Application of dielectric spectroscopy for real-time
CHAPTER 3 3.5.5 Cell size and viability studies
Offline cell size and viability analysis were performed to validate the application of capacitance as a PAT tool to estimate the physiological properties of recombinant E. coli considered in this study. As the recombinant therapeutic protein considered in this study is obtained in the form of inclusion bodies, changes in cell size or cell shape could be expected to occur (Randek and Mandenius, 2020). The viability analyses were performed using flow cytometry and fluorescence microscopy techniques. The percentage of viability was calculated using the flow cytometry dual staining analysis.
3.5.5.1 Cell size analysis
The cell size analysis was carried out using a particle size analyzer, and FESEM is shown in Figure 3.10. The cell diameter was measured using particle size analysis, which measures the average size of the particles in the suspension using the principle of dynamic light scattering (DLS) (Loske et al., 2014). As observed from the results of particle size analysis shown in Figure 3.10 (A), the cell diameter increased up to 6 h and then is observed to be maintained around 1 µm throughout the fermentation. The particle size analyzer measures the average size of the particles suspended in the sample, and it could be noted that the measured average value of 1 µm can be correlated to the average diameter of the typical rod-shaped E. coli cells present in the suspension.
Additionally, the difference between the cell size at the time of induction and harvest (samples c and d) was also observed from the FESEM analysis as presented in Figure 3.10 (B). The FESEM analysis also confirmed the rod-shaped appearance of the E. coli cells, and it was observed that there was no significant change in cell shape between the induction and harvest samples and that there is a minor change with respect to cell size.
The slight decrease in cell size from induction to harvest could be attributed to the stress- induced due to the formation of inclusion bodies. The observations obtained from the FESEM can be corroborated with the measured capacitance values to reveal more insights related to the physiological changes occurring throughout the fermentation (Randek and Mandenius, 2020).
3.5 Results and discussion
Figure 3.10. Bacterial cell size analysis. (A). Cell diameter measurement using particle size analyzer. (B). Scanning electron microscopy images of bacterial cells at the time of induction (c) and harvest (d)
3.5.5.2 Cell viability analysis
The results from the cell viability studies are summarized in Figure 3.11. Figure 3.11 (A) represents the percentage of viable and dead cells analyzed through quadrant gating of flow cytometry. Figure 3.11 (B) shows the quadrant gating for the control and dual stained samples for samples a, b, c, and d, respectively. The quadrants were segregated to represent the different subpopulations of the samples, with the bottom left quadrant indicating the unstained cells. In the contour plot of dual staining studies, the top left quadrant corresponded to the percentage of live cells in the samples and the bottom right quadrant corresponded to the percentage of dead cells in the respective samples. It could be observed that viability decreased up to 42% towards the end of the fermentation, with a proportional increase in the percentage of dead cells by 24%. These cell viability values were used for calculating the corrected value of viable biomass concentration (VCC) from the measured DCW using the equation 3.1. The cell viability decreased significantly after the induction, as reflected in the viability values of samples c and d in Figure 3.11 (A, B).
This observation further indicates that the measured capacitance values can correlate well with the viable cell concentration present in the bioreactor. The changes in cell viability were visualized qualitatively from the fluorescence microscopy studies. The decrease in the viability was also observed in the fluorescence microscopy with PI staining from Figure 3.11 (C).
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Figure 3.11. Bacterial cell viability analysis. (A). Cell viability percentage obtained from flow cytometry. (B). Quadrant gating for control and dual stain obtained from flow cytometry for four different time intervals of fermentation, namely, a (batch), b (fed- batch), c (induction) and d (harvest). (C). Propidium iodide staining studies using fluorescence microscopy for the samples a, b, c and d.
3.6 Real-time biomass estimation from capacitance measurements 3.6 Real-time biomass estimation from capacitance measurements 3.6.1 Linear modelling
Application of different models for the correlation of measured capacitance and biomass concentration is a significant step in developing a capacitance-based control strategy (Knabben, 2011). Linear modeling is the most commonly used approach for establishing a direct correlation between biomass concentration and capacitance data. Figure 3.12 represents the linear correlation of pre-processed capacitance data with the offline DCW and corrected VCC values. Three representative frequencies, 384 kHz, 1000 kHz, and 4472 kHz, were chosen to represent the linear correlation model for estimating biomass concentration. The slope and R2 values of the represented linear correlations are presented in Table 3.2. It is observed that the average R2 values of the linear correlation established using VCC values were higher than that of those developed using DCW values. The R2 values indicate that the measured capacitance values have a better correlation with the VCC values, which was also observed from steeper slopes with a three-fold increase than the slope values from the DCW correlation.
Figure 3.12. Linear correlation for DCW vs capacitance represented in triangles (▲) and correlation for VCC vs capacitance represented in circles (●). Three representative frequencies, 368 kHz (grey), 1120 kHz (red), 4472 kHz (blue), are represented.
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