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Huang H, Liu H, Ma W, Qin L, Chen L, Guo H, Xu H, Li J, Yang C, Hu H, Wu R, Chen D, Feng J, Zhou Y, Wang J, Wang X. High-throughput MALDI-MSI metabolite analysis of plant tissue microarrays. PLANT BIOTECHNOLOGY JOURNAL 2023; 21:2574-2584. [PMID: 37561662 PMCID: PMC10651148 DOI: 10.1111/pbi.14154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/21/2023] [Accepted: 08/01/2023] [Indexed: 08/12/2023]
Abstract
A novel metabolomics analysis technique, termed matrix-assisted laser desorption/ionization mass spectrometry imaging-based plant tissue microarray (MALDI-MSI-PTMA), was successfully developed for high-throughput metabolite detection and imaging from plant tissues. This technique completely overcomes the disadvantage that metabolites cannot be accessible on an intact plant tissue due to the limitations of the special structures of plant cells (e.g. epicuticular wax, cuticle and cell wall) through homogenization of plant tissues, preparation of PTMA moulds and matrix spraying of PTMA sections. Our study shows several properties of MALDI-MSI-PTMA, including no need of sample separation and enrichment, high-throughput metabolite detection and imaging (>1000 samples per day), high-stability mass spectrometry data acquisition and imaging reconstruction and high reproducibility of data. This novel technique was successfully used to quickly evaluate the effects of two plant growth regulator treatments (i.e. 6-benzylaminopurine and N-phenyl-N'-1,2,3-thiadiazol-5-ylurea) on endogenous metabolite expression in plant tissue culture specimens of Dracocephalum rupestre Hance (D. rupestre). Intra-day and inter-day evaluations indicated that the metabolite data detected on PTMA sections had good reproducibility and stability. A total of 312 metabolite ion signals in leaves tissues of D. rupestre were detected, of which 228 metabolite ion signals were identified, they were composed of 122 primary metabolites, 90 secondary metabolites and 16 identified metabolites of unknown classification. The results demonstrated the advantages of MALDI-MSI-PTMA technique for enhancing the overall detection ability of metabolites in plant tissues, indicating that MALDI-MSI-PTMA has the potential to become a powerful routine practice for high-throughput metabolite study in plant science.
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Affiliation(s)
- Hangjun Huang
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
| | - Haiqiang Liu
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Weiwei Ma
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
| | - Liang Qin
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Lulu Chen
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Hua Guo
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Hualei Xu
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Jinrong Li
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Chenyu Yang
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Hao Hu
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Ran Wu
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Difan Chen
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Jinchao Feng
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Yijun Zhou
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
| | - Junli Wang
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
| | - Xiaodong Wang
- College of Life and Environmental SciencesMinzu University of ChinaBeijingChina
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (State Ethnic Affairs Commission), Centre for Imaging & Systems BiologyMinzu University of ChinaBeijingChina
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2
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Fuica-Carrasco C, Toro-Núñez Ó, Lira-Noriega A, Pérez AJ, Hernández V. Metabolome expression in Eucryphia cordifolia populations: Role of seasonality and ecological niche centrality hypothesis. JOURNAL OF PLANT RESEARCH 2023; 136:827-839. [PMID: 37486392 DOI: 10.1007/s10265-023-01483-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/13/2023] [Indexed: 07/25/2023]
Abstract
The ecological niche centrality hypothesis states that population abundance is determined by the position in the ecological niche, expecting higher abundances towards the center of the niche and lower at the periphery. However, the variations in the conditions that favor the persistence of populations between the center and the periphery of the niche can be a surrogate of stress factors that are reflected in the production of metabolites in plants. In this study we tested if metabolomic similarity and diversity in populations of the tree species Eucryphia cordifolia Cav. vary according to their position with respect to the structure of the ecological niche. We hypothesize that populations growing near the centroid should exhibit lower metabolites diversity than plants growing at the periphery of the niche. The ecological niche of the species was modeled using correlative approaches and bioclimatic variables to define central and peripheral localities from which we chose four populations to obtain their metabolomic information using UHPLC-DAD-QTOF-MS. We observed that populations farther away from the centroid tend to have higher metabolome diversity, thus supporting our expectation of the niche centrality hypothesis. Nonetheless, the Shannon index showed a marked variation in metabolome diversity at the seasonal level, with summer and autumn being the periods with higher metabolite diversity compared to winter and spring. We conclude that both the environmental variation throughout the year in combination with the structure of the ecological niche are relevant to understand the variation in expression of metabolites in plants.
