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Hao JD, Chen YY, Wang YZ, An N, Bai PR, Zhu QF, Feng YQ. Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics. Anal Chem 2023; 95:13330-13337. [PMID: 37609864 DOI: 10.1021/acs.analchem.3c02583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.
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Affiliation(s)
- Jun-Di Hao
- Department of Chemistry, Wuhan University, Wuhan 430072, China
| | - Yao-Yu Chen
- Department of Chemistry, Wuhan University, Wuhan 430072, China
| | - Yan-Zhen Wang
- Department of Chemistry, Wuhan University, Wuhan 430072, China
| | - Na An
- Department of Chemistry, Wuhan University, Wuhan 430072, China
| | - Pei-Rong Bai
- Department of Chemistry, Wuhan University, Wuhan 430072, China
| | - Quan-Fei Zhu
- School of Public Health, Wuhan University, Wuhan 430071, China
| | - Yu-Qi Feng
- Department of Chemistry, Wuhan University, Wuhan 430072, China
- Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan 430071, China
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2
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Koussiouris J, Looby N, Kulasingam V, Chandran V. A Solid-Phase Microextraction-Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease. Metabolites 2023; 13:963. [PMID: 37623906 PMCID: PMC10456752 DOI: 10.3390/metabo13080963] [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: 07/13/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
Approximately 25% of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). There is strong interest in identifying and validating biomarkers that can accurately and reliably predict conversion from psoriasis to PsA using novel technologies such as metabolomics. Lipids, in particular, are of key interest in psoriatic disease. We sought to develop a liquid chromatography-mass spectrometry (LC-MS) method to be used in conjunction with solid-phase microextraction (SPME) for analyzing fatty acids and similar molecules. A total of 25 chromatographic methods based on published lipid studies were tested on two LC columns. As a proof of concept, serum samples from psoriatic disease patients (n = 27 psoriasis and n = 26 PsA) were processed using SPME and run on the selected LC-MS method. The method that was best for analyzing fatty acids and fatty acid-like molecules was optimized and applied to serum samples. A total of 18 tentatively annotated features classified as fatty acids and other lipid compounds were statistically significant between psoriasis and PsA groups using both multivariate and univariate approaches. The SPME-LC-MS method developed and optimized was capable of detecting fatty acids and similar lipids that may aid in differentiating psoriasis and PsA patients.
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Affiliation(s)
- John Koussiouris
- Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada; (J.K.); (N.L.)
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada;
| | - Nikita Looby
- Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada; (J.K.); (N.L.)
| | - Vathany Kulasingam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada;
- Division of Clinical Biochemistry, Laboratory Medicine Program, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Vinod Chandran
- Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada; (J.K.); (N.L.)
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada;
- Division of Rheumatology, Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Medicine, Memorial University, St. John’s, NL A1B 3V6, Canada
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3
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Lim SY, Lim FLS, Criado-Navarro I, Yeo XH, Dayal H, Vemulapalli SD, Seah SJ, Laserna AKC, Yang X, Tan SH, Chan MY, Li SFY. Multi-Omics Investigation into Acute Myocardial Infarction: An Integrative Method Revealing Interconnections amongst the Metabolome, Lipidome, Glycome, and Metallome. Metabolites 2022; 12:metabo12111080. [PMID: 36355163 PMCID: PMC9693522 DOI: 10.3390/metabo12111080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/01/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. This work aims to investigate the translational potential of a multi-omics study (comprising metabolomics, lipidomics, glycomics, and metallomics) in revealing biomechanistic insights into AMI. Following the N-glycomics and metallomics studies performed by our group previously, untargeted metabolomic and lipidomic profiles were generated and analysed in this work via the use of a simultaneous metabolite/lipid extraction and liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis workflow. The workflow was applied to blood plasma samples from AMI cases (n = 101) and age-matched healthy controls (n = 66). The annotated metabolomic (number of features, n = 27) and lipidomic (n = 48) profiles, along with the glycomic (n = 37) and metallomic (n = 30) profiles of the same set of AMI and healthy samples were integrated and analysed. The integration method used here works by identifying a linear combination of maximally correlated features across the four omics datasets, via utilising both block-partial least squares-discriminant analysis (block-PLS-DA) based on sparse generalised canonical correlation analysis. Based on the multi-omics mapping of biomolecular interconnections, several postulations were derived. These include the potential roles of glycerophospholipids in N-glycan-modulated immunoregulatory effects, as well as the augmentation of the importance of Ca–ATPases in cardiovascular conditions, while also suggesting contributions of phosphatidylethanolamine in their functions. Moreover, it was shown that combining the four omics datasets synergistically enhanced the classifier performance in discriminating between AMI and healthy subjects. Fresh and intriguing insights into AMI, otherwise undetected via single-omics analysis, were revealed in this multi-omics study. Taken together, we provide evidence that a multi-omics strategy may synergistically reinforce and enhance our understanding of diseases.
