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Liang D, Li Z, Vlaanderen J, Tang Z, Jones DP, Vermeulen R, Sarnat JA. A State-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: Current Progress, Analytical Challenges, and Recommendations for Future Direction. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:56002. [PMID: 37192319 PMCID: PMC10187974 DOI: 10.1289/ehp11851] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 03/22/2023] [Accepted: 03/30/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Understanding the mechanistic basis of air pollution toxicity is dependent on accurately characterizing both exposure and biological responses. Untargeted metabolomics, an analysis of small-molecule metabolic phenotypes, may offer improved estimation of exposures and corresponding health responses to complex environmental mixtures such as air pollution. The field remains nascent, however, with questions concerning the coherence and generalizability of findings across studies, study designs and analytical platforms. OBJECTIVES We aimed to review the state of air pollution research from studies using untargeted high-resolution metabolomics (HRM), highlight the areas of concordance and dissimilarity in methodological approaches and reported findings, and discuss a path forward for future use of this analytical platform in air pollution research. METHODS We conducted a state-of-the-science review to a) summarize recent research of air pollution studies using untargeted metabolomics and b) identify gaps in the peer-reviewed literature and opportunities for addressing these gaps in future designs. We screened articles published within Pubmed and Web of Science between 1 January 2005 and 31 March 2022. Two reviewers independently screened 2,065 abstracts, with discrepancies resolved by a third reviewer. RESULTS We identified 47 articles that applied untargeted metabolomics on serum, plasma, whole blood, urine, saliva, or other biospecimens to investigate the impact of air pollution exposures on the human metabolome. Eight hundred sixteen unique features confirmed with level-1 or -2 evidence were reported to be associated with at least one or more air pollutants. Hypoxanthine, histidine, serine, aspartate, and glutamate were among the 35 metabolites consistently exhibiting associations with multiple air pollutants in at least 5 independent studies. Oxidative stress and inflammation-related pathways-including glycerophospholipid metabolism, pyrimidine metabolism, methionine and cysteine metabolism, tyrosine metabolism, and tryptophan metabolism-were the most commonly perturbed pathways reported in > 70 % of studies. More than 80% of the reported features were not chemically annotated, limiting the interpretability and generalizability of the findings. CONCLUSIONS Numerous investigations have demonstrated the feasibility of using untargeted metabolomics as a platform linking exposure to internal dose and biological response. Our review of the 47 existing untargeted HRM-air pollution studies points to an underlying coherence and consistency across a range of sample analytical quantitation methods, extraction algorithms, and statistical modeling approaches. Future directions should focus on validation of these findings via hypothesis-driven protocols and technical advances in metabolic annotation and quantification. https://doi.org/10.1289/EHP11851.
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Affiliation(s)
- Donghai Liang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Zhenjiang Li
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Jelle Vlaanderen
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Ziyin Tang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Dean P. Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Roel Vermeulen
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Jeremy A. Sarnat
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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2
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Parastar H, Tauler R. Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.201801134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Hadi Parastar
- Department of Chemistry Sharif University of Technology Tehran Iran
| | - Roma Tauler
- Department of Environmental Chemistry IDAEA-CSIC 08034 Barcelona Spain
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3
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Rose B, May JC, Reardon AR, McLean JA. Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1229-1237. [PMID: 35653638 PMCID: PMC9516683 DOI: 10.1021/jasms.2c00067] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Structures for lossless ion manipulation-based high-resolution ion mobility (HRIM) interfaced with mass spectrometry has emerged as a powerful tool for the separation and analysis of many isomeric systems. IM-derived collision cross section (CCS) is increasingly used as a molecular descriptor for structural analysis and feature annotation, but there are few studies on the calibration of CCS from HRIM measurements. Here, we examine the accuracy, reproducibility, and practical applicability of CCS calibration strategies for a broad range of lipid subclasses and develop a straightforward and generalizable framework for obtaining high-resolution CCS values. We explore the utility of using structurally similar custom calibrant sets as well as lipid subclass-specific empirically derived correction factors. While the lipid calibrant sets lowered overall bias of reference CCS values from ∼2-3% to ∼0.5%, application of the subclass-specific correction to values calibrated with a broadly available general calibrant set resulted in biases <0.4%. Using this method, we generated a high-resolution CCS database containing over 90 lipid values with HRIM. To test the applicability of this method to a broader class range typical of lipidomics experiments, a standard lipid mix was analyzed. The results highlight the importance of both class and arrival time range when correcting or scaling CCS values and provide guidance for implementation of the method for more general applications.
