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Zhang F, Shan S, Fu C, Guo S, Liu C, Wang S. Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications. Molecules 2024; 29:2530. [PMID: 38893405 PMCID: PMC11173766 DOI: 10.3390/molecules29112530] [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: 02/08/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 06/21/2024] Open
Abstract
Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.
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Affiliation(s)
- Feixue Zhang
- Hubei Key Laboratory of Diabetes and Angiopathy, Medicine Research Institute, Medical College, Hubei University of Science and Technology, Xianning 437100, China; (F.Z.); (C.F.); (S.G.)
| | - Shan Shan
- College of Life Science, National R&D Center for Freshwater Fish Processing, Jiangxi Normal University, Nanchang 330022, China;
| | - Chenlu Fu
- Hubei Key Laboratory of Diabetes and Angiopathy, Medicine Research Institute, Medical College, Hubei University of Science and Technology, Xianning 437100, China; (F.Z.); (C.F.); (S.G.)
- School of Pharmacy, Medical College, Hubei University of Science and Technology, Xianning 437100, China
| | - Shuang Guo
- Hubei Key Laboratory of Diabetes and Angiopathy, Medicine Research Institute, Medical College, Hubei University of Science and Technology, Xianning 437100, China; (F.Z.); (C.F.); (S.G.)
| | - Chao Liu
- Hubei Key Laboratory of Diabetes and Angiopathy, Medicine Research Institute, Medical College, Hubei University of Science and Technology, Xianning 437100, China; (F.Z.); (C.F.); (S.G.)
| | - Shuanglong Wang
- Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation, East China University of Technology, Nanchang 330013, China
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2
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Pang Z, Xu L, Viau C, Lu Y, Salavati R, Basu N, Xia J. MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics. Nat Commun 2024; 15:3675. [PMID: 38693118 PMCID: PMC11063062 DOI: 10.1038/s41467-024-48009-6] [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: 09/15/2023] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.
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Affiliation(s)
- Zhiqiang Pang
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Xu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Charles Viau
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | - Reza Salavati
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada.
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3
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Pang Z, Lu Y, Zhou G, Hui F, Xu L, Viau C, Spigelman AF, MacDonald PE, Wishart DS, Li S, Xia J. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res 2024:gkae253. [PMID: 38587201 DOI: 10.1093/nar/gkae253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/14/2024] [Accepted: 03/26/2024] [Indexed: 04/09/2024] Open
Abstract
We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Fiona Hui
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Lei Xu
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Charles Viau
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Aliya F Spigelman
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Patrick E MacDonald
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - David S Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- University of Connecticut School of Medicine, Farmington, CT, USA
| | - Jianguo Xia
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
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4
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Calabrese V, Brunet TA, Degli-Esposti D, Chaumot A, Geffard O, Salvador A, Clément Y, Ayciriex S. Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum. Anal Bioanal Chem 2024:10.1007/s00216-024-05232-w. [PMID: 38492024 DOI: 10.1007/s00216-024-05232-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/18/2024]
Abstract
The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.
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Affiliation(s)
- Valentina Calabrese
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France.
| | - Thomas Alexandre Brunet
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | | | - Arnaud Chaumot
- Laboratoire d'écotoxicologie, INRAE, UR RiverLy, 69625, Villeurbanne, France
| | - Olivier Geffard
- Laboratoire d'écotoxicologie, INRAE, UR RiverLy, 69625, Villeurbanne, France
| | - Arnaud Salvador
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | - Yohann Clément
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | - Sophie Ayciriex
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France.
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Jia W, Wu X, Shu J, Shi L. 3-Monochloropropane-1,2-diol reduced bioaccessibility of sn-2 palmitate via binding with pancreatic lipase in infant formula during gastrointestinal digestion. J Dairy Sci 2023; 106:8449-8468. [PMID: 37690726 DOI: 10.3168/jds.2023-23730] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 07/24/2023] [Indexed: 09/12/2023]
Abstract
Infant formula contains 3-monochloropropane-1,2-diol esters (3-MCPDE), which are formed during the deodorization step of vegetable oil refining. The European Food Safety Authority stated that 3-MCPDE can be hydrolyzed in the gastrointestinal tract to free-form 3-monochloropropane-1,2-diol (3-MCPD), which has potential toxicity and can be rapidly absorbed. Evaluating the effect of 3-MCPD on nutrition absorption is a prerequisite for establishing effective management strategies. A total of 66 crucial lipid molecules associated with 3-MCPD were identified based on debiased sparse partial correlation analysis. 3-MCPD affected triglyceride hydrolyzation and increased the concentration of undigested sn-2 palmitate (9.57 to 17.06 mg kg-1). 3-Monochloropropane-1,2-diol reduced the bioaccessibility of fatty acids, and more short- (31.42 to 58.02 mg kg-1) and medium-chain fatty acids (17.03 to 26.43 mg kg-1) remained unabsorbed. Lipidomic profiles of infant formula models spiked with different 3-MCPDE levels were investigated, and the results were consistent with the experiments with the commercial formula indicating lipid alteration was mainly affected by the digestive 3-MCPD. The formation of 3-MCPD ester-pancreatic lipase with the binding energy of -4.9 kcal mol-1 was more stable than triglyceride-pancreatic lipase (-4.0 kcal mol-1), affecting triglyceride hydrolyzation. 3-Monochloropropane-1,2-diol was bound to Glu13 and Asp331 residues of the pancreatic lipase via hydrogen bonds, which resulted in a conformational change of pancreatic lipase and spatial shielding effect. The existence of the spatial-shielding effect reduced the accessibility of pancreatic lipase and further affected triglyceride hydrolyzation. These findings indicated that 3-MCPD obstructed nutrient acquisition and laid the foundation for the subsequent nutrition enhancement design.