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Affiliation(s)
- Si Ying Lim
- NUS Graduate School’s Integrative Sciences & Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
- Department of Chemistry, National University of Singapore, Singapore 117543, Singapore
| | - Felicia Li Shea Lim
- Department of Chemistry, National University of Singapore, Singapore 117543, Singapore
| | | | - Xin Hao Yeo
- Department of Chemistry, National University of Singapore, Singapore 117543, Singapore
| | - Hiranya Dayal
- Department of Chemistry, National University of Singapore, Singapore 117543, Singapore
| | | | - Song Jie Seah
- Department of Chemistry, National University of Singapore, Singapore 117543, Singapore
| | - Anna Karen Carrasco Laserna
- Department of Chemistry, National University of Singapore, Singapore 117543, Singapore
- Central Instrumentation Facility (Laguna Campus), Office of the Vice President for Research and Innovation, De La Salle University, Manila 1004, Philippines
| | - Xiaoxun Yang
- Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
| | - Sock Hwee Tan
- Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
| | - Mark Y. Chan
- Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
| | - Sam Fong Yau Li
- NUS Graduate School’s Integrative Sciences & Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
- Department of Chemistry, National University of Singapore, Singapore 117543, Singapore
- Correspondence: ; Tel.: +65-6516-2681; Fax: +65-6779-1691
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A rapid and robust method for amino acid quantification using a simple N-hydroxysuccinimide ester derivatization and liquid chromatography-ion mobility-mass spectrometry. Anal Bioanal Chem 2022; 414:5549-5559. [PMID: 35338375 DOI: 10.1007/s00216-022-03993-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/14/2022] [Accepted: 02/25/2022] [Indexed: 11/01/2022]
Abstract
The vast majority of mass spectrometry (MS)-based metabolomics studies employ reversed-phase liquid chromatography (RPLC) to separate analytes prior to MS detection. Highly polar metabolites, such as amino acids (AAs), are poorly retained by RPLC, making quantitation of these key species challenging across the broad concentration ranges typically observed in biological specimens, such as cell extracts. To improve the detection and quantitation of AAs in microglial cell extracts, the implementation of a 4-dimethylaminobenzoylamido acetic acid N-hydroxysuccinimide ester (DBAA-NHS) derivatization agent was explored for its ability to improve both analyte retention and detection limits in RPLC-MS. In addition to the introduction of the DBAA-NHS labeling reagent, a uniformly (U) 13C-labeled yeast extract was also introduced during the sample preparation workflow as an internal standard (IS) to eliminate artifacts and to enable targeted quantitation of AAs, as well as untargeted amine submetabolome profiling. To improve method sensitivity and selectivity, multiplexed drift-tube ion mobility (IM) was integrated into the LC-MS workflow, facilitating the separation of isomeric metabolites, and improving the structural identification of unknown metabolites. Implementation of the U-13C-labeled yeast extract during the multiplexed LC-IM-MS analysis enabled the quantitation of 19 of the 20 common AAs, supporting a linear dynamic range spanning up to three orders of magnitude in concentration for microglial cell extracts, in addition to reducing the required cell count for reliable quantitation from 10 to 5 million cells per sample.
