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

O'Shea, Keiron; Misra, Biswapriya B.

doi:10.1007/s11306-020-01657-3

Cited by 78 publications

(53 citation statements)

References 108 publications

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“…An overview of the most commonly used techniques and critical points to consider during study design is presented in Fig 1. Such approaches have been utilized in fungal research to perform untargeted monitoring of primary and secondary metabolites (SMs), targeted search for biomarkers (e.g., lipidomics), and in vivo measurement of metabolic fluxes [4][5][6][7]. For downstream analysis of metabolomic data, an increasing number of databases, software, and tools are available (summarized in [8,9]).…”

Section: What Methods Are Available To Study Metabolomics?mentioning

confidence: 99%

Catch the wave: Metabolomic analyses in human pathogenic fungi

2020

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Section: What Methods Are Available To Study Metabolomics?mentioning

confidence: 99%

Catch the wave: Metabolomic analyses in human pathogenic fungi

2020

View full text Add to dashboard Cite

“…On the other hand, untargeted metabolomics studies capture more comprehensive pictures of biological systems by collecting all possible data available from a given technique, without any a priori on the compounds detected. Untargeted analytical approaches provide access to hundreds or thousands of metabolite signals [ 34 ], and sophisticated statistical methods are required to extract and identify relevant metabolite alterations and biomarkers associated with chemical exposure [ 60 , 68 ].…”

Section: Metabolic Phenotyping Of Cell Culture Metabolism: Analytimentioning

confidence: 99%

“…NMR is universally quantitative, which means metabolites concentrations can be determined from signal integration using a single reference (e.g., lactate) of known concentrations [ 87 ]. Metabolites concentrations are routinely determined from 1H 1D NOESY spectra, either automatically or interactively, by the integration of well-resolved peaks belonging to a metabolite, or using more advanced curve-fitting dedicated software or algorithms [ 60 , 73 , 88 ]. Using more advanced spectral editing NMR techniques, metabolite peaks intensities depend on the specific properties for each metabolite, such as relaxation times, and relative metabolite concentrations are obtained following equivalent procedures [ 86 ].…”

Section: Metabolic Phenotyping Of Cell Culture Metabolism: Analytimentioning

confidence: 99%

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Balcerczyk

Damblon

Elena-Herrmann

et al. 2020

IJMS

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Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.

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“…Lipidomics is the analysis of the large-scale identification and quantification of individual lipid species, which relies—at its finest—on the analytical technology of chromatographic separation coupled to mass spectrometry (MS) and dedicated software 1 , 2 . Lipidomics is among the most juvenile—omic technologies, yet recent advances in MS, lipid biochemistry and software pipelines allow for high-throughput, unbiased analysis of diverse biomolecules.…”

Section: Introductionmentioning

confidence: 99%

liputils: a Python module to manage individual fatty acid moieties from complex lipids

Manzini

Busnelli

Colombo

et al. 2020

Sci Rep

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Lipidomic analyses address the problem of characterizing the lipid components of given cells, tissues and organisms by means of chromatographic separations coupled to high-resolution, tandem mass spectrometry analyses. A number of software tools have been developed to help in the daunting task of mass spectrometry signal processing and cleaning, peak analysis and compound identification, and a typical finished lipidomic dataset contains hundreds to thousands of individual molecular lipid species. To provide researchers without a specific technical expertise in mass spectrometry the possibility of broadening the exploration of lipidomic datasets, we have developed liputils, a Python module that specializes in the extraction of fatty acid moieties from individual molecular lipids. there is no prerequisite data format, as liputils extracts residues from RefMet-compliant textual identifiers and from annotations of other commercially available services. We provide three examples of realworld data processing with liputils, as well as a detailed protocol on how to readily process an existing dataset that can be followed with basic informatics skills. Lipidomics is the analysis of the large-scale identification and quantification of individual lipid species, which relies-at its finest-on the analytical technology of chromatographic separation coupled to mass spectrometry (MS) and dedicated software 1,2. Lipidomics is among the most juvenile-omic technologies, yet recent advances in MS, lipid biochemistry and software pipelines allow for high-throughput, unbiased analysis of diverse biomolecules. A typical workflow of a high-resolution, high-throughput lipidomics involves MS-based studies of the structure, composition, and quantity of lipids in biological systems-typically organs, cells, and bodily fluids. Analytes are then identified and quantified with software tools that can process MS data in an automated or semi-automated manner 3,4. Most of the tools that are available on dedicated portals, such as LIPID MAPS 5 , are designed to tackle the problems such as fragments recognition, lipids reconstruction from fragment analysis and artefactual signals removal 4. As of beginning of 2020, ~ 3,000 papers featuring "lipidomics" and "mass spectrometry" could be retrieved from Web of Science, showing an ever growing trend since 2003 6. A modern lipidomic analysis can contain hundreds of individual molecular lipid species, of the 43,645 annotated in databases such as LIPID MAPS, as of March 2020 5. Lipids-generically defined as water-insoluble molecules, or molecules soluble in organic solvents-have been grouped into eight categories: fatty acyls (9,374), glycerolipids (7,608), glycerophospholipids (9,918), sphingolipids (4,438), sterol lipids (2,828), prenol lipids (1,353), saccharolipids (1,316) and polyketides (6,810), further divided into sub-categories 7. In living organisms, they are used as energy reserve 8 , structural components of biological membranes, transporters, lipoprotein components 9,10 , signalling molecules 1...

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

Cited by 78 publications

References 108 publications

Catch the wave: Metabolomic analyses in human pathogenic fungi

Catch the wave: Metabolomic analyses in human pathogenic fungi

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

liputils: a Python module to manage individual fatty acid moieties from complex lipids

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