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Koomen DC, May JC, McLean JA. Insights and prospects for ion mobility-mass spectrometry in clinical chemistry. Expert Rev Proteomics 2022; 19:17-31. [PMID: 34986717 PMCID: PMC8881341 DOI: 10.1080/14789450.2022.2026218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
INTRODUCTION Ion mobility-mass spectrometry is an emerging technology in the clinical setting for high throughput and high confidence molecular characterization from complex biological samples. Ion mobility spectrometry can provide isomer separations on the basis of molecular structure, the ability of which is increasing through technological developments that afford enhanced resolving power. Integrating multiple separation dimensions, such as liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) provide dramatic enhancements in the mitigation of molecular interferences for high accuracy clinical measurements. AREAS COVERED Multidimensional separations with LC-IM-MS provide better selectivity and sensitivity in molecular analysis. Mass spectrometry imaging of tissues to inform spatial molecular distribution is improved by complementary ion mobility analyses. Biomarker identification in surgical environments is enhanced by intraoperative biochemical analysis with mass spectrometry and holds promise for integration with ion mobility spectrometry. New prospects in high resolving power ion mobility are enhancing analysis capabilities, such as distinguishing isomeric compounds. EXPERT OPINION Ion mobility-mass spectrometry holds many prospects for the field of isomer identification, molecular imaging, and intraoperative tumor margin delineation in clinical settings. These advantages are afforded while maintaining fast analysis times and subsequently high throughput. High resolving power ion mobility will enhance these advantages further, in particular for analyses requiring high confidence isobaric selectivity and detection.
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Affiliation(s)
- David C. Koomen
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Jody C. May
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - John A. McLean
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
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5
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Miller DR, McClain ES, Dodds JN, Balinski A, May JC, McLean JA, Cliffel DE. Chlorpyrifos Disrupts Acetylcholine Metabolism Across Model Blood-Brain Barrier. Front Bioeng Biotechnol 2021; 9:622175. [PMID: 34513802 PMCID: PMC8431803 DOI: 10.3389/fbioe.2021.622175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 07/16/2021] [Indexed: 01/25/2023] Open
Abstract
Despite the significant progress in both scientific understanding and regulations, the safety of agricultural pesticides continues to be called into question. The need for complementary analytics to identify dysregulation events associated with chemical exposure and leverage this information to predict biological responses remains. Here, we present a platform that combines a model organ-on-chip neurovascular unit (NVU) with targeted mass spectrometry (MS) and electrochemical analysis to assess the impact of organophosphate (OP) exposure on blood-brain barrier (BBB) function. Using the NVU to simulate exposure, an escalating dose of the organophosphate chlorpyrifos (CPF) was administered. With up to 10 μM, neither CPF nor its metabolites were detected across the BBB (limit of quantitation 0.1 µM). At 30 µM CPF and above, targeted MS detected the main urinary metabolite, trichloropyridinol (TCP), across the BBB (0.025 µM) and no other metabolites. In the vascular chamber where CPF was directly applied, two primary metabolites of CPF, TCP and diethylthiophosphate (DETP), were both detected (0.1–5.7 µM). In a second experiment, a constant dose of 10 µM CPF was administered to the NVU, and though neither CPF nor its metabolites were detected across the BBB after 24 h, electrochemical analysis detected increases in acetylcholine levels on both sides of the BBB (up to 24.8 ± 3.4 µM) and these levels remained high over the course of treatment. In the vascular chamber where CPF was directly applied, only TCP was detected (ranging from 0.06 μM at 2 h to 0.19 μM at 24 h). These results provide chemical evidence of the substantial disruption induced by this widely used commercial pesticide. This work reinforces previously observed OP metabolism and mechanisms of impact, validates the use of the NVU for OP toxicology testing, and provides a model platform for analyzing these organotypic systems.
