Plants are ideal systems for self-organization processes among small molecules. According to Münch-Gamalei model, plant organism may be generally represented as three enclosed tubular domains: the inner one–endoplast, the middle one–symplast, and the outer one–apoplast. Therefore, the cytoplasm is essentially a diffuse layer at the border between two oppositely directed transport streams, and it appears to be the most suitable medium for self-organization processes to develop.
Serial analysis of small biomolecules in metabolomics (metabolite profiling) demonstrates the following features of self-organization:
1. Stable metabolite profiles are formed which are typical for given plant species and their ontogenesis.
2. Application of dimension reduction methods to metabolite concentration matrix reveals the temporal trends.
Plant metabolomics mostly deals with metabolite content in transport network within plant bodies. Given that, detailed information on content and dynamics of sugars and other transport system components is required for comprehensive metabolite profiling. Traditional chemical derivatization approaches for wide range of small biomolecules hardly facilitate further analysis of sugars due to their mass spectrometrical similarity, hence their (chromatographical) retention parameters become informative for their resolution. Therefore, plant metabolomics obviously benefits from methods based on retention parameters, especially GC×GC mode in LECO 4D GC-MS machines that gives the second dimension to retention parameter. We provide several examples of 4D mode application to plant metabolite profiling.