratory), University Hospital Tübingen, Tübingen, Germany.,German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Xiaolin Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Andreas Fritsche
- Division of Clinical Chemistry and Pathobiochemistry (Central Laboratory), University Hospital Tübingen, Tübingen, Germany.,Core Facility German Center for Diabetes Research (DZD) Clinical Chemistry Laboratory, Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany.,German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Andreas Peter
- Division of Clinical Chemistry and Pathobiochemistry (Central Laboratory), University Hospital Tübingen, Tübingen, Germany.,Core Facility German Center for Diabetes Research (DZD) Clinical Chemistry Laboratory, Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany.,German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Hans-Ulrich Häring
- Division of Clinical Chemistry and Pathobiochemistry (Central Laboratory), University Hospital Tübingen, Tübingen, Germany.,Core Facility German Center for Diabetes Research (DZD) Clinical Chemistry Laboratory, Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, 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, Dalian, China;
| | - Rainer Lehmann
- Division of Clinical Chemistry and Pathobiochemistry (Central Laboratory), University Hospital Tübingen, Tübingen, Germany; .,Core Facility German Center for Diabetes Research (DZD) Clinical Chemistry Laboratory, Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany.,German Center for Diabetes Research (DZD), Tübingen, Germany
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23
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Gonzales GB, De Saeger S. Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition. Sci Rep 2018; 8:3659. [PMID: 29483546 PMCID: PMC5826937 DOI: 10.1038/s41598-018-21851-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 02/06/2018] [Indexed: 12/11/2022] Open
Abstract
In this paper, the stability of the plasma metabolome at −20 °C for up to 30 days was evaluated using liquid chromatography-high resolution mass spectrometric metabolomics analysis. To follow the time-series deterioration of the plasma metabolome, the use of an elastic net regularized regression model for the prediction of storage time at −20 °C based on the plasma metabolomic profile, and the selection and ranking of metabolites with high temporal changes was demonstrated using the glmnet package in R. Out of 1229 (positive mode) and 1483 (negative mode) metabolite features, the elastic net model extracted 32 metabolites of interest in both positive and negative modes. L-gamma-glutamyl-L-(iso)leucine (tentative identification) was found to have the highest time-dependent change and significantly increased proportionally to the storage time of plasma at −20 °C (R2 = 0.6378 [positive mode], R2 = 0.7893 [negative mode], p-value < 0.00001). Based on the temporal profiles of the extracted metabolites by the model, results show only minimal deterioration of the plasma metabolome at −20 °C up to 1 month. However, majority of the changes appeared at around 12–15 days of storage. This allows scientists to better plan logistics and storage strategies for samples obtained from low-resource settings, where −80 °C storage is not guaranteed.
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Affiliation(s)
- Gerard Bryan Gonzales
- Gastroenterology and Hepatology, Department of Internal Medicine, Faculty of Medicine and Health Sciences, Ghent University, C. Heymanslaan 10, 9000, Ghent, Belgium. .,Laboratory of Food Analysis, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium.
| | - Sarah De Saeger
- Laboratory of Food Analysis, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
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24
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Jacobs G, Wolf A, Krawczak M, Lieb W. Biobanks in the Era of Digital Medicine. Clin Pharmacol Ther 2017; 103:761-762. [PMID: 29285753 PMCID: PMC5946992 DOI: 10.1002/cpt.968] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 11/27/2017] [Accepted: 11/30/2017] [Indexed: 12/17/2022]
Abstract
Digitalization is currently permeating virtually all sectors of modern societies, including biomedical research and medical care. At the same time, biobanks engaged in the long-term storage of biological samples that are fit for purpose have become key drivers in both fields. The present article highlights some of the challenges and opportunities that biobanking is facing in the current proverbial "era of digitalization."
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Affiliation(s)
- Gunnar Jacobs
- Institute of Epidemiology, University of Kiel, and PopGen Biobank, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Andreas Wolf
- Institute of Medical Informatics and Statistics, University of Kiel, Kiel, Germany
| | - Michael Krawczak
- Institute of Medical Informatics and Statistics, University of Kiel, Kiel, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, University of Kiel, and PopGen Biobank, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
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25
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Haid M, Muschet C, Wahl S, Römisch-Margl W, Prehn C, Möller G, Adamski J. Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C. J Proteome Res 2017; 17:203-211. [PMID: 29064256 DOI: 10.1021/acs.jproteome.7b00518] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at -80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ p180 targeted-metabolomics assay at 193 time points. Modeling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% or -14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: + 15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: acylcarnitines, -12.1%; lysophosphatidylcholines, -15.1%; diacyl-phosphatidylcholines, -17.0%; acyl-alkyl-phosphatidylcholines, -13.3%; sphingomyelins, -14.8%. We conclude that storage of plasma samples at -80 °C for up to five years can lead to altered concentration levels of amino acids, acylcarnitines, glycerophospholipids, sphingomyelins, and the sum of hexoses. These alterations must be considered when analyzing metabolomics data from long-term epidemiological studies.