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Affiliation(s)
- Camila Fuica-Carrasco
- Laboratorio de Química de Productos Naturales, Departamento de Botánica, Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción, Casilla 160-C, Concepción, CP 40300000, Chile.
| | - Óscar Toro-Núñez
- Departamento de Botánica, Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción, Casilla 160-C, Concepción, CP 40300000, Chile
| | - Andrés Lira-Noriega
- CONAHCyT Research Fellow, Red de Estudios Moleculares Avanzados, Instituto de Ecología, Mexico City, A.C, México
| | - Andy J Pérez
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Casilla 160-C, Concepción, CP 40300000, Chile
| | - Víctor Hernández
- Laboratorio de Química de Productos Naturales, Departamento de Botánica, Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción, Casilla 160-C, Concepción, CP 40300000, Chile
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3
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Decoding Metabolic Reprogramming in Plants under Pathogen Attacks, a Comprehensive Review of Emerging Metabolomics Technologies to Maximize Their Applications. Metabolites 2023; 13:metabo13030424. [PMID: 36984864 PMCID: PMC10055942 DOI: 10.3390/metabo13030424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/01/2023] [Accepted: 03/09/2023] [Indexed: 03/15/2023] Open
Abstract
In their environment, plants interact with a multitude of living organisms and have to cope with a large variety of aggressions of biotic or abiotic origin. What has been known for several decades is that the extraordinary variety of chemical compounds the plants are capable of synthesizing may be estimated in the range of hundreds of thousands, but only a fraction has been fully characterized to be implicated in defense responses. Despite the vast importance of these metabolites for plants and also for human health, our knowledge about their biosynthetic pathways and functions is still fragmentary. Recent progress has been made particularly for the phenylpropanoids and oxylipids metabolism, which is more emphasized in this review. With an increasing interest in monitoring plant metabolic reprogramming, the development of advanced analysis methods should now follow. This review capitalizes on the advanced technologies used in metabolome mapping in planta, including different metabolomics approaches, imaging, flux analysis, and interpretation using bioinformatics tools. Advantages and limitations with regards to the application of each technique towards monitoring which metabolite class or type are highlighted, with special emphasis on the necessary future developments to better mirror such intricate metabolic interactions in planta.
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Defossez E, Bourquin J, von Reuss S, Rasmann S, Glauser G. Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics. MASS SPECTROMETRY REVIEWS 2023; 42:131-143. [PMID: 34145627 PMCID: PMC10078780 DOI: 10.1002/mas.21715] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/28/2021] [Accepted: 06/04/2021] [Indexed: 05/10/2023]
Abstract
In recent years, metabolomics has emerged as a pivotal approach for the holistic analysis of metabolites in biological systems. The rapid progress in analytical equipment, coupled to the rise of powerful data processing tools, now provides unprecedented opportunities to deepen our understanding of the relationships between biochemical processes and physiological or phenotypic conditions in living organisms. However, to obtain unbiased data coverage of hundreds or thousands of metabolites remains a challenging task. Among the panel of available analytical methods, targeted and untargeted mass spectrometry approaches are among the most commonly used. While targeted metabolomics usually relies on multiple-reaction monitoring acquisition, untargeted metabolomics use either data-independent acquisition (DIA) or data-dependent acquisition (DDA) methods. Unlike DIA, DDA offers the possibility to get real, selective MS/MS spectra and thus to improve metabolite assignment when performing untargeted metabolomics. Yet, DDA settings are more complex to establish than DIA settings, and as a result, DDA is more prone to errors in method development and application. Here, we present a tutorial which provides guidelines on how to optimize the technical parameters essential for proper DDA experiments in metabolomics applications. This tutorial is organized as a series of rules describing the impact of the different parameters on data acquisition and data quality. It is primarily intended to metabolomics users and mass spectrometrists that wish to acquire both theoretical background and practical tips for developing effective DDA methods.
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Affiliation(s)
- Emmanuel Defossez
- Laboratory of Functional Ecology, Institute of BiologyUniversity of NeuchâtelNeuchâtelSwitzerland
| | | | - Stephan von Reuss
- Laboratory of Bioanalytical Chemistry, Institute of ChemistryUniversity of NeuchâtelNeuchâtelSwitzerland
- Neuchâtel Platform of Analytical ChemistryUniversity of NeuchâtelNeuchâtelSwitzerland
| | - Sergio Rasmann
- Laboratory of Functional Ecology, Institute of BiologyUniversity of NeuchâtelNeuchâtelSwitzerland
| | - Gaétan Glauser
- Neuchâtel Platform of Analytical ChemistryUniversity of NeuchâtelNeuchâtelSwitzerland
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5
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Jurburg SD, Buscot F, Chatzinotas A, Chaudhari NM, Clark AT, Garbowski M, Grenié M, Hom EFY, Karakoç C, Marr S, Neumann S, Tarkka M, van Dam NM, Weinhold A, Heintz-Buschart A. The community ecology perspective of omics data. MICROBIOME 2022; 10:225. [PMID: 36510248 PMCID: PMC9746134 DOI: 10.1186/s40168-022-01423-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 11/10/2022] [Indexed: 06/17/2023]
Abstract
The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract.
