51
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Connolly JRFB, Munoz-Muriedas J, Lapthorn C, Higton D, Vissers JPC, Webb A, Beaumont C, Dear GJ. Investigation into Small Molecule Isomeric Glucuronide Metabolite Differentiation Using In Silico and Experimental Collision Cross-Section Values. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1976-1986. [PMID: 34296869 DOI: 10.1021/jasms.0c00427] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Identifying isomeric metabolites remains a challenging and time-consuming process with both sensitivity and unambiguous structural assignment typically only achieved through the combined use of LC-MS and NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce timely and accurate data using a single technique to identify drug metabolites, including isomers, without the requirement for in-depth interpretation (cf. MS/MS data) using an automated computational pipeline by comparison of experimental collision cross-section (CCS) values with predicted CCS values. An ion mobility enabled Q-Tof mass spectrometer was used to determine the CCS values of 28 (14 isomeric pairs of) small molecule glucuronide metabolites, which were then compared to two different in silico models; a quantum mechanics (QM) and a machine learning (ML) approach to test these approaches. The difference between CCS values within isomer pairs was also assessed to evaluate if the difference was large enough for unambiguous structural identification through in silico prediction. A good correlation was found between both the QM- and ML-based models and experimentally determined CCS values. The predicted CCS values were found to be similar between ML and QM in silico methods, with the QM model more accurately describing the difference in CCS values between isomer pairs. Of the 14 isomeric pairs, only one (naringenin glucuronides) gave a sufficient difference in CCS values for the QM model to distinguish between the isomers with some level of confidence, with the ML model unable to confidently distinguish the studied isomer pairs. An evaluation of analyte structures was also undertaken to explore any trends or anomalies within the data set.
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Affiliation(s)
- John R F B Connolly
- RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin D02 YN77, Ireland
| | | | - Cris Lapthorn
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom
| | - David Higton
- Waters Corporation, Stamford Ave, Wilmslow SK9 4AX, United Kingdom
| | | | - Alison Webb
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom
| | - Claire Beaumont
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom
| | - Gordon J Dear
- GlaxoSmithKline, Park Road, Ware, Hertfordshire SG12 0DP, United Kingdom
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52
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Li T, Yin Y, Zhou Z, Qiu J, Liu W, Zhang X, He K, Cai Y, Zhu ZJ. Ion mobility-based sterolomics reveals spatially and temporally distinctive sterol lipids in the mouse brain. Nat Commun 2021; 12:4343. [PMID: 34267224 PMCID: PMC8282640 DOI: 10.1038/s41467-021-24672-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022] Open
Abstract
Aberrant sterol lipid metabolism is associated with physiological dysfunctions in the aging brain and aging-dependent disorders such as neurodegenerative diseases. There is an unmet demand to comprehensively profile sterol lipids spatially and temporally in different brain regions during aging. Here, we develop an ion mobility-mass spectrometry based four-dimensional sterolomics technology leveraged by a machine learning-empowered high-coverage library (>2000 sterol lipids) for accurate identification. We apply this four-dimensional technology to profile the spatially resolved landscapes of sterol lipids in ten functional regions of the mouse brain, and quantitatively uncover ~200 sterol lipids uniquely distributed in specific regions with concentrations spanning up to 8 orders of magnitude. Further spatial analysis pinpoints age-associated differences in region-specific sterol lipid metabolism, revealing changes in the numbers of altered sterol lipids, concentration variations, and age-dependent coregulation networks. These findings will contribute to our understanding of abnormal sterol lipid metabolism and its role in brain diseases.
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Affiliation(s)
- Tongzhou Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yandong Yin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zhiwei Zhou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jiaqian Qiu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wenbin Liu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Xueting Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Kaiwen He
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
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53
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Odenkirk MT, Reif DM, Baker ES. Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments. Anal Chem 2021; 93:7763-7773. [PMID: 34029068 PMCID: PMC8465926 DOI: 10.1021/acs.analchem.0c04850] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - David M Reif
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
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54
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Borges R, Colby SM, Das S, Edison AS, Fiehn O, Kind T, Lee J, Merrill AT, Merz KM, Metz TO, Nunez JR, Tantillo DJ, Wang LP, Wang S, Renslow RS. Quantum Chemistry Calculations for Metabolomics. Chem Rev 2021; 121:5633-5670. [PMID: 33979149 PMCID: PMC8161423 DOI: 10.1021/acs.chemrev.0c00901] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Indexed: 02/07/2023]
Abstract
A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.