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Affiliation(s)
- Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China; Shaanxi Testing Institute of Product Quality Supervision, Xi'an, Shaanxi 710048, China; Shaanxi Sky Pet Biotechnology Co. Ltd., Xi'an 710075, China.
| | - Xixuan Wu
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
| | - Jing Shu
- Shaanxi Testing Institute of Product Quality Supervision, Xi'an, Shaanxi 710048, China
| | - Lin Shi
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
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6
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Chen CJ, Lee DY, Yu J, Lin YN, Lin TM. Recent advances in LC-MS-based metabolomics for clinical biomarker discovery. MASS SPECTROMETRY REVIEWS 2023; 42:2349-2378. [PMID: 35645144 DOI: 10.1002/mas.21785] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/14/2021] [Accepted: 11/18/2021] [Indexed: 06/15/2023]
Abstract
The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.
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Affiliation(s)
- Chao-Jung Chen
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Proteomics Core Laboratory, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Der-Yen Lee
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Jiaxin Yu
- AI Innovation Center, China Medical University Hospital, Taichung, Taiwan
| | - Yu-Ning Lin
- Proteomics Core Laboratory, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Tsung-Min Lin
- Proteomics Core Laboratory, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
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7
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Sun MX, Li XH, Jiang MT, Zhang L, Ding MX, Zou YD, Gao XM, Yang WZ, Wang HD, Guo DA. A practical strategy enabling more reliable identification of ginsenosides from Panax quinquefolius flower by dimension-enhanced liquid chromatography/mass spectrometry and quantitative structure-retention relationship-based retention behavior prediction. J Chromatogr A 2023; 1706:464243. [PMID: 37567002 DOI: 10.1016/j.chroma.2023.464243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023]
Abstract
To accurately identify the metabolites is crucial in a number of research fields, and discovery of new compounds from the natural products can benefit the development of new drugs. However, the preferable phytochemistry or liquid chromatography/mass spectrometry approach is time-/labor-extensive or receives unconvincing identifications. Herein, we presented a strategy, by integrating offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), exclusion list-containing high-definition data-dependent acquisition (HDDDA-EL), and quantitative structure-retention relationship (QSRR) prediction of the retention time (tR), to facilitate the in-depth and more reliable identification of herbal components and thus to discover new compounds more efficiently. Using the saponins in Panax quinquefolius flower (PQF) as a case, high orthogonality (0.79) in separating ginsenosides was enabled by configuring the XBridge Amide and CSH C18 columns. HDDDA-EL could improve the coverage in MS2 acquisition by 2.26 folds compared with HDDDA (2933 VS 1298). Utilizing 106 reference compounds, an accurate QSRR prediction model (R2 = 0.9985 for the training set and R2 = 0.88 for the validation set) was developed based on Gradient Boosting Machine (GBM), by which the predicted tR matching could significantly reduce the isomeric candidates identification for unknown ginsenosides. Isolation and establishment of the structures of two malonylginsenosides by NMR partially verified the practicability of the integral strategy. By these efforts, 421 ginsenosides were identified or tentatively characterized, and 284 thereof were not ever reported from the Panax species. The current strategy is thus powerful in the comprehensive metabolites characterization and rapid discovery of new compounds from the natural products.
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Affiliation(s)
- Meng-Xiao Sun
- 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
| | - Xiao-Hang 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
| | - Mei-Ting 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
| | - Lin Zhang
- 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; Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meng-Xiang 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
| | - Ya-Dan 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
| | - Xiu-Mei Gao
- 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; Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Wen-Zhi 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; Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China.
| | - Hong-da 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.
| | - De-An Guo
- 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; Shanghai Research Center for Modernization of Traditional Chinese Medicine, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China.
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8
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Zhao T, Xing S, Yu H, Huan T. De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics. Anal Chem 2023; 95:13018-13028. [PMID: 37603462 DOI: 10.1021/acs.analchem.3c00736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a de novo approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo MS2Purifier), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.
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Affiliation(s)
- Tingting Zhao
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
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9
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Ledesma-Escobar CA, Priego-Capote F, Calderón-Santiago M. MetaboMSDIA: A tool for implementing data-independent acquisition in metabolomic-based mass spectrometry analysis. Anal Chim Acta 2023; 1266:341308. [PMID: 37244659 DOI: 10.1016/j.aca.2023.341308] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 04/25/2023] [Accepted: 04/30/2023] [Indexed: 05/29/2023]
Abstract
Data-dependent acquisition (DDA) is the most widely used mode in untargeted metabolomic analysis despite its limited tandem mass spectrometry (MS2) detection coverage. We present MetaboMSDIA for complete processing of data-independent acquisition (DIA) files by the extraction of multiplexed MS2 spectra and further identification of metabolites in open libraries. In the analysis of polar extracts from lemon and olive fruits, DIA allows one to obtain multiplexed MS2 spectra for 100% of precursor ions compared to 64% of precursor ions from average MS2 acquisition in DDA. MetaboMSDIA is compatible with MS2 repositories and homemade libraries prepared by analysis of standards. An additional option is based on filtering molecular entities by searching for selective fragmentation patterns according to selective neutral losses or product ions to target the annotation of families of metabolites. Combining both options, the applicability of MetaboMSDIA was tested by annotating 50 and 35 metabolites in polar extracts from lemon and olive fruit, respectively. MetaboMSDIA is particularly proposed to increase the acquisition coverage in untargeted metabolomics and to improve spectral quality, which are two critical pillars for the tentative annotation of metabolites. The R script used in MetaboMSDIA workflow is available at github repository (https://github.com/MonicaCalSan/MetaboMSDIA).