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Recent Advances in Understanding of Alzheimer's Disease Progression through Mass Spectrometry-Based Metabolomics. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:1-17. [PMID: 35656096 PMCID: PMC9159642 DOI: 10.1007/s43657-021-00036-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Alzheimer's disease (AD) is the leading cause of dementia in the aging population, but despite extensive research, there is no consensus on the biological cause of AD. While AD research is dominated by protein/peptide-centric research based on the amyloid hypothesis, a theory that designates dysfunction in beta-amyloid production, accumulation, or disposal as the primary cause of AD, many studies focus on metabolomics as a means of understanding the biological processes behind AD progression. In this review, we discuss mass spectrometry (MS)-based AD metabolomics studies, including sample type and preparation, mass spectrometry specifications, and data analysis, as well as biological insights gleaned from these studies, with the hope of informing future AD metabolomic studies.
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Goh KKK, Toh WGH, Hee DKH, Ting EZW, Chua NGS, Zulkifli FIB, Sin LJ, Tan TT, Kwa ALH, Lim TP. Quantification of Fosfomycin in Combination with Nine Antibiotics in Human Plasma and Cation-Adjusted Mueller-Hinton II Broth via LCMS. Antibiotics (Basel) 2022; 11:antibiotics11010054. [PMID: 35052932 PMCID: PMC8772704 DOI: 10.3390/antibiotics11010054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/28/2021] [Accepted: 12/29/2021] [Indexed: 02/05/2023] Open
Abstract
Fosfomycin-based combination therapy has emerged as an attractive option in our armamentarium due to its synergistic activity against carbapenem-resistant Gram-negative bacteria (CRGNB). The ability to simultaneously measure fosfomycin and other antibiotic drug levels will support in vitro and clinical investigations to develop rational antibiotic combination dosing regimens against CRGNB infections. We developed an analytical assay to measure fosfomycin with nine important antibiotics in human plasma and cation-adjusted Mueller–Hinton II broth (CAMHB). We employed a liquid-chromatography tandem mass spectrometry method and validated the method based on accuracy, precision, matrix effect, limit-of-detection, limit-of-quantification, specificity, carryover, and short-term and long-term stability on U.S. Food & Drug Administration (FDA) guidelines. Assay feasibility was assessed in a pilot clinical study in four patients on antibiotic combination therapy. Simultaneous quantification of fosfomycin, levofloxacin, meropenem, doripenem, aztreonam, piperacillin/tazobactam, ceftolozane/tazobactam, ceftazidime/avibactam, cefepime, and tigecycline in plasma and CAMHB were achieved within 4.5 min. Precision, accuracy, specificity, and carryover were within FDA guidelines. Fosfomycin combined with any of the nine antibiotics were stable in plasma and CAMHB up to 4 weeks at −80 °C. The assay identified and quantified the respective antibiotics administered in the four subjects. Our assay can be a valuable tool for in vitro and clinical applications.
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Affiliation(s)
- Kelvin Kau-Kiat Goh
- Department of Pharmacy, Singapore General Hospital, Outram Road, Singapore 169608, Singapore; (K.K.-K.G.); (W.G.-H.T.); (N.G.S.C.); (F.I.B.Z.); (L.-J.S.)
- SingHealth Duke-NUS Pathology Academic Clinical Programme, 8 College Road, Singapore 169857, Singapore
| | - Wilson Ghim-Hon Toh
- Department of Pharmacy, Singapore General Hospital, Outram Road, Singapore 169608, Singapore; (K.K.-K.G.); (W.G.-H.T.); (N.G.S.C.); (F.I.B.Z.); (L.-J.S.)
| | - Daryl Kim-Hor Hee
- Shimadzu (Asia Pacific) Pte Ltd., 79 Science Park Dr, #02-01/08 Cintech IV, Singapore 118264, Singapore; (E.Z.-W.T.); (D.K.-H.H.)
| | - Edwin Zhi-Wei Ting
- Shimadzu (Asia Pacific) Pte Ltd., 79 Science Park Dr, #02-01/08 Cintech IV, Singapore 118264, Singapore; (E.Z.-W.T.); (D.K.-H.H.)
