Implementation of liquid chromatography–high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Analytica Chimica Acta, Journal Year: 2020, Volume and Issue: 1105, P. 28 - 44

Published: Jan. 2, 2020

Language: Английский

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Software tools, databases and resources in metabolomics: updates from 2018 to 2019

Metabolomics, Journal Year: 2020, Volume and Issue: 16(3)

Published: March 1, 2020

Language: Английский

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From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Metabolites, Journal Year: 2019, Volume and Issue: 9(12), P. 308 - 308

Published: Dec. 17, 2019

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset progression, response intervention. Each step of the analytical statistical pipeline crucial for generation high-quality, robust data. Metabolite identification remains bottleneck in these studies; therefore, confidence data produced paramount order maximize biological output. Here, we outline key steps workflow provide details on important parameters considerations. Studies should be designed carefully ensure appropriate power adequate controls. Subsequent sample handling preparation avoid introduction bias, which can significantly affect downstream interpretation. It not possible cover entire metabolome with single platform; platform reflect under investigation question(s) consideration. The large, complex datasets need pre-processed extract meaningful information. Finally, most time-consuming are metabolite identification, as well metabolic pathway network analysis. Here discuss some widely used tools pitfalls each workflow, ultimate aim guiding reader towards efficient their studies.

Language: Английский

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Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Analytical and Bioanalytical Chemistry, Journal Year: 2021, Volume and Issue: 414(2), P. 759 - 789

Published: Aug. 25, 2021

Abstract Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification frame research projects clinical studies, much remains be done move this approach practice. This is especially true perspective being applied personalized/precision medicine, which aims at stratifying patients according their risk developing diseases, tailoring medical treatments individual characteristics order improve efficacy limit toxicity. In review article, we discuss main challenges linked analytical chemistry that need addressed foster implementation metabolomics clinics use data produced by personalized medicine. First all, there are well-known issues related untargeted workflows levels production (lack standardization), metabolite identification (small proportion annotated features identified metabolites), processing (from automatic detection multi-omic integration) hamper inter-operability reusability data. Furthermore, outputs complex molecular signatures few tens metabolites, often abundance variations, obtained expensive laboratory equipment. It thus necessary simplify these so they can used field. last point, still poorly community, may crucial a near future increased availability societal demand participatory Graphical abstract

Language: Английский

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Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics

TrAC Trends in Analytical Chemistry, Journal Year: 2022, Volume and Issue: 158, P. 116903 - 116903

Published: Dec. 24, 2022

Language: Английский

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Untargeted metabolomics by liquid chromatography‐mass spectrometry for food authentication: A review

Comprehensive Reviews in Food Science and Food Safety, Journal Year: 2022, Volume and Issue: 21(3), P. 2455 - 2488

Published: March 29, 2022

Abstract Food fraud is currently a growing global concern with far‐reaching consequences. authenticity attributes, including biological identity, geographical origin, agricultural production, and processing technology, are susceptible to food fraud. Metabolic markers their corresponding authentication methods considered as promising choice for authentication. However, few metabolic were available develop robust analytical in routine control. Untargeted metabolomics by liquid chromatography‐mass spectrometry (LC‐MS) increasingly used discover markers. This review summarizes the general workflow, recent applications, advantages, advances, limitations, future needs of untargeted LC‐MS identifying In conclusion, shows great efficiency assessment freshness, cause animals’ death, so on, through three main steps, namely, data acquisition, biomarker discovery, validation. The application prospects selected require be valued, need eventually applicable at targeted analysis assessing unknown samples.

Language: Английский

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BUDDY: molecular formula discovery via bottom-up MS/MS interrogation

Nature Methods, Journal Year: 2023, Volume and Issue: 20(6), P. 881 - 890

Published: April 13, 2023

Language: Английский

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FMO rewires metabolism to promote longevity through tryptophan and one carbon metabolism in C. elegans

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Feb. 2, 2023

Abstract Flavin containing monooxygenases (FMOs) are promiscuous enzymes known for metabolizing a wide range of exogenous compounds. In C. elegans , fmo-2 expression increases lifespan and healthspan downstream multiple longevity-promoting pathways through an unknown mechanism. Here, we report that, beyond its classification as xenobiotic enzyme, leads to rewiring endogenous metabolism principally changes in one carbon (OCM). These likely relevant, find that genetically modifying OCM enzyme alterations longevity interact with expression. Using computer modeling, identify decreased methylation the major flux modified by FMO-2 is sufficient recapitulate benefits. We further tryptophan mammalian FMO overexpression models validated substrate FMO-2. Our resulting model connects single two previously unconnected key metabolic provides framework interconnectivity such dietary restriction. FMOs well-conserved also induced lifespan-extending interventions mice, supporting conserved important role promoting health remodeling.

Language: Английский

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PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: April 28, 2023

Abstract Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological environmental bio-chemical processes. However, the lack of rapid analytical methods robust algorithms these heterogeneous data has limited its application. Here, we develop evaluate a sensitive high-throughput computational workflow to enable accurate metabolite profiling. Our combines liquid chromatography, ion mobility data-independent acquisition with PeakDecoder, machine learning-based algorithm that learns distinguish true co-elution co-mobility from raw calculates identification error rates. We apply PeakDecoder profiling various engineered strains Aspergillus pseudoterreus, niger, Pseudomonas putida Rhodosporidium toruloides . Results, validated manually against selected reaction monitoring gas-chromatography platforms, show 2683 features could be confidently annotated quantified across 116 microbial sample runs library built 64 standards.

Language: Английский

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Untargeted metabolomics and machine learning unveil quality and authenticity interactions in grated Parmigiano Reggiano PDO cheese

Food Chemistry, Journal Year: 2024, Volume and Issue: 447, P. 138938 - 138938

Published: March 5, 2024

The chemical composition of Parmigiano Reggiano (PR) hard cheese can be significantly affected by different factors across the dairy supply chain, including ripening, altimetric zone, and rind inclusion levels in grated cheeses. present study proposes an untargeted metabolomics approach combined with machine learning chemometrics to evaluate effect these three critical parameters. Specifically, ripening was found exert a pivotal role defining signature PR cheeses, amino acids lipid derivatives that exhibited their as key discriminant compounds. In parallel, random forest classifier used predict (> 18%) cheeses authenticate specific altimetry production, achieving high prediction ability both model performances (i.e., ∼60% > 90%, respectively). Overall, results open novel perspective identifying quality authenticity markers metabolites cheese.

Language: Английский

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