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Affiliation(s)
| | | | | | | | | | | | - Jerzy Adamski
- Lehrstuhl für Experimentelle Genetik, Technische Universität München , 85350 Freising-Weihenstephan, Germany.,German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
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26
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Lee JE, Kim YY. Impact of Preanalytical Variations in Blood-Derived Biospecimens on Omics Studies: Toward Precision Biobanking? OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2017; 21:499-508. [PMID: 28873014 DOI: 10.1089/omi.2017.0109] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Research data and outcomes do vary across populations and persons, but this is not always due to experimental or true biological variation. Preanalytical components of experiments, be they biospecimen acquisition, preparation, storage, or transportation to the laboratory, may all contribute to apparent variability in research data, outcomes, and interpretation. The present review article and biobanking innovation analysis offer new insights with a summary of such preanalytical variables, for example, the type of blood collection tube, centrifugation conditions, long-term sample storage temperature, and duration, on output of omics analyses of blood-derived biospecimens: whole blood, serum, plasma, buffy coat, and peripheral blood mononuclear cells. Furthermore, we draw parallels from the field of precision medicine in this study, with a view to the future of "precision biobanking" wherein such preanalytical variations are carefully taken into consideration so as to minimize their influence on outcomes of omics data, analyses, and sensemaking, particularly in clinical omics applications. We underscore the need for using broadly framed, critical, independent, social and political science, and humanities research so as to understand the multiple possible future trajectories of, and the motivations and values embedded in, precision biobanking that is increasingly relevant in the current age of Big Data.
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Affiliation(s)
- Jae-Eun Lee
- Division of Biobank for Health Sciences, Center for Genome Science, Korea National Institute of Health , Korea Centers for Disease Control and Prevention, Cheongju-si, Korea
| | - Young-Youl Kim
- Division of Biobank for Health Sciences, Center for Genome Science, Korea National Institute of Health , Korea Centers for Disease Control and Prevention, Cheongju-si, Korea
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27
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Dudzik D, Barbas-Bernardos C, García A, Barbas C. Quality assurance procedures for mass spectrometry untargeted metabolomics. a review. J Pharm Biomed Anal 2017; 147:149-173. [PMID: 28823764 DOI: 10.1016/j.jpba.2017.07.044] [Citation(s) in RCA: 201] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 07/28/2017] [Accepted: 07/29/2017] [Indexed: 12/16/2022]
Abstract
Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.
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Affiliation(s)
- Danuta Dudzik
- Center for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, San Pablo CEU University, Boadilla del Monte, ES-28668, Madrid, Spain.
| | - Cecilia Barbas-Bernardos
- Center for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, San Pablo CEU University, Boadilla del Monte, ES-28668, Madrid, Spain.
| | - Antonia García
- Center for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, San Pablo CEU University, Boadilla del Monte, ES-28668, Madrid, Spain.
| | - Coral Barbas
- Center for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, San Pablo CEU University, Boadilla del Monte, ES-28668, Madrid, Spain.
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28
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Abstract
Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.
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Affiliation(s)
- Biswapriya B Misra
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Johannes F Fahrmann
- Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, TX, USA
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29
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Hernandes VV, Barbas C, Dudzik D. A review of blood sample handling and pre-processing for metabolomics studies. Electrophoresis 2017; 38:2232-2241. [PMID: 28543881 DOI: 10.1002/elps.201700086] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Revised: 05/10/2017] [Accepted: 05/11/2017] [Indexed: 01/06/2023]
Abstract
Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre-analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes. Many researchers are mainly focused on the instrumental aspects of the biomarker discovery process, and sample related variables sometimes seem to be overlooked. To bridge the gap, critical information and standardized protocols regarding experimental design and sample handling and pre-processing are highly desired. Characterization of a range variation among sample collection methods is necessary to prevent results misinterpretation and to ensure that observed differences are not due to an experimental bias caused by inconsistencies in sample processing. Herein, a systematic discussion of pre-analytical variables affecting metabolomics studies based on blood derived samples is performed. Furthermore, we provide a set of recommendations concerning experimental design, collection, pre-processing procedures and storage conditions as a practical review that can guide and serve for the standardization of protocols and reduction of undesirable variation.