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Affiliation(s)
- Ricardo
M. Borges
- Walter
Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil
| | - Sean M. Colby
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Susanta Das
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Arthur S. Edison
- Departments
of Genetics and Biochemistry and Molecular Biology, Complex Carbohydrate
Research Center and Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
| | - Oliver Fiehn
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Tobias Kind
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Jesi Lee
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Amy T. Merrill
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Kenneth M. Merz
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Thomas O. Metz
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Jamie R. Nunez
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Dean J. Tantillo
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Lee-Ping Wang
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Shunyang Wang
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Ryan S. Renslow
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
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55
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Zamith-Miranda D, Peres da Silva R, Couvillion SP, Bredeweg EL, Burnet MC, Coelho C, Camacho E, Nimrichter L, Puccia R, Almeida IC, Casadevall A, Rodrigues ML, Alves LR, Nosanchuk JD, Nakayasu ES. Omics Approaches for Understanding Biogenesis, Composition and Functions of Fungal Extracellular Vesicles. Front Genet 2021; 12:648524. [PMID: 34012462 PMCID: PMC8126698 DOI: 10.3389/fgene.2021.648524] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 04/06/2021] [Indexed: 12/13/2022] Open
Abstract
Extracellular vesicles (EVs) are lipid bilayer structures released by organisms from all kingdoms of life. The diverse biogenesis pathways of EVs result in a wide variety of physical properties and functions across different organisms. Fungal EVs were first described in 2007 and different omics approaches have been fundamental to understand their composition, biogenesis, and function. In this review, we discuss the role of omics in elucidating fungal EVs biology. Transcriptomics, proteomics, metabolomics, and lipidomics have each enabled the molecular characterization of fungal EVs, providing evidence that these structures serve a wide array of functions, ranging from key carriers of cell wall biosynthetic machinery to virulence factors. Omics in combination with genetic approaches have been instrumental in determining both biogenesis and cargo loading into EVs. We also discuss how omics technologies are being employed to elucidate the role of EVs in antifungal resistance, disease biomarkers, and their potential use as vaccines. Finally, we review recent advances in analytical technology and multi-omic integration tools, which will help to address key knowledge gaps in EVs biology and translate basic research information into urgently needed clinical applications such as diagnostics, and immuno- and chemotherapies to fungal infections.
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Affiliation(s)
- Daniel Zamith-Miranda
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, United States
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, United States
| | | | - Sneha P. Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Erin L. Bredeweg
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Meagan C. Burnet
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Carolina Coelho
- MRC Centre for Medical Mycology, University of Exeter, Exeter, United Kingdom
| | - Emma Camacho
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Leonardo Nimrichter
- Laboratório de Glicobiologia de Eucariotos, Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Rosana Puccia
- Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina-Universidade Federal de São Paulo, São Paulo, Brazil
| | - Igor C. Almeida
- Department of Biological Sciences, Border Biomedical Research Center, University of Texas at El Paso, El Paso, TX, United States
| | - Arturo Casadevall
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Marcio L. Rodrigues
- Laboratório de Regulação da Expressão Gênica, Instituto Carlos Chagas-FIOCRUZ PR, Curitiba, Brazil
- Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Lysangela R. Alves
- Laboratório de Regulação da Expressão Gênica, Instituto Carlos Chagas-FIOCRUZ PR, Curitiba, Brazil
| | - Joshua D. Nosanchuk
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, United States
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
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56
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Zhang Y, Lei H, Tao J, Yuan W, Zhang W, Ye J. An integrated approach for structural characterization of Gui Ling Ji by traveling wave ion mobility mass spectrometry and molecular network. RSC Adv 2021; 11:15546-15556. [PMID: 35481180 PMCID: PMC9029087 DOI: 10.1039/d1ra01834e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 04/21/2021] [Indexed: 12/11/2022] Open
Abstract
Gui Ling Ji (GLJ), an ancient reputable traditional Chinese medicine (TCM) formula prescription, has been applied for the treatment of oligospermia and asthenospermia in clinical practice. However, its inherent compounds have not yet been systematically elucidated, which hampers developing standards or guidelines for quality evaluation and even the understanding of pharmacological effects. In this study, an integrated approach has been established for comprehensive structural characterization of GLJ. Mass spectrometry datasets of GLJ and each of the single herb medicines in this prescription have been developed by dynamic exclusion fast data-dependent acquisition and high-definition data-independent acquisition modes on ultra-high-performance liquid chromatography coupled with travelling wave ion mobility quadrupole time-of-flight mass spectrometry (UPLC-TWIMS-QTOF-MS). A global natural product social molecular networking (GNPS) platform was then applied for the visualization of chemical space of GLJ and further for the high throughput identification of the targeted or untargeted compounds due to the support of data-transmitting from each single herbal medicine to the formula GLJ. Moreover, drift time, predicted CCS, and diagnostic fragment ions were induced for annotating isomer compounds. Consequently, based on molecular network and library hits, a total of 257 compounds from GLJ, which were classified into 4 structural types, were positively or tentatively characterized. Among them, 20 potential new compounds were detected and 30 pairs of isomers were comprehensively distinguished. The established strategy was effective for attribution, classification, recognition of various constituents, and also was valuable for integrating large amounts of disordered MS/MS data and mining trace compounds in other complex chemical or biochemical systems. An integrated approach for structural characterization of Gui Ling Ji by traveling wave ion mobility mass spectrometry and molecular network.![]()