| | - Nathalie Grace Sy Chua
- Department of Pharmacy, Singapore General Hospital, Outram Road, Singapore 169608, Singapore; (K.K.-K.G.); (W.G.-H.T.); (N.G.S.C.); (F.I.B.Z.); (L.-J.S.)
| | - Farah Iffah Binte Zulkifli
- Department of Pharmacy, Singapore General Hospital, Outram Road, Singapore 169608, Singapore; (K.K.-K.G.); (W.G.-H.T.); (N.G.S.C.); (F.I.B.Z.); (L.-J.S.)
| | - Li-Jiao Sin
- Department of Pharmacy, Singapore General Hospital, Outram Road, Singapore 169608, Singapore; (K.K.-K.G.); (W.G.-H.T.); (N.G.S.C.); (F.I.B.Z.); (L.-J.S.)
| | - Thuan-Tong Tan
- SingHealth Duke-NUS Medicine Academic Clinical Programme, 8 College Road, Singapore 169857, Singapore;
- Department of Infectious Diseases, Singapore General Hospital, Outram Road, Singapore 169608, Singapore
| | - Andrea Lay-Hoon Kwa
- Department of Pharmacy, Singapore General Hospital, Outram Road, Singapore 169608, Singapore; (K.K.-K.G.); (W.G.-H.T.); (N.G.S.C.); (F.I.B.Z.); (L.-J.S.)
- SingHealth Duke-NUS Medicine Academic Clinical Programme, 8 College Road, Singapore 169857, Singapore;
- Emerging Infectious Diseases Program, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
- Correspondence: (A.L.-H.K.); (T.-P.L.); Tel.: +65-6321-3401 (A.L.-H.K.); +65-6326-6959 (T.-P.L.)
| | - Tze-Peng Lim
- Department of Pharmacy, Singapore General Hospital, Outram Road, Singapore 169608, Singapore; (K.K.-K.G.); (W.G.-H.T.); (N.G.S.C.); (F.I.B.Z.); (L.-J.S.)
- SingHealth Duke-NUS Pathology Academic Clinical Programme, 8 College Road, Singapore 169857, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, 8 College Road, Singapore 169857, Singapore;
- Correspondence: (A.L.-H.K.); (T.-P.L.); Tel.: +65-6321-3401 (A.L.-H.K.); +65-6326-6959 (T.-P.L.)
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Feng J, Zhong Q, Kuang J, Liu J, Huang T, Zhou T. Simultaneous Analysis of the Metabolome and Lipidome Using Polarity Partition Two-Dimensional Liquid Chromatography-Mass Spectrometry. Anal Chem 2021; 93:15192-15199. [PMID: 34739231 DOI: 10.1021/acs.analchem.1c03905] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Comprehensive metabolic profiling is a considerable challenge for systems biology since the metabolites in biological samples have significant polarity differences. A heart-cutting two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method-based polarity partition was established to analyze both the metabolome and lipidome in a single run. Based on the polarity partition strategy, metabolites with high polarity were retained and separated by one-dimensional hydrophilic chromatography, while low- and medium-polarity lipids were collected into a sample loop and injected into two-dimensional reversed-phase chromatography for separation. A simple online dilution strategy realized the online coupling of the 2D-LC-MS, which effectively solved band broadening and peak distortion caused by solvent incompatibility. Moreover, a dual gradient elution procedure was introduced to further broaden the coverage of low-polarity lipids. The metabolites' log P values, which this 2D-LC-MS method could analyze, ranged from -8.79 to 26.86. The feasibility of the 2D-LC-MS system was demonstrated by simultaneous analysis of the metabolome and lipidome in rat plasma related to depression. A total of 319 metabolites were determined within 40 min, including organic acids, nucleosides, carbohydrate derivatives, amino acids, lipids, and other organic compounds. Finally, 44 depression-related differential metabolites were screened. Compared with conventional LC-MS-based methods, the 2D-LC method covered over 99% of features obtained by two conventional methods. In addition, the selectivity and resolution of the hydrophilic metabolites were improved, and the matrix effects of the hydrophobic metabolites were reduced in the developed method. The results indicated that the established 2D-LC system is a powerful tool for comprehensive metabolomics studies.
