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MetabolomicsMatrix Effect on Chemical Isotope Labeling and Its Implication in Metabolomic Sample Preparation for Quantitative Metabolomics
Matrix effect from various constituents in biological samples can reduce the accuracy of quantitative metabolomics. Differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) can overcome the matrix effect on MS detection based on measuring the intensity ratios of met...
2015-07-01 | MTBLS194 | MetaboLights
MetabolomicsTwo-Channel Chemical Isotope Labeling Metabolomics Analysis of Human Fecal Samples

Global metabolomics study of the amine and carboxyl submetabolomes of 16 human fecal samples.

2026-09-23 | MTBLS13531 | MetaboLights
MetabolomicsUntargeted extraction of metabolites 13C labeling profiles from time course labeling switch experiment
Dynamic isotope labeling data provide crucial information about the operation of metabolic pathways, and are commonly generated via liquid chromatography mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We devel...
2015-10-19 | MTBLS228 | MetaboLights
MetabolomicsUntargeted extraction of metabolites 13C labeling profiles from time course labeling switch experiment
Dynamic isotope labeling data provide crucial information about the operation of metabolic pathways, and are commonly generated via liquid chromatography mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We devel...
2015-10-19 | MTBLS229 | MetaboLights
MetabolomicsStable Isotope–Assisted Plant Metabolomics: Combination of Global and Tracer-Based Labeling for Enhanced Untargeted Profiling and Compound Annotation
Untargeted approaches and thus biological interpretation of metabolomics results are still hampered by the reliable assignment of the global metabolome as well as classification and (putative) identification of metabolites. In this work we present an liquid chromatography-mass spectrometry (LC-MS)-b...
2020-02-09 | MTBLS1217 | MetaboLights
MetabolomicsgeoRge: A Computational Tool To Detect the Presence of Stable Isotope Labeling in LC/MS-Based Untargeted Metabolomics
Studying the flow of chemical moieties through the complex set of metabolic reactions that happen in the cell is essential to understanding the alterations in homeostasis that occur in disease. Recently, LC/MS-based untargeted metabolomics and isotopically labeled metabolites have been used to facil...
2015-09-15 | MTBLS213 | MetaboLights
MetabolomicsPronounced uptake and metabolism of organic substrates by diatoms revealed by pulse-labeling metabolomics
Diatoms contribute as a major group of microalgae to approximately 20% of the global carbon fixation. In the plankton, these algae are exposed to a plethora of metabolites, especially when competing algae are lysed. In line with evidence that these phototrophs can also engage in heterotrophy we aske...
2021-12-09 | MTBLS2333 | MetaboLights
GenomicsSelective chemical labeling reveals intragenic enrichment of 5-hydroxymethylcytosine in mouse cerebellum
5-Methylcytosine (5-mC) is an important DNA modification found in eukaryotes that impacts gene regulation and disease pathogenesis. Recently, 5-hydroxymethylcytosine (5-hmC), another form of DNA modification, has been identified in substantial amounts in certain mammalian cell types; however, its ro...
ORGANISM(S): Mus musculus 
2010-12-12 | E-GEOD-25398 | biostudies-arrayexpress
TranscriptomicsInfluence of RNA Labeling on Expression Profiling of MicroRNAs
Several technical parameters are now being optimized for microRNA expression profiling experiments, but the finite amount of total RNA available continues to bottleneck clinical investigations. We used microarrays to determine the influence of the labeling reagent on total RNA requirements and data ...
ORGANISM(S): synthetic construct 
2012-05-21 | E-GEOD-30045 | biostudies-arrayexpress
TranscriptomicsComparison of Labeling Methods for gene expression analysis on the NimbleGen platform
Comparison of ds-cDNA, Indirect and Direct Random Labeling Methods for gene expression analysis on the NimbleGen platform. Expression profiles from artemisinic acid-producing S. cerevisiae strain EPY330 and non-producing strain EPY338 are compared for each labeling method tested. The labeling metho...
ORGANISM(S): Saccharomyces cerevisiae 
2010-05-19 | E-GEOD-15816 | biostudies-arrayexpress
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