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Gerhardtova I, Jankech T, Majerova P, Piestansky J, Olesova D, Kovac A, Jampilek J. Recent Analytical Methodologies in Lipid Analysis. Int J Mol Sci 2024; 25:2249. [PMID: 38396926 PMCID: PMC10889185 DOI: 10.3390/ijms25042249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/09/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024] Open
Abstract
Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.
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Affiliation(s)
- Ivana Gerhardtova
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, SK-842 15 Bratislava, Slovakia
| | - Timotej Jankech
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, SK-842 15 Bratislava, Slovakia
| | - Petra Majerova
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
| | - Juraj Piestansky
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Toxicological and Antidoping Center, Faculty of Pharmacy, Comenius University in Bratislava, Odbojarov 10, SK-832 32 Bratislava, Slovakia
- Department of Galenic Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Odbojarov 10, SK-832 32 Bratislava, Slovakia
| | - Dominika Olesova
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 05 Bratislava, Slovakia
| | - Andrej Kovac
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Department of Pharmacology and Toxicology, University of Veterinary Medicine and Pharmacy in Kosice, Komenskeho 68/73, SK-041 81 Kosice, Slovakia
| | - Josef Jampilek
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, SK-842 15 Bratislava, Slovakia
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2
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Rischke S, Hahnefeld L, Burla B, Behrens F, Gurke R, Garrett TJ. Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects. J Mass Spectrom Adv Clin Lab 2023; 28:47-55. [PMID: 36872952 PMCID: PMC9982001 DOI: 10.1016/j.jmsacl.2023.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
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Key Words
- (U)HPLC (Ultra-), High pressure liquid chromatography
- Biomarker Discovery Study
- HILIC, Hydrophilic interaction liquid chromatography
- HRMS, High resolution mass spectrometry
- LC-MS, Liquid chromatography – mass spectrometry
- LC-MS-Based Clinical Research
- Lipidomics
- MRM, Multiple reaction monitoring
- Metabolomics
- PCA, Principal component analysis
- QA, Quality assurance
- QC, Quality control
- RF, Random Forest
- RP, Reversed phase
- SVA, Support vector machine
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Affiliation(s)
- S Rischke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - L Hahnefeld
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - B Burla
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - F Behrens
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.,Division of Rheumatology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - R Gurke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - T J Garrett
- Department of Pathology, Immunology and Laboratory Medicine and Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL 32611, USA
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Sens A, Rischke S, Hahnefeld L, Dorochow E, Schäfer SMG, Thomas D, Köhm M, Geisslinger G, Behrens F, Gurke R. Pre-analytical sample handling standardization for reliable measurement of metabolites and lipids in LC-MS-based clinical research. J Mass Spectrom Adv Clin Lab 2023; 28:35-46. [PMID: 36872954 PMCID: PMC9975683 DOI: 10.1016/j.jmsacl.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/09/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023] Open
Abstract
The emerging disciplines of lipidomics and metabolomics show great potential for the discovery of diagnostic biomarkers, but appropriate pre-analytical sample-handling procedures are critical because several analytes are prone to ex vivo distortions during sample collection. To test how the intermediate storage temperature and storage period of plasma samples from K3EDTA whole-blood collection tubes affect analyte concentrations, we assessed samples from non-fasting healthy volunteers (n = 9) for a broad spectrum of metabolites, including lipids and lipid mediators, using a well-established LC-MS-based platform. We used a fold change-based approach as a relative measure of analyte stability to evaluate 489 analytes, employing a combination of targeted LC-MS/MS and LC-HRMS screening. The concentrations of many analytes were found to be reliable, often justifying less strict sample handling; however, certain analytes were unstable, supporting the need for meticulous processing. We make four data-driven recommendations for sample-handling protocols with varying degrees of stringency, based on the maximum number of analytes and the feasibility of routine clinical implementation. These protocols also enable the simple evaluation of biomarker candidates based on their analyte-specific vulnerability to ex vivo distortions. In summary, pre-analytical sample handling has a major effect on the suitability of certain metabolites as biomarkers, including several lipids and lipid mediators. Our sample-handling recommendations will increase the reliability and quality of samples when such metabolites are necessary for routine clinical diagnosis.
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Key Words
- 1-AG, 1-arachidonoyl glycerol
- 1-LG, 1-linoleoyl glycerol
- 2-AG, 2-arachidonoyl glycerol
- 2-LG, 2- linoleoyl glycerol
- ACN, acetonitrile
- AEA, arachidonoyl ethanolamide
- BHT, 2,6-di-tert-butyl-4-methylphenol
- CAR, carnitine
- EC, endocannabinoid
- FC, fold change
- FT, freezing temperature/storage in ice water
- HETE, hydroxyeicosatetraenoate
- HRMS, high-resolution mass spectrometry
- IRB, Institutional Review Board
- IS, internal standard
- K3EDTA plasma sampling
- K3EDTA, tripotassium ethylenediaminetetraacetic acid
- LC, liquid chromatography
- LEA, linoleoyl ethanolamide
- LLE, liquid–liquid extraction
- LLOQ, lowest limit of quantification
- LPA, lysophosphatidic acid
- LPC O, lysophosphatidylcholine-ether
- LPC, lysophosphatidylcholine
- LPE, lysophosphatidylethanolamine
- LPG, lysophosphatidylglycerol
- LPI, lysophosphatic inositol
- Lipidomics
- MS/MS, tandem mass spectrometry
- MTBE, methyl tertiary-butyl ether
- MeOH, methanol
- Metabolomics
- OEA, oleoyl ethanolamide
- PBS, phosphate-buffered saline
- PC, phohsphatidylcholine
- PE, phosphotidylethanolamine
- PEA, palmitoyl ethanolamide
- PI, phosphatidylinositol
- Pre-analytics
- QC, quality control
- REC, Research Ethics Committee
- RT, room temperature
- Ref, reference sample
- SEA, stearoyl ethanolamide
- SPE, solid-phase extraction
- STD, calibration standard
- Sampling protocol
- VEA, vaccenic acid ethanolamid
- WB, whole blood
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Affiliation(s)
- A Sens
- Pharmazentrum Frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - S Rischke
- Pharmazentrum Frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - L Hahnefeld
- Pharmazentrum Frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - E Dorochow
- Pharmazentrum Frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - S M G Schäfer
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - D Thomas
- Pharmazentrum Frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - M Köhm
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.,Rheumatology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - G Geisslinger
- Pharmazentrum Frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - F Behrens
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.,Rheumatology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - R Gurke
- Pharmazentrum Frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
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4
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Olshansky G, Giles C, Salim A, Meikle PJ. Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies. Prog Lipid Res 2022; 87:101177. [PMID: 35780914 DOI: 10.1016/j.plipres.2022.101177] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/19/2022] [Accepted: 06/26/2022] [Indexed: 10/17/2022]
Abstract
Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort sizes and complexity has led to new challenges in sample collection, handling, storage, and preparation stages. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.