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Affiliation(s)
- Yuhao Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
| | - Huibo Lei
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
| | - Jianfei Tao
- College of Pharmacy, The Second Military Medical University Shanghai 200433 China +86 021 81871248.,Pharmacy Department, Shanghai Yang Si Hospital Shanghai 200126 China
| | - Wenlin Yuan
- College of Pharmacy, The Second Military Medical University Shanghai 200433 China +86 021 81871248
| | - Weidong Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244.,College of Pharmacy, The Second Military Medical University Shanghai 200433 China +86 021 81871248
| | - Ji Ye
- College of Pharmacy, The Second Military Medical University Shanghai 200433 China +86 021 81871248
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57
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Taylor M, Lukowski JK, Anderton CR. Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:872-894. [PMID: 33656885 PMCID: PMC8033567 DOI: 10.1021/jasms.0c00439] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 05/02/2023]
Abstract
Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.
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Affiliation(s)
- Michael
J. Taylor
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jessica K. Lukowski
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Christopher R. Anderton
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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58
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Mass spectrometry based untargeted metabolomics for plant systems biology. Emerg Top Life Sci 2021; 5:189-201. [DOI: 10.1042/etls20200271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/04/2021] [Accepted: 02/22/2021] [Indexed: 12/12/2022]
Abstract
Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.
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59
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Broeckling CD, Yao L, Isaac G, Gioioso M, Ianchis V, Vissers JPC. Application of Predicted Collisional Cross Section to Metabolome Databases to Probabilistically Describe the Current and Future Ion Mobility Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:661-669. [PMID: 33539078 DOI: 10.1021/jasms.0c00375] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Metabolomics is a powerful phenotyping platform with potential for high-throughput analyses. The primary technology for metabolite profiling is mass spectrometry. In recent years, the coupling of mass spectrometry with ion mobility spectrometry (IMS) has offered the promise of faster analysis time and greater resolving power. Our understanding of the potential impact of IMS on the field of metabolomics is limited by availability of comprehensive experimental data. In this analysis, we use a probabilistic approach to enumerate the strengths and limitations, the present and future, of this technology. This is accomplished through use of "model" metabolomes, predicted physicochemical properties, and probabilistic descriptions of resolving power. This analysis advances our understanding of the importance of orthogonality in resolving (separation) dimensions, describes the impact of the metabolome composition on resolution demands, and offers a system resolution landscape that may serve to guide practitioners in the coming years.
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Affiliation(s)
- Corey D Broeckling
- Analytical Resources Core, Bioanalysis and Omics Center, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Linxing Yao
- Analytical Resources Core, Bioanalysis and Omics Center, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Giorgis Isaac
- Waters Corporation, Milford, Massachusetts 01757, United States
| | - Marisa Gioioso
- Waters Corporation, Milford, Massachusetts 01757, United States
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60
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Nielson FF, Colby SM, Thomas DG, Renslow RS, Metz TO. Exploring the Impacts of Conformer Selection Methods on Ion Mobility Collision Cross Section Predictions. Anal Chem 2021; 93:3830-3838. [PMID: 33606495 DOI: 10.1021/acs.analchem.0c04341] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The prediction of structure dependent molecular properties, such as collision cross sections as measured using ion mobility spectrometry, are crucially dependent on the selection of the correct population of molecular conformers. Here, we report an in-depth evaluation of multiple conformation selection techniques, including simple averaging, Boltzmann weighting, lowest energy selection, low energy threshold reductions, and similarity reduction. Generating 50 000 conformers each for 18 molecules, we used the In Silico Chemical Library Engine (ISiCLE) to calculate the collision cross sections for the entire data set. First, we employed Monte Carlo simulations to understand the variability between conformer structures as generated using simulated annealing. Then we employed Monte Carlo simulations to the aforementioned conformer selection techniques applied on the simulated molecular property: the ion mobility collision cross section. Based on our analyses, we found Boltzmann weighting to be a good trade-off between precision and theoretical accuracy. Combining multiple techniques revealed that energy thresholds and root-mean-squared deviation-based similarity reductions can save considerable computational expense while maintaining property prediction accuracy. Molecular dynamic conformer generation tools like AMBER can continue to generate new lowest energy conformers even after tens of thousands of generations, decreasing precision between runs. This reduced precision can be ameliorated and theoretical accuracy increased by running density functional theory geometry optimization on carefully selected conformers.
