Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting

Vcelar, Sabine; Jadhav, Vaibhav; Melcher, Michael; Auer, Norbert; Hrdina, Astrid; Sagmeister, Rebecca; Heffner, Kelley; Puklowski, Anja; Betenbaugh, Michael J.; Wenger, Till; Leisch, Friedrich; Baumann, Martina; Borth, Nicole

doi:10.1002/bit.26453

Cited by 74 publications

(73 citation statements)

References 28 publications

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“…The preparation of metaphase spreads and the counting of chromosomes per spreaded cell was done as described in Vcelar et al (2018) . For each sample, chromosomes of at least 75 cells were counted.…”

Section: Methodsmentioning

confidence: 99%

“…These lead to phenotypic instability and hence problems with respect to process reproducibility or production instability . As a consequence of such genotypic drifts, CHO cells are quasi‐diploid with a chromosome number that diverges from that of the primary Chinese hamster and with different karyotypes and modal chromosome numbers for each cell line analyzed …”

Section: Introductionmentioning

confidence: 99%

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Changes in Chromosome Counts and Patterns in CHO Cell Lines upon Generation of Recombinant Cell Lines and Subcloning

Vcelar

Melcher

Auer

et al. 2018

Biotechnology Journal

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Chinese hamster ovary (CHO) cells are the number one production system for therapeutic proteins. A pre-requirement for their use in industrial production of biopharmaceuticals is to be clonal, thus originating from a single cell in order to be phenotypically and genomically identical. In the present study it was evaluated whether standard procedures, such as the generation of a recombinant cell line in combination with selection for a specific and stable phenotype (expression of the recombinant product) or subcloning have any impact on karyotype stability or homogeneity in CHO cells. Analyses used were the distribution of chromosome counts per cell as well as chromosome painting to identify specific karyotype patterns within a population. Results indicate that subclones both of the host and the recombinant cell line are of comparable heterogeneity and (in)stability as the original pool. In contrast, the rigorous selection for a stably expressing phenotype generated cell lines with fewer variation and more stable karyotypes, both at the level of the sorted pool and derivative subclones. We conclude that the process of subcloning itself does not contribute to an improved karyotypic homogeneity of a population, while the selection for a specific cell property inherently can provide evolutionary pressure that may lead to improved chromosomal stability as well as to a more homogenous population.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Changes in Chromosome Counts and Patterns in CHO Cell Lines upon Generation of Recombinant Cell Lines and Subcloning

Vcelar

Melcher

Auer

et al. 2018

Biotechnology Journal

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“…[mM] have been identified in cell line generation (Patel et al, 2018;Scarcelli et al, 2018) and clonal long term stability (Chusainow et al, 2009;Vcelar et al, 2018). To assess heterogeneities under bioreactor cultivation conditions, a combined workflow was developed using near-physiological cell cycle synchronization (review see Jandt,…”

Section: Nomenclature Variablementioning

confidence: 99%

“…Changes in the cell populations of mammalian cell cultures have received increasing attention