Model‐based identification of cell‐cycle‐dependent metabolism and putative autocrine effects in antibody producing CHO cell culture

Möller, J.; Korte, Katrin; Pörtner, Ralf; Zeng, An‐Ping; Jandt, Uwe

doi:10.1002/bit.26828

Cited by 22 publications

(43 citation statements)

References 71 publications

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“…The aim of this study was to gain an understanding of process‐induced oscillations of cell cycle distribution. The basis of the processes (repeated‐batch and fed‐batch) was a population‐resolved model, which describes cell cycle‐dependent growth and metabolism of mammalian cells (Jandt et al, ; Möller et al, ). Materials and methods are partially based on previous publications for the FUCCI‐transduced, nonproducing mammalian cell line derivatives CHO‐K1 FUCCI CN and CHO‐K1 FUCCI CM (Fuge et al, ) and the model‐based identification of cell cycle‐dependent population dynamics in mammalian cell lines (Castillo, Fuge, Jandt, & Zeng, ; Jandt et al, ; Möller et al, ; Platas Barradas et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, the cell cycle‐dependent metabolic regulations are included for glucose, glutamine, lactate, ammonium, and the antibody (Equations S6–S10). Möller et al () identified the formation of a putative autocrine factor and showed its interactions with the metabolism, which is also considered in the model (Equations S11–S13). The cell cycle‐specificity is implemented with individual model parameter sets for all

normalΩ

, that is G1, S, and G2/M‐dependent parameters, allowing the description of cell cycle‐dependent metabolic up‐ and downregulation.…”

Section: Methodsmentioning

confidence: 99%

“…The cell cycle‐specificity is implemented with individual model parameter sets for all

normalΩ

, that is G1, S, and G2/M‐dependent parameters, allowing the description of cell cycle‐dependent metabolic up‐ and downregulation. This approach has so far been applied to identify cell cycle dependencies such as increasing antibody productivity during the S‐phase of CHO DP‐12 cells (Möller et al, ). The model is set up as a single cell model simulating 1,000 cells with individual growth and metabolism in accordance with the cell cycle.…”

Section: Methodsmentioning

confidence: 99%

“…So far, variations in cell populations have been identified in cell line generation (Patel et al, ; Scarcelli et al, ) and clonal long term stability (Chusainow et al, ; Vcelar et al, ). To assess heterogeneities under bioreactor cultivation conditions, a combined workflow was developed using near‐physiological cell cycle synchronization (review see Jandt, Platas Barradas, Pörtner, & Zeng, ) and population‐resolved modeling (Jandt, Platas Barradas, Pörtner, & Zeng, ; Möller, Korte, Pörtner, Zeng, & Jandt, ). Using this approach, strong metabolic up‐ and downregulation with changing productivities were identified during the cell cycle of nonproducing CHO K1 and

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(Jandt et al, ) cells and antibody‐producing Chinese hamster ovary (CHO) cells (Möller et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…To assess heterogeneities under bioreactor cultivation conditions, a combined workflow was developed using near‐physiological cell cycle synchronization (review see Jandt, Platas Barradas, Pörtner, & Zeng, ) and population‐resolved modeling (Jandt, Platas Barradas, Pörtner, & Zeng, ; Möller, Korte, Pörtner, Zeng, & Jandt, ). Using this approach, strong metabolic up‐ and downregulation with changing productivities were identified during the cell cycle of nonproducing CHO K1 and

{AGE1.hn}_{ATT}

(Jandt et al, ) cells and antibody‐producing Chinese hamster ovary (CHO) cells (Möller et al, ). However, it is still not understood, to which extent bulk process conditions can trigger, amplify or damp cell cycle oscillations in otherwise nonsynchronized cultures under near‐production conditions.…”

Section: Introductionmentioning

confidence: 99%

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Process‐induced cell cycle oscillations in CHO cultures: Online monitoring and model‐based investigation

Möller

Bhat

Riecken

et al. 2019

Biotech & Bioengineering

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The influence of process strategies on the dynamics of cell population heterogeneities in mammalian cell culture is still not well understood. We recently found that the progression of cells through the cell cycle causes metabolic regulations with variable productivities in antibody‐producing Chimese hamster ovary (CHO) cells. On the other hand, it is so far unknown how bulk cultivation conditions, for example, variable nutrient concentrations depending on process strategies, can influence cell cycle‐derived population dynamics. In this study, process‐induced cell cycle synchronization was assessed in repeated‐batch and fed‐batch cultures. An automated flow cytometry set‐up was developed to measure the cell cycle distribution online, using antibody‐producing CHO DP‐12 cells transduced with the cell cycle‐specific fluorescent ubiquitination‐based cell cycle indicator (FUCCI) system. On the basis of the population‐resolved model, feeding‐induced partial self‐synchronization was predicted and the results were evaluated experimentally. In the repeated‐batch culture, stable cell cycle oscillations were confirmed with an oscillating G1 phase distribution between 41% and 72%. Furthermore, oscillations of the cell cycle distribution were simulated and determined in a (bolus) fed‐batch process with up to 25×106 cells/ml. The cell cycle synchronization arose with pulse feeding only and ceased with continuous feeding. Both simulated and observed oscillations occurred at higher frequencies than those observable based on regular (e.g., daily) sample analysis, thus demonstrating the need for high‐frequency online cell cycle analysis. In summary, we showed how experimental methods combined with simulations enable the improved assessment of the effects of process strategies on the dynamics of cell cycle‐dependent population heterogeneities. This provides a novel approach to understand cell cycle regulations, control cell population dynamics, avoid inadvertently induced oscillations of cell cycle distributions and thus to improve process stability and efficiency.