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Affiliation(s)
- Felicity F Nielson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington United States
| | - Sean M Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington United States
| | - Dennis G Thomas
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington United States
| | - Ryan S Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington United States
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington United States
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61
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Witting M, Schmidt U, Knölker HJ. UHPLC-IM-Q-ToFMS analysis of maradolipids, found exclusively in Caenorhabditis elegans dauer larvae. Anal Bioanal Chem 2021; 413:2091-2102. [PMID: 33575816 PMCID: PMC7943524 DOI: 10.1007/s00216-021-03172-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/22/2020] [Accepted: 01/12/2021] [Indexed: 12/29/2022]
Abstract
Lipid identification is one of the current bottlenecks in lipidomics and lipid profiling, especially for novel lipid classes, and requires multidimensional data for correct annotation. We used the combination of chromatographic and ion mobility separation together with data-independent acquisition (DIA) of tandem mass spectrometric data for the analysis of lipids in the biomedical model organism Caenorhabditis elegans. C. elegans reacts to harsh environmental conditions by interrupting its normal life cycle and entering an alternative developmental stage called dauer stage. Dauer larvae show distinct changes in metabolism and morphology to survive unfavorable environmental conditions and are able to survive for a long time without feeding. Only at this developmental stage, dauer larvae produce a specific class of glycolipids called maradolipids. We performed an analysis of maradolipids using ultrahigh performance liquid chromatography-ion mobility spectrometry-quadrupole-time of flight-mass spectrometry (UHPLC-IM-Q-ToFMS) using drift tube ion mobility to showcase how the integration of retention times, collisional cross sections, and DIA fragmentation data can be used for lipid identification. The obtained results show that combination of UHPLC and IM separation together with DIA represents a valuable tool for initial lipid identification. Using this analytical tool, a total of 45 marado- and lysomaradolipids have been putatively identified and 10 confirmed by authentic standards directly from C. elegans dauer larvae lipid extracts without the further need for further purification of glycolipids. Furthermore, we putatively identified two isomers of a lysomaradolipid not known so far. ![]()
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Affiliation(s)
- 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, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 2, 85354, Freising, Germany.
| | - Ulrike Schmidt
- Faculty of Chemistry, Technische Universität Dresden, Bergstraße 66, 01069, Dresden, Germany
| | - Hans-Joachim Knölker
- Faculty of Chemistry, Technische Universität Dresden, Bergstraße 66, 01069, Dresden, Germany
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62
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Chang HY, Colby SM, Du X, Gomez JD, Helf MJ, Kechris K, Kirkpatrick CR, Li S, Patti GJ, Renslow RS, Subramaniam S, Verma M, Xia J, Young JD. A Practical Guide to Metabolomics Software Development. Anal Chem 2021; 93:1912-1923. [PMID: 33467846 PMCID: PMC7859930 DOI: 10.1021/acs.analchem.0c03581] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
![]()
A growing number
of software tools have been developed for metabolomics
data processing and analysis. Many new tools are contributed by metabolomics
practitioners who have limited prior experience with software development,
and the tools are subsequently implemented by users with expertise
that ranges from basic point-and-click data analysis to advanced coding.
This Perspective is intended to introduce metabolomics software users
and developers to important considerations that determine the overall
impact of a publicly available tool within the scientific community.
The recommendations reflect the collective experience of an NIH-sponsored
Metabolomics Consortium working group that was formed with the goal
of researching guidelines and best practices for metabolomics tool
development. The recommendations are aimed at metabolomics researchers
with little formal background in programming and are organized into
three stages: (i) preparation, (ii) tool development, and (iii) distribution
and maintenance.
