Morphological Characterization and Viability Assessment of Trichoderma reesei by Image Analysis

Abstract

The production of cellulase from the filamentous fungus Trichoderma reesei is a critical step in the industrial process leading to cellulose ethanol. As a result of the lack of quantitative analysis tools, the intimate relationship that exists between the morphological and physiological states of the microorganism, the shear field in the bioreactor, and the process performance is not yet fully understood. A semiautomatic image analysis protocol was developed to characterize the mycelium morphology and to estimate its percentage viability during the fermentation process based on four morphological types (unbranched, branched, entangled, and clumped microorganisms). Pictures taken under bright field microscopy combined with images of fluorescein diacetate stained fungi were used to assess the morphological parameters and the percentage viability of microorganisms simultaneously. The method was tested during the course of fed-batch fermentation in a reciprocating plate bioreactor. The use of the image analysis protocol was found to be successful in quantifying the variations in the morphology and the viability of T. reesei throughout the fermentation.

Influence of buffer systems on Trichoderma reesei Rut C-30 morphology and cellulase production

“…Fungal fermentations are complex systems into which the operating conditions, the broth rheology, the enzyme production, the morphology of the microorganisms, and their physiological state are all interrelated (Lecault et al 2007). Many fungi can grow in submerged culture in different forms ranging from dispersed filaments to pellets, depending on culture conditions and the strain of organism used.…”

A novel method for fast and statistically verified morphological characterization of filamentous fungi

“…In 1998, Paul and Thomas described image analysis procedures, which allowed the determination of morphological shape parameters from microscopic raw images (Paul and Thomas, 1998). Although this approach and modified versions thereof have been frequently employed by the scientific community in recent years (Casas Lopez et al, 2005;Lecault et al, 2007;Papagianni and Moo-Young, 2002), they could only be applied for sequential evaluation of single microscopic images using dedicated proprietary software (Wucherpfennig et al, 2010). Thus, recent reviews report only a limited use of morphological data in industry due to the currently used time-consuming and costly procedures and underline the need for more powerful and automated process analytical tools for morphological image analysis of filamentous fungi (Wucherpfennig et al, 2010).…”

“…Despite the fact that different computational workflows which minimize the requirement for manual intervention of the operator during morphological analysis of sequentially recorded microscopic images were introduced (Barry, 2010;Lecault et al, 2007), there is still a pressing need for an integrated approach covering fully automated image acquisition as well as concomitant evaluation and verification of results. Thus, any manual intervention of the operator can be avoided, fully automated processes can be developed and statistically consistent data can be obtained.…”

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

“…In Lecault et al (2007), a semi-automatic method is proposed to classify four morphological types of Trichoderma reesei, where eight shape features are first extracted, and then decision values are used for classification.…”

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