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Affiliation(s)
- Gavriel Olshansky
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia
| | - Corey Giles
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia
| | - Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC 3010, Australia; School of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Australia
| | - Peter J Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia; Faculty of Medicine, Nursing and Health Sciences, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
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5
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Chen Q, Zhang Y, Ye L, Gong S, Sun H, Su G. Identifying active xenobiotics in humans by use of a suspect screening technique coupled with lipidomic analysis. ENVIRONMENT INTERNATIONAL 2021; 157:106844. [PMID: 34455192 DOI: 10.1016/j.envint.2021.106844] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/16/2021] [Accepted: 08/20/2021] [Indexed: 06/13/2023]
Abstract
Lipidomic analysis has been proven to be a powerful technique to explore the underlying associations between xenobiotics and health status of organisms. Here, we established a strategy that combined the lipidomic analysis with high-throughput suspect contaminant screening technique with an aim to efficiently identify active xenobiotics in humans. Firstly, in the light of single liquid phase equilibrium of chloroform-methanol-water (15:14:2, v/v/v), we developed an efficient method that was able to simultaneously extract both polar and nonpolar lipids in serum samples. By use of this method, targeted and non-targeted lipid analyses were conducted for n = 120 serum samples collected from Wuxi city, China. Secondly, we established a suspect database containing 1450 contaminants that have been previously reported in human samples, and contaminants in this database were screened in the same batch of serum samples by use of high-resolution mass spectrometry (HR-MS). Thirdly, the underlying associations between suspect contaminants and lipids were explored and discussed, and we observed that levels of some lipids were statistically correlated with concentrations of numerous contaminants. Among these active contaminants, 23 ones were identified on the basis of HR MS1 and MS2 characteristics, and these contaminants belonged to the classes of phthalates, phenols, parabens, or perfluorinated compounds (PFCs). Three active xenobiotics were fully validated by comparison with authentic standards, and they were perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and diethyl phthalate (DEP). There were statistically significant changes in levels of triglyceride (TG), lysophosphocholine (LPC), and sphingomyelin (SM) as peak areas of xenobiotics increase. We also observed that, among target lipid molecules, 18:0 lysophosphatidylethanolamine (LPE(18:0)) was very sensitive, and this lipid responded to exposure of various contaminants. Our present study provides novel knowledge on potential alteration of lipid metabolism in humans following exposure to xenobiotics, and provides an efficient strategy for efficiently identifying active xenobiotics in humans.
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Affiliation(s)
- Qianyu Chen
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, 210094 Nanjing, People's Republic of China
| | - Yayun Zhang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, 210094 Nanjing, People's Republic of China
| | - Langjie Ye
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, 210094 Nanjing, People's Republic of China
| | - Shuai Gong
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, 210094 Nanjing, People's Republic of China
| | - Hong Sun
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu 210009, China
| | - Guanyong Su
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, 210094 Nanjing, People's Republic of China.
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6
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Malkusch S, Hahnefeld L, Gurke R, Lötsch J. Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP). CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1371-1381. [PMID: 34598320 PMCID: PMC8592507 DOI: 10.1002/psp4.12704] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/06/2021] [Accepted: 08/10/2021] [Indexed: 01/05/2023]
Abstract
The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R-based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine-learning-based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k-nearest-neighbors-based imputation followed by k-means clustering and density-based spatial clustering of applications with noise. The R package provides a Shiny-based web interface designed to be easy to use for non-data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r-project.org/web/packages/pguIMP/index.html).
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Affiliation(s)
- Sebastian Malkusch
- Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany
| | - Lisa Hahnefeld
- Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany
| | - Robert Gurke
- Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
| | - Jörn Lötsch
- Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
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7
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Meikle TG, Huynh K, Giles C, Meikle PJ. Clinical lipidomics: realizing the potential of lipid profiling. J Lipid Res 2021; 62:100127. [PMID: 34582882 PMCID: PMC8528718 DOI: 10.1016/j.jlr.2021.100127] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 11/17/2022] Open
Abstract
Dysregulation of lipid metabolism plays a major role in the etiology and sequelae of inflammatory disorders, cardiometabolic and neurological diseases, and several forms of cancer. Recent advances in lipidomic methodology allow comprehensive lipidomic profiling of clinically relevant biological samples, enabling researchers to associate lipid species and metabolic pathways with disease onset and progression. The resulting data serve not only to advance our fundamental knowledge of the underlying disease process but also to develop risk assessment models to assist in the diagnosis and management of disease. Currently, clinical applications of in-depth lipidomic profiling are largely limited to the use of research-based protocols in the analysis of population or clinical sample sets. However, we foresee the development of purpose-built clinical platforms designed for continuous operation and clinical integration-assisting health care providers with disease risk assessment, diagnosis, and monitoring. Herein, we review the current state of clinical lipidomics, including the use of research-based techniques and platforms in the analysis of clinical samples as well as assays already available to clinicians. With a primary focus on MS-based strategies, we examine instrumentation, analysis techniques, statistical models, prospective design of clinical platforms, and the possible pathways toward implementation of clinical lipidomics.
