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Roach J, Mital R, Haffner JJ, Colwell N, Coats R, Palacios HM, Liu Z, Godinho JLP, Ness M, Peramuna T, McCall LI. Microbiome metabolite quantification methods enabling insights into human health and disease. Methods 2024; 222:81-99. [PMID: 38185226 DOI: 10.1016/j.ymeth.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/27/2023] [Accepted: 12/13/2023] [Indexed: 01/09/2024] Open
Abstract
Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.
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Affiliation(s)
- Jarrod Roach
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Rohit Mital
- Department of Biology, University of Oklahoma
| | - Jacob J Haffner
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Nathan Colwell
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Randy Coats
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Horvey M Palacios
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Zongyuan Liu
- Department of Chemistry and Biochemistry, University of Oklahoma
| | | | - Monica Ness
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Thilini Peramuna
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Laura-Isobel McCall
- Department of Chemistry and Biochemistry, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma; Department of Chemistry and Biochemistry, San Diego State University.
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2
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Norris AC, Yazlovitskaya EM, Zhu L, Rose BS, May JC, Gibson-Corley KN, McLean JA, Stafford JM, Graham TR. Deficiency of the lipid flippase ATP10A causes diet-induced dyslipidemia in female mice. Sci Rep 2024; 14:343. [PMID: 38172157 PMCID: PMC10764864 DOI: 10.1038/s41598-023-50360-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
Genetic association studies have linked ATP10A and closely related type IV P-type ATPases (P4-ATPases) to insulin resistance and vascular complications, such as atherosclerosis. ATP10A translocates phosphatidylcholine and glucosylceramide across cell membranes, and these lipids or their metabolites play important roles in signal transduction pathways regulating metabolism. However, the influence of ATP10A on lipid metabolism in mice has not been explored. Here, we generated gene-specific Atp10A knockout mice and show that Atp10A-/- mice fed a high-fat diet did not gain excess weight relative to wild-type littermates. However, Atp10A-/- mice displayed female-specific dyslipidemia characterized by elevated plasma triglycerides, free fatty acids and cholesterol, as well as altered VLDL and HDL properties. We also observed increased circulating levels of several sphingolipid species along with reduced levels of eicosanoids and bile acids. The Atp10A-/- mice also displayed hepatic insulin resistance without perturbations to whole-body glucose homeostasis. Thus, ATP10A has a sex-specific role in regulating plasma lipid composition and maintaining hepatic liver insulin sensitivity in mice.
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Affiliation(s)
- Adriana C Norris
- Department of Biological Sciences, Vanderbilt University, 465 21St Ave S, Nashville, TN, 37212, USA
| | - Eugenia M Yazlovitskaya
- Department of Biological Sciences, Vanderbilt University, 465 21St Ave S, Nashville, TN, 37212, USA
| | - Lin Zhu
- Division of Endocrinology, Diabetes and Metabolism, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bailey S Rose
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Jody C May
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Katherine N Gibson-Corley
- Division of Comparative Medicine, Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John A McLean
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Innovative Technology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - John M Stafford
- Division of Endocrinology, Diabetes and Metabolism, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System, Veterans Affairs, Nashville, TN, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Todd R Graham
- Department of Biological Sciences, Vanderbilt University, 465 21St Ave S, Nashville, TN, 37212, USA.
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3
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Rāciņš O, Nagy G. Implementation of charged microdroplet-based derivatization of bile acids on a cyclic ion mobility spectrometry-mass spectrometry platform. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5577-5581. [PMID: 37853730 PMCID: PMC10638862 DOI: 10.1039/d3ay01447a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Herein, we report the first implementation of charged microdroplet-based derivatization on a commercially-available cyclic ion mobility spectrometry-mass spectrometry platform. We have demonstrated the potential of our approach to improve separability of challenging isomers, but more importantly to rapidly screen derivatization reactions through droplet chemistry. Additionally, the use of cyclic ion mobility separations and tandem mass spectrometry reveals insights into product formation that would be lost with single stage mass spectrometry. Overall, we anticipate broad utility of our methodology owing to the simple design and setup for performing these droplet-based reactions and future work coupling these reactions online with liquid chromatography.
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Affiliation(s)
- Olavs Rāciņš
- Department of Chemistry, University of Utah, 315 South 1400 East, Room 2020, Salt Lake City, Utah 84112, USA.
| | - Gabe Nagy
- Department of Chemistry, University of Utah, 315 South 1400 East, Room 2020, Salt Lake City, Utah 84112, USA.