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Affiliation(s)
- Dusty R Miller
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
| | - Ethan S McClain
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
| | - James N Dodds
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States.,Center for Innovative Technology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, United States
| | - Andrzej Balinski
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States.,Center for Innovative Technology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, United States
| | - Jody C May
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States.,Center for Innovative Technology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, United States
| | - John A McLean
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States.,Center for Innovative Technology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, United States
| | - David E Cliffel
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, United States.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, United States
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6
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Davis DE, Leaptrot KL, Koomen DC, May JC, Cavalcanti GDA, Padilha MC, Pereira HMG, McLean JA. Multidimensional Separations of Intact Phase II Steroid Metabolites Utilizing LC-Ion Mobility-HRMS. Anal Chem 2021; 93:10990-10998. [PMID: 34319704 DOI: 10.1021/acs.analchem.1c02163] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The detection and unambiguous identification of anabolic-androgenic steroid metabolites are essential in clinical, forensic, and antidoping analyses. Recently, sulfate phase II steroid metabolites have received increased attention in steroid metabolism and drug testing. In large part, this is because phase II steroid metabolites are excreted for an extended time, making them a potential long-term chemical marker of choice for tracking steroid misuse in sports. Comprehensive analytical methods, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS), have been used to detect and identify glucuronide and sulfate steroids in human urine with high sensitivity and reliability. However, LC-MS/MS identification strategies can be hindered by the fact that phase II steroid metabolites generate nonselective ion fragments across the different metabolite markers, limiting the confidence in metabolite identifications that rely on exact mass measurement and MS/MS information. Additionally, liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is sometimes insufficient at fully resolving the analyte peaks from the sample matrix (commonly urine) chemical noise, further complicating accurate identification efforts. Therefore, we developed a liquid chromatography-ion mobility-high resolution mass spectrometry (LC-IM-HRMS) method to increase the peak capacity and utilize the IM-derived collision cross section (CCS) values as an additional molecular descriptor for increased selectivity and to improve identifications of intact steroid analyses at low concentrations.
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Affiliation(s)
- Don E Davis
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Katrina L Leaptrot
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - David C Koomen
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jody C May
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Gustavo de A Cavalcanti
- Brazilian Doping Control Laboratory (LBCD), Chemistry Institute, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ 21941-598, Brazil
| | - Monica C Padilha
- Brazilian Doping Control Laboratory (LBCD), Chemistry Institute, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ 21941-598, Brazil
| | - Henrique M G Pereira
- Brazilian Doping Control Laboratory (LBCD), Chemistry Institute, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ 21941-598, Brazil
| | - John A McLean
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
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7
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Davis DE, Sherrod SD, Gant-Branum RL, Colby JM, McLean JA. Targeted Strategy to Analyze Antiepileptic Drugs in Human Serum by LC-MS/MS and LC-Ion Mobility-MS. Anal Chem 2020; 92:14648-14656. [PMID: 33047601 PMCID: PMC10103591 DOI: 10.1021/acs.analchem.0c03172] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Routine small-molecule analysis is challenging owing to the need for high selectivity and/or low limits of quantification. This work reports a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify 14 antiepileptic drugs (AEDs) in human serum. For the optimized LC-MS/MS method described herein, we applied the guidelines outlined in the Clinical and Laboratory Standards Institute (CLSI) LC-MS C62-A document and the U.S. Food and Drug Administration (FDA) Bioanalytical Method Validation Guidance for Industry to evaluate the quality of the assay. In these studies, AED linearity, analyte recovery, matrix effects, precision, and accuracy were assessed. Using liquid chromatography-drift tube ion mobility-mass spectrometry (LC-DTIM-MS), a qualitative method was also used to increase confidence in AED identification using accurate mass and collision cross section (CCS) measurements. The LC-DTIM-MS method was also used to assess the ability of drift tube CCS measurements to aid in the separation and identification of AED structural isomers and other AEDs. These data show that another dimension of information, namely CCS measurements, provides an orthogonal dimension of structural information needed for AED analysis. Multiplexed AED measurements using LC-MS/MS and LC-DTIM-MS have the potential to enable better optimization of dosing owing to the high precision capabilities available in these types of analytical studies. Taken together, these data also show the ability to increase confidence in small-molecule identification and quantification using these analytical technologies.