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Affiliation(s)
- Jieqing Feng
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Qisheng Zhong
- Guangzhou Analytical Applications Center, Shimadzu (China) Co., LTD, Guangzhou 510010, China
| | - Jiangmeng Kuang
- Guangzhou Analytical Applications Center, Shimadzu (China) Co., LTD, Guangzhou 510010, China
| | - Jiaqi Liu
- Guangzhou Analytical Applications Center, Shimadzu (China) Co., LTD, Guangzhou 510010, China
| | - Taohong Huang
- Shanghai Analytical Applications Center, Shimadzu (China) Co., LTD, Shanghai 200233, China
| | - Ting Zhou
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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Furlani IL, da Cruz Nunes E, Canuto GAB, Macedo AN, Oliveira RV. Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1336:179-213. [PMID: 34628633 DOI: 10.1007/978-3-030-77252-9_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.
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Affiliation(s)
- Izadora L Furlani
- Núcleo de Pesquisa em Cromatografia (Separare), Department of Chemistry, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Estéfane da Cruz Nunes
- Department of Analytical Chemistry, Institute of Chemistry, Federal University of Bahia, Salvador, BA, Brazil
| | - Gisele A B Canuto
- Department of Analytical Chemistry, Institute of Chemistry, Federal University of Bahia, Salvador, BA, Brazil
| | - Adriana N Macedo
- Department of Chemistry, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Regina V Oliveira
- Núcleo de Pesquisa em Cromatografia (Separare), Department of Chemistry, Federal University of São Carlos, São Carlos, SP, Brazil.
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Zhong P, Wei X, Xu Y, Zhang L, Koidis A, Liu Y, Lei Y, Wu S, Lei H. Integration of Untargeted and Pseudotargeted Metabolomics for Authentication of Three Shrimp Species Using UHPLC-Q-Orbitrap. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:8861-8873. [PMID: 34319107 DOI: 10.1021/acs.jafc.1c02630] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this work, an untargeted and pseudotargeted metabolomics combination approach was used for authentication of three shrimp species (Litopenaeus vanmamei, Penaeus japonicus, and Penaeus monodon). The monophasic extraction-based untargeted metabolomics approach enabled comprehensive-coverage and high-throughput analysis of shrimp tissue and revealed 26 potential markers. The pseudotargeted metabolomics approach confirmed 21 markers (including 9 key markers), which realized at least putative identification. The 21 confirmed markers, as well as 9 key markers, were used to develop PLS-DA models, correctly classifying 60/60 testing samples. Furthermore, DD-SIMCA and PLS-DA models were integrated based on the 9 key markers, with 59/60 and 20/20 samples of the species that were involved and uninvolved in model training correctly classified. The results demonstrated the potential of this untargeted and pseudotargeted metabolomics combination approach for shrimp species authentication.
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Affiliation(s)
- Peng Zhong
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiaoqun Wei
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Yi Xu
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Lulu Zhang
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Anastasios Koidis
- Institute for Global Food Security, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DJ, United Kingdom
| | - Yunle Liu
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Yi Lei
- Guangdong Institute of Food Inspection, Guangzhou 510435, China
| | - Shaozong Wu
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
| | - Hongtao Lei
- Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
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10
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Diederen T, Delabrière A, Othman A, Reid ME, Zamboni N. Metabolomics. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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11
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Wolfer AM, Correia GDS, Sands CJ, Camuzeaux S, Yuen AHY, Chekmeneva E, Takáts Z, Pearce JTM, Lewis MR. peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets. Bioinformatics 2021; 37:4886-4888. [PMID: 34125879 PMCID: PMC8665750 DOI: 10.1093/bioinformatics/btab433] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 04/09/2021] [Accepted: 06/12/2021] [Indexed: 11/12/2022] Open
Abstract
Untargeted LC-MS profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC-MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real time data quality assessment. AVAILABILITY peakPantheR is available via Bioconductor (https://bioconductor.org/packages/peakPantheR/). Documentation and worked examples are available at https://phenomecentre.github.io/peakPantheR.github.io/ and https://github.com/phenomecentre/metabotyping-dementia-urine. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Arnaud M Wolfer
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.,Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Gonçalo D S Correia
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, UK
| | - Caroline J Sands
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, UK
| | - Stephane Camuzeaux
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, UK
| | - Ada H Y Yuen
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, UK
| | - Elena Chekmeneva