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Affiliation(s)
- Hui-Yin Chang
- Department of Pathology, University of Michigan, 1301 Catherine Street, Ann Arbor, Michigan 48109, United States.,Department of Biomedical Sciences and Engineering, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan City 320, Taiwan
| | - Sean M Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K8-98, Richland, Washington 99352, United States
| | - Xiuxia Du
- Department of Bioinformatics & Genomics, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, North Carolina 28223, United States
| | - Javier D Gomez
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, Tennessee 37235, United States
| | - Maximilian J Helf
- Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, 533 Tower Road, Ithaca, New York 14853, United States
| | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 East 17th Place B119, Aurora, Colorado 80045, United States
| | - Christine R Kirkpatrick
- San Diego Supercomputer Center, University of California San Diego, MC 0505, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, Connecticut 06032, United States
| | - Gary J Patti
- Department of Chemistry, Department of Medicine, and Siteman Cancer Center, Washington University in St. Louis, CB 1134, One Brookings Drive, St. Louis, Missouri 63130, United States
| | - Ryan S Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K8-98, Richland, Washington 99352, United States.,Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, P.O. Box 646515, Pullman, Washington 99164, United States
| | - Shankar Subramaniam
- San Diego Supercomputer Center, University of California San Diego, MC 0505, 9500 Gilman Drive, La Jolla, California 92093, United States.,Department of Bioengineering, Department of Computer Science and Engineering, Department of Cellular and Molecular Medicine, and Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive #0412, La Jolla, California 92093, United States
| | - Mukesh Verma
- Epidemiology and Genomics Research Program, National Cancer Institute, National Institutes of Health, Suite 4E102, 9609 Medical Center Drive, MSC 9763, Rockville, Maryland 20850, United States
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, 21111 Lakeshore Road, Ste. Anne de Bellevue, Quebec H9X 3 V9, Canada
| | - Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, Tennessee 37235, United States.,Department of Molecular Physiology and Biophysics, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, Tennessee 37235, United States
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63
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Masike K, Stander MA, de Villiers A. Recent applications of ion mobility spectrometry in natural product research. J Pharm Biomed Anal 2021; 195:113846. [PMID: 33422832 DOI: 10.1016/j.jpba.2020.113846] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/08/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022]
Abstract
Ion mobility spectrometry (IMS) is a rapid separation technique capable of extracting complementary structural information to chromatography and mass spectrometry (MS). IMS, especially in combination with MS, has experienced inordinate growth in recent years as an analytical technique, and elicited intense interest in many research fields. In natural product analysis, IMS shows promise as an additional tool to enhance the performance of analytical methods used to identify promising drug candidates. Potential benefits of the incorporation of IMS into analytical workflows currently used in natural product analysis include the discrimination of structurally similar secondary metabolites, improving the quality of mass spectral data, and the use of mobility-derived collision cross-section (CCS) values as an additional identification criterion in targeted and untargeted analyses. This review aims to provide an overview of the application of IMS to natural product analysis over the last six years. Instrumental aspects and the fundamental background of IMS will be briefly covered, and recent applications of the technique for natural product analysis will be discussed to demonstrate the utility of the technique in this field.
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Affiliation(s)
- Keabetswe Masike
- Department of Biochemistry, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa
| | - Maria A Stander
- Department of Biochemistry, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa; Central Analytical Facility, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa
| | - André de Villiers
- Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa.
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64
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Nuñez JR, Mcgrady M, Yesiltepe Y, Renslow RS, Metz TO. Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets. J Chem Inf Model 2020; 60:6251-6257. [PMID: 33283505 DOI: 10.1021/acs.jcim.0c00899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader "chemical space." These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable Python module, chespa, built to easily assess different chemical space definitions through clustering of compounds in these spaces and visualizing trends of these clusters. To demonstrate this, chespa currently streamlines prediction of various molecular descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test six different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, that is, the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's nontargeted analysis collaborative trial, where blinded samples had been analyzed previously, providing 1398 data points. Improved understanding of observability would offer many advantages in small-molecule identification, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property nontrivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.
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Affiliation(s)
- Jamie R Nuñez
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.,The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, Washington 99164, United States
| | - Monee Mcgrady
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Yasemin Yesiltepe
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.,The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, Washington 99164, United States
| | - Ryan S Renslow
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.,The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, Washington 99164, United States
| | - Thomas O Metz
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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65
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Using metacommunity ecology to understand environmental metabolomes. Nat Commun 2020; 11:6369. [PMID: 33311510 PMCID: PMC7732844 DOI: 10.1038/s41467-020-19989-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 10/29/2020] [Indexed: 12/26/2022] Open
Abstract
Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail. Despite growing interest in environmental metabolomics, we lack conceptual frameworks for considering how metabolites vary across space and time in ecological systems. Here, the authors apply (species) community assembly concepts to metabolomics data, offering a way forward in understanding the assembly of metabolite assemblages.