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Affiliation(s)
- Don E Davis
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Stacy D Sherrod
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Randi L Gant-Branum
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jennifer M Colby
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37235, United States
| | - John A McLean
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
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8
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Allwardt V, Ainscough AJ, Viswanathan P, Sherrod SD, McLean JA, Haddrick M, Pensabene V. Translational Roadmap for the Organs-on-a-Chip Industry toward Broad Adoption. Bioengineering (Basel) 2020; 7:E112. [PMID: 32947816 PMCID: PMC7552662 DOI: 10.3390/bioengineering7030112] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/09/2020] [Accepted: 09/14/2020] [Indexed: 12/11/2022] Open
Abstract
Organs-on-a-Chip (OOAC) is a disruptive technology with widely recognized potential to change the efficiency, effectiveness, and costs of the drug discovery process; to advance insights into human biology; to enable clinical research where human trials are not feasible. However, further development is needed for the successful adoption and acceptance of this technology. Areas for improvement include technological maturity, more robust validation of translational and predictive in vivo-like biology, and requirements of tighter quality standards for commercial viability. In this review, we reported on the consensus around existing challenges and necessary performance benchmarks that are required toward the broader adoption of OOACs in the next five years, and we defined a potential roadmap for future translational development of OOAC technology. We provided a clear snapshot of the current developmental stage of OOAC commercialization, including existing platforms, ancillary technologies, and tools required for the use of OOAC devices, and analyze their technology readiness levels. Using data gathered from OOAC developers and end-users, we identified prevalent challenges faced by the community, strategic trends and requirements driving OOAC technology development, and existing technological bottlenecks that could be outsourced or leveraged by active collaborations with academia.
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Affiliation(s)
- Vanessa Allwardt
- Center for Innovative Technology, Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; (V.A.); (S.D.S.); (J.A.M.)
| | | | - Priyalakshmi Viswanathan
- Medicines Discovery Catapult, Alderley Park, Alderley Edge, Macclesfield SK10 4TG, UK; (P.V.); (M.H.)
| | - Stacy D. Sherrod
- Center for Innovative Technology, Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; (V.A.); (S.D.S.); (J.A.M.)
| | - John A. McLean
- Center for Innovative Technology, Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; (V.A.); (S.D.S.); (J.A.M.)
- Vanderbilt Institute of Chemical Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
| | - Malcolm Haddrick
- Medicines Discovery Catapult, Alderley Park, Alderley Edge, Macclesfield SK10 4TG, UK; (P.V.); (M.H.)
| | - Virginia Pensabene
- School of Electronic and Electrical Engineering, School of Medicine, Leeds Institute of Medical Research at St. James’s, University of Leeds, Leeds LS2 9JT, UK
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9
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Picache JA, May JC, McLean JA. Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization. Anal Chem 2020; 92:10759-10767. [DOI: 10.1021/acs.analchem.0c02137] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jaqueline A. Picache
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jody C. May
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - John A. McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
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10
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Poland JC, Leaptrot KL, Sherrod SD, Flynn CR, McLean JA. Collision Cross Section Conformational Analyses of Bile Acids via Ion Mobility-Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:10.1021/jasms.0c00015. [PMID: 32525305 PMCID: PMC8059067 DOI: 10.1021/jasms.0c00015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Bile acids serve as one of the most important classes of biological molecules in the gastrointestinal system. Due to their structural similarity, bile acids have historically been difficult to accurately annotate in complex biological matrices using mass spectrometry. They often have identical or nominally similar mass-to-charge ratios and similar fragmentation patterns that make identification by mass spectrometry arduous, normally involving chemical derivatization and separation via liquid chromatography. Here, we demonstrate the use of drift tube ion mobility (DTIM) to derive collision cross section (CCS) values in nitrogen drift gas (DTCCSN2) for use as an additional descriptor to facilitate expedited bile acid identification. We also explore trends in DTIM measurements and detail structural characteristics for differences in DTCCSN2 values between subclasses of bile acid molecules.