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Affiliation(s)
- Thomas G Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Kevin Huynh
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia
| | - Corey Giles
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia
| | - Peter J Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia; Faculty of Medicine, Nursing and Health Sciences, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
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8
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Sen P, Lamichhane S, Mathema VB, McGlinchey A, Dickens AM, Khoomrung S, Orešič M. Deep learning meets metabolomics: a methodological perspective. Brief Bioinform 2020; 22:1531-1542. [PMID: 32940335 DOI: 10.1093/bib/bbaa204] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/08/2020] [Accepted: 08/10/2020] [Indexed: 12/15/2022] Open
Abstract
Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.,School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Vivek B Mathema
- Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Alex M Dickens
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Sakda Khoomrung
- Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Center for Innovation in Chemistry (PERCH), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.,School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
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9
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Yan C, Guo H, Ding Q, Shao Y, Kang D, Yu T, Li C, Huang H, Du Y, Wang H, Hu K, Xie L, Wang G, Liang Y. Multiomics Profiling Reveals Protective Function of Schisandra Lignans against Acetaminophen-Induced Hepatotoxicity. Drug Metab Dispos 2020; 48:1092-1103. [PMID: 32719086 DOI: 10.1124/dmd.120.000083] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 06/30/2020] [Indexed: 12/13/2022] Open
Abstract
The action principles of traditional Chinese medicines (TCMs) feature multiactive components, multitarget sites, and weak combination with action targets. In the present study, we performed an integrated analysis of metabonomics, proteomics, and lipidomics to establish a scientific research system on the underlying mechanism of TCMs, and Schisandra lignan extract (SLE) was selected as a model TCM. In metabonomics, several metabolic pathways were found to mediate the liver injury induced by acetaminophen (APAP), and SLE could regulate the disorder of lipid metabolism. The proteomic study further proved that the hepatoprotective effect of SLE was closely related to the regulation of lipid metabolism. Indeed, the results of lipidomics demonstrated that SLE dosing has an obvious callback effect on APAP-induced lipidic profile shift. The contents of 25 diglycerides (DAGs) and 21 triglycerides (TAGs) were enhanced significantly by APAP-induced liver injury, which could further induce liver injury and inflammatory response by upregulating protein kinase C (PKCβ, PKCγ, PKCδ, and PKCθ). The upregulated lipids and PKCs could be reversed to the normal level by SLE dosing. More importantly, phosphatidic acid phosphatase, fatty acid transport protein 5, and diacylglycerol acyltransferase 2 were proved to be positively associated with the regulation of DAGs and TAGs. SIGNIFICANCE STATEMENT: Integrated multiomics was first used to reveal the mechanism of APAP-induced acute liver failure (ALF) and the hepatoprotective role of SLE. The results showed that the ALF caused by APAP was closely related to lipid regulation and that SLE dosing could exert a hepatoprotective role by reducing intrahepatic diglyceride and triglyceride levels. Our research can not only promote the application of multicomponent technology in the study of the mechanism of traditional Chinese medicines but also provide an effective approach for the prevention and treatment of ALF.
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Affiliation(s)
- Caixia Yan
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Huimin Guo
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Qingqing Ding
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Yuhao Shao
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Dian Kang
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Tengjie Yu
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Changjian Li
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Haoran Huang
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Yisha Du
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - He Wang
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Kangrui Hu
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Lin Xie
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Guangji Wang
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
| | - Yan Liang
- Key Laboratory of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, P.R. China (C.Y., H.G., Y.S., D.K., T.Y., C.L., H.H., Y.D., H.W., K.H., L.X., G.W., Y.L.) and Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University (Jiangsu People's Hospital), Nanjing, P.R. China (Q.D.)
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10
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Plasma Lipid Profile Reveals Plasmalogens as Potential Biomarkers for Colon Cancer Screening. Metabolites 2020; 10:metabo10060262. [PMID: 32630389 PMCID: PMC7345851 DOI: 10.3390/metabo10060262] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 06/12/2020] [Accepted: 06/12/2020] [Indexed: 12/24/2022] Open
Abstract
In this era of precision medicine, there is an increasingly urgent need for highly sensitive tests for detecting tumors such as colon cancer (CC), a silent disease where the first symptoms may take 10–15 years to appear. Mass spectrometry-based lipidomics is an emerging tool for such clinical diagnosis. We used ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry operating in high energy collision spectral acquisition mode (MSE) mode (UPLC-QTOF-MSE) and gas chromatography (GC) to investigate differences between the plasmatic lipidic composition of CC patients and control (CTR) subjects. Key enzymes in lipidic metabolism were investigated using immuno-based detection assays. Our partial least squares discriminant analysis (PLS-DA) resulted in a suitable discrimination between CTR and CC plasma samples. Forty-two statistically significant discriminating lipids were putatively identified. Ether lipids showed a prominent presence and accordingly, a decrease in glyceronephosphate O-acyltransferase (GNPAT) enzyme activity was found. A receiver operating characteristic (ROC) curve built for three plasmalogens of phosphatidylserine (PS), named PS(P-36:1), PS(P-38:3) and PS(P-40:5), presented an area under the curve (AUC) of 0.998, and sensitivity and specificity of 100 and 85.7% respectively. These results show significant differences in CC patients’ plasma lipid composition that may be useful in discriminating them from CTR individuals with a special role for plasmalogens.