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Affiliation(s)
- Carlos Augusto Ledesma-Escobar
- Department of Analytical Chemistry, Annex Marie Curie Building, Campus of Rabanales, University of Córdoba, Córdoba, Spain; Maimónides Institute of Biomedical Research (IMIBIC), Reina Sofía University Hospital, University of Córdoba, Córdoba, Spain; Nanochemistry University Institute (IUNAN), Campus of Rabanales, University of Córdoba, Córdoba, Spain
| | - Feliciano Priego-Capote
- Department of Analytical Chemistry, Annex Marie Curie Building, Campus of Rabanales, University of Córdoba, Córdoba, Spain; Maimónides Institute of Biomedical Research (IMIBIC), Reina Sofía University Hospital, University of Córdoba, Córdoba, Spain; Nanochemistry University Institute (IUNAN), Campus of Rabanales, University of Córdoba, Córdoba, Spain; CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Spain.
| | - Mónica Calderón-Santiago
- Department of Analytical Chemistry, Annex Marie Curie Building, Campus of Rabanales, University of Córdoba, Córdoba, Spain; Maimónides Institute of Biomedical Research (IMIBIC), Reina Sofía University Hospital, University of Córdoba, Córdoba, Spain; Nanochemistry University Institute (IUNAN), Campus of Rabanales, University of Córdoba, Córdoba, Spain.
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10
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Pérez-López C, Oró-Nolla B, Lacorte S, Tauler R. Regions of Interest Multivariate Curve Resolution Liquid Chromatography with Data-Independent Acquisition Tandem Mass Spectrometry. Anal Chem 2023; 95:7519-7527. [PMID: 37146285 DOI: 10.1021/acs.analchem.2c05704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
New data-independent acquisition (DIA) modes coupled to chromatographic separations are opening new perspectives in the processing of massive mass spectrometric (MS) data using chemometric methods. In this work, the application of the regions of interest multivariate curve resolution (ROIMCR) method is shown for the simultaneous analysis of MS1 and MS2 DIA raw data obtained by liquid chromatography coupled to quadrupole-time-of-flight MS analysis. The ROIMCR method proposed in this work relies on the intrinsic bilinear structure of the MS1 and MS2 experimental data which allows us for the fast direct resolution of the elution and spectral profiles of all sample constituents giving measurable MS signals, without needing any further data pretreatment such as peak matching, alignment, or modeling. Compound annotation and identification can be achieved directly by the comparison of the ROIMCR-resolved MS1 and MS2 spectra with those from standards or from mass spectral libraries. ROIMCR elution profiles of the resolved components can be used to build calibration curves for the prediction of their concentrations in complex unknown samples. The application of the proposed procedure is shown for the analysis of mixtures of per- and polyfluoroalkyl substances in standard mixtures, spiked hen eggs, and gull egg samples, where these compounds tend to accumulate.
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Affiliation(s)
- Carlos Pérez-López
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, Barcelona 08034, Spain
| | - Bernat Oró-Nolla
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, Barcelona 08034, Spain
| | - Silvia Lacorte
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, Barcelona 08034, Spain
| | - Romà Tauler
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, Barcelona 08034, Spain
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11
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Xing S, Shen S, Xu B, Li X, Huan T. BUDDY: molecular formula discovery via bottom-up MS/MS interrogation. Nat Methods 2023:10.1038/s41592-023-01850-x. [PMID: 37055660 DOI: 10.1038/s41592-023-01850-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 03/15/2023] [Indexed: 04/15/2023]
Abstract
A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).
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Affiliation(s)
- Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sam Shen
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Banghua Xu
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Xiaoxiao Li
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada.
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12
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Luo M, Yin Y, Zhou Z, Zhang H, Chen X, Wang H, Zhu ZJ. A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics. Nat Commun 2023; 14:1813. [PMID: 37002244 PMCID: PMC10066191 DOI: 10.1038/s41467-023-37539-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 03/17/2023] [Indexed: 04/03/2023] Open
Abstract
Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.
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Affiliation(s)
- Mingdu Luo
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yandong Yin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
| | - Zhiwei Zhou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
| | - Haosong Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Xi Chen
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Hongmiao Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, 201210, P. R. China.