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Section: Methodsmentioning

confidence: 99%

normalΩ

, that is G1, S, and G2/M‐dependent parameters, allowing the description of cell cycle‐dependent metabolic up‐ and downregulation.…”

Section: Methodsmentioning

confidence: 99%

“…The cell cycle‐specificity is implemented with individual model parameter sets for all

normalΩ

Section: Methodsmentioning

confidence: 99%

{AGE1.hn}_{ATT}

(Jandt et al, ) cells and antibody‐producing Chinese hamster ovary (CHO) cells (Möller et al, ).…”

Section: Introductionmentioning

confidence: 99%

{AGE1.hn}_{ATT}

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Process‐induced cell cycle oscillations in CHO cultures: Online monitoring and model‐based investigation

Möller

Bhat

Riecken

et al. 2019

Biotech & Bioengineering

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

Arndt

Kuchemüller

Baganz

et al. 2021

Biotechnology Progress

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Miniaturized bioreactor (MBR) systems are routinely used in the development of mammalian cell culture processes. However, scale-up of process strategies obtained in MBR-to larger scale is challenging due to mainly non-holistic scale-up approaches. In this study, a model-based workflow is introduced to quantify differences in the process dynamics between bioreactor scales and thus enable a more knowledge-driven scaleup. The workflow is applied to two case studies with antibody-producing Chinese hamster ovary cell lines. With the workflow, model parameter distributions are estimated first under consideration of experimental variability for different scales. Second, the obtained individual model parameter distributions are tested for statistical differences.In case of significant differences, model parametric distributions are transferred between the scales. In case study I, a fed-batch process in a microtiter plate (4 ml working volume) and lab-scale bioreactor (3750 ml working volume) was mathematically modeled and evaluated. No significant differences were identified for model parameter distributions reflecting process dynamics. Therefore, the microtiter plate can be applied as scale-down tool for the lab-scale bioreactor. In case study II, a fed-batch process in a 24-Deep-Well-Plate (2 ml working volume) and shake flask (40 ml working volume) with two feed media was investigated. Model parameter distributions showed significant differences. Thus, process strategies were mathematically transferred, and model predictions were simulated for a new shake flask culture setup and confirmed in validation experiments. Overall, the workflow enables a knowledge-driven evaluation of scale-up for a more efficient bioprocess design and optimization.

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Characterization of cell cycle and apoptosis in Chinese hamster ovary cell culture using flow cytometry for bioprocess monitoring

Lee

Eyster

et al. 2021

Biotechnology Progress

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Chinese hamster ovary (CHO) cells are by far the most important mammalian cell lines used for producing antibodies and other therapeutic proteins. It is critical to fully understand their physiological conditions during a bioprocess in order to achieve the highest productivity and the desired product quality. Flow cytometry technology possesses unique advantages for measuring multiple cellular attributes for a given cell and examining changes in cell culture heterogeneity over time that can be used as metrics for enhanced process understanding and control strategy. Flow cytometrybased assays were utilized to examine the progression of cell cycle and apoptosis in three case studies using different antibody-producing CHO cell lines in both fedbatch and perfusion bioprocesses. In our case studies, we found that G0/G1 phase distribution and early apoptosis accumulation responded to subtle changes in culture conditions, such as pH shifting or momentary glucose depletion. In a perfusion process, flow cytometry provided an insightful understanding of the cell physiological status under a hypothermic condition. More importantly, these changes in cell cycle and apoptosis were not detected by a routine trypan blue exclusion-based cell counting and viability measurement. In summary, integration of flow cytometry into bioprocesses as a process analytical technology tool can be beneficial for establishing optimum process conditions and process control.

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Model‐based identification of cell‐cycle‐dependent metabolism and putative autocrine effects in antibody producing CHO cell culture

Cited by 22 publications

References 71 publications

Process‐induced cell cycle oscillations in CHO cultures: Online monitoring and model‐based investigation

Process‐induced cell cycle oscillations in CHO cultures: Online monitoring and model‐based investigation

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

Characterization of cell cycle and apoptosis in Chinese hamster ovary cell culture using flow cytometry for bioprocess monitoring

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