4.7 Article

Sample amount alternatives for data adjustment in comparative cyanobacterial metabolomics

Journal

ANALYTICAL AND BIOANALYTICAL CHEMISTRY
Volume 399, Issue 10, Pages 3503-3517

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s00216-011-4678-z

Keywords

Synechocystis; Nostoc; Oscillatoria; Comparative metabolomics; Spectral adjustment

Funding

  1. Max Planck Society
  2. Institut Pasteur
  3. Centre National de la Recherche Scientifique [URA 2172]

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Here we describe an integrative protocol for metabolite extraction and the measurement of three cellular constituents, chlorophyll a, total protein, and glycogen from the same small volume of cyanobacterial cultures that can be used as alternative sample amount parameters for data adjustment in comparative metabolome studies. We conducted recovery experiments to assess the robustness and reproducibility of the measurements obtained for the cellular constituents. Also, we have chosen three profile-intrinsic parameters derived from gas chromatography-mass spectrometry (GC/MS) data in order to test their utility for spectral data adjustment. To demonstrate the relevance of these six parameters, we analyzed three cyanobacteria with greatly different morphologies, comprising a unicellular, a filamentous, and a filamentous biofilm-forming strain. Comparative analysis of GC/MS data from cultures grown under standardized conditions indicated that adjustment of the corresponding metabolite profiles by any of the measured cellular constituents or chosen intrinsic parameters led to similar results with respect to sample cohesion and strain separation. Twenty-one metabolites significantly enriched for the carbohydrate and amine superclasses are mainly responsible for strain separation, with a majority of the remaining metabolites contributing to sample group cohesion. Therefore, we conclude that any of the parameters tested in this study can be used for spectral data adjustment of cyanobacterial strains grown under controlled conditions. However, their use for the differentiation between different stresses or physiological states within a strain remains to be shown. Interestingly, both the adjustment approaches and statistical tests applied effected the detection of metabolic differences and their patterns among the analyzed strains.

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