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11
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Zhao X, Zhu S, Liu H. Recent progresses of derivatization approaches in the targeted lipidomics analysis by mass spectrometry. J Sep Sci 2020; 43:1838-1846. [DOI: 10.1002/jssc.201901346] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/07/2020] [Accepted: 02/07/2020] [Indexed: 12/14/2022]
Affiliation(s)
- Xian‐En Zhao
- Key Laboratory of Life‐organic Analysis of Shandong Province and Key Laboratory of Pharmaceutical Intermediates and Natural Medicine Analysis, College of Chemistry and Chemical EngineeringQufu Normal University Qufu P.R. China
| | - Shuyun Zhu
- Key Laboratory of Life‐organic Analysis of Shandong Province and Key Laboratory of Pharmaceutical Intermediates and Natural Medicine Analysis, College of Chemistry and Chemical EngineeringQufu Normal University Qufu P.R. China
| | - Huwei Liu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Institute of Analytical Chemistry, College of Chemistry and Molecular EngineeringPeking University Beijing P.R. China
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12
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Pousinis P, Gowler PRW, Burston JJ, Ortori CA, Chapman V, Barrett DA. Lipidomic identification of plasma lipids associated with pain behaviour and pathology in a mouse model of osteoarthritis. Metabolomics 2020; 16:32. [PMID: 32108917 PMCID: PMC7046574 DOI: 10.1007/s11306-020-01652-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 02/19/2020] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Osteoarthritis (OA) is the most common form of joint disease, causing pain and disability. Previous studies have demonstrated the role of lipid mediators in OA pathogenesis. OBJECTIVES To explore potential alterations in the plasma lipidomic profile in an established mouse model of OA, with a view to identification of potential biomarkers of pain and/or pathology. METHODS Pain behaviour was assessed following destabilisation of the medial meniscus (DMM) model of OA (n = 8 mice) and compared to sham controls (n = 7). Plasma and knee joints were collected at 16 weeks post-surgery. Plasma samples were analysed using ultra-high performance liquid chromatography accurate mass high resolution mass spectrometry (UHPLC-HR-MS) to identify potential differences in the lipidome, using multivariate and univariate statistical analyses. Correlations between pain behaviour, joint pathology and levels of lipids were investigated. RESULTS 24 lipids, predominantly from the lipid classes of cholesterol esters (CE), fatty acids (FA), phosphatidylcholines (PC), N-acylethanolamines (NAE) and sphingomyelins (SM), were differentially expressed in DMM plasma compared to sham plasma. Six of these lipids which were increased in the DMM model were identified as CE(18:2), CE(20:4), CE(22:6), PC(18:0/18:2), PC(38:7) and SM(d34:1). CEs were positively correlated with pain behaviour and all six lipid species were positively correlated with cartilage damage. Pathways shown to be involved in altered lipid homeostasis in OA were steroid biosynthesis and sphingolipid metabolism. CONCLUSION We identify plasma lipid species associated with pain and/or pathology in a DMM model of OA.
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Affiliation(s)
- P Pousinis
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technology Division, School of Pharmacy, University of Nottingham, Nottingham, UK
| | - P R W Gowler
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - J J Burston
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - C A Ortori
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technology Division, School of Pharmacy, University of Nottingham, Nottingham, UK
| | - V Chapman
- Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK.
- School of Life Sciences, University of Nottingham, Nottingham, UK.
| | - D A Barrett
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technology Division, School of Pharmacy, University of Nottingham, Nottingham, UK
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13
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Abstract
Lipidomics data generated using untargeted mass spectrometry techniques can offer great biological insight to metabolic status and disease diagnoses. As the community's ability to conduct large-scale studies with deep coverage of the lipidome expands, approaches to analyzing untargeted data and extracting biological insight are needed. Currently, the function of most individual lipids are not known; however, meaningful biological information can be extracted. Here, I will describe a step-by-step approach to identify patterns and trends in untargeted mass spectrometry lipidomics data to assist users in extracting information leading to a greater understanding of biological systems.
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14
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Wei F, Lamichhane S, Orešič M, Hyötyläinen T. Lipidomes in health and disease: Analytical strategies and considerations. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.115664] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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15
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Colombo S, Domingues P, Domingues MR. Mass spectrometry strategies to unveil modified aminophospholipids of biological interest. MASS SPECTROMETRY REVIEWS 2019; 38:323-355. [PMID: 30597614 DOI: 10.1002/mas.21584] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 10/30/2018] [Indexed: 06/09/2023]
Abstract
The biological functions of modified aminophospholipids (APL) have become a topic of interest during the last two decades, and distinct roles have been found for these biomolecules in both physiological and pathological contexts. Modifications of APL include oxidation, glycation, and adduction to electrophilic aldehydes, altogether contributing to a high structural variability of modified APL. An outstanding technique used in this challenging field is mass spectrometry (MS). MS has been widely used to unveil modified APL of biological interest, mainly when associated with soft ionization methods (electrospray and matrix-assisted laser desorption ionization) and coupled with separation techniques as liquid chromatography. This review summarizes the biological roles and the chemical mechanisms underlying APL modifications, and comprehensively reviews the current MS-based knowledge that has been gathered until now for their analysis. The interpretation of the MS data obtained by in vitro-identification studies is explained in detail. The perspective of an analytical detection of modified APL in clinical samples is explored, highlighting the fundamental role of MS in unveiling APL modifications and their relevance in pathophysiology.
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Affiliation(s)
- Simone Colombo
- Mass Spectrometry Centre, Department of Chemistry and QOPNA, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
| | - Pedro Domingues
- Mass Spectrometry Centre, Department of Chemistry and QOPNA, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
| | - M Rosário Domingues
- Mass Spectrometry Centre, Department of Chemistry and QOPNA, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
- Department of Chemistry and CESAM, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
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16
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Cao Z, Schmitt TC, Varma V, Sloper D, Beger RD, Sun J. Evaluation of the Performance of Lipidyzer Platform and Its Application in the Lipidomics Analysis in Mouse Heart and Liver. J Proteome Res 2019; 19:2742-2749. [PMID: 31310547 DOI: 10.1021/acs.jproteome.9b00289] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Lipids play important roles in cell signaling, energy storage, and as major structural components of cell membranes. To date, little work has been conducted to show the extent of tissue specificity of lipid compositions. Here, the recently acquired Lipidyzer platform was employed in this pilot study: (i) to assess the performance of the Lipidyzer platform, (ii) to explore lipid profiles in liver and cardiac tissue in mice, (iii) to examine sex-specific differences in lipids in the liver tissue, and (iv) to evaluate biological variances in lipidomes present in animals. In total, 787 lipid species from 13 lipid classes were measured in the liver and heart. Lipidomics data from the Lipidyzer platform were very reproducible with the coefficient of variations of the quality control (QC) samples, ∼10%. The total concentration of the cholesterol esters (CE) lipid class, and specifically CE(16:1) and CE(18:1) species, showed sex differences in the liver. Cardiac tissue had higher levels of phospholipids containing docosahexaenoic acid, which could be related to heart health status and function. Our results demonstrate the usefulness of the Lipidyzer platform in identifying differences in lipid profile at the tissue level and between male and female mice in specific tissues.
