Quantification of the dynamics of population heterogeneities in CHO cultures with stably integrated fluorescent markers

Abstract

Cell population heterogeneities and their changes in mammalian cell culture processes are still not well characterized. In this study, the formation and dynamics of cell population heterogeneities were investigated with flow cytometry and stably integrated fluorescent markers based on the lentiviral gene ontology (LeGO) vector system. To achieve this, antibodyproducing CHO cells were transduced with different LeGO vectors to stably express single or multiple fluorescent proteins. This enables the tracking of the transduced populations and is discussed in two case studies from the field of bioprocess engineering: In case study I, cells were co-transduced to express red, green, and blue fluorescent proteins and the development of sub-populations and expression heterogeneities were investigated in high passage cultivations (total 130 days). The formation of a fast-growing and more productive population was observed with a simultaneous increase in cell density and product titer. In case study II, different preculture growth phases and their influence on the population dynamics were investigated in mixed batch cultures with flow cytometry (offline and automated). Four cell line derivatives, each expressing a different fluorescent protein, were generated and cultivated for different time intervals, corresponding to different growth phases. Mixed cultures were inoculated from them, and changes in the composition of the cell populations were observed during the first 48 h of cultivation with reduced process productivity. In summary, we showed how the dynamics of population heterogeneities can be characterized. This represents a novel approach to investigate the dynamics of cell population heterogeneities under near-physiological conditions with changing productivity in mammalian cell culture processes.

Regulation of pyruvate dehydrogenase complex related to lactate switch in CHO cells

“…The peak antibody titer was at the end of the cultivation, as seen in Figure 2H. The product concentrations were comparable to previous studies using the same medium and cell line [43, 48–50].…”

Model‐based workflow for scale‐up of process strategies developed in miniaturized bioreactor systems

“…The over‐prediction of the product titer mainly resulted from high antibody formation rates q Ab and the direct relation to the X v . The antibody formation for CHO DP‐12 cells was found to be dependent on a variety of different effects, for example, sub‐population formation, 53 cell‐cycle‐dependent metabolic regulations (i.e., regulation of pyruvate dehydrogenase complex) and so far, not well identified effects 23 …”

Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses

“…The factor settings of the DoE designs were determined as described in the following: First, a large number of points ( ) were randomly distributed in the three-dimensional design space (i.e., three investigated factors). Then, clusters in these points were determined using the k -means algorithm [ 48 , 49 ]. These clusters are partitioned into the k sets corresponding to the number of experiments in the DoE design.…”