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13
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Wang C, Zhao R, Zhao Z, Liu N, Cheng J, Guo M. Proteomic characterization and comparison of milk fat globule membrane proteins of Saanen goat milk from 3 habitats in China using SWATH-MS technique. J Dairy Sci 2023; 106:2289-2302. [PMID: 36870831 DOI: 10.3168/jds.2022-22393] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/01/2022] [Indexed: 03/06/2023]
Abstract
Saanen goats are among the major dairy goats in China. In present study, variation of milk fat globule membrane proteins profile of Saanen goat milk caused by geographic location was investigated using sequential window acquisition of all theoretical fragment ions data-independent acquisition mass spectrometry based proteomic approach. A total of 1,001 proteins were quantified in goat milk collected from 3 habitats of China [Guangdong (GD); Inner Mongolia (IM); Shannxi (SX)]. Most of the proteins were found to act cellular process of biological process, cell of cellular component, binding of molecular function after Gene Ontology annotation and metabolic of pathway indicated by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Differentially expressed proteins (DEP) for GD versus IM, GD versus SX, IM versus SX were identified to be 81, 91, and 44, respectively. Gene Ontology enrichment analysis showed that the greatest DEP for 3 groups (GD vs. IM, GD vs. SX, IM vs. SX) were cellular process, cellular process and organonitrogen compound biosynthetic process/immune system process for biological process. For cellular component, the largest number of DEP for 3 comparison groups were organelle, organelle and organelle/intracellular. For molecular function, DEP of the 3 comparison groups were expressed most in structural molecule activity, binding and anion binding, respectively. Pathways with the majority of DEP were ribosome, systemic lupus erythematosus and primary immunodeficiency/systemic lupus erythematosus/amoebiasis/PI3K-Akt signaling pathway for GD versus IM, GD versus SX and IM versus SX, severally. Protein-protein interaction network analysis showed that DEP interacted most were 40S ribosomal protein S5, fibronectin and Cytochrome b-c1 complex subunit 2, mitochondrial for GD versus IM, GD versus SX and IM versus SX, separately. Data may give useful information for goat milk selection and milk authenticity in China.
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Affiliation(s)
- Cuina Wang
- Department of Food Science, Jilin University, Changchun, 130062, China
| | - Ru Zhao
- Department of Food Science, Jilin University, Changchun, 130062, China
| | - Zixuan Zhao
- Department of Food Science, Northeast Agriculture University, Harbin, 150030, China
| | - Ning Liu
- Department of Food Science, Northeast Agriculture University, Harbin, 150030, China
| | - Jianjun Cheng
- Department of Food Science, Northeast Agriculture University, Harbin, 150030, China
| | - Mingruo Guo
- Department of Nutrition and Food Sciences, College of Agriculture and Life Sciences, University of Vermont, Burlington 05405.
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14
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Wandy J, McBride R, Rogers S, Terzis N, Weidt S, van der Hooft JJJ, Bryson K, Daly R, Davies V. Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics. Front Mol Biosci 2023; 10:1130781. [PMID: 36959982 PMCID: PMC10027714 DOI: 10.3389/fmolb.2023.1130781] [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: 12/23/2022] [Accepted: 02/24/2023] [Indexed: 03/09/2023] Open
Abstract
Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for further advancements in LC-MS/MS data acquisition methods.
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Affiliation(s)
- Joe Wandy
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom
| | - Ross McBride
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Nikolaos Terzis
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
| | - Stefan Weidt
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom
| | | | - Kevin Bryson
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Rónán Daly
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom
| | - Vinny Davies
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
- *Correspondence: Vinny Davies,
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15
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Cai Y, Zhou Z, Zhu ZJ. Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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16
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Toward building mass spectrometry-based metabolomics and lipidomics atlases for biological and clinical research. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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17
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Liu M, Xu X, Wang X, Wang H, Mi Y, Gao X, Guo D, Yang W. Enhanced Identification of Ginsenosides Simultaneously from Seven Panax Herbal Extracts by Data-Dependent Acquisition Including a Preferred Precursor Ions List Derived from an In-House Programmed Virtual Library. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:13796-13807. [PMID: 36239255 DOI: 10.1021/acs.jafc.2c06781] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Data-dependent acquisition (DDA) is widely utilized for metabolite identification in natural product research and food science, which, however, can suffer from low coverage. A potential solution to improve DDA coverage is to include the precursor ions list (PIL). Here, we aimed to construct a PIL-containing DDA strategy based on an in-house library of ginsenosides (VLG) and identify ginsenosides simultaneously from seven Panax herbal extracts. VLG, combined with mass defect filtering, could efficiently screen the ginsenoside precursors and elaborate the separate PIL involved in DDA for each ginseng extract. Consequently, we could characterize 500 ginsenosides, including 176 ones with unknown masses. Using the Panax ginseng extract, the superiority of this strategy was embodied in targeting more known ginsenoside masses and newly acquiring the MS2 spectra of 13 components. Conclusively, knowledge-based large-scale molecular prediction and PIL-DDA can represent a powerful targeted/untargeted strategy beneficial to novel natural compound discovery.
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Affiliation(s)
- Meiyu Liu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiaoyan Xu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiaoyan Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Hongda Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Yueguang Mi
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiumei Gao
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Dean Guo
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China
| | - Wenzhi Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
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18
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Yan S, Bhawal R, Yin Z, Thannhauser TW, Zhang S. Recent advances in proteomics and metabolomics in plants. MOLECULAR HORTICULTURE 2022; 2:17. [PMID: 37789425 PMCID: PMC10514990 DOI: 10.1186/s43897-022-00038-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/20/2022] [Indexed: 10/05/2023]
Abstract
Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.
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Affiliation(s)
- Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Ruchika Bhawal
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA
| | - Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | | | - Sheng Zhang
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA.
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19
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Shah SMZ, Ali A, Khan MN, Khadim A, Asmari M, Uddin J, Musharraf SG. Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools. Pharmaceuticals (Basel) 2022; 15:ph15070901. [PMID: 35890199 PMCID: PMC9317224 DOI: 10.3390/ph15070901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/07/2022] [Accepted: 07/11/2022] [Indexed: 12/19/2022] Open
Abstract
Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.
