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Martínez Bilesio AR, Puig-Castellví F, Tauler R, Sciara M, Fay F, Rasia RM, Burdisso P, García-Reiriz AG. Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts. Anal Chim Acta 2024; 1309:342689. [PMID: 38772669 DOI: 10.1016/j.aca.2024.342689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/03/2024] [Indexed: 05/23/2024]
Abstract
BACKGROUND Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics. RESULTS This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers. SIGNIFICANCE Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.
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Affiliation(s)
- Andrés R Martínez Bilesio
- Instituto de Biología Molecular y Celular de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (IBR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Ocampo y Esmeralda, Rosario 2000, Argentina
| | - Francesc Puig-Castellví
- European Genomics Institute for Diabetes, INSERM U1283, CNRS UMR8199, Institut Pasteur de Lille, Lille University Hospital, University of Lille, Lille, France
| | - Romà Tauler
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034, Barcelona, Spain
| | - Mariela Sciara
- Centro de Diagnóstico Médico de Alta Complejidad (CIBIC), Rosario, Argentina
| | - Fabián Fay
- Centro de Diagnóstico Médico de Alta Complejidad (CIBIC), Rosario, Argentina
| | - Rodolfo M Rasia
- Instituto de Biología Molecular y Celular de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (IBR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Ocampo y Esmeralda, Rosario 2000, Argentina; Plataforma Argentina de Biología Estructural y Metabolómica (PLABEM), Rosario, Santa Fe, Argentina
| | - Paula Burdisso
- Instituto de Biología Molecular y Celular de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (IBR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Ocampo y Esmeralda, Rosario 2000, Argentina; Plataforma Argentina de Biología Estructural y Metabolómica (PLABEM), Rosario, Santa Fe, Argentina.
| | - Alejandro G García-Reiriz
- Instituto de Química Rosario (IQUIR-CONICET) Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Ocampo y Esmeralda, Rosario 2000, Argentina.
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Wu S, Huang W, Wang F, Zou X, Li X, Liu CM, Zhang W, Yan S. Integrated metabolomics and lipidomics analyses suggest the temperature-dependent lipid desaturation promotes aflatoxin biosynthesis in Aspergillus flavus. Front Microbiol 2023; 14:1137643. [PMID: 37065116 PMCID: PMC10102665 DOI: 10.3389/fmicb.2023.1137643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/17/2023] [Indexed: 04/03/2023] Open
Abstract
Temperature is one of the main factors affecting aflatoxin (AF) biosynthesis in Aspergillus flavus. Previous studies showed that AF biosynthesis is elevated in A. flavus at temperatures between 28°C-30°C, while it is inhibited at temperatures above 30°C. However, little is known about the metabolic mechanism underlying temperature-regulated AF biosynthesis. In this study, we integrated metabolomic and lipidomic analyses to investigate the endogenous metabolism of A. flavus across 6 days of mycelia growth at 28°C (optimal AF production) and 37°C (no AF production). Results showed that both metabolite and lipid profiles were significantly altered at different temperatures. In particular, metabolites involved in carbohydrate and amino acid metabolism were up-regulated at 37°C on the second day but down-regulated from days three to six. Moreover, lipidomics and targeted fatty acids analyses of mycelia samples revealed a distinct pattern of lipid species and free fatty acids desaturation. High degrees of polyunsaturation of most lipid species at 28°C were positively correlated with AF production. These results provide new insights into the underlying metabolic changes in A. flavus under temperature stress.
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Affiliation(s)
- Shaowen Wu
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Wenjie Huang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Fenghua Wang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Xinlu Zou
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Xuan Li
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Chun-Ming Liu
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | - Wenyang Zhang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- *Correspondence: Shijuan Yan,
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Yang C, Dong A, Deng L, Wang F, Liu J. Deciphering the change pattern of lipid metabolism in Saccharomyces cerevisiae responding to low temperature. Biochem Eng J 2023. [DOI: 10.1016/j.bej.2023.108884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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Pérez-Cova M, Platikanov S, Tauler R, Jaumot J. Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches. Talanta 2022; 247:123586. [PMID: 35671578 DOI: 10.1016/j.talanta.2022.123586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 11/16/2022]
Abstract
In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.
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Affiliation(s)
- Miriam Pérez-Cova
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, E08034 Barcelona, Spain; Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Diagonal 647, E08028, Barcelona, Spain.
| | - Stefan Platikanov
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, E08034 Barcelona, Spain
| | - Romà Tauler
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, E08034 Barcelona, Spain
| | - Joaquim Jaumot
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, E08034 Barcelona, Spain
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Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies. Molecules 2022; 27:molecules27103304. [PMID: 35630781 PMCID: PMC9147242 DOI: 10.3390/molecules27103304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/08/2022] [Accepted: 05/19/2022] [Indexed: 02/01/2023] Open
Abstract
The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis—ASCA, regularized MANOVA–rMANOVA, and Group-wise ANOVA-simultaneous component analysis—GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.