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Affiliation(s)
- Vinicius Veri Hernandes
- Center for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, San Pablo CEU University, Madrid, Spain.,ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas-UNICAMP, Campinas, Brazil
| | - Coral Barbas
- Center for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, San Pablo CEU University, Madrid, Spain
| | - Danuta Dudzik
- Center for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, San Pablo CEU University, Madrid, Spain
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30
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A novel turn-on fluorescent strategy for sensing ascorbic acid using graphene quantum dots as fluorescent probe. Biosens Bioelectron 2017; 92:229-233. [DOI: 10.1016/j.bios.2017.02.005] [Citation(s) in RCA: 99] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 01/15/2017] [Accepted: 02/03/2017] [Indexed: 02/08/2023]
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31
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Trezzi JP, Jäger C, Galozzi S, Barkovits K, Marcus K, Mollenhauer B, Hiller K. Metabolic profiling of body fluids and multivariate data analysis. MethodsX 2017; 4:95-103. [PMID: 28275554 PMCID: PMC5329063 DOI: 10.1016/j.mex.2017.02.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 02/10/2017] [Indexed: 11/18/2022] Open
Abstract
Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC–MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC–MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process Small sample volume required Rapid and cost-effective processing of biological samples Logistic regression based determination of biomarker signatures for in-depth data analysis
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Affiliation(s)
- Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
- Integrated BioBank of Luxembourg, Strassen, Luxembourg
| | - Christian Jäger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sara Galozzi
- Functional Proteomics, Medizinisches Proteom-Center, Ruhr-University Bochum, Germany
| | - Katalin Barkovits
- Functional Proteomics, Medizinisches Proteom-Center, Ruhr-University Bochum, Germany
| | - Katrin Marcus
- Functional Proteomics, Medizinisches Proteom-Center, Ruhr-University Bochum, Germany
| | - Brit Mollenhauer
- Paracelsus-Elena Klinik, Kassel, Germany
- University Medical Center Goettingen, Institute of Neuropathology and Department of Neurosurgery, Goettingen, Germany
| | - Karsten Hiller
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
- Braunschweig Integrated Centre of Systems Biology, University of Braunschweig, Rebenring 56, Braunschweig, Germany
- Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Corresponding author at: Braunschweig Integrated Centre of Systems Biology, University of Braunschweig, Rebenring 56, Braunschweig, Germany.Braunschweig Integrated Centre of Systems BiologyUniversity of BraunschweigRebenring 56BraunschweigGermany
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Jobard E, Trédan O, Postoly D, André F, Martin AL, Elena-Herrmann B, Boyault S. A Systematic Evaluation of Blood Serum and Plasma Pre-Analytics for Metabolomics Cohort Studies. Int J Mol Sci 2016; 17:ijms17122035. [PMID: 27929400 PMCID: PMC5187835 DOI: 10.3390/ijms17122035] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 11/14/2016] [Accepted: 11/29/2016] [Indexed: 12/11/2022] Open
Abstract
The recent thriving development of biobanks and associated high-throughput phenotyping studies requires the elaboration of large-scale approaches for monitoring biological sample quality and compliance with standard protocols. We present a metabolomic investigation of human blood samples that delineates pitfalls and guidelines for the collection, storage and handling procedures for serum and plasma. A series of eight pre-processing technical parameters is systematically investigated along variable ranges commonly encountered across clinical studies. While metabolic fingerprints, as assessed by nuclear magnetic resonance, are not significantly affected by altered centrifugation parameters or delays between sample pre-processing (blood centrifugation) and storage, our metabolomic investigation highlights that both the delay and storage temperature between blood draw and centrifugation are the primary parameters impacting serum and plasma metabolic profiles. Storing the blood drawn at 4 °C is shown to be a reliable routine to confine variability associated with idle time prior to sample pre-processing. Based on their fine sensitivity to pre-analytical parameters and protocol variations, metabolic fingerprints could be exploited as valuable ways to determine compliance with standard procedures and quality assessment of blood samples within large multi-omic clinical and translational cohort studies.
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Affiliation(s)
- Elodie Jobard
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, Institut des Sciences Analytiques UMR 5280, 5 rue de la Doua, F-69100 Villeurbanne, France.
- Centre Léon Bérard, Département de Recherche Translationnelle et de l'Innovation, 28 rue Laënnec, 69373 Lyon, CEDEX 08, France.
| | - Olivier Trédan
- Centre Léon Bérard, Département d'oncologie Médicale, 28 rue Laënnec, 69373 Lyon, CEDEX 08, France.
| | - Déborah Postoly
- Centre Léon Bérard, Département de Recherche Translationnelle et de l'Innovation, Génomique des Cancers, 28 rue Laënnec, 69373 Lyon, CEDEX 08, France.
| | - Fabrice André
- Department of Medical Oncology, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France.
| | | | - Bénédicte Elena-Herrmann
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, Institut des Sciences Analytiques UMR 5280, 5 rue de la Doua, F-69100 Villeurbanne, France.
| | - Sandrine Boyault
- Centre Léon Bérard, Département de Recherche Translationnelle et de l'Innovation, Génomique des Cancers, 28 rue Laënnec, 69373 Lyon, CEDEX 08, France.