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66
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Celma A, Sancho JV, Schymanski EL, Fabregat-Safont D, Ibáñez M, Goshawk J, Barknowitz G, Hernández F, Bijlsma L. Improving Target and Suspect Screening High-Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:15120-15131. [PMID: 33207875 DOI: 10.1021/acs.est.0c05713] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Currently, the most powerful approach to monitor organic micropollutants (OMPs) in environmental samples is the combination of target, suspect, and nontarget screening strategies using high-resolution mass spectrometry (HRMS). However, the high complexity of sample matrices and the huge number of OMPs potentially present in samples at low concentrations pose an analytical challenge. Ion mobility separation (IMS) combined with HRMS instruments (IMS-HRMS) introduces an additional analytical dimension, providing extra information, which facilitates the identification of OMPs. The collision cross-section (CCS) value provided by IMS is unaffected by the matrix or chromatographic separation. Consequently, the creation of CCS databases and the inclusion of ion mobility within identification criteria are of high interest for an enhanced and robust screening strategy. In this work, a CCS library for IMS-HRMS, which is online and freely available, was developed for 556 OMPs in both positive and negative ionization modes using electrospray ionization. The inclusion of ion mobility data in widely adopted confidence levels for identification in environmental reporting is discussed. Illustrative examples of OMPs found in environmental samples are presented to highlight the potential of IMS-HRMS and to demonstrate the additional value of CCS data in various screening strategies.
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Affiliation(s)
- Alberto Celma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Juan V Sancho
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - David Fabregat-Safont
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - María Ibáñez
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Jeff Goshawk
- Waters Corporation, Stamford Avenue, Altrincham Road, Wilmslow, Cheshire SK9 4AX, U.K
| | - Gitte Barknowitz
- Waters Corporation, Stamford Avenue, Altrincham Road, Wilmslow, Cheshire SK9 4AX, U.K
| | - Félix Hernández
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
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67
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Zhou Z, Luo M, Chen X, Yin Y, Xiong X, Wang R, Zhu ZJ. Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics. Nat Commun 2020; 11:4334. [PMID: 32859911 PMCID: PMC7455731 DOI: 10.1038/s41467-020-18171-8] [Citation(s) in RCA: 164] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 08/07/2020] [Indexed: 01/04/2023] Open
Abstract
The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.
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Affiliation(s)
- Zhiwei Zhou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049, Beijing, People's Republic of China
| | - Mingdu Luo
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049, Beijing, People's Republic of China
| | - Xi Chen
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049, Beijing, People's Republic of China
| | - Yandong Yin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
| | - Xin Xiong
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
| | - Ruohong Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049, Beijing, People's Republic of China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China.
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68
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Mullin L, Jobst K, DiLorenzo RA, Plumb R, Reiner EJ, Yeung LW, Jogsten IE. Liquid chromatography-ion mobility-high resolution mass spectrometry for analysis of pollutants in indoor dust: Identification and predictive capabilities. Anal Chim Acta 2020; 1125:29-40. [DOI: 10.1016/j.aca.2020.05.052] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 05/16/2020] [Accepted: 05/21/2020] [Indexed: 01/01/2023]
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69
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Couvillion SP, Agrawal N, Colby SM, Brandvold KR, Metz TO. Who Is Metabolizing What? Discovering Novel Biomolecules in the Microbiome and the Organisms Who Make Them. Front Cell Infect Microbiol 2020; 10:388. [PMID: 32850487 PMCID: PMC7410922 DOI: 10.3389/fcimb.2020.00388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 06/25/2020] [Indexed: 12/14/2022] Open
Abstract
Even as the field of microbiome research has made huge strides in mapping microbial community composition in a variety of environments and organisms, explaining the phenotypic influences on the host by microbial taxa-both known and unknown-and their specific functions still remain major challenges. A pressing need is the ability to assign specific functions in terms of enzymes and small molecules to specific taxa or groups of taxa in the community. This knowledge will be crucial for advancing personalized therapies based on the targeted modulation of microbes or metabolites that have predictable outcomes to benefit the human host. This perspective article advocates for the combined use of standards-free metabolomics and activity-based protein profiling strategies to address this gap in functional knowledge in microbiome research via the identification of novel biomolecules and the attribution of their production to specific microbial taxa.