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, UK
| | - Zoltán Takáts
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, UK
| | - Jake T M Pearce
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK
| | - Matthew R Lewis
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, UK
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12
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Yoshino M, Yoshino J, Kayser BD, Patti GJ, Franczyk MP, Mills KF, Sindelar M, Pietka T, Patterson BW, Imai SI, Klein S. Nicotinamide mononucleotide increases muscle insulin sensitivity in prediabetic women. Science 2021; 372:1224-1229. [PMID: 33888596 DOI: 10.1126/science.abe9985] [Citation(s) in RCA: 173] [Impact Index Per Article: 57.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 04/08/2021] [Indexed: 12/14/2022]
Abstract
In rodents, obesity and aging impair nicotinamide adenine dinucleotide (NAD+) biosynthesis, which contributes to metabolic dysfunction. Nicotinamide mononucleotide (NMN) availability is a rate-limiting factor in mammalian NAD+ biosynthesis. We conducted a 10-week, randomized, placebo-controlled, double-blind trial to evaluate the effect of NMN supplementation on metabolic function in postmenopausal women with prediabetes who were overweight or obese. Insulin-stimulated glucose disposal, assessed by using the hyperinsulinemic-euglycemic clamp, and skeletal muscle insulin signaling [phosphorylation of protein kinase AKT and mechanistic target of rapamycin (mTOR)] increased after NMN supplementation but did not change after placebo treatment. NMN supplementation up-regulated the expression of platelet-derived growth factor receptor β and other genes related to muscle remodeling. These results demonstrate that NMN increases muscle insulin sensitivity, insulin signaling, and remodeling in women with prediabetes who are overweight or obese (clinicaltrial.gov NCT03151239).
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Affiliation(s)
- Mihoko Yoshino
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA
| | - Jun Yoshino
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA
| | - Brandon D Kayser
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA
| | - Gary J Patti
- Department of Chemistry, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael P Franczyk
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA
| | - Kathryn F Mills
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Miriam Sindelar
- Department of Chemistry, Washington University School of Medicine, St. Louis, MO, USA
| | - Terri Pietka
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA
| | - Bruce W Patterson
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA
| | - Shin-Ichiro Imai
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Samuel Klein
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA.
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13
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Abstract
![]()
Helminths
represent a diverse category of parasitic organisms that
can thrive within a host for years, if not decades, in the absence
of treatment. As such, they must establish mechanisms to subsist off
their hosts, evade the immune system, and develop a niche among the
other cohabiting microbial communities. The complex interplay of biologically
small molecules (collectively known as the metabolome) derived from,
utilized by, or in response to the presence of helminths within a
host is an emerging field of study. In this Perspective, we briefly
summarize the current existing literature, categorize key host–pathogen–microbiome
interfaces that could be studied in the context of the metabolome,
and provide background on mass spectrometry-based metabolomic methodology.
Overall, we hope to provide a comprehensive guide for utilizing metabolomics
in the context of helminthic disease.
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Affiliation(s)
- Jeffrey D. Whitman
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, California 94110, United States
| | - Judy A. Sakanari
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Makedonka Mitreva
- Department of Medicine, Washington University School of Medicine, St. Louis, Missouri 63130, United States
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14
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A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing. Methods Mol Biol 2021; 2276:357-382. [PMID: 34060055 PMCID: PMC9284939 DOI: 10.1007/978-1-0716-1266-8_27] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Untargeted metabolomics has rapidly become a profiling method of choice in many areas of research, including mitochondrial biology. Most commonly, untargeted metabolomics is performed with liquid chromatography/mass spectrometry because it enables measurement of a relatively wide range of physiochemically diverse molecules. Specifically, to assess energy pathways that are associated with mitochondrial metabolism, hydrophilic interaction liquid chromatography (HILIC) is often applied before analysis with a high-resolution accurate mass instrument. The workflow produces large, complex data files that are impractical to analyze manually. Here, we present a protocol to perform untargeted metabolomics on biofluids such as plasma, urine, and cerebral spinal fluid with a HILIC separation and an Orbitrap mass spectrometer. Our protocol describes each step of the analysis in detail, from preparation of solvents for chromatography to selecting parameters during data processing.