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Affiliation(s)
- Stephanie D Jurburg
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
- Institute of Biology, Leipzig University, Leipzig, Germany.
| | - François Buscot
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Soil Ecology, Helmholtz Centre for Environmental Research- UFZ, Halle, Germany
| | - Antonis Chatzinotas
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biology, Leipzig University, Leipzig, Germany
| | - Narendrakumar M Chaudhari
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University, Jena, Germany
| | - Adam T Clark
- Institute of Biology, University of Graz, Graz, Austria
| | - Magda Garbowski
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Botany, University of Wyoming, Wyoming, USA
| | - Matthias Grenié
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biology, Leipzig University, Leipzig, Germany
| | - Erik F Y Hom
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Biology and Center for Biodiversity and Conservation Research, University of Mississippi, Oxford, Mississippi, USA
| | - Canan Karakoç
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Biology, Indiana University, Indiana, USA
| | - Susanne Marr
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biology, Geobotany and Botanical Garden, Martin Luther University Halle Wittenberg, Halle, Germany
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data, Halle, Germany
| | - Steffen Neumann
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data, Halle, Germany
| | - Mika Tarkka
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Soil Ecology, Helmholtz Centre for Environmental Research- UFZ, Halle, Germany
| | - Nicole M van Dam
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University, Jena, Germany
- Leibniz Institute of Vegetable and Ornamental Crops (IGZ), Großbeeren, Germany
| | - Alexander Weinhold
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University, Jena, Germany
| | - Anna Heintz-Buschart
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
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6
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Waris M, Koçak E, Gonulalan EM, Demirezer LO, Kır S, Nemutlu E. Metabolomics analysis insight into medicinal plant science. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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7
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Metabology: Analysis of metabolomics data using community ecology tools. Anal Chim Acta 2022; 1232:340469. [DOI: 10.1016/j.aca.2022.340469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/15/2022] [Accepted: 09/28/2022] [Indexed: 11/17/2022]
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8
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Honeker LK, Hildebrand GA, Fudyma JD, Daber LE, Hoyt D, Flowers SE, Gil-Loaiza J, Kübert A, Bamberger I, Anderton CR, Cliff J, Leichty S, AminiTabrizi R, Kreuzwieser J, Shi L, Bai X, Velickovic D, Dippold MA, Ladd SN, Werner C, Meredith LK, Tfaily MM. Elucidating Drought-Tolerance Mechanisms in Plant Roots through 1H NMR Metabolomics in Parallel with MALDI-MS, and NanoSIMS Imaging Techniques. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:2021-2032. [PMID: 35048708 DOI: 10.1021/acs.est.1c06772] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
As direct mediators between plants and soil, roots play an important role in metabolic responses to environmental stresses such as drought, yet these responses are vastly uncharacterized on a plant-specific level, especially for co-occurring species. Here, we aim to examine the effects of drought on root metabolic profiles and carbon allocation pathways of three tropical rainforest species by combining cutting-edge metabolomic and imaging technologies in an in situ position-specific 13C-pyruvate root-labeling experiment. Further, washed (rhizosphere-depleted) and unwashed roots were examined to test the impact of microbial presence on root metabolic pathways. Drought had a species-specific impact on the metabolic profiles and spatial distribution in Piper sp. and Hibiscus rosa sinensis roots, signifying different defense mechanisms; Piper sp. enhanced root structural defense via recalcitrant compounds including lignin, while H. rosa sinensis enhanced biochemical defense via secretion of antioxidants and fatty acids. In contrast, Clitoria fairchildiana, a legume tree, was not influenced as much by drought but rather by rhizosphere presence where carbohydrate storage was enhanced, indicating a close association with symbiotic microbes. This study demonstrates how multiple techniques can be combined to identify how plants cope with drought through different drought-tolerance strategies and the consequences of such changes on below-ground organic matter composition.
