Determining total protein content is a routine operation in many laboratories. Despite substantial work on assay optimization interferences, the widely used bicinchoninic acid (BCA) assay remains widely recognized for its robustness. Especially in the field of bioprocess engineering the inaccuracy caused by interfering substances remains hardly predictable and not well understood. Since the introduction of the assay, sample pre-treatment by trichloroacetic acid (TCA) precipitation has been indicated as necessary and sufficient to minimize interferences. However, the sample matrix in cultivation media is not only highly complex but also dynamically changing over process time in terms of qualitative and quantitative composition. A significant misestimation of the total protein concentration of bioprocess samples is often observed when following standard work-up schemes such as TCA precipitation, indicating that this step alone is not an adequate means to avoid measurement bias. Here, we propose a modification of the BCA assay, which is less influenced by sample complexity. The dynamically changing sample matrix composition of bioprocessing samples impairs the conventional approach of compensating for interfering substances via a static offset. Hence, we evaluated the use of a correction factor based on an internal spike measurement for the respective samples. Using protein spikes, the accuracy of the BCA protein quantification could be improved fivefold, taking the BCA protein quantification to a level of accuracy comparable to other, more expensive methods. This will allow reducing expensive iterations in bioprocess development to due inaccurate total protein analytics.Graphical abstractElectronic supplementary materialThe online version of this article (doi:10.1007/s10295-016-1796-9) contains supplementary material, which is available to authorized users.

During the cultivation of E. coli for recombinant protein production, substrate accumulation is often observed in induction phase. Uncontrolled substrate accumulation leads to difficulties in transferring or scaling processes and even to failed batches. The phenomenon of metabolite/substrate accumulation occurs as a result of exceeding the physiological capacity to metabolize substrate (q ). In contrast to the common understanding of q as "static" value, we hypothesize that q essentially has a dynamic nature. Following the state of the art approach of physio logical strain characterization, substrate pulse experiments were used to quantify q in induction phase. The q was found to be temperature and time dependent. Subsequently, q was expressed through a linear equation, to serve as boundary for physiologically controlled experiments. Nevertheless, accumulation was observed within a physiologically controlled verification experiment, although the q boundary was not exceeded. A second set of experiments was conducted, by oscillating the q set point between discrete plateaus during physiologically controlled experiments. From the results, we deduced a significant interrelation between the metabolic activity and the timely decline of qScrit. This finding highlights the necessity of a comprehensive but laborious physiological characterization for each strain or alternatively, to use physio logical feedback control to facilitate real time monitoring of q , in order to effectively avoid substrate accumulation.

Within this contribution we have outlined and compared four generic applicable methods for biomass estimation in early bioprocess development. The accuracy and robustness of a hard sensor, softsensor and hybrid sensor were discussed based on the coefficient of variation of the root mean squared error (cvRMSE) for two strains and three different levels of metabolic activity. This comparison facilitates a comprehensive overview of appropriate methods for biomass estimation in bioprocess development. Depending on the scope of the planned experiments as well as on the available infrastructure and historic data, the outlined data ease method selection. Hereby, we aim to alleviate physiologic bioprocess development requiring physiological process control which is necessarily based on real time biomass estimation.www.els-journal.com Page 2 Engineering in Life SciencesThis article is protected by copyright. All rights reserved. 2 AbstractAdvanced bioprocess development strategies focus on the control of physiological entities which rely on accurate real time determination of biomass concentration. Various methods have been proposed in literature but up to this date a comprehensive and differentiated comparison of biomass estimation approaches for early stage bioprocess development is missing. In this contribution, we compared hard sensor, soft-sensor and data-driven approaches for real-time biomass estimation in respect to accuracy, transferability and costs. The outlined methods were tested with two different microbial strains and recombinant products using E. coli. To investigate the applicability of the outlined methods, method performance was assessed in correspondence to metabolic activity. Based on statistical descriptors the methods were compared and discussed. The results indicate no significant impact of strain or biomass estimation approach on the measurement quality. The average relative error of 11-13% can be greatly reduced by over 85% combining the outlined methods by the means of weighted average. This approach proved to be highly robust even during highly dynamic process conditions of oscillating specific substrate uptake rates. Concluding, the combination of low cost first principle soft-sensor approaches in combination with a hybrid soft-sensor yields the best information to effort ratio.

The expression of pharmaceutical relevant proteins in Escherichia coli frequently triggers inclusion body (IB) formation caused by protein aggregation. In the scientific literature, substantial effort has been devoted to the quantification of IB size. However, particle-based methods used up to this point to analyze the physical properties of representative numbers of IBs lack sensitivity and/or orthogonal verification. Using high pressure freezing and automated freeze substitution for transmission electron microscopy (TEM) the cytosolic inclusion body structure was preserved within the cells. TEM imaging in combination with manual grey scale image segmentation allowed the quantification of relative areas covered by the inclusion body within the cytosol. As a high throughput method nano particle tracking analysis (NTA) enables one to derive the diameter of inclusion bodies in cell homogenate based on a measurement of the Brownian motion. The NTA analysis of fixated (glutaraldehyde) and non-fixated IBs suggests that high pressure homogenization annihilates the native physiological shape of IBs. Nevertheless, the ratio of particle counts of non-fixated and fixated samples could potentially serve as factor for particle stickiness. In this contribution, we establish image segmentation of TEM pictures as an orthogonal method to size biologic particles in the cytosol of cells. More importantly, NTA has been established as a particle-based, fast and high throughput method (1000-3000 particles), thus constituting a much more accurate and representative analysis than currently available methods.

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