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15
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Guan S, Armbruster MR, Huang T, Edwards JL, Bythell BJ. Isomeric Differentiation and Acidic Metabolite Identification by Piperidine-Based Tagging, LC–MS/MS, and Understanding of the Dissociation Chemistries. Anal Chem 2020; 92:9305-9311. [DOI: 10.1021/acs.analchem.0c01640] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Shanshan Guan
- Department of Chemistry and Biochemistry, Ohio University, 391 Clippinger Laboratories, Athens, Ohio 45701, United States
- Department of Chemistry and Biochemistry, University of Missouri, 1 University Blvd, St. Louis, Missouri 63121, United States
| | - Michael R. Armbruster
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63102, United States
| | - Tianjiao Huang
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63102, United States
| | - James L. Edwards
- Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Avenue, St. Louis, Missouri 63102, United States
| | - Benjamin J. Bythell
- Department of Chemistry and Biochemistry, Ohio University, 391 Clippinger Laboratories, Athens, Ohio 45701, United States
- Department of Chemistry and Biochemistry, University of Missouri, 1 University Blvd, St. Louis, Missouri 63121, United States
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16
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Abstract
Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.
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Affiliation(s)
- Miriam Sindelar
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Gary J. Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
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17
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Teclemariam ET, Pergande MR, Cologna SM. Considerations for mass spectrometry-based multi-omic analysis of clinical samples. Expert Rev Proteomics 2020; 17:99-107. [PMID: 31996049 DOI: 10.1080/14789450.2020.1724540] [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] [Indexed: 10/25/2022]
Abstract
Introduction: The role of mass spectrometry in biomolecule analysis has become paramount over the last several decades ranging in the analysis across model systems and human specimens. Accordingly, the presence of mass spectrometers in clinical laboratories has also expanded alongside the number of researchers investigating the protein, lipid, and metabolite composition of an array of biospecimens. With this increase in the number of omic investigations, it is important to consider the entire experimental strategy from sample collection and storage, data collection and analysis.Areas covered: In this short review, we outline considerations for working with clinical (e.g. human) specimens including blood, urine, and cerebrospinal fluid, with emphasis on sample handling, profiling composition, targeted measurements and relevance to disease. Discussions of integrated genomic or transcriptomic datasets are not included. A brief commentary is also provided regarding new technologies with clinical relevance.Expert opinion: The role of mass spectrometry to investigate clinically related specimens is on the rise and the ability to integrate multiple omics datasets from mass spectrometry measurements will be crucial to further understanding human health and disease.
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Affiliation(s)
- Esei T Teclemariam
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Melissa R Pergande
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA.,Laboratory of Integrated Neuroscience, University of Illinois at Chicago, Chicago, IL, USA
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18
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Pezzatti J, Boccard J, Codesido S, Gagnebin Y, Joshi A, Picard D, González-Ruiz V, Rudaz S. Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial. Anal Chim Acta 2020; 1105:28-44. [PMID: 32138924 DOI: 10.1016/j.aca.2019.12.062] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 11/18/2019] [Accepted: 12/20/2019] [Indexed: 12/23/2022]
Abstract
Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.
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Affiliation(s)
- Julian Pezzatti
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Julien Boccard
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Santiago Codesido
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Yoric Gagnebin
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Abhinav Joshi
- Department of Cell Biology, Faculty of Science, University of Geneva, 1211, Geneva, Switzerland
| | - Didier Picard
- Department of Cell Biology, Faculty of Science, University of Geneva, 1211, Geneva, Switzerland
| | - Víctor González-Ruiz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Serge Rudaz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1211, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland.