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Affiliation(s)
- Linnea K Honeker
- BIO5 Institute, The University of Arizona, 1657 East Helen Street., Tucson, Arizona 85719, United States
- Biosphere 2, University of Arizona, 32540 South Biosphere Road, Oracle, Arizona 85739, United States
| | - Gina A Hildebrand
- Department of Environmental Science, University of Arizona, 1177 East Fourth Street, Tucson, Arizona 85721, United States
| | - Jane D Fudyma
- Department of Environmental Science, University of Arizona, 1177 East Fourth Street, Tucson, Arizona 85721, United States
| | - L Erik Daber
- Chair of Ecosystem Physiology, Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany
| | - David Hoyt
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Sarah E Flowers
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Juliana Gil-Loaiza
- School of Natural Resources and the Environment, University of Arizona, 1064 East Lowell Sreet, Tucson, Arizona 85721, United States
| | - Angelika Kübert
- Chair of Ecosystem Physiology, Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany
| | - Ines Bamberger
- Chair of Ecosystem Physiology, Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany
| | - Christopher R Anderton
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - John Cliff
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Sarah Leichty
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Roya AminiTabrizi
- Department of Environmental Science, University of Arizona, 1177 East Fourth Street, Tucson, Arizona 85721, United States
| | - Jürgen Kreuzwieser
- Chair of Ecosystem Physiology, Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany
| | - Lingling Shi
- Biogeochemistry of Agroecosystems, Department of Crop Science, Georg August University of Göttingen, Büsgenweg 2, 37077 Göttingen, Germany
| | - Xuejuan Bai
- State Key Laboratory of Soil Erosion and Dry Land Farming on Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, 712100 Shaanxi, China
| | - Dusan Velickovic
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Michaela A Dippold
- Biogeochemistry of Agroecosystems, Department of Crop Science, Georg August University of Göttingen, Büsgenweg 2, 37077 Göttingen, Germany
| | - S Nemiah Ladd
- Chair of Ecosystem Physiology, Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany
| | - Christiane Werner
- Chair of Ecosystem Physiology, Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany
| | - Laura K Meredith
- BIO5 Institute, The University of Arizona, 1657 East Helen Street., Tucson, Arizona 85719, United States
- Biosphere 2, University of Arizona, 32540 South Biosphere Road, Oracle, Arizona 85739, United States
- School of Natural Resources and the Environment, University of Arizona, 1064 East Lowell Sreet, Tucson, Arizona 85721, United States
| | - Malak M Tfaily
- BIO5 Institute, The University of Arizona, 1657 East Helen Street., Tucson, Arizona 85719, United States
- Department of Environmental Science, University of Arizona, 1177 East Fourth Street, Tucson, Arizona 85721, United States
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
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9
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Plyushchenko IV, Fedorova ES, Potoldykova NV, Polyakovskiy KA, Glukhov AI, Rodin IA. Omics Untargeted Key Script: R-Based Software Toolbox for Untargeted Metabolomics with Bladder Cancer Biomarkers Discovery Case Study. J Proteome Res 2021; 21:833-847. [PMID: 34161108 DOI: 10.1021/acs.jproteome.1c00392] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Large-scale untargeted LC-MS-based metabolomic profiling is a valuable source for systems biology and biomarker discovery. Data analysis and processing are major tasks due to the high complexity of generated signals and the presence of unwanted variations. In the present study, we introduce an R-based open-source collection of scripts called OUKS (Omics Untargeted Key Script), which provides comprehensive data processing. OUKS is developed by integrating various R packages and metabolomics software tools and can be easily set up and prepared to create a custom pipeline. Novel computational features are related to quality control samples-based signal processing and are implemented by gradient boosting, tree-based, and other nonlinear regression algorithms. Bladder cancer biomarkers discovery study which is based on untargeted LC-MS profiling of urine samples is performed to demonstrate exhaustive functionality of the developed software tool. Unique examination among dozens of metabolomics-specific data curation methods was carried out at each processing step. As a result, potential biomarkers were identified, statistically validated, and described by metabolism disorders. Our study demonstrates that OUKS helps to make untargeted LC-MS metabolomic profiling with the latest computational features readily accessible in a ready-to-use unified manner to a research community.
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Affiliation(s)
- Ivan V Plyushchenko
- Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Elizaveta S Fedorova
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 119071 Moscow, Russia
| | - Natalia V Potoldykova
- Institute for Urology and Reproductive Health, Sechenov First Moscow State Medical University, 119992 Moscow, Russia
| | - Konstantin A Polyakovskiy
- Institute for Urology and Reproductive Health, Sechenov First Moscow State Medical University, 119992 Moscow, Russia
| | - Alexander I Glukhov
- Biology Department, Lomonosov Moscow State University, 119991 Moscow, Russia.,Department of Biochemistry, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Igor A Rodin
- Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia.,Department of Epidemiology and Evidence-Based Medicine, Sechenov First Moscow State Medical University, 119435 Moscow, Russia
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