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Affiliation(s)
- Sneha P. Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Neha Agrawal
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Sean M. Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Kristoffer R. Brandvold
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, United States
| | - Thomas O. Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
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70
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Abstract
We present Mass Spectrometry-Data Independent Analysis software version 4 (MS-DIAL 4), a comprehensive lipidome atlas with retention time, collision cross-section and tandem mass spectrometry information. We formulated mass spectral fragmentations of lipids across 117 lipid subclasses and included ion mobility tandem mass spectrometry. Using human, murine, algal and plant biological samples, we annotated and semiquantified 8,051 lipids using MS-DIAL 4 with a 1-2% estimated false discovery rate. MS-DIAL 4 helps standardize lipidomics data and discover lipid pathways.
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71
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Bonini P, Kind T, Tsugawa H, Barupal DK, Fiehn O. Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics. Anal Chem 2020; 92:7515-7522. [PMID: 32390414 PMCID: PMC8715951 DOI: 10.1021/acs.analchem.9b05765] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Two large, publicly available data sets were used for model training in machine learning: the Fiehn hydrophilic interaction liquid chromatography data set (HILIC) of 981 primary metabolites and biogenic amines,and the RIKEN plant specialized metabolome annotation (PlaSMA) database of 852 secondary metabolites that uses reversed-phase liquid chromatography (RPLC). Five different machine learning algorithms have been integrated into the Retip R package: the random forest, Bayesian-regularized neural network, XGBoost, light gradient-boosting machine (LightGBM), and Keras algorithms for building the retention time prediction models. A complete workflow for retention time prediction was developed in R. It can be freely downloaded from the GitHub repository (https://www.retip.app). Keras outperformed other machine learning algorithms in the test set with minimum overfitting, verified by small error differences between training, test, and validation sets. Keras yielded a mean absolute error of 0.78 min for HILIC and 0.57 min for RPLC. Retip is integrated into the mass spectrometry software tools MS-DIAL and MS-FINDER, allowing a complete compound annotation workflow. In a test application on mouse blood plasma samples, we found a 68% reduction in the number of candidate structures when searching all isomers in MS-FINDER compound identification software. Retention time prediction increases the identification rate in liquid chromatography and subsequently leads to an improved biological interpretation of metabolomics data.
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Affiliation(s)
- Paolo Bonini
- NGAlab, La Riera de Gaia, Tarragona 43762, Spain
| | - Tobias Kind
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, California 95616, United States
| | - Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, Yokohama 230-0045, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Dinesh Kumar Barupal
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, California 95616, United States
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, California 95616, United States
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72
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Luo MD, Zhou ZW, Zhu ZJ. The Application of Ion Mobility-Mass Spectrometry in Untargeted Metabolomics: from Separation to Identification. JOURNAL OF ANALYSIS AND TESTING 2020. [DOI: 10.1007/s41664-020-00133-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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73
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Graton J, Hernández-Mesa M, Normand S, Dervilly G, Le Questel JY, Le Bizec B. Characterization of Steroids through Collision Cross Sections: Contribution of Quantum Chemistry Calculations. Anal Chem 2020; 92:6034-6042. [DOI: 10.1021/acs.analchem.0c00357] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Jérôme Graton
- CNRS, CEISAM, UMR 6230, Université de Nantes, Nantes F-44000, France
| | - Maykel Hernández-Mesa
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), INRAE, Oniris, Nantes F-44307, France
| | - Samuel Normand
- CNRS, CEISAM, UMR 6230, Université de Nantes, Nantes F-44000, France
| | - Gaud Dervilly
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), INRAE, Oniris, Nantes F-44307, France
| | | | - Bruno Le Bizec
- Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), INRAE, Oniris, Nantes F-44307, France
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74
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Ross DH, Cho JH, Xu L. Breaking Down Structural Diversity for Comprehensive Prediction of Ion-Neutral Collision Cross Sections. Anal Chem 2020; 92:4548-4557. [PMID: 32096630 DOI: 10.1021/acs.analchem.9b05772] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or drug metabolite identification. Ion mobility-mass spectrometry (IM-MS) provides rapid two-dimensional separation of ions based on their mobility through a neutral buffer gas. The mobility of an ion is related to its collision cross section (CCS) with the buffer gas, a physical property that is determined by the size and shape of the ion. This structural dependency makes CCS a promising characteristic for compound identification, but this utility is limited by the availability of high-quality reference CCS values. CCS prediction using machine learning (ML) has recently shown promise in the field, but accurate and broadly applicable models are still lacking. Here we present a novel ML approach that employs a comprehensive collection of CCS values covering a wide range of chemical space. Using this diverse database, we identified the structural characteristics, represented by molecular quantum numbers (MQNs), that contribute to variance in CCS and assessed the performance of a variety of ML algorithms in predicting CCS. We found that by breaking down the chemical structural diversity using unsupervised clustering based on the MQNs, specific and accurate prediction models for each cluster can be trained, which showed superior performance than a single model trained with all data. Using this approach, we have robustly trained and characterized a CCS prediction model with high accuracy on diverse chemical structures. An all-in-one web interface (https://CCSbase.net) was built for querying the CCS database and accessing the predictive model to support unknown compound identifications.
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Affiliation(s)
- Dylan H Ross
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Jang Ho Cho
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Libin Xu
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
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75
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Colby SM, Nuñez JR, Hodas NO, Corley CD, Renslow RR. Deep Learning to Generate in Silico Chemical Property Libraries and Candidate Molecules for Small Molecule Identification in Complex Samples. Anal Chem 2019; 92:1720-1729. [DOI: 10.1021/acs.analchem.9b02348] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Sean M. Colby
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jamie R. Nuñez
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Nathan O. Hodas
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Courtney D. Corley
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ryan R. Renslow
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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76
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Wojcik R, Nagy G, Attah IK, Webb IK, Garimella SVB, Weitz KK, Hollerbach A, Monroe ME, Ligare MR, Nielson FF, Norheim RV, Renslow RS, Metz TO, Ibrahim YM, Smith RD. SLIM Ultrahigh Resolution Ion Mobility Spectrometry Separations of Isotopologues and Isotopomers Reveal Mobility Shifts due to Mass Distribution Changes. Anal Chem 2019; 91:11952-11962. [PMID: 31450886 PMCID: PMC7188075 DOI: 10.1021/acs.analchem.9b02808] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We report on separations of ion isotopologues and isotopomers using ultrahigh-resolution traveling wave-based Structures for Lossless Ion Manipulations with serpentine ultralong path and extended routing ion mobility spectrometry coupled to mass spectrometry (SLIM SUPER IMS-MS). Mobility separations of ions from the naturally occurring ion isotopic envelopes (e.g., [M], [M+1], [M+2], ... ions) showed the first and second isotopic peaks (i.e., [M+1] and [M+2]) for various tetraalkylammonium ions could be resolved from their respective monoisotopic ion peak ([M]) after SLIM SUPER IMS with resolving powers of ∼400-600. Similar separations were obtained for other compounds (e.g., tetrapeptide ions). Greater separation was obtained using argon versus helium drift gas, as expected from the greater reduced mass contribution to ion mobility described by the Mason-Schamp relationship. To more directly explore the role of isotopic substitutions, we studied a mixture of specific isotopically substituted (15N, 13C, and 2H) protonated arginine isotopologues. While the separations in nitrogen were primarily due to their reduced mass differences, similar to the naturally occurring isotopologues, their separations in helium, where higher resolving powers could also be achieved, revealed distinct additional relative mobility shifts. These shifts appeared correlated, after correction for the reduced mass contribution, with changes in the ion center of mass due to the different locations of heavy atom substitutions. The origin of these apparent mass distribution-induced mobility shifts was then further explored using a mixture of Iodoacetyl Tandem Mass Tag (iodoTMT) isotopomers (i.e., each having the same exact mass, but with different isotopic substitution sites). Again, the observed mobility shifts appeared correlated with changes in the ion center of mass leading to multiple monoisotopic mobilities being observed for some isotopomers (up to a ∼0.04% difference in mobility). These mobility shifts thus appear to reflect details of the ion structure, derived from the changes due to ion rotation impacting collision frequency or momentum transfer, and highlight the potential for new approaches for ion structural characterization.
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Affiliation(s)
- Roza Wojcik
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Gabe Nagy
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Isaac. K. Attah
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ian K. Webb
- Department of Chemistry, Indiana University Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Sandilya V. B. Garimella
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Karl K. Weitz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Adam Hollerbach
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Matthew E. Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Marshall R. Ligare
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Felicity F. Nielson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Randolph V. Norheim
- Environmental Molecular Sciences Laboratory, 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
| | - Yehia M. Ibrahim
- 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
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