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19
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Abstract
There are thousands of published methods for profiling metabolites with liquid chromatography/mass spectrometry (LC/MS). While many have been evaluated and optimized for a small number of select metabolites, very few have been assessed on the basis of global metabolite coverage. Thus, when performing untargeted metabolomics, researchers often question which combination of extraction techniques, chromatographic separations, and mass spectrometers is best for global profiling. Method comparisons are complicated because thousands of LC/MS signals (so-called features) in a typical untargeted metabolomic experiment cannot be readily identified with current resources. It is therefore challenging to distinguish methods that increase signal number due to improved metabolite coverage from methods that increase signal number due to contamination and artifacts. Here, we present the credentialing protocol to remove the latter from untargeted metabolomic datasets without having to identify metabolite structures. This protocol can be used to compare or optimize methods pertaining to any step of the untargeted metabolomic workflow (e.g., extraction, chromatography, mass spectrometer, informatic software, etc.).
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Affiliation(s)
- Lingjue Wang
- Department of Chemistry, Washington University, St. Louis, MO, USA
| | - Fuad J Naser
- Department of Chemistry, Washington University, St. Louis, MO, USA
| | - Jonathan L Spalding
- Department of Chemistry, Washington University, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Gary J Patti
- Department of Chemistry, Washington University, St. Louis, MO, USA.
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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20
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Spalding JL, Naser FJ, Mahieu NG, Johnson SL, Patti GJ. Trace Phosphate Improves ZIC-pHILIC Peak Shape, Sensitivity, and Coverage for Untargeted Metabolomics. J Proteome Res 2018; 17:3537-3546. [PMID: 30160483 DOI: 10.1021/acs.jproteome.8b00487] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Existing hydrophilic interaction liquid chromatography (HILIC) methods, considered individually, each exhibit poor chromatographic performance for a substantial fraction of polar metabolites. In addition to limiting metabolome coverage, such deficiencies also complicate automated data processing. Here we show that some of these analytical challenges can be addressed for the ZIC-pHILIC, a zwitterionic stationary phase commonly used in metabolomics, with the addition of trace levels of phosphate. Specifically, micromolar phosphate extended metabolome coverage by hundreds of credentialed features, improved peak shapes, and reduced peak-detection errors during informatic processing. Although the addition of high levels of phosphate (millimolar) as a HILIC mobile phase buffer has been explored previously, such concentrations interfere with mass spectrometric (MS) detection. We show that using phosphate as a trace additive at micromolar concentrations improves analysis by electrospray MS, increasing signal for a diverse set of polar standards. Given the small amount of phosphate needed, comparable chromatographic improvements were also achieved by direct addition of phosphate to the sample during reconstitution. Our results suggest that defects in ZIC-pHILIC performance are predominantly driven by electrostatic interactions, which can be modulated by phosphate. These findings constitute both a methodological improvement for untargeted metabolomics and an advance in our understanding of the mechanisms limiting HILIC coverage.
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Affiliation(s)
- Jonathan L Spalding
- Department of Chemistry , Washington University in St. Louis , St. Louis , MO 63130 , United States.,Department of Genetics , Washington University in St. Louis , St. Louis , MO 63110 , United States.,Department of Medicine , Washington University in St. Louis , St. Louis , MO 63110 , United States
| | - Fuad J Naser
- Department of Chemistry , Washington University in St. Louis , St. Louis , MO 63130 , United States
| | - Nathaniel G Mahieu
- Department of Chemistry , Washington University in St. Louis , St. Louis , MO 63130 , United States
| | - Stephen L Johnson
- Department of Genetics , Washington University in St. Louis , St. Louis , MO 63110 , United States
| | - Gary J Patti
- Department of Chemistry , Washington University in St. Louis , St. Louis , MO 63130 , United States.,Department of Medicine , Washington University in St. Louis , St. Louis , MO 63110 , United States
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21
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Vuckovic D. Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery. Chem Commun (Camb) 2018; 54:6728-6749. [PMID: 29888773 DOI: 10.1039/c8cc02592d] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.
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Affiliation(s)
- Dajana Vuckovic
- Department of Chemistry and Biochemistry, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec H4B 1R6, Canada.
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