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Affiliation(s)
- James C Poland
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Katrina L Leaptrot
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Stacy D Sherrod
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Charles Robb Flynn
- Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37235, United States
| | - John A McLean
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
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Odenkirk MT, Baker ES. Utilizing Drift Tube Ion Mobility Spectrometry for the Evaluation of Metabolites and Xenobiotics. Methods Mol Biol 2020; 2084:35-54. [PMID: 31729652 DOI: 10.1007/978-1-0716-0030-6_2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Metabolites and xenobiotics are small molecules with a molecular weight that often falls below 600 Da. Over the last few decades, multiple small molecule databases have been curated listing structures, masses, and fragmentation spectra possible in metabolomic and exposomic measurements. To date only a small portion of the spectra in these databases are experimentally derived due to the high expense of obtaining, synthesizing, and analyzing standards. A vast majority of spectra have thus been created using theoretical programs to fit the available experimental data. The errors associated with theoretical data have however caused problems with current small molecule identifications, and accurate quantitation as searching the databases using just one or two analysis dimensions (i.e., chromatography retention times and mass spectrometry (MS) m/z values) results in numerous annotations for each experimental feature. Additional analysis dimensions are therefore needed to better annotate and identify small molecules. Drift tube ion mobility spectrometry coupled with MS (DTIMS-MS) is a promising technique to address this challenge as it is able to perform rapid structural evaluations of small molecules in complex matrices by assessing the collision cross section values for each in addition to their m/z values. The use of IMS in conjunction with other separation techniques such as gas or liquid chromatography and MS has therefore enabled more accurate identifications for the small molecules present in complex biological and environmental samples. Here, we present a review of relevant parameter considerations for DTIMS application with emphasis on xenobiotics and metabolomics isomer separations.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.
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12
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Poland JC, Schrimpe-Rutledge AC, Sherrod SD, Flynn CR, McLean JA. Utilizing Untargeted Ion Mobility-Mass Spectrometry To Profile Changes in the Gut Metabolome Following Biliary Diversion Surgery. Anal Chem 2019; 91:14417-14423. [PMID: 31573190 DOI: 10.1021/acs.analchem.9b02924] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Obesity and obesity-related disorders are a global epidemic affecting over 10% of the world's population. Treatment of these diseases has become increasingly challenging and expensive. The most effective and durable treatment for Class III obesity (body mass index ≥35 kg/m2) is bariatric surgery, namely, Roux-en-Y gastric bypass (RYGB) and vertical sleeve gastrectomy. These procedures are associated with increased circulating bile acids, molecules that not only facilitate intestinal fat absorption but are also potent hormones regulating numerous metabolic pathways. We recently reported on a novel surgical procedure in mice, termed distal gallbladder bile diversion to the ileum (GB-ILdist), that emulates the altered bile flow after RYGB without other manipulations of gastrointestinal anatomy. GB-ILdist improves oral glucose tolerance in mice made obese with high-fat diet. This is accompanied by fat malabsorption and weight loss, which complicates studying the role of elevated circulating bile acids in metabolic control. A less aggressive surgery in which the gallbladder bile is diverted to the proximal ileum, termed GB-ILprox, also improves glucose control but is not accompanied by fat malabsorption. To better understand the differential effects achieved by these bile diversion procedures, an untargeted ultraperformance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS) method was optimized for fecal samples derived from mice that have undergone bile diversion surgery. Utilizing the UPLC-IM-MS method, we were able to identify dysregulation of bile acids, short-chain fatty acids, and cholesterol derivatives that contribute to the differential metabolism resulting from these surgeries.