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4
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Stewart AK, Foley MH, Dougherty MK, McGill SK, Gulati AS, Gentry EC, Hagey LR, Dorrestein PC, Theriot CM, Dodds JN, Baker ES. Using Multidimensional Separations to Distinguish Isomeric Amino Acid-Bile Acid Conjugates and Assess Their Presence and Perturbations in Model Systems. Anal Chem 2023; 95:15357-15366. [PMID: 37796494 PMCID: PMC10613829 DOI: 10.1021/acs.analchem.3c03057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Bile acids play key roles in nutrient uptake, inflammation, signaling, and microbiome composition. While previous bile acid analyses have primarily focused on profiling 5 canonical primary and secondary bile acids and their glycine and taurine amino acid-bile acid (AA-BA) conjugates, recent studies suggest that many other microbial conjugated bile acids (or MCBAs) exist. MCBAs are produced by the gut microbiota and serve as biomarkers, providing information about early disease onset and gut health. Here we analyzed 8 core bile acids synthetically conjugated with 22 proteinogenic and nonproteogenic amino acids totaling 176 MCBAs. Since many of the conjugates were isomeric and only 42 different m/z values resulted from the 176 MCBAs, a platform coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) was used for their separation. Their molecular characteristics were then used to create an in-house extended bile acid library for a combined total of 182 unique compounds. Additionally, ∼250 rare bile acid extracts were also assessed to provide additional resources for bile acid profiling and identification. This library was then applied to healthy mice dosed with antibiotics and humans having fecal microbiota transplantation (FMT) to assess the MCBA presence and changes in the gut before and after each perturbation.
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Affiliation(s)
- Allison K Stewart
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Matthew H Foley
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina 27607, United States
- Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Michael K Dougherty
- Department of Medicine, Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, United States
| | - Sarah K McGill
- Department of Medicine, Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, United States
| | - Ajay S Gulati
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
- Department of Pediatrics, Division of Gastroenterology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Emily C Gentry
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Lee R Hagey
- Division of Gastroenterology, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, California 92093, United States
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, Departments of Pharmacology and Pediatrics, University of California at San Diego, La Jolla, California 92093, United States
| | - Casey M Theriot
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina 27607, United States
| | - James N Dodds
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27607, United States
| | - Erin S Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27607, United States
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Wen JH, Guo AQ, Li MN, Yang H. A structural similarity networking assisted collision cross-section prediction interval filtering strategy for multi-compound identification of complex matrix by ion-mobility mass spectrometry. Anal Chim Acta 2023; 1278:341720. [PMID: 37709461 DOI: 10.1016/j.aca.2023.341720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/28/2023] [Accepted: 08/14/2023] [Indexed: 09/16/2023]
Abstract
Ion mobility coupled with mass spectrometry (IM-MS), an emerging technology for analysis of complex matrix, has been facing challenges due to the complexities of chemical structures and original data, as well as low-efficiency and error-proneness of manual operations. In this study, we developed a structural similarity networking assisted collision cross-section prediction interval filtering (SSN-CCSPIF) strategy. We first carried out a structural similarity networking (SSN) based on Tanimoto similarities among Morgan fingerprints to classify the authentic compounds potentially existing in complex matrix. By performing automatic regressive prediction statistics on mass-to-charge ratios (m/z) and collision cross-sections (CCS) with a self-built Python software, we explored the IM-MS feature trendlines, established filtering intervals and filtered potential compounds for each SSN classification. Chemical structures of all filtered compounds were further characterized by interpreting their multidimensional IM-MS data. To evaluate the applicability of SSN-CCSPIF, we selected Ginkgo biloba extract and dripping pills. The SSN-CCSPIF subtracted more background interferences (43.24%∼43.92%) than other similar strategies with conventional ClassyFire criteria (10.71%∼12.13%) or without compound classification (35.73%∼36.63%). Totally, 229 compounds, including eight potential new compounds, were characterized. Among them, seven isomeric pairs were discriminated with the integration of IM-separation. Using SSN-CCSPIF, we can achieve high-efficient analysis of complex IM-MS data and comprehensive chemical profiling of complex matrix to reveal their material basis.
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Affiliation(s)
- Jia-Hui Wen
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China
| | - An-Qi Guo
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China
| | - Meng-Ning Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China.
| | - Hua Yang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China.
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6
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Kemperman RHJ, Chouinard CD, Yost RA. Characterization of Bile Acid Isomers and the Implementation of High-Resolution Demultiplexing with Ion Mobility-Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37319333 DOI: 10.1021/jasms.3c00143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Bile acids (BAs) are a complex suite of clinically relevant metabolites that include many isomers. Liquid chromatography coupled to mass spectrometry (LC-MS) is an increasingly popular technique due to its high specificity and sensitivity; nonetheless, acquisition times are generally 10-20 min, and isomers are not always resolved. In this study, the application of ion mobility (IM) spectrometry coupled to MS was investigated to separate, characterize, and measure BAs. A subset of 16 BAs was studied, including three groups of isomers belonging to unconjugated, glycine-conjugated, and taurine-conjugated BA classes. A variety of strategies were explored to increase BA isomer separation such as changing the drift gas, measuring different ionic species (i.e., multimers and cationized species), and enhancing the instrumental resolving power. In general, Ar, N2, and CO2 provided the best peak shape, resolving power (Rp), and separation, especially CO2; He and SF6 were less preferable. Furthermore, measuring dimers versus monomers improved isomer separation due to enhanced gas-phase structural differences. A variety of cation adducts other than sodium were characterized. Mobility arrival times and isomer separation were affected by the choice of adduct, which was shown to be used to target certain BAs. Finally, a novel workflow that involves high-resolution demultiplexing in combination with dipivaloylmethane ion-neutral clusters was implemented to improve Rp dramatically. A maximum Rp increase was observed with lower IM field strengths to obtain longer drift times, increasing Rp from 52 to 187. A combination of these separation enhancement strategies demonstrates great potential for rapid BA analysis.
