Optimized bioprocessing of filamentous fungi through CytoSense derived data

29.04.2020  by  Tina Silovic

Figure 1. Left: confocal microscopy of a pellet with enhanced contrast depicting pellet diameter, viable layer (vl), compact core region (red circle) and hairy outer region (green circle). White line = 50 Ķm. Right: corresponding signal profiles from flow cytometry depicting (a) the viable area across the pellets diameter and (b) the degraded area in the pelletís core according to Veiter and Herwig (Figure taken from Veiter et al., 2020)

Figure 2. Spatially resolved pellet signal profiles, FSC signal (black) and SSC signal (blue). Pellet with low compactness (a) according to SSC signal. Pellet with high compactness according to SSC signal (b). Saturated SSC signal and pellet breakage according to FSC signals at elevated pellet diameters and high overall compactness (c) (Figure taken from Veiter et al., 2020)

In their new study Veiter et al., ("Optimal process design space to ensure maximum viability and productivity in Penicillium chrysogenum pellets during fed-batch cultivations through morphological and physiological control") used a CytoSense to maximise viability and productivity in cultivation of fungi through morphological and physiological control.
Cultivation strategies of filamentous fungi are characterized by specific fungal morphologies encompassing several forms; ranging from homogeneously dispersed hyphae to dense agglomerates. Industrial bioprocesses using the fungi Penicillium chrysogenum favour the sphere-like pellet form, but this will often lead to biomass degradation, due to metabolic differences between the core and outer layers of the pellets. Therefore, at-line monitoring of pellets (as done with the CytoSense) is needed for robust process control, while subsequent analysis of derived morphological descriptors can help in maximizing viability and productivity in their fed-batch processing.  
The goal of this study was to study the impact of the fermentation parameters (power input, dissolved oxygen concentration, substrate uptake) on morphology, biomass viability and productivity of fungi. Responses were analysed using novel morphological descriptors (pellet compactness and viable pellet layer) measured by a CytoSense.
Altogether, the following responses were determine by a CytoSense:

  • Volume ratio of pellets in relation to all morphological classes (pellet ratios in %)(Fig. 1)
  • Average size of pellets (pellet size in micrometers)
  • Pellet compactness (can be obtained from SSC (Sideward Scatter) signal length in combination with particle size as explained in Fig. 2)
  • Viable pellet layer (in micrometers) (Further details on the method including data evaluation can be found in Veiter and Herwig, 2019)

The authors envision the  presented methodology (optimizing bioprocessing through morphological and physiological control) to be suitable for any organism where process performance is highly dependent on morphology, as for instance already adapted for glyco-engineered yeast (Pekarsky et al., 2018).

Besides the pelletí signal profiles, new features of CytoClus include scaling options (to prevent signal saturation) and interactive viewing of all imaged particles and their pulse shapes for faster definition of changes in cells.