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Affiliation(s)
- James C Poland
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , United States
| | - Alexandra C Schrimpe-Rutledge
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , United States
| | - Stacy D Sherrod
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , United States
| | - Charles Robb Flynn
- Department of Surgery , Vanderbilt University Medical Center , Nashville , Tennessee 37235 , United States
| | - John A McLean
- Center for Innovative Technology, Department of Chemistry, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , United States
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13
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Tebani A, Afonso C, Bekri S. Advances in metabolome information retrieval: turning chemistry into biology. Part I: analytical chemistry of the metabolome. J Inherit Metab Dis 2018; 41:379-391. [PMID: 28840392 PMCID: PMC5959978 DOI: 10.1007/s10545-017-0074-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 06/28/2017] [Accepted: 07/14/2017] [Indexed: 12/20/2022]
Abstract
Metabolites are small molecules produced by enzymatic reactions in a given organism. Metabolomics or metabolic phenotyping is a well-established omics aimed at comprehensively assessing metabolites in biological systems. These comprehensive analyses use analytical platforms, mainly nuclear magnetic resonance spectroscopy and mass spectrometry, along with associated separation methods to gather qualitative and quantitative data. Metabolomics holistically evaluates biological systems in an unbiased, data-driven approach that may ultimately support generation of hypotheses. The approach inherently allows the molecular characterization of a biological sample with regard to both internal (genetics) and environmental (exosome, microbiome) influences. Metabolomics workflows are based on whether the investigator knows a priori what kind of metabolites to assess. Thus, a targeted metabolomics approach is defined as a quantitative analysis (absolute concentrations are determined) or a semiquantitative analysis (relative intensities are determined) of a set of metabolites that are possibly linked to common chemical classes or a selected metabolic pathway. An untargeted metabolomics approach is a semiquantitative analysis of the largest possible number of metabolites contained in a biological sample. This is part I of a review intending to give an overview of the state of the art of major metabolic phenotyping technologies. Furthermore, their inherent analytical advantages and limits regarding experimental design, sample handling, standardization and workflow challenges are discussed.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Carlos Afonso
- Normandie Université, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000, Rouen, France
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000, Rouen, France.
- Normandie Université, UNIROUEN, CHU Rouen, IRIB, INSERM U1245, 76000, Rouen, France.
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14
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Tauler R, Parastar H. Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists. Angew Chem Int Ed Engl 2018; 61:e201801134. [DOI: 10.1002/anie.201801134] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Indexed: 11/08/2022]
Affiliation(s)
- Roma Tauler
- IDAEA-CSIC Environmental Chemistry Jordi Girona 18-26 08034 Barcelona SPAIN
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15
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Abstract
In this review, we focus on an important aspect of ion mobility (IM) research, namely the reporting of quantitative ion mobility measurements in the form of the gas-phase collision cross section (CCS), which has provided a common basis for comparison across different instrument platforms and offers a unique form of structural information, namely size and shape preferences of analytes in the absence of bulk solvent. This review surveys the over 24,000 CCS values reported from IM methods spanning the era between 1975 to 2015, which provides both a historical and analytical context for the contributions made thus far, as well as insight into the future directions that quantitative ion mobility measurements will have in the analytical sciences. The analysis was conducted in 2016, so CCS values reported in that year are purposely omitted. In another few years, a review of this scope will be intractable, as the number of CCS values which will be reported in the next three to five years is expected to exceed the total amount currently published in the literature.
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Affiliation(s)
- Jody C May
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Caleb B Morris
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - John A McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University , Nashville, Tennessee 37235, United States
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16
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Brown JA, Codreanu SG, Shi M, Sherrod SD, Markov DA, Neely MD, Britt CM, Hoilett OS, Reiserer RS, Samson PC, McCawley LJ, Webb DJ, Bowman AB, McLean JA, Wikswo JP. Metabolic consequences of inflammatory disruption of the blood-brain barrier in an organ-on-chip model of the human neurovascular unit. J Neuroinflammation 2016; 13:306. [PMID: 27955696 PMCID: PMC5153753 DOI: 10.1186/s12974-016-0760-y] [Citation(s) in RCA: 108] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 11/07/2016] [Indexed: 11/24/2022] Open
Abstract
Background Understanding blood-brain barrier responses to inflammatory stimulation (such as lipopolysaccharide mimicking a systemic infection or a cytokine cocktail that could be the result of local or systemic inflammation) is essential to understanding the effect of inflammatory stimulation on the brain. It is through the filter of the blood-brain barrier that the brain responds to outside influences, and the blood-brain barrier is a critical point of failure in neuroinflammation. It is important to note that this interaction is not a static response, but one that evolves over time. While current models have provided invaluable information regarding the interaction between cytokine stimulation, the blood-brain barrier, and the brain, these approaches—whether in vivo or in vitro—have often been only snapshots of this complex web of interactions. Methods We utilize new advances in microfluidics, organs-on-chips, and metabolomics to examine the complex relationship of inflammation and its effects