1
|
Cajka T, Hricko J, Rakusanova S, Brejchova K, Novakova M, Rudl Kulhava L, Hola V, Paucova M, Fiehn O, Kuda O. Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics. Int J Mol Sci 2024; 25:2899. [PMID: 38474147 DOI: 10.3390/ijms25052899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/17/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.
Collapse
Affiliation(s)
- Tomas Cajka
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Jiri Hricko
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Stanislava Rakusanova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Kristyna Brejchova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Michaela Novakova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Lucie Rudl Kulhava
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Veronika Hola
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Michaela Paucova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Ondrej Kuda
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| |
Collapse
|
2
|
Habra H, Meijer JL, Shen T, Fiehn O, Gaul DA, Fernández FM, Rempfert KR, Metz TO, Peterson KE, Evans CR, Karnovsky A. metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics. Metabolites 2024; 14:125. [PMID: 38393017 PMCID: PMC10891690 DOI: 10.3390/metabo14020125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.
Collapse
Affiliation(s)
- Hani Habra
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
| | - Jennifer L. Meijer
- Department of Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA;
| | - Tong Shen
- West Coast Metabolomics Center, University of California, Davis, CA 95616, USA; (T.S.); (O.F.)
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, CA 95616, USA; (T.S.); (O.F.)
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, GA 30332, USA; (D.A.G.); (F.M.F.)
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, GA 30332, USA; (D.A.G.); (F.M.F.)
| | - Kaitlin R. Rempfert
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA; (K.R.R.); (T.O.M.)
| | - Thomas O. Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA; (K.R.R.); (T.O.M.)
| | - Karen E. Peterson
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA;
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Charles R. Evans
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
| |
Collapse
|
3
|
Yanshole VV, Melnikov AD, Yanshole LV, Zelentsova EA, Snytnikova OA, Osik NA, Fomenko MV, Savina ED, Kalinina AV, Sharshov KA, Dubovitskiy NA, Kobtsev MS, Zaikovskii AA, Mariasina SS, Tsentalovich YP. Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data. Metabolites 2023; 13:1088. [PMID: 37887413 PMCID: PMC10609207 DOI: 10.3390/metabo13101088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/07/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.
Collapse
Affiliation(s)
- Vadim V. Yanshole
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
- Department of Physics, Novosibirsk State University, Pirogova Str. 1, Novosibirsk 630090, Russia
| | - Arsenty D. Melnikov
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Lyudmila V. Yanshole
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Ekaterina A. Zelentsova
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Olga A. Snytnikova
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Nataliya A. Osik
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
- Department of Physics, Novosibirsk State University, Pirogova Str. 1, Novosibirsk 630090, Russia
| | - Maxim V. Fomenko
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
- Department of Physics, Novosibirsk State University, Pirogova Str. 1, Novosibirsk 630090, Russia
| | - Ekaterina D. Savina
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Anastasia V. Kalinina
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Kirill A. Sharshov
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Federal Research Center of Fundamental and Translational Medicine, Timakova Str. 2, Novosibirsk 630117, Russia; (K.A.S.); (N.A.D.)
| | - Nikita A. Dubovitskiy
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Federal Research Center of Fundamental and Translational Medicine, Timakova Str. 2, Novosibirsk 630117, Russia; (K.A.S.); (N.A.D.)
| | - Mikhail S. Kobtsev
- Department of Information Technologies, Novosibirsk State University, Pirogova Str. 1, Novosibirsk 630090, Russia;
| | - Anatolii A. Zaikovskii
- Department of Mathematics and Computer Science, Saint Petersburg State University, 14th Line V. O. 29, Saint Petersburg 199178, Russia;
| | - Sofia S. Mariasina
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia;
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow 119991, Russia
- RUDN University, Miklukho-Maklaya Str. 6, Moscow 117198, Russia
| | - Yuri P. Tsentalovich
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| |
Collapse
|
4
|
Ebbels TMD, van der Hooft JJJ, Chatelaine H, Broeckling C, Zamboni N, Hassoun S, Mathé EA. Recent advances in mass spectrometry-based computational metabolomics. Curr Opin Chem Biol 2023; 74:102288. [PMID: 36966702 PMCID: PMC11075003 DOI: 10.1016/j.cbpa.2023.102288] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 04/03/2023]
Abstract
The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".
Collapse
Affiliation(s)
- Timothy M D Ebbels
- Section of Bioinformatics, Department of Metabolism, Digestion & Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University & Research, Wageningen 6708 PB, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Haley Chatelaine
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Corey Broeckling
- Bioanalysis and Omics Center, Analytical Resources Core, Colorado State University, Fort Collins, CO, USA
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, USA; Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Ewy A Mathé
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA.
| |
Collapse
|
5
|
Ryan MJ, Grant-St James A, Lawler NG, Fear MW, Raby E, Wood FM, Maker GL, Wist J, Holmes E, Nicholson JK, Whiley L, Gray N. Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry. J Proteome Res 2023; 22:1419-1433. [PMID: 36828482 PMCID: PMC10167688 DOI: 10.1021/acs.jproteome.2c00682] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.