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Affiliation(s)
- Syed Muhammad Zaki Shah
- International Center for Chemical and Biological Sciences, H.E.J. Research Institute of Chemistry, University of Karachi, Karachi 75270, Pakistan; (S.M.Z.S.); (M.N.K.); (A.K.)
| | - Arslan Ali
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan
- Correspondence: or (A.A.); or or (S.G.M.); Tel.: +92-34819010-174 (A.A.); +92-34819010-134 (S.G.M.)
| | - Muhammad Noman Khan
- International Center for Chemical and Biological Sciences, H.E.J. Research Institute of Chemistry, University of Karachi, Karachi 75270, Pakistan; (S.M.Z.S.); (M.N.K.); (A.K.)
| | - Adeeba Khadim
- International Center for Chemical and Biological Sciences, H.E.J. Research Institute of Chemistry, University of Karachi, Karachi 75270, Pakistan; (S.M.Z.S.); (M.N.K.); (A.K.)
| | - Mufarreh Asmari
- Department of Pharmaceutical Chemistry, College of Pharmacy, Abha 62529, Saudi Arabia; (M.A.); (J.U.)
| | - Jalal Uddin
- Department of Pharmaceutical Chemistry, College of Pharmacy, Abha 62529, Saudi Arabia; (M.A.); (J.U.)
| | - Syed Ghulam Musharraf
- International Center for Chemical and Biological Sciences, H.E.J. Research Institute of Chemistry, University of Karachi, Karachi 75270, Pakistan; (S.M.Z.S.); (M.N.K.); (A.K.)
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan
- The Affiliated T.C.M Hospital of Southwest Medical University, Luzhou 646099, China
- Correspondence: or (A.A.); or or (S.G.M.); Tel.: +92-34819010-174 (A.A.); +92-34819010-134 (S.G.M.)
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20
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Colby SM, Chang CH, Bade JL, Nunez JR, Blumer MR, Orton DJ, Bloodsworth KJ, Nakayasu ES, Smith RD, Ibrahim YM, Renslow RS, Metz TO. DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data. Anal Chem 2022; 94:6130-6138. [PMID: 35430813 PMCID: PMC9047447 DOI: 10.1021/acs.analchem.1c05017] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/05/2022] [Indexed: 01/06/2023]
Abstract
We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.
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Affiliation(s)
- Sean M. Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Christine H. Chang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Jessica L. Bade
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Jamie R. Nunez
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Madison R. Blumer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Daniel J. Orton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Kent J. Bloodsworth
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Richard D. Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Yehia M. Ibrahim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Ryan S. Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Thomas O. Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
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21
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Alka O, Shanthamoorthy P, Witting M, Kleigrewe K, Kohlbacher O, Röst HL. DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics. Nat Commun 2022; 13:1347. [PMID: 35292629 PMCID: PMC8924252 DOI: 10.1038/s41467-022-29006-z] [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: 10/05/2021] [Accepted: 02/18/2022] [Indexed: 11/09/2022] Open
Abstract
The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.
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Affiliation(s)
- Oliver Alka
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany. .,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.
| | - Premy Shanthamoorthy
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany.,Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany.,Chair of Analytical Food Chemistry, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Karin Kleigrewe
- Bavarian Center for Biomolecular Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Oliver Kohlbacher
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Hannes L Röst
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Canada. .,Department of Computer Science, University of Toronto, Toronto, Canada.
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22
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Qian YX, Zhao DX, Wang HD, Sun H, Xiong Y, Xu XY, Hu WD, Liu MY, Chen BX, Hu Y, Li X, Jiang MT, Yang WZ, Gao XM. An ion mobility-enabled and high-efficiency hybrid scan approach in combination with ultra-high performance liquid chromatography enabling the comprehensive characterization of the multicomponents from Carthamus tinctorius. J Chromatogr A 2022; 1667:462904. [DOI: 10.1016/j.chroma.2022.462904] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 01/09/2023]
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23
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Li X, Liu J, Zuo TT, Hu Y, Li Z, Wang HD, Xu XY, Yang WZ, Guo DA. Advances and challenges in ginseng research from 2011 to 2020: the phytochemistry, quality control, metabolism, and biosynthesis. Nat Prod Rep 2022; 39:875-909. [PMID: 35128553 DOI: 10.1039/d1np00071c] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Covering: 2011 to the end of 2020Panax species (Araliaceae), particularly P. ginseng, P. quinquefolius, and P. notoginseng, have a long history of medicinal use because of their remarkable tonifying effects, and currently serve as crucial sources for various healthcare products, functional foods, and cosmetics, aside from their vast clinical preparations. The huge market demand on a global scale prompts the continuous prosperity in ginseng research concerning the discovery of new compounds, precise quality control, ADME (absorption/disposition/metabolism/excretion), and biosynthesis pathways. Benefitting from the ongoing rapid development of analytical technologies, e.g. multi-dimensional chromatography (MDC), personalized mass spectrometry (MS) scan strategies, and multi-omics, highly recognized progress has been made in driving ginseng analysis towards "systematicness, integrity, personalization, and intelligentization". Herein, we review the advances in the phytochemistry, quality control, metabolism, and biosynthesis pathway of ginseng over the past decade (2011-2020), with 410 citations. Emphasis is placed on the introduction of new compounds isolated (saponins and polysaccharides), and the emerging novel analytical technologies and analytical strategies that favor ginseng's authentic use and global consumption. Perspectives on the challenges and future trends in ginseng analysis are also presented.
