Dataset Information

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

ABSTRACT: Premise of the Study:We describe a field collection, sample processing, and ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) instrumental and bioinformatics method developed for untargeted metabolomics of plant tissue and suitable for molecular networking applications. Methods and Results:A total of 613 leaf samples from 204 tree species was collected in the field and analyzed using UHPLC-MS/MS. Matching of molecular fragmentation spectra generated over 125,000 consensus spectra representing unique molecular structures, 26,410 of which were linked to at least one structurally similar compound. Conclusions:Our workflow is able to generate molecular networks of hundreds of thousands of compounds representing broad classes of plant secondary chemistry and a wide range of molecular masses, from 100 to 2500 daltons, making possible large-scale comparative metabolomics, as well as studies of chemical community ecology and macroevolution in plants.

SUBMITTER: Sedio BE

PROVIDER: S-EPMC5895185 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

Sedio Brian E BE Boya P Cristopher A CA Rojas Echeverri Juan Camilo JC

Applications in plant sciences 20180301 3

<h4>Premise of the study</h4>We describe a field collection, sample processing, and ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) instrumental and bioinformatics method developed for untargeted metabolomics of plant tissue and suitable for molecular networking applications.<h4>Methods and results</h4>A total of 613 leaf samples from 204 tree species was collected in the field and analyzed using UHPLC-MS/MS. Matching of molecular fragmentation spectra generat ...[more]

PMID: 29732263

Similar Datasets

Project description:Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in all data sets. This work highlights the importance of technical replication in mass spectrometry-based studies, and presents a new application of HCA as a tool for evaluating the effectiveness of data filtering prior to statistical analysis.

Dataset Information

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

Publications

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets