Advanced digital image analysis method dedicated to the characterization of the morphology of filamentous fungus

Area of Science:

• Biotechnology
• Microbial Fermentation
• Image Analysis

Background:

• Filamentous fungi exhibit complex morphology, leading to challenges in fermentation processes like increased viscosity and diffusion limitations.
• Understanding the relationship between fungal morphology and fermentation parameters is crucial for process optimization.

Purpose of the Study:

• To develop and validate an accurate image analysis method for quantitative morphological analysis of filamentous fungi during fermentation.
• To investigate the impact of shear stress on the morphology of Trichoderma reesei and its correlation with fermentation medium viscosity.

Main Methods:

• Development of a novel algorithm named FACE (Fungal Algorithm for Characterization and Enhancement) for image processing.
• Application of skeleton and topological approaches for detailed morphological analysis.
• Validation using an industrial strain of Trichoderma reesei subjected to varying shear stress conditions.

Main Results:

• The FACE algorithm successfully generated sharp images, segmented fungal structures, and enabled comprehensive morphological analysis.
• Nine key morphological criteria were extracted, demonstrating the method's ability to identify significant changes.
• The study revealed the impact of shear stress on fungal morphology and its direct influence on fermentation medium viscosity.

Conclusions:

• The developed FACE method provides quantitative and representative data for fungal morphology, crucial for fermentation development.
• This approach offers valuable insights into the relationship between shear stress, fungal morphology, and viscosity in Trichoderma reesei fermentations.
• The findings contribute to optimizing fermentation processes by better managing fungal growth and its physical effects.