Collapse
Affiliation(s)
- Monique J Ryan
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Alanah Grant-St James
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Nathan G Lawler
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Mark W Fear
- Burn Injury Research Unit, University of Western Australia, Perth, Western Australia 6009, Australia.,Fiona Wood Foundation, Perth, Western Australia 6150, Australia
| | - Edward Raby
- Department of Microbiology, PathWest Laboratory Medicine, Perth, Western Australia 6009, Australia.,Department of Infectious Diseases, Fiona Stanley Hospital, Perth, Western Australia 6150, Australia
| | - Fiona M Wood
- Burn Injury Research Unit, University of Western Australia, Perth, Western Australia 6009, Australia.,WA Department of Health, Burns Service WA, Perth, Western Australia 6009, Australia.,Fiona Wood Foundation, Perth, Western Australia 6150, Australia
| | - Garth L Maker
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Julien Wist
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Elaine Holmes
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Jeremy K Nicholson
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Luke Whiley
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Perron Institute for Neurological and Translational Science, Nedlands, Western Australia 6009, Australia
| | - Nicola Gray
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| |
Collapse
|
6
|
Östman JR, Pinto RC, Ebbels TMD, Thysell E, Hallmans G, Moazzami AA. Identification of prediagnostic metabolites associated with prostate cancer risk by untargeted mass spectrometry-based metabolomics: A case-control study nested in the Northern Sweden Health and Disease Study. Int J Cancer 2022; 151:2115-2127. [PMID: 35866293 PMCID: PMC9804595 DOI: 10.1002/ijc.34223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 06/13/2022] [Accepted: 06/29/2022] [Indexed: 01/07/2023]
Abstract
Prostate cancer (PCa) is the most common cancer form in males in many European and American countries, but there are still open questions regarding its etiology. Untargeted metabolomics can produce an unbiased global metabolic profile, with the opportunity for uncovering new plasma metabolites prospectively associated with risk of PCa, providing insights into disease etiology. We conducted a prospective untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis using prediagnostic fasting plasma samples from 752 PCa case-control pairs nested within the Northern Sweden Health and Disease Study (NSHDS). The pairs were matched by age, BMI, and sample storage time. Discriminating features were identified by a combination of orthogonal projection to latent structures-effect projections (OPLS-EP) and Wilcoxon signed-rank tests. Their prospective associations with PCa risk were investigated by conditional logistic regression. Subgroup analyses based on stratification by disease aggressiveness and baseline age were also conducted. Various free fatty acids and phospholipids were positively associated with overall risk of PCa and in various stratification subgroups. Aromatic amino acids were positively associated with overall risk of PCa. Uric acid was positively, and glucose negatively, associated with risk of PCa in the older subgroup. This is the largest untargeted LC-MS based metabolomics study to date on plasma metabolites prospectively associated with risk of developing PCa. Different subgroups of disease aggressiveness and baseline age showed different associations with metabolites. The findings suggest that shifts in plasma concentrations of metabolites in lipid, aromatic amino acid, and glucose metabolism are associated with risk of developing PCa during the following two decades.
Collapse
Affiliation(s)
- Johnny R Östman
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Rui C Pinto
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,UK Dementia Research Institute, Imperial College London, London, UK
| | - Timothy M D Ebbels
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Elin Thysell
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Sweden
| | - Ali A Moazzami
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| |
Collapse
|
7
|
Advanced Microsamples: Current Applications and Considerations for Mass Spectrometry-Based Metabolic Phenotyping Pipelines. SEPARATIONS 2022. [DOI: 10.3390/separations9070175] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Microsamples are collections usually less than 50 µL, although all devices that we have captured as part of this review do not fit within this definition (as some can perform collections of up to 600 µL); however, they are considered microsamples that can be self-administered. These microsamples have been introduced in pre-clinical, clinical, and research settings to overcome obstacles in sampling via traditional venepuncture. However, venepuncture remains the sampling gold standard for the metabolic phenotyping of blood. This presents several challenges in metabolic phenotyping workflows: accessibility for individuals in rural and remote areas (due to the need for trained personnel), the unamenable nature to frequent sampling protocols in longitudinal research (for its invasive nature), and sample collection difficulty in the young and elderly. Furthermore, venous sample stability may be compromised when the temperate conditions necessary for cold-chain transport are beyond control. Alternatively, research utilising microsamples extends phenotyping possibilities to inborn errors of metabolism, therapeutic drug monitoring, nutrition, as well as sport and anti-doping. Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into the metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the ‘haematocrit effect’ in dried blood spot microsampling can be overcome. In this review, we provide an overview of the common uses and workflows for microsampling in metabolic phenotyping research. We discuss the advancements in technologies, highlighting key considerations and remaining knowledge gaps for the employment of microsamples in metabolic phenotyping research. This review supports the translation of research from the ‘bench to the community’.
Collapse
|