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Affiliation(s)
- Xue Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China.
| | - Jie Liu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China.
| | - Tian-Tian Zuo
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China.
| | - Ying Hu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China.
| | - Zheng Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China. .,College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Jinghai, Tianjin 301617, China
| | - Hong-da Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China.
| | - Xiao-Yan Xu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China.
| | - Wen-Zhi Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China.
| | - De-An Guo
- State Key Laboratory of Component-based Chinese Medicine, Tianjin Key Laboratory of TCM Chemistry and Analysis, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China. .,Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China
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24
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Chen YC, Wu HY, Chang CW, Liao PC. Post-Deconvolution MS/MS Spectra Extraction with Data-Independent Acquisition for Comprehensive Profiling of Urinary Glucuronide-Conjugated Metabolome. Anal Chem 2022; 94:2740-2748. [PMID: 35119834 DOI: 10.1021/acs.analchem.1c03557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Conjugation reactions are of critical significance in human metabolism. Identification of these conjugated metabolites is still challenging. Here, we propose a strategy, post-deconvolution MS/MS spectra extraction with data-independent acquisition (PDMS2E-DIA), to comprehensively profile the glucuronide-conjugated metabolome. PDMS2E-DIA enables the identification of conjugated and unconjugated metabolite pairs through neutral loss filtering combined with a significant change in abundance after the deconjugation reaction. Purified DIA MS/MS spectra were constructed by extracting MS/MS fragments shared between spectra derived from conjugated and unconjugated metabolites. The feasibility of this approach was first demonstrated by the identification of two glucuronide-conjugated metabolite standards spiked in urine samples. For human urine samples, 479 features were structurally annotated as potential glucuronide-conjugated metabolites, resulting in the identification of 211 metabolites. Fragment peaks derived from interferents were found to be removed by PDMS2E-DIA, which increased about 6 times the number of identified urine metabolites compared with those calculated from raw DIA deconvoluted MS/MS spectra. This approach was found to have great potential for identifying glucuronide-conjugated metabolites, as well as other kinds of chemical conjugations.
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Affiliation(s)
- Yuan-Chih Chen
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
| | - Hsin-Yi Wu
- Instrumentation Center, National Taiwan University, Taipei 106, Taiwan
| | - Chih-Wei Chang
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
| | - Pao-Chi Liao
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
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25
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Harrieder EM, Kretschmer F, Böcker S, Witting M. Current state-of-the-art of separation methods used in LC-MS based metabolomics and lipidomics. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1188:123069. [PMID: 34879285 DOI: 10.1016/j.jchromb.2021.123069] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/10/2021] [Accepted: 11/24/2021] [Indexed: 12/23/2022]
Abstract
Metabolomics deals with the large-scale analysis of metabolites, belonging to numerous compound classes and showing an extremely high chemical diversity and complexity. Lipidomics, being a subcategory of metabolomics, analyzes the cellular lipid species. Both require state-of-the-art analytical methods capable of accessing the underlying chemical complexity. One of the major techniques used for the analysis of metabolites and lipids is Liquid Chromatography-Mass Spectrometry (LC-MS), offering both different selectivities in LC separation and high sensitivity in MS detection. Chromatography can be divided into different modes, based on the properties of the employed separation system. The most popular ones are Reversed-Phase (RP) separation for non- to mid-polar molecules and Hydrophilic Interaction Liquid Chromatography (HILIC) for polar molecules. So far, no single analysis method exists that can cover the entire range of metabolites or lipids, due to the huge chemical diversity. Consequently, different separation methods have been used for different applications and research questions. In this review, we explore the current use of LC-MS in metabolomics and lipidomics. As a proxy, we examined the use of chromatographic methods in the public repositories EBI MetaboLights and NIH Metabolomics Workbench. We extracted 1484 method descriptions, collected separation metadata and generated an overview on the current use of columns, eluents, etc. Based on this overview, we reviewed current practices and identified potential future trends as well as required improvements that may allow us to increase metabolite coverage, throughput or both simultaneously.
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Affiliation(s)
- Eva-Maria Harrieder
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Fleming Kretschmer
- Chair of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany
| | - Sebastian Böcker
- Chair of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; Metabolomics and Proteomics Core, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; Chair of Analytical Food Chemistry, Technical University of Munich, Maximus-von-Imhof-Forum 2, 85354 Freising, Germany.
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26
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HERMES: a molecular-formula-oriented method to target the metabolome. Nat Methods 2021; 18:1370-1376. [PMID: 34725482 PMCID: PMC9284938 DOI: 10.1038/s41592-021-01307-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 09/22/2021] [Indexed: 01/14/2023]
Abstract
Comprehensive metabolome analyses are essential for biomedical, environmental, and biotechnological research. However, current MS1- and MS2-based acquisition and data analysis strategies in untargeted metabolomics result in low identification rates of metabolites. Here we present HERMES, a molecular-formula-oriented and peak-detection-free method that uses raw LC/MS1 information to optimize MS2 acquisition. Investigating environmental water, Escherichia coli, and human plasma extracts with HERMES, we achieved an increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared with a state-of-the-art data-dependent acquisition (DDA) approach. Thus, HERMES improves sensitivity, selectivity, and annotation of metabolites. HERMES is available as an R package with a user-friendly graphical interface for data analysis and visualization.
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27
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Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU. Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses. Metabolites 2021; 11:460. [PMID: 34357354 PMCID: PMC8304377 DOI: 10.3390/metabo11070460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/14/2022] Open
Abstract
Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.
