Towards an efficient phenotypic classification of fungal cultures from environmental samples using digital imagery

The use of the analysis technique proposed here, based on functions of the digital imagery software eCognition professional 4.0, provides an objective and effective method for the assessment of fungal diversity in the context of environmental screening projects. It is demonstrated that strains of cultivated fungi can be quantitatively segregated with regard to specific false-color patterns, which reflect even the merest differences in pigment composition, indicating genotypic or phylogenetic disparities. Due to resolving subtle differences of phenotypic traits, a rapid recognition of (duplicate) genotypes is possible which allows the direct inference of the mycobial diversity of given environmental samples and a semi-quantitative or qualitative estimation of the fungal community structure. Two sets of image data from cultures were used in the current study: a minor set being applied for the definition of color classes and for usage in an image reference array, and a second, extended dataset for method validation. An objective assignment, based on false-color classification, was carried out by cluster analysis. High reproducibility using standardized methods makes this design an effective pre-screening option in the field of microbial environmental research. The application of false-color imagery may therefore be applied in fungal monitoring studies as a meaningful procedure supplementing molecular analyses by the identification of new strains irrespective of their relatedness.