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Affiliation(s)
- Robin H J Kemperman
- University of Florida, Department of Chemistry, Gainesville, Florida 32611, USA
| | | | - Richard A Yost
- University of Florida, Department of Chemistry, Gainesville, Florida 32611, USA
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7
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Li X, Wang H, Jiang M, Ding M, Xu X, Xu B, Zou Y, Yu Y, Yang W. Collision Cross Section Prediction Based on Machine Learning. Molecules 2023; 28:molecules28104050. [PMID: 37241791 DOI: 10.3390/molecules28104050] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.
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Affiliation(s)
- Xiaohang Li
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Hongda Wang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meiting Jiang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Mengxiang Ding
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaoyan Xu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Bei Xu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yadan Zou
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yuetong Yu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Wenzhi Yang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
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8
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Rose BS, May JC, Picache JA, Codreanu SG, Sherrod SD, McLean JA. Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction. Bioinformatics 2022; 38:2872-2879. [PMID: 35561172 PMCID: PMC9306740 DOI: 10.1093/bioinformatics/btac197] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/22/2022] [Accepted: 03/29/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS). RESULTS We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class. AVAILABILITY AND IMPLEMENTATION All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bailey S Rose
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
| | - Jody C May
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
| | - Jaqueline A Picache
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
| | - Simona G Codreanu
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
| | - Stacy D Sherrod
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
| | - John A McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA
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9
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Song XC, Canellas E, Dreolin N, Goshawk J, Nerin C. A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:4457-4466. [PMID: 35380813 PMCID: PMC9011387 DOI: 10.1021/acs.jafc.2c00724] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The chemicals in food contact materials (FCMs) can migrate into food and endanger human health. In this study, we developed a database of traveling wave collision cross section in nitrogen (TWCCSN2) values for extractables and leachables from FCMs. The database contains a total of 1038 TWCCSN2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The TWCCSN2 values in the database were compared to previously published values, and 85.7, 87.7, and 64.9% [M + H]+, [M + Na]+, and [M - H]- adducts showed deviations <2%, with the presence of protomers, post-ion mobility spectrometry dissociation of noncovalent clusters and inconsistent calibration are possible sources of CCS deviations. Our experimental TWCCSN2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The TWCCSN2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.
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Affiliation(s)
- Xue-Chao Song
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Elena Canellas
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Nicola Dreolin
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, United Kingdom
| | - Jeff Goshawk
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, United Kingdom
| | - Cristina Nerin
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
- . Phone: +34 976761873
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10
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Hadavi D, Borzova M, Siegel TP, Honing M. Uncovering the behaviour of ions in the gas-phase to predict the ion mobility separation of isomeric steroid compounds. Anal Chim Acta 2022; 1200:339617. [DOI: 10.1016/j.aca.2022.339617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 01/19/2022] [Accepted: 02/14/2022] [Indexed: 11/30/2022]
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11
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Li MN, Shen BQ, Lu X, Gao W, Wen SS, Zhang X, Yang H, Li P. An integrated two-step filtering strategy of collision cross-section interval predicting and mass defect filtering for targeted identification of analogues in herbal medicines using liquid chromatography-ion mobility-mass spectrometry. J Chromatogr A 2021; 1657:462572. [PMID: 34601257 DOI: 10.1016/j.chroma.2021.462572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/10/2021] [Accepted: 09/13/2021] [Indexed: 12/14/2022]
Abstract
Rapid identification of chemical analogues in herbal medicines using liquid chromatography-mass spectrometry was an efficient tool for discoveries of potentially active ingredients. Multi-dimensional combination of various separation technologies could significantly enhance the capacities for detection of trace components and discrimination of multiple isomers. In this study, an integrated two-step filtering strategy on liquid chromatography-ion mobility tandem with quadrupole-time-of-flight mass spectrometry (LC-IM-QTOF MS) was developed for identification of analogues in complex matrixes. The extracted raw data were preliminarily filtered by a collision-cross section (CCS) interval generated from power regression with confidence level at 99% for prediction of analogues. Then, the remained ions were further screened using a mass defect filtering (MDF) window based on m/z and decimal m/z of potential skeletons and substituents. By applying this strategy, 86, 102, 73, and 57 isoquinoline alkaloids were identified in herbal materials of Coptis chinensis Franch (CC), C. deltoidea C.Y.Cheng et Hsiao (CD), C. teeta Wall (CT), and Corydalis yanhusuo W.T.Wang (CY). The integrated two-step filtering presented higher efficiencies on exclusion of the background interference and reducing the false-positive rates than previously reported approaches. This study facilitated the application of LC-IM-MS on small molecular analysis and promoted the discoveries of bioactive components of herbal medicines for further pharmacological researches and quality control.
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Affiliation(s)
- Meng-Ning Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Bing-Qing Shen
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Xu Lu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Wen Gao
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Shan-Shan Wen
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Xuan Zhang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Hua Yang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China.
| | - Ping Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China.
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