Dataset Information

Accurate mass-time tag library for LC/MS-based metabolite profiling of medicinal plants.

ABSTRACT: We report the development and testing of an accurate mass-time (AMT) tag approach for the LC/MS-based identification of plant natural products (PNPs) in complex extracts. An AMT tag library was developed for approximately 500 PNPs with diverse chemical structures, detected in electrospray and atmospheric pressure chemical ionization modes (both positive and negative polarities). In addition, to enable peak annotations with high confidence, MS/MS spectra were acquired with three different fragmentation energies. The LC/MS and MS/MS data sets were integrated into online spectral search tools and repositories (Spektraris and MassBank), thus allowing users to interrogate their own data sets for the potential presence of PNPs. The utility of the AMT tag library approach is demonstrated by the detection and annotation of active principles in 27 different medicinal plant species with diverse chemical constituents.

SUBMITTER: Cuthbertson DJ

PROVIDER: S-EPMC3697863 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Accurate mass-time tag library for LC/MS-based metabolite profiling of medicinal plants.

Cuthbertson Daniel J DJ Johnson Sean R SR Piljac-Žegarac Jasenka J Kappel Julia J Schäfer Sarah S Wüst Matthias M Ketchum Raymond E B RE Croteau Rodney B RB Marques Joaquim V JV Davin Laurence B LB Lewis Norman G NG Rolf Megan M Kutchan Toni M TM Soejarto D Doel DD Lange B Markus BM

Phytochemistry 20130416

We report the development and testing of an accurate mass-time (AMT) tag approach for the LC/MS-based identification of plant natural products (PNPs) in complex extracts. An AMT tag library was developed for approximately 500 PNPs with diverse chemical structures, detected in electrospray and atmospheric pressure chemical ionization modes (both positive and negative polarities). In addition, to enable peak annotations with high confidence, MS/MS spectra were acquired with three different fragmen ...[more]

PMID: 23597491

Similar Datasets

Project description:Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture before analysis (≥24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures. In the first training step, libraries of peptides are obtained on pure bacterial colonies in DDA mode, their detection in urine is then verified in DIA mode, followed by the use of machine learning classifiers (NaiveBayes, BayesNet and Hoeffding tree) to define a peptidic signature to distinguish each bacterial species from the others. Then, in the second step, this signature is monitored in unknown urine samples using targeted proteomics. This method, allowing bacterial identification in less than 4 h, has been applied to fifteen species representing 84% of all Urinary Tract Infections. More than 31,000 peptides in 190 samples were quantified by DIA and classified by machine learning to determine an 82 peptides signature and build a prediction model. This signature was validated for its use in routine using Parallel Reaction Monitoring on two different instruments. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the standard threshold. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.

Dataset Information

Accurate mass-time tag library for LC/MS-based metabolite profiling of medicinal plants.

Publications

Accurate mass-time tag library for LC/MS-based metabolite profiling of medicinal plants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets