Dataset Information


More is not always better: Evaluation of 1D- and 2D-LC-MS2 methods for metaproteomics

ABSTRACT: Metaproteomics is a powerful tool to characterize the structure of microbial communities and the physiology, metabolism and interactions of the species in these communities. Metaproteomics seeks to identify and quantify proteins from microbial communities on a large scale using gel electrophoresis or advanced liquid chromatography (LC) combined with high-resolution, accurate-mass mass spectrometry. To achieve extensive coverage of a metaproteome using shotgun proteomics, the sample complexity has to be decreased, for which a main approach is the on-line separation of peptides using one or more LC dimensions. The aim of this study is to test different 1D- and 2D-LC methods, also in comparison with the standard GeLC (pre-separation of proteins via gel electrophoresis) to find the best approach for analyzing metaproteome samples, using a mock community with 32 species in different abundances.


ORGANISM(S): Bacteria   Archaea   Eukaryota   Viruses  

TISSUE(S): Tissue Not Applicable To Dataset

DISEASE(S): Not Available

SUBMITTER: Tjorven Hinzke  

LAB HEAD: Manuel Kleiner

PROVIDER: PXD008017 | Pride | 2019-02-12


Dataset's files

Action DRS
1D_12h_50.raw Raw Other
1D_12h_75.raw Raw Other
1D_8h_50_1.6_1.raw Raw
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More Is Not Always Better: Evaluation of 1D and 2D-LC-MS/MS Methods for Metaproteomics.

Hinzke Tjorven T   Kouris Angela A   Hughes Rebecca-Ayme RA   Strous Marc M   Kleiner Manuel M  

Frontiers in microbiology 20190214

Metaproteomics, the study of protein expression in microbial communities, is a versatile tool for environmental microbiology. Achieving sufficiently high metaproteome coverage to obtain a comprehensive picture of the activities and interactions in microbial communities is one of the current challenges in metaproteomics. An essential step to maximize the number of identified proteins is peptide separation via liquid chromatography (LC) prior to mass spectrometry (MS). Thorough optimization and co  ...[more]

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