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Affiliation(s)
- Zhijun Cao
- Division of System Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Thomas C Schmitt
- Division of System Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Vijayalakshmi Varma
- Division of System Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Daniel Sloper
- Division of System Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Richard D Beger
- Division of System Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Jinchun Sun
- Division of System Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States
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17
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Lamichhane S, Ahonen L, Dyrlund TS, Siljander H, Hyöty H, Ilonen J, Toppari J, Veijola R, Hyötyläinen T, Knip M, Orešič M. A longitudinal plasma lipidomics dataset from children who developed islet autoimmunity and type 1 diabetes. Sci Data 2018; 5:180250. [PMID: 30422126 PMCID: PMC6233478 DOI: 10.1038/sdata.2018.250] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 09/26/2018] [Indexed: 12/12/2022] Open
Abstract
Early prediction and prevention of type 1 diabetes (T1D) are currently unmet medical needs. Previous metabolomics studies suggest that children who develop T1D are characterised by a distinct metabolic profile already detectable during infancy, prior to the onset of islet autoimmunity. However, the specificity of persistent metabolic disturbances in relation T1D development has not yet been established. Here, we report a longitudinal plasma lipidomics dataset from (1) 40 children who progressed to T1D during follow-up, (2) 40 children who developed single islet autoantibody but did not develop T1D and (3) 40 matched controls (6 time points: 3, 6, 12, 18, 24 and 36 months of age). This dataset may help other researchers in studying age-dependent progression of islet autoimmunity and T1D as well as of the age-dependence of lipidomic profiles in general. Alternatively, this dataset could more broadly used for the development of methods for the analysis of longitudinal multivariate data.
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Affiliation(s)
- Santosh Lamichhane
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Linda Ahonen
- Steno Diabetes Center Copenhagen, 2820 Gentofte, Denmark
| | | | - Heli Siljander
- Children’s Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland
- Research Program Unit, Diabetes and Obesity, University of Helsinki, 00290 Helsinki, Finland
| | - Heikki Hyöty
- Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
- Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
- Clinical Microbiology, Turku University Hospital, Turku, Finland
| | - Jorma Toppari
- Institute of Biomedicine, Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
- Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Riitta Veijola
- Department of Paediatrics, PEDEGO Research Unit, Medical Research Centre, University of Oulu, Oulu, Finland
- Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
- Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | | | - Mikael Knip
- Children’s Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland
- Research Program Unit, Diabetes and Obesity, University of Helsinki, 00290 Helsinki, Finland
- Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku 20520, Finland
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
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18
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Jonasdottir HS, Brouwers H, Toes REM, Ioan-Facsinay A, Giera M. Effects of anticoagulants and storage conditions on clinical oxylipid levels in human plasma. Biochim Biophys Acta Mol Cell Biol Lipids 2018; 1863:1511-1522. [PMID: 30308322 DOI: 10.1016/j.bbalip.2018.10.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/30/2018] [Accepted: 10/05/2018] [Indexed: 01/16/2023]
Abstract
Metabolomics and lipidomics are of fundamental importance to personalized healthcare. Particularly the analysis of bioactive lipids is of relevance to a better understanding of various diseases. Within clinical routines, blood derived samples are widely used for diagnostic and research purposes. Hence, standardized and validated procedures for blood collection and storage are mandatory, in order to guarantee sample integrity and relevant study outcomes. We here investigated different plasma storage conditions and their effect on plasma fatty acid and oxylipid levels. Our data clearly indicate the importance of storage conditions for plasma lipidomic analysis. Storage at very low temperature (-80 °C) and the addition of methanol directly after sampling are the most important measures to avoid ex vivo synthesis of oxylipids. Furthermore, we identified critical analytes being affected under certain storage conditions. Finally, we carried out chiral analysis and found possible residual enzymatic activity to be one of the contributors to the ex vivo formation of oxylipids even at -20 °C.
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Affiliation(s)
- Hulda S Jonasdottir
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2300RC Leiden, the Netherlands; Leiden University Medical Center, Department of Rheumatology, Albinusdreef 2, 2300RC Leiden, the Netherlands
| | - Hilde Brouwers
- Leiden University Medical Center, Department of Rheumatology, Albinusdreef 2, 2300RC Leiden, the Netherlands
| | - René E M Toes
- Leiden University Medical Center, Department of Rheumatology, Albinusdreef 2, 2300RC Leiden, the Netherlands
| | - Andreea Ioan-Facsinay
- Leiden University Medical Center, Department of Rheumatology, Albinusdreef 2, 2300RC Leiden, the Netherlands
| | - Martin Giera
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2300RC Leiden, the Netherlands.
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19
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Wang C, Wang C, Liu F, Rainosek S, Patterson TA, Slikker W, Han X. Lipidomics Reveals Changes in Metabolism, Indicative of Anesthetic-Induced Neurotoxicity in Developing Brains. Chem Res Toxicol 2018; 31:825-835. [PMID: 30132657 DOI: 10.1021/acs.chemrestox.8b00186] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Numerous studies have demonstrated that treatment with high dose anesthetics for a prolonged duration induces brain injury in infants. However, whether anesthetic treatment leading to neurotoxicity is associated with alterations in lipid metabolism and homeostasis is still unclear. This review first outlines the lipidomics tools for analysis of lipid molecular species that can inform alterations in lipid species after anesthetic exposure. Then the available data indicating anesthetics cause changes in lipid profiles in the brain and serum of infant monkeys in preclinical studies are summarized, and the potential mechanisms leading to the altered lipid metabolism and their association with anesthetic-induced brain injury are also discussed. Finally, whether lipid changes identified in serum of infant monkeys can serve as indicators for the early detection of anesthetic-induced brain injury is described. We believe extensive studies on alterations in lipids after exposure to anesthetics will allow us to better understand anesthetic-induced neurotoxicity, unravel its underlying biochemical mechanisms, and develop powerful biomarkers for early detection/monitoring of the toxicity.