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Affiliation(s)
- Ulla T. Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Robin Kosch
- Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Michael Altenbuchinger
- Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
| | - Helena U. Zacharias
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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28
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Stancliffe E, Schwaiger-Haber M, Sindelar M, Patti GJ. DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution. Nat Methods 2021; 18:779-787. [PMID: 34239103 PMCID: PMC9302972 DOI: 10.1038/s41592-021-01195-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 05/24/2021] [Indexed: 02/03/2023]
Abstract
Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied on specific experimental methods that introduce variation in the ratios of precursor ions between multiple tandem mass spectrometry (MS/MS) scans. DecoID provides a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum by using LASSO regression. We validated that DecoID increases the number of identified metabolites in MS/MS datasets from both data-independent and data-dependent acquisition without increasing the false discovery rate. We applied DecoID to publicly available data from the MetaboLights repository and to data from human plasma, where DecoID increased the number of identified metabolites from data-dependent acquisition data by over 30% compared to direct spectral matching. DecoID is compatible with any user-defined MS/MS database and provides automated searching for some of the largest MS/MS databases currently available.
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Affiliation(s)
- Ethan Stancliffe
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - 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.
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29
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Wang H, Wang S, Zhao D, Xie H, Wang H, Sun M, Yang X, Qian Y, Wang X, Li X, Gao X, Yang W. A novel ion mobility separation-enabled and precursor ions list-included high-definition data-dependent acquisition (HDDDA) approach: Method development and its application to the comprehensive multicomponent characterization of Fangji Huangqi Decoction. ARAB J CHEM 2021. [DOI: 10.1016/j.arabjc.2021.103087] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
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30
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Cho K, Schwaiger-Haber M, Naser FJ, Stancliffe E, Sindelar M, Patti GJ. Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics. Anal Chim Acta 2021; 1149:338210. [PMID: 33551064 PMCID: PMC8189644 DOI: 10.1016/j.aca.2021.338210] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/16/2020] [Accepted: 01/05/2021] [Indexed: 12/22/2022]
Abstract
When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is common to detect thousands of features from a biological extract. Although it is impractical to collect non-chimeric MS/MS data for each in a single chromatographic run, this is generally unnecessary because most features do not correspond to unique metabolites of biological relevance. Here we show that relatively simple data-processing strategies that can be applied on the fly during acquisition of data with an Orbitrap ID-X, such as blank subtraction and well-established adduct or isotope calculations, decrease the number of features to target for MS/MS analysis by up to an order of magnitude for various types of biological matrices. We demonstrate that annotating these non-biological contaminants and redundancies in real time during data acquisition enables comprehensive MS/MS data to be acquired on each remaining feature at a single collision energy. To ensure that an appropriate collision energy is applied, we introduce a method using a series of hidden ion-trap scans in an Orbitrap ID-X to find an optimal value for each feature that can then be applied in a subsequent high-resolution Orbitrap scan. Data from 100 metabolite standards indicate that this real-time optimization of collision energies leads to more informative MS/MS patterns compared to using a single fixed collision energy alone. As a benchmark to evaluate the overall workflow, we manually annotated unique biological features by independently subjecting E. coli samples to a credentialing analysis. While credentialing led to a more rigorous reduction in feature number, on-the-fly annotation with blank subtraction on an Orbitrap ID-X did not inappropriately discard unique biological metabolites. Taken together, our results reveal that optimal fragmentation data can be obtained in a single LC/MS/MS run for >90% of the unique biological metabolites in a sample when features are annotated during acquisition and collision energies are selected by using parallel mass spectrometry detection.
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Affiliation(s)
- Kevin Cho
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Fuad J Naser
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ethan Stancliffe
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - 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.
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31
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Qian Y, Li W, Wang H, Hu W, Wang H, Zhao D, Hu Y, Li X, Gao X, Yang W. A four-dimensional separation approach by offline 2D-LC/IM-TOF-MS in combination with database-driven computational peak annotation facilitating the in-depth characterization of the multicomponents from Atractylodis Macrocephalae Rhizoma (Atractylodes macrocephala). ARAB J CHEM 2021. [DOI: 10.1016/j.arabjc.2020.102957] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
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32
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Chaker J, Gilles E, Léger T, Jégou B, David A. From Metabolomics to HRMS-Based Exposomics: Adapting Peak Picking and Developing Scoring for MS1 Suspect Screening. Anal Chem 2020; 93:1792-1800. [DOI: 10.1021/acs.analchem.0c04660] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Erwann Gilles
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Thibaut Léger
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Bernard Jégou
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
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33
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Xu T, Hu C, Xuan Q, Xu G. Recent advances in analytical strategies for mass spectrometry-based lipidomics. Anal Chim Acta 2020; 1137:156-169. [PMID: 33153599 PMCID: PMC7525665 DOI: 10.1016/j.aca.2020.09.060] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 09/25/2020] [Accepted: 09/28/2020] [Indexed: 12/20/2022]
Abstract
Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed.
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Affiliation(s)
- Tianrun Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chunxiu Hu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qiuhui Xuan
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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34
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Qiu J, Li T, Zhu ZJ. Multi-dimensional characterization and identification of sterols in untargeted LC-MS analysis using all ion fragmentation technology. Anal Chim Acta 2020; 1142:108-117. [PMID: 33280688 DOI: 10.1016/j.aca.2020.10.058] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 12/11/2022]
Abstract
Sterols are an important type of lipids, and play many important roles in physiological and pathological processes. However, comprehensive analysis of sterols especially identification of unknown sterols is challenging. In this work, LC-MS with all ion fragmentation (AIF) technology was developed for untargeted analysis of sterols in biological samples. AIF technology provided holistic and multi-dimensional characterization for both knowns and unknowns sterols, including accurate m/z, isotope pattern, retention time (RT), and co-eluted peak profiles between MS1 and MS2 ions in one analysis. We further developed an analysis strategy by integrating the multi-dimensional properties to support unambiguous identification of sterols, including distinguishing sterol isomers. The developed strategy enabled to identify a total of 23 sterols in mouse samples, and quantified 19 sterols in mouse liver tissues. More importantly, we demonstrated that AIF based multi-dimensional analysis provided a possibility to identify sterols without chemical standards and facilitated to discover novel compounds with sterol-like structures in biological samples. In summary, we employed the LC-MS based AIF technology to develop multi-dimensional characterization and identification of both known and unknown sterols in complex biological samples. The comprehensive analysis of sterols facilitates to provide molecular insights to many physiological and pathological activities in biology.