on blood-brain barrier function ex vivo and the metabolic consequences of these responses and repair mechanisms. In this study, we pair a novel dual-chamber, organ-on-chip microfluidic device, the NeuroVascular Unit, with small-volume cytokine detection and mass spectrometry analysis to investigate how the blood-brain barrier responds to two different but overlapping drivers of neuroinflammation, lipopolysaccharide and a cytokine cocktail of IL-1β, TNF-α, and MCP1,2. Results In this study, we show that (1) during initial exposure to lipopolysaccharide, the blood-brain barrier is compromised as expected, with increased diffusion and reduced presence of tight junctions, but that over time, the barrier is capable of at least partial recovery; (2) a cytokine cocktail also contributes to a loss of barrier function; (3) from this time-dependent cytokine activation, metabolic signature profiles can be obtained for both the brain and vascular sides of the blood-brain barrier model; and (4) collectively, we can use metabolite analysis to identify critical pathways in inflammatory response. Conclusions Taken together, these findings present new data that allow us to study the initial effects of inflammatory stimulation on blood-brain barrier disruption, cytokine activation, and metabolic pathway changes that drive the response and recovery of the barrier during continued inflammatory exposure. Electronic supplementary material The online version of this article (doi:10.1186/s12974-016-0760-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jacquelyn A Brown
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37235, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA
| | - Simona G Codreanu
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA.,Center for Innovative Technology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Mingjian Shi
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, 37235, USA
| | - Stacy D Sherrod
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA.,Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA.,Center for Innovative Technology, Vanderbilt University, Nashville, TN, 37235, USA.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Dmitry A Markov
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - M Diana Neely
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, 37232, USA
| | - Clayton M Britt
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37235, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA
| | - Orlando S Hoilett
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA
| | - Ronald S Reiserer
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37235, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA
| | - Philip C Samson
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37235, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA
| | - Lisa J McCawley
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA.,Department of Cancer Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Donna J Webb
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA.,Department of Biological Sciences, Vanderbilt University, Nashville, TN, 37235, USA
| | - Aaron B Bowman
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, 37232, USA.,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.,Department of Biochemistry, Vanderbilt University, Nashville, TN, 37232, USA
| | - John A McLean
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA.,Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA.,Center for Innovative Technology, Vanderbilt University, Nashville, TN, 37235, USA.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - John P Wikswo
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37235, USA. .,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, 6301 Stevenson Center, Nashville, TN, 37235, USA. .,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA. .,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA.
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Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, McLean JA. Untargeted Metabolomics Strategies-Challenges and Emerging Directions. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2016; 27:1897-1905. [PMID: 27624161 PMCID: PMC5110944 DOI: 10.1007/s13361-016-1469-y] [Citation(s) in RCA: 667] [Impact Index Per Article: 83.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 07/27/2016] [Accepted: 07/29/2016] [Indexed: 05/05/2023]
Abstract
Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies-specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described. Graphical Abstract ᅟ.
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Affiliation(s)
- Alexandra C Schrimpe-Rutledge
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, 37235, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, 37235, USA
| | - Simona G Codreanu
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, 37235, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, 37235, USA
| | - Stacy D Sherrod
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, 37235, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, 37235, USA
| | - John A McLean
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA.
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, 37235, USA.
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, 37235, USA.
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, 37235, USA.
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18
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Tebani A, Abily-Donval L, Afonso C, Marret S, Bekri S. Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era. Int J Mol Sci 2016; 17:ijms17071167. [PMID: 27447622 PMCID: PMC4964538 DOI: 10.3390/ijms17071167] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Revised: 07/12/2016] [Accepted: 07/15/2016] [Indexed: 12/29/2022] Open
Abstract
Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.
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Affiliation(s)
- Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76031, France.
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, Rouen 76000, France.
| | - Lenaig Abily-Donval
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Department of Neonatal Pediatrics and Intensive Care, Rouen University Hospital, Rouen 76031, France.
| | - Carlos Afonso
- Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, Rouen 76000, France.
| | - Stéphane Marret
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
- Department of Neonatal Pediatrics and Intensive Care, Rouen University Hospital, Rouen 76031, France.
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76031, France.
- Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
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19
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May JC, McLean JA. Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:387-409. [PMID: 27306312 PMCID: PMC5763907 DOI: 10.1146/annurev-anchem-071015-041734] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.
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Affiliation(s)
- Jody C May
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235;
| | - John A McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235;
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20
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Hutson MS, Alexander PG, Allwardt V, Aronoff DM, Bruner-Tran KL, Cliffel DE, Davidson JM, Gough A, Markov DA, McCawley LJ, McKenzie JR, McLean JA, Osteen KG, Pensabene V, Samson PC, Senutovitch NK, Sherrod SD, Shotwell MS, Taylor DL, Tetz LM, Tuan RS, Vernetti LA, Wikswo JP. Organs-on-Chips as Bridges for Predictive Toxicology. ACTA ACUST UNITED AC 2016. [DOI: 10.1089/aivt.2016.0003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- M. Shane Hutson
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Physics & Astronomy, Vanderbilt University, Nashville, Tennessee
| | - Peter G. Alexander
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Vanessa Allwardt
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
| | - David M. Aronoff
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kaylon L. Bruner-Tran
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Obstetrics & Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - David E. Cliffel
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee
| | - Jeffrey M. Davidson
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
- Research Service, Department of Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee
| | - Albert Gough
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Dmitry A. Markov
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lisa J. McCawley
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jennifer R. McKenzie
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee
| | - John A. McLean
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee
| | - Kevin G. Osteen
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Obstetrics & Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee
- Research Service, Department of Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee
| | - Virginia Pensabene
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Philip C. Samson
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Physics & Astronomy, Vanderbilt University, Nashville, Tennessee
| | - Nina K. Senutovitch
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Stacy D. Sherrod
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee
| | - Matthew S. Shotwell
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - D. Lansing Taylor
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lauren M. Tetz
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rocky S. Tuan
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
- Department of Bioengineering, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
- Center for Cellular and Molecular Engineering, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
- Center for Military Medicine Research, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Lawrence A. Vernetti
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - John P. Wikswo
- Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology, Vanderbilt University, Nashville, Tennessee, and University of Pittsburgh, Pittsburgh, Pennsylvania
- Vanderbilt Institute for Integrative Biosystems Research & Education, Vanderbilt University, Nashville, Tennessee
- Department of Physics & Astronomy, Vanderbilt University, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Molecular Physiology & Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee
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May JC, Gant-Branum RL, McLean JA. Targeting the untargeted in molecular phenomics with structurally-selective ion mobility-mass spectrometry. Curr Opin Biotechnol 2016; 39:192-197. [PMID: 27132126 DOI: 10.1016/j.copbio.2016.04.013] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 04/06/2016] [Accepted: 04/13/2016] [Indexed: 12/25/2022]
Abstract
Systems-wide molecular phenomics is rapidly expanding through technological advances in instrumentation and bioinformatics. Strategies such as structural mass spectrometry, which utilizes size and shape measurements with molecular weight, serve to characterize the sum of molecular expression in biological contexts, where broad-scale measurements are made that are interpreted through big data statistical techniques to reveal underlying patterns corresponding to phenotype. The data density, data dimensionality, data projection, and data interrogation are all critical aspects of these approaches to turn data into salient information. Untargeted molecular phenomics is already having a dramatic impact in discovery science from drug discovery to synthetic biology. It is evident that these emerging techniques will integrate closely in broad efforts aimed at precision medicine.
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Affiliation(s)
- Jody Christopher May
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
| | - Randi Lee Gant-Branum
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
| | - John Allen McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA.
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22
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Szymańska E, Davies AN, Buydens LMC. Chemometrics for ion mobility spectrometry data: recent advances and future prospects. Analyst 2016; 141:5689-5708. [DOI: 10.1039/c6an01008c] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
This is the first comprehensive review on chemometric techniques used in ion mobility spectrometry data analysis.
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Affiliation(s)
- Ewa Szymańska
- Radboud University
- Institute for Molecules and Materials
- 6500 GL Nijmegen
- The Netherlands
- TI-COAST
| | - Antony N. Davies
- School of Applied Sciences
- Faculty of Computing
- Engineering and Science
- University of South Wales
- UK
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