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Dias DA, Koal T. Progress in Metabolomics Standardisation and its Significance in Future Clinical Laboratory Medicine. EJIFCC 2016; 27:331-343. [PMID: 28149265 PMCID: PMC5282916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Today, the technology of 'targeted' based metabolomics is pivotal in the clinical analysis workflow as it provides information of metabolic phenotyping (metabotypes) by enhancing our understanding of metabolism of complex diseases, biomarker discovery for disease development, progression, treatment, and drug function and assessment. This review is focused on surveying and providing a gap analysis on metabolic phenotyping with a focus on targeted based metabolomics from an instrumental, technical point-of-view discussing the state-of-the-art instrumentation, pre- to post- analytical aspects as well as an overall future necessity for biomarker discovery and future (pre-) clinical routine application.
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Affiliation(s)
- Daniel A. Dias
- School of Health and Biomedical Sciences, Discipline of Laboratory Medicine, RMIT University, Victoria, Australia
| | - Therese Koal
- Biocrates Life Sciences AG, Eduard-Bodem-Gasse 8, 6020 Innsbruck, Austria
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Betsou F, Bulla A, Cho SY, Clements J, Chuaqui R, Coppola D, De Souza Y, De Wilde A, Grizzle W, Guadagni F, Gunter E, Heil S, Hodgkinson V, Kessler J, Kiehntopf M, Kim HS, Koppandi I, Shea K, Singh R, Sobel M, Somiari S, Spyropoulos D, Stone M, Tybring G, Valyi-Nagy K, Van den Eynden G, Wadhwa L. Assays for Qualification and Quality Stratification of Clinical Biospecimens Used in Research: A Technical Report from the ISBER Biospecimen Science Working Group. Biopreserv Biobank 2016; 14:398-409. [PMID: 27046294 PMCID: PMC5896556 DOI: 10.1089/bio.2016.0018] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
This technical report presents quality control (QC) assays that can be performed in order to qualify clinical biospecimens that have been biobanked for use in research. Some QC assays are specific to a disease area. Some QC assays are specific to a particular downstream analytical platform. When such a qualification is not possible, QC assays are presented that can be performed to stratify clinical biospecimens according to their biomolecular quality.
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Affiliation(s)
- Fay Betsou
- Integrated BioBank of Luxemburg (IBBL), Luxembourg, Luxembourg
| | - Alexandre Bulla
- Biotheque-SML, Division of Genetics and Laboratory Medicine (DMGL), University Hospital of Geneva, Geneva, Switzerland
| | - Sang Yun Cho
- National Biobank of Korea, Cheongju, South Korea
| | - Judith Clements
- Australian Prostate Cancer Bioresource/Queensland University of Technology, Brisbane, Australia
| | - Rodrigo Chuaqui
- Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis (DCTD), National Cancer Institute, Rockville, Maryland
| | - Domenico Coppola
- Moffitt Cancer Center, Department of Anatomic Pathology, University of South Florida, Tampa, Florida
| | - Yvonne De Souza
- University of California, San Francisco, AIDS Specimen Bank, San Francisco, California
| | | | | | | | | | - Stacey Heil
- Coriell Institute for Medical Research, Camden, New Jersey
| | - Verity Hodgkinson
- Cancer Research Division, Cancer Council NSW, Woolloomooloo, Australia
| | | | | | - Hee Sung Kim
- Department of Pathology, Chung-Ang University College of Medicine, Dongjak-gu, South Korea
| | | | | | - Rajeev Singh
- Houston Methodist Research Institute, Biorepository, Houston, Texas
| | - Marc Sobel
- American Society for Investigative Pathology, Bethesda, Maryland
| | - Stella Somiari
- Biobank and Biospecimen Science Research, Windber Research Institute, Windber, Pennsylvania
| | - Demetri Spyropoulos
- Department of Pathology and Laboratory Medicine, Children's Research Institute, Medical University of South Carolina, Charleston, South Carolina
| | - Mars Stone
- Blood Systems Research Institute, San Francisco, California
| | | | - Klara Valyi-Nagy
- University of Illinois Biorepository, Department of Pathology, College of Medicine, University of Illinois at Chicago, Chicago, Illinois
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