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Affiliation(s)
- Jiaqian Qiu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, PR China; University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Tongzhou Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, PR China; University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, PR China.
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35
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Tada I, Chaleckis R, Tsugawa H, Meister I, Zhang P, Lazarinis N, Dahlén B, Wheelock CE, Arita M. Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in Multisample Studies. Anal Chem 2020; 92:11310-11317. [PMID: 32648737 DOI: 10.1021/acs.analchem.0c01980] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Data-independent acquisition mass spectrometry (DIA-MS) is essential for information-rich spectral annotations in untargeted metabolomics. However, the acquired MS2 spectra are highly complex, posing significant annotation challenges. We have developed a correlation-based deconvolution (CorrDec) method that uses ion abundance correlations in multisample studies using DIA-MS as an update of our MS-DIAL software. CorrDec is based on the assumption that peak intensities of precursor and fragment ions correlate across samples and exploits this quantitative information to deconvolute complex DIA spectra. CorrDec clearly improved deconvolution of the original MS-DIAL deconvolution method (MS2Dec) in a dilution series of chemical standards and a 224-sample urinary metabolomics study. The primary advantage of CorrDec over MS2Dec is the ability to discriminate coeluting low-abundance compounds. CorrDec requires the measurement of multiple samples to successfully deconvolute DIA spectra; however, our randomized assessment demonstrated that CorrDec can contribute to studies with as few as 10 unique samples. The presented methodology improves compound annotation and identification in multisample studies and will be useful for applications in large cohort studies.
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Affiliation(s)
- Ipputa Tada
- Department of Genetics, The Graduate University for Advanced Studies, SOKENDAI, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
| | - Romanas Chaleckis
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan.,Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, Sweden
| | - Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, Japan.,RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, Japan
| | - Isabel Meister
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan.,Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, Sweden
| | - Pei Zhang
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan.,Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, Sweden
| | - Nikolaos Lazarinis
- Division of Respiratory Medicine and Allergy, Department of Medicine, Karolinska University Hospital Huddinge, Stockholm, 141-86, Sweden
| | - Barbro Dahlén
- Division of Respiratory Medicine and Allergy, Department of Medicine, Karolinska University Hospital Huddinge, Stockholm, 141-86, Sweden
| | - Craig E Wheelock
- Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan.,Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, Sweden
| | - Masanori Arita
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, Japan.,National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
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Guo J, Huan T. Comparison of Full-Scan, Data-Dependent, and Data-Independent Acquisition Modes in Liquid Chromatography–Mass Spectrometry Based Untargeted Metabolomics. Anal Chem 2020; 92:8072-8080. [DOI: 10.1021/acs.analchem.9b05135] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Jian Guo
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1, British Columbia Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1, British Columbia Canada
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Xue J, Domingo-Almenara X, Guijas C, Palermo A, Rinschen MM, Isbell J, Benton HP, Siuzdak G. Enhanced in-Source Fragmentation Annotation Enables Novel Data Independent Acquisition and Autonomous METLIN Molecular Identification. Anal Chem 2020; 92:6051-6059. [DOI: 10.1021/acs.analchem.0c00409] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Jingchuan Xue
- Scripps Center for Metabolomics and Department of Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Xavier Domingo-Almenara
- Centre for Omic Sciences, EURECAT − Technology Centre of Catalonia and Rovira i Virgili University Joint Unit, Reus, Catalonia, Spain
| | - Carlos Guijas
- Scripps Center for Metabolomics and Department of Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Amelia Palermo
- Scripps Center for Metabolomics and Department of Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Markus M. Rinschen
- Scripps Center for Metabolomics and Department of Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - John Isbell
- Discovery Chemistry, Genomics Institute of the Novartis Research Foundation, Metabolism and Pharmacokinetics, San Diego, California 92121, United States
| | - H. Paul Benton
- Scripps Center for Metabolomics and Department of Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Gary Siuzdak
- Scripps Center for Metabolomics and Department of Molecular and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
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O'Shea K, Misra BB. Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics 2020; 16:36. [PMID: 32146531 DOI: 10.1007/s11306-020-01657-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/01/2020] [Indexed: 12/24/2022]
Abstract
Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.
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Affiliation(s)
- Keiron O'Shea
- Institute of Biological, Environmental, and Rural Studies, Aberystwyth University, Ceredigion, Wales, SY23 3DA, UK
| | - Biswapriya B Misra
- Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
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Ivanisevic J, Want EJ. From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data. Metabolites 2019; 9:metabo9120308. [PMID: 31861212 PMCID: PMC6950334 DOI: 10.3390/metabo9120308] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 12/31/2022] Open
Abstract
Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.
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Affiliation(s)
- Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 19, 1005 Lausanne, Switzerland
- Correspondence: (J.I.); (E.J.W.)
| | - Elizabeth J. Want
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
- Correspondence: (J.I.); (E.J.W.)
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40
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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