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Affiliation(s)
| | | | | | - Shuo Rainosek
- Department of Anesthesiology , Central Arkansas Veterans Health System , 4300 West Seventh Street, VA 704-110 , Little Rock , Arkansas 72205 , United States
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20
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Schlotterbeck J, Chatterjee M, Gawaz M, Lämmerhofer M. Comprehensive MS/MS profiling by UHPLC-ESI-QTOF-MS/MS using SWATH data-independent acquisition for the study of platelet lipidomes in coronary artery disease. Anal Chim Acta 2018; 1046:1-15. [PMID: 30482286 DOI: 10.1016/j.aca.2018.08.060] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 01/13/2023]
Abstract
A non-targeted lipidomics workflow based on C8 core-shell particle ultra high-performance liquid chromatography (UHPLC) hyphenated to ESI-QTOF-MS in data-independent acquisition (DIA) mode with sequential window acquisition of all theoretical fragment ion spectra (SWATH) was developed and applied to differential platelet lipidomics profiling of cardiovascular disease patients (stable angina pectoris (n = 10), ST-elevated myocardial infarction (n = 13)) against healthy controls (n = 10). DIA with SWATH generates comprehensive MS and MS/MS data throughout the entire chromatograms and all study samples. Hence, chromatograms can be extracted based on precursors or fragments which provided some benefits in terms of assay specificity in some cases. SWATH acquisition offers flexible experimental design with variable Q1 isolation windows. Liquid chromatography as well as SWATH settings were optimized to cover the lipidome of human platelets. The flexibility of the SWATH experiment design was utilized to implement target SWATH windows with narrow 5 Da Q1 precursor ion selection width (multiple reaction monitoring (MRM)-like SWATH windows) for the detection of low abundant oxidized phospholipids. Data processing was performed with MS-DIAL, and its feasibilities and caveats are discussed by illustrative examples. Thereby, identification of lipids is still a bottleneck in non-targeted lipidomics workflow. MS-DIAL, however, offers automatic identification via spectral matching using an in silico library. In total 1971 molecular features were detected cross the samples of which 611 were identified (total score >70%). The quality of the acquired data was validated with embedded quality control samples (n = 11). 80.3% of all features detected in the QC samples showed a coefficient of variation of below 30%. Multivariate statistics were used to visualize differences in the lipidome of distinct sample groups at a false discovery rate of 5%.
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Affiliation(s)
- Jörg Schlotterbeck
- University of Tübingen, Institute of Pharmaceutical Sciences, Pharmaceutical (Bio-)Analysis, Auf der Morgenstelle 8, 72076, Tübingen, Germany
| | - Madhumita Chatterjee
- Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Otfried-Müller-Strasse 10, 72076, Tübingen, Germany
| | - Meinrad Gawaz
- Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Otfried-Müller-Strasse 10, 72076, Tübingen, Germany
| | - Michael Lämmerhofer
- University of Tübingen, Institute of Pharmaceutical Sciences, Pharmaceutical (Bio-)Analysis, Auf der Morgenstelle 8, 72076, Tübingen, Germany.
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21
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Schröter J, Popkova Y, Süß R, Schiller J. Combined Use of MALDI-TOF Mass Spectrometry and 31P NMR Spectroscopy for Analysis of Phospholipids. Methods Mol Biol 2018; 1609:107-122. [PMID: 28660578 DOI: 10.1007/978-1-4939-6996-8_11] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Lipids are important and abundant constituents of all biological tissues and body fluids. In particular, phospholipids (PL) constitute a major part of the cellular membrane, play a role in signal transduction, and some selected PL are increasingly considered as potential disease markers. However, methods of lipid analysis are less established in comparison to techniques of protein analysis. Mass spectrometry (MS) is an increasingly used technique to analyze lipids, especially in combination with electrospray ionization (ESI) MS which is the so far best established ionization method. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS has itself proven to be also useful in the field of lipid analysis. 31P nuclear magnetic resonance (NMR) spectroscopy is another powerful method of PL analysis, represents a direct quantitative method, and does not suffer from suppression effects.This chapter gives an overview of methodological aspects of MALDI-TOF MS and 31P NMR in lipid research and summarizes the specific advantages and drawbacks of both methods. In particular, suppression effects in MS will be highlighted and possible ways to overcome this problem (use of different matrices, separation of the relevant lipid mixture prior to analysis) will be discussed.
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Affiliation(s)
- Jenny Schröter
- Institute for Medical Physics and Biophysics, University of Leipzig, Leipzig 04107, Germany
| | - Yulia Popkova
- Institute for Medical Physics and Biophysics, University of Leipzig, Leipzig 04107, Germany
| | - Rosmarie Süß
- Institute for Medical Physics and Biophysics, University of Leipzig, Leipzig 04107, Germany
| | - Jürgen Schiller
- Institute for Medical Physics and Biophysics, University of Leipzig, Leipzig 04107, Germany.
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22
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Bowden JA, Heckert A, Ulmer CZ, Jones CM, Koelmel JP, Abdullah L, Ahonen L, Alnouti Y, Armando AM, Asara JM, Bamba T, Barr JR, Bergquist J, Borchers CH, Brandsma J, Breitkopf SB, Cajka T, Cazenave-Gassiot A, Checa A, Cinel MA, Colas RA, Cremers S, Dennis EA, Evans JE, Fauland A, Fiehn O, Gardner MS, Garrett TJ, Gotlinger KH, Han J, Huang Y, Neo AH, Hyötyläinen T, Izumi Y, Jiang H, Jiang H, Jiang J, Kachman M, Kiyonami R, Klavins K, Klose C, Köfeler HC, Kolmert J, Koal T, Koster G, Kuklenyik Z, Kurland IJ, Leadley M, Lin K, Maddipati KR, McDougall D, Meikle PJ, Mellett NA, Monnin C, Moseley MA, Nandakumar R, Oresic M, Patterson R, Peake D, Pierce JS, Post M, Postle AD, Pugh R, Qiu Y, Quehenberger O, Ramrup P, Rees J, Rembiesa B, Reynaud D, Roth MR, Sales S, Schuhmann K, Schwartzman ML, Serhan CN, Shevchenko A, Somerville SE, St John-Williams L, Surma MA, Takeda H, Thakare R, Thompson JW, Torta F, Triebl A, Trötzmüller M, Ubhayasekera SJK, Vuckovic D, Weir JM, Welti R, Wenk MR, Wheelock CE, Yao L, Yuan M, Zhao XH, Zhou S. Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma. J Lipid Res 2017; 58:2275-2288. [PMID: 28986437 PMCID: PMC5711491 DOI: 10.1194/jlr.m079012] [Citation(s) in RCA: 270] [Impact Index Per Article: 38.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Revised: 10/02/2017] [Indexed: 12/22/2022] Open
Abstract
As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.