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Hu X, Luo Y, Man Y, Tang X, Bi Z, Ren L. Lipidomic and transcriptomic analysis reveals the self-regulation mechanism of Schizochytrium sp. in response to temperature stresses. ALGAL RES 2022. [DOI: 10.1016/j.algal.2022.102664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Adverse Effects of Arsenic Uptake in Rice Metabolome and Lipidome Revealed by Untargeted Liquid Chromatography Coupled to Mass Spectrometry (LC-MS) and Regions of Interest Multivariate Curve Resolution. SEPARATIONS 2022. [DOI: 10.3390/separations9030079] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Rice crops are especially vulnerable to arsenic exposure compared to other cereal crops because flooding growing conditions facilitates its uptake. Besides, there are still many unknown questions about arsenic’s mode of action in rice. Here, we apply two untargeted approaches using liquid chromatography coupled to mass spectrometry (LC-MS) to unravel the effects on rice lipidome and metabolome in the early stages of growth. The exposure is evaluated through two different treatments, watering with arsenic-contaminated water and soil containing arsenic. The combination of regions of interest (ROI) and multivariate curve resolution (MCR) strategies in the ROIMCR data analyses workflow is proposed and complemented with other multivariate analyses such as partial least square discriminant analysis (PLS-DA) for the identification of potential markers of arsenic exposure and toxicity effects. The results of this study showed that rice metabolome (and lipidome) in root tissues seemed to be more affected by the watering and soil treatment. In contrast, aerial tissues alterations were accentuated by the arsenic dose, rather than with the watering and soil treatment itself. Up to a hundred lipids and 40 metabolites were significantly altered due to arsenic exposure. Major metabolic alterations were found in glycerophospholipids, glycerolipids, and amino acid-related pathways.
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State-of-the-art in analytical methods for metabolic profiling of Saccharomyces cerevisiae. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Lu H, Li F, Yuan L, Domenzain I, Yu R, Wang H, Li G, Chen Y, Ji B, Kerkhoven EJ, Nielsen J. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Mol Syst Biol 2021; 17:e10427. [PMID: 34676984 PMCID: PMC8532513 DOI: 10.15252/msb.202110427] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 10/02/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022] Open
Abstract
Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.
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Affiliation(s)
- Hongzhong Lu
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Feiran Li
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Le Yuan
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Iván Domenzain
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Rosemary Yu
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Hao Wang
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- National Bioinformatics Infrastructure SwedenScience for Life LaboratoryChalmers University of TechnologyGothenburgSweden
| | - Gang Li
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Yu Chen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Boyang Ji
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
| | - Eduard J Kerkhoven
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Jens Nielsen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
- BioInnovation InstituteCopenhagen NDenmark
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Sailwal M, Das AJ, Gazara RK, Dasgupta D, Bhaskar T, Hazra S, Ghosh D. Connecting the dots: Advances in modern metabolomics and its application in yeast system. Biotechnol Adv 2020; 44:107616. [DOI: 10.1016/j.biotechadv.2020.107616] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 12/15/2022]
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Fabri JHTM, de Sá NP, Malavazi I, Del Poeta M. The dynamics and role of sphingolipids in eukaryotic organisms upon thermal adaptation. Prog Lipid Res 2020; 80:101063. [PMID: 32888959 DOI: 10.1016/j.plipres.2020.101063] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 08/18/2020] [Accepted: 08/27/2020] [Indexed: 01/09/2023]
Abstract
All living beings have an optimal temperature for growth and survival. With the advancement of global warming, the search for understanding adaptive processes to climate changes has gained prominence. In this context, all living beings monitor the external temperature and develop adaptive responses to thermal variations. These responses ultimately change the functioning of the cell and affect the most diverse structures and processes. One of the first structures to detect thermal variations is the plasma membrane, whose constitution allows triggering of intracellular signals that assist in the response to temperature stress. Although studies on this topic have been conducted, the underlying mechanisms of recognizing thermal changes and modifying cellular functioning to adapt to this condition are not fully understood. Recently, many reports have indicated the participation of sphingolipids (SLs), major components of the plasma membrane, in the regulation of the thermal stress response. SLs can structurally reinforce the membrane or/and send signals intracellularly to control numerous cellular processes, such as apoptosis, cytoskeleton polarization, cell cycle arresting and fungal virulence. In this review, we discuss how SLs synthesis changes during both heat and cold stresses, focusing on fungi, plants, animals and human cells. The role of lysophospholipids is also discussed.
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Affiliation(s)
- João Henrique Tadini Marilhano Fabri
- Department of Microbiology and Immunology, Stony Brook University, Stony Brook, New York, USA; Departamento de Genética e Evolução, Centro de Ciências Biológicas e da Saúde, Universidade Federal de São Carlos, São Carlos, SP, Brazil
| | - Nivea Pereira de Sá
- Department of Microbiology and Immunology, Stony Brook University, Stony Brook, New York, USA
| | - Iran Malavazi
- Departamento de Genética e Evolução, Centro de Ciências Biológicas e da Saúde, Universidade Federal de São Carlos, São Carlos, SP, Brazil
| | - Maurizio Del Poeta
- Department of Microbiology and Immunology, Stony Brook University, Stony Brook, New York, USA; Division of Infectious Diseases, School of Medicine, Stony Brook University, Stony Brook, New York, USA; Veterans Administration Medical Center, Northport, New York, USA.
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Gorrochategui E, Jaumot J, Tauler R. ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets. BMC Bioinformatics 2019; 20:256. [PMID: 31101001 PMCID: PMC6525397 DOI: 10.1186/s12859-019-2848-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 04/25/2019] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field. RESULTS Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications. CONCLUSIONS The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.
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Affiliation(s)
- Eva Gorrochategui
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas (CSIC), Jorsi Girona 18-25, Barcelona, 08034, Catalonia, Spain
| | - Joaquim Jaumot
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas (CSIC), Jorsi Girona 18-25, Barcelona, 08034, Catalonia, Spain
| | - Romà Tauler
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas (CSIC), Jorsi Girona 18-25, Barcelona, 08034, Catalonia, Spain.
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de Juan A, Tauler R. Data Fusion by Multivariate Curve Resolution. DATA HANDLING IN SCIENCE AND TECHNOLOGY 2019. [DOI: 10.1016/b978-0-444-63984-4.00008-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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