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Affiliation(s)
- John A Bowden
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Alan Heckert
- Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD
| | - Candice Z Ulmer
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Christina M Jones
- Marine Biochemical Sciences Group, Chemical Sciences Division, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Jeremy P Koelmel
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | | | - Linda Ahonen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Yazen Alnouti
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE
| | - Aaron M Armando
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - John M Asara
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Takeshi Bamba
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - John R Barr
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Jonas Bergquist
- Department of Chemistry-BMC, Analytical Chemistry, Uppsala University, Uppsala, Sweden
| | - Christoph H Borchers
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada
- Gerald Bronfman Department of Oncology McGill University, Montreal, Quebec, Canada
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
| | - Joost Brandsma
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Susanne B Breitkopf
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
| | - Tomas Cajka
- National Institutes of Health West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Antonio Checa
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Michelle A Cinel
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Romain A Colas
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Experimental Therapeutics and Reperfusion Injury, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Serge Cremers
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Edward A Dennis
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | | | - Alexander Fauland
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Oliver Fiehn
- National Institutes of Health West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Michael S Gardner
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Timothy J Garrett
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | - Katherine H Gotlinger
- Department of Pharmacology, New York Medical College School of Medicine, Valhalla, NY
| | - Jun Han
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
| | | | - Aveline Huipeng Neo
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | | | - Yoshihiro Izumi
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Hongfeng Jiang
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Houli Jiang
- Department of Pharmacology, New York Medical College School of Medicine, Valhalla, NY
| | - Jiang Jiang
- Departments of Chemistry and Biochemistry and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Maureen Kachman
- Metabolomics Core, BRCF, University of Michigan, Ann Arbor, MI
| | | | | | | | - Harald C Köfeler
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | - Johan Kolmert
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Grielof Koster
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Zsuzsanna Kuklenyik
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Irwin J Kurland
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Michael Leadley
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Karen Lin
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, Victoria, British Columbia, Canada
| | - Krishna Rao Maddipati
- Lipidomics Core Facility and Department of Pathology, Wayne State University, Detroit, MI
| | - Danielle McDougall
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | | | - Cian Monnin
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - M Arthur Moseley
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | - Renu Nandakumar
- Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Rainey Patterson
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL
| | | | - Jason S Pierce
- Department of Biochemistry and Molecular Biology Medical University of South Carolina, Charleston, SC
| | - Martin Post
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Anthony D Postle
- Faculty of Medicine, Academic Unit of Clinical and Experimental Sciences, Southampton General Hospital, University of Southampton, Southampton, United Kingdom
| | - Rebecca Pugh
- Chemical Sciences Division, Environmental Specimen Bank Group, Hollings Marine Laboratory, National Institute of Standards and Technology, Charleston, SC
| | - Yunping Qiu
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Oswald Quehenberger
- Departments of Medicine and Pharmacology, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Parsram Ramrup
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - Jon Rees
- Division of Laboratory Sciences, Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA
| | - Barbara Rembiesa
- Department of Biochemistry and Molecular Biology Medical University of South Carolina, Charleston, SC
| | - Denis Reynaud
- Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Mary R Roth
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Susanne Sales
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Kai Schuhmann
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | | | - Charles N Serhan
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Experimental Therapeutics and Reperfusion Injury, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Andrej Shevchenko
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Stephen E Somerville
- Hollings Marine Laboratory, Medical University of South Carolina, Charleston, SC
| | - Lisa St John-Williams
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | | | - Hiroaki Takeda
- Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Rhishikesh Thakare
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE
| | - J Will Thompson
- Proteomics and Metabolomics Shared Resource, Levine Science Research Center, Duke University School of Medicine, Durham, NC
| | - Federico Torta
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Alexander Triebl
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | - Martin Trötzmüller
- Core Facility for Mass Spectrometry, Medical University of Graz, Graz, Austria
| | | | - Dajana Vuckovic
- Department of Chemistry and Biochemistry, Concordia University, Montréal, Québec, Canada
| | - Jacquelyn M Weir
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Ruth Welti
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Markus R Wenk
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore and Singapore Lipidomic Incubator (SLING), Life Sciences Institute, Singapore
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Libin Yao
- Division of Biology, Kansas Lipidomics Research Center, Kansas State University, Manhattan, KS
| | - Min Yuan
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA
| | - Xueqing Heather Zhao
- Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
| | - Senlin Zhou
- Lipidomics Core Facility and Department of Pathology, Wayne State University, Detroit, MI
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23
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Fu Y, Zhao C, Lu X, Xu G. Nontargeted screening of chemical contaminants and illegal additives in food based on liquid chromatography–high resolution mass spectrometry. Trends Analyt Chem 2017. [DOI: 10.1016/j.trac.2017.07.014] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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24
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Kohler I, Hankemeier T, van der Graaf PH, Knibbe CA, van Hasselt JC. Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine. Eur J Pharm Sci 2017; 109S:S15-S21. [DOI: 10.1016/j.ejps.2017.05.018] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 05/10/2017] [Indexed: 12/21/2022]
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25
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Hyötyläinen T, Ahonen L, Pöhö P, Orešič M. Lipidomics in biomedical research-practical considerations. Biochim Biophys Acta Mol Cell Biol Lipids 2017; 1862:800-803. [PMID: 28408341 DOI: 10.1016/j.bbalip.2017.04.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 04/06/2017] [Accepted: 04/08/2017] [Indexed: 02/06/2023]
Abstract
Lipids have many central physiological roles including as structural components of cell membranes, energy storage sources and intermediates in signaling pathways. Lipid-related disturbances are known to underlie many diseases and their co-morbidities. The emergence of lipidomics has empowered researchers to study lipid metabolism at the cellular as well as physiological levels at a greater depth than was previously possible. The key challenges ahead in the field of lipidomics in medical research lie in the development of experimental protocols and in silico techniques needed to study lipidomes at the systems level. Clinical questions where lipidomics may have an impact in healthcare settings also need to be identified, both from the health outcomes and health economics perspectives. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.
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Affiliation(s)
| | - Linda Ahonen
- Steno Diabetes Center A/S, DK-2820 Gentofte, Denmark
| | - Päivi Pöhö
- Faculty of Pharmacy, University of Helsinki, FI-00014 Helsinki, Finland
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
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26
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Han X. Lipidomics for precision medicine and metabolism: A personal view. Biochim Biophys Acta Mol Cell Biol Lipids 2017; 1862:804-807. [PMID: 28238864 DOI: 10.1016/j.bbalip.2017.02.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 02/18/2017] [Accepted: 02/21/2017] [Indexed: 12/24/2022]
Affiliation(s)
- Xianlin Han
- Center for Metabolic Origins of Disease, Sanford Burnham Prebys Medical Discovery Institute, 6400 Sanger Road, Orlando, FL 32827, USA.
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27
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Metabolomics: Definitions and Significance in Systems Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 965:3-17. [DOI: 10.1007/978-3-319-47656-8_1] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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28
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Jurowski K, Kochan K, Walczak J, Barańska M, Piekoszewski W, Buszewski B. Comprehensive review of trends and analytical strategies applied for biological samples preparation and storage in modern medical lipidomics: State of the art. Trends Analyt Chem 2017. [DOI: 10.1016/j.trac.2016.10.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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29
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Yang K, Han X. Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences. Trends Biochem Sci 2016; 41:954-969. [PMID: 27663237 DOI: 10.1016/j.tibs.2016.08.010] [Citation(s) in RCA: 355] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 08/16/2016] [Accepted: 08/19/2016] [Indexed: 12/16/2022]
Abstract
Lipidomics is a newly emerged discipline that studies cellular lipids on a large scale based on analytical chemistry principles and technological tools, particularly mass spectrometry. Recently, techniques have greatly advanced and novel applications of lipidomics in the biomedical sciences have emerged. This review provides a timely update on these aspects. After briefly introducing the lipidomics discipline, we compare mass spectrometry-based techniques for analysis of lipids and summarize very recent applications of lipidomics in health and disease. Finally, we discuss the status of the field, future directions, and advantages and limitations of the field.
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Affiliation(s)
- Kui Yang
- Division of Bioorganic Chemistry and Molecular Pharmacology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Xianlin Han
- Center for Metabolic Origins of Disease, Sanford Burnham Prebys Medical Discovery Institute, Orlando, Florida 32827, USA; College of Basic Medical Sciences, Zhejiang Chinese Medical University, 548 Bingwen Road, Hangzhou, Zhejiang 310053, China.
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30
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Fu Y, Zhou Z, Kong H, Lu X, Zhao X, Chen Y, Chen J, Wu Z, Xu Z, Zhao C, Xu G. Nontargeted Screening Method for Illegal Additives Based on Ultrahigh-Performance Liquid Chromatography-High-Resolution Mass Spectrometry. Anal Chem 2016; 88:8870-7. [PMID: 27480407 DOI: 10.1021/acs.analchem.6b02482] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Identification of illegal additives in complex matrixes is important in the food safety field. In this study a nontargeted screening strategy was developed to find illegal additives based on ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). First, an analytical method for possible illegal additives in complex matrixes was established including fast sample pretreatment, accurate UHPLC separation, and HRMS detection. Second, efficient data processing and differential analysis workflow were suggested and applied to find potential risk compounds. Third, structure elucidation of risk compounds was performed by (1) searching online databases [Metlin and the Human Metabolome Database (HMDB)] and an in-house database which was established at the above-defined conditions of UHPLC-HRMS analysis and contains information on retention time, mass spectra (MS), and tandem mass spectra (MS/MS) of 475 illegal additives, (2) analyzing fragment ions, and (3) referring to fragmentation rules. Fish was taken as an example to show the usefulness of the nontargeted screening strategy, and six additives were found in suspected fish samples. Quantitative analysis was further carried out to determine the contents of these compounds. The satisfactory application of this strategy in fish samples means that it can also be used in the screening of illegal additives in other kinds of food samples.
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Affiliation(s)
- Yanqing Fu
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science , Dalian 116023, China.,University of Chinese Academy of Sciences , Beijing 100049, China
| | - Zhihui Zhou
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science , Dalian 116023, China.,University of Chinese Academy of Sciences , Beijing 100049, China
| | - Hongwei Kong
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science , Dalian 116023, China.,University of Chinese Academy of Sciences , Beijing 100049, China
| | - Xin Lu
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science , Dalian 116023, China.,University of Chinese Academy of Sciences , Beijing 100049, China
| | - Xinjie Zhao
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science , Dalian 116023, China.,University of Chinese Academy of Sciences , Beijing 100049, China
| | - Yihui Chen
- Xiangshan Entry-Exit Inspection and Quarantine Bureau, Ningbo 315000, China
| | - Jia Chen
- Hangzhou Pooke Testing Technology Company, Limited, Hangzhou 310000, China
| | - Zeming Wu
- Thermo Fisher Scientific, China, Application Center, Shanghai 210623, China
| | - Zhiliang Xu
- Hangzhou Pooke Testing Technology Company, Limited, Hangzhou 310000, China
| | - Chunxia Zhao
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science , Dalian 116023, China.,University of Chinese Academy of Sciences , Beijing 100049, China
| | - Guowang Xu
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science , Dalian 116023, China.,University of Chinese Academy of Sciences , Beijing 100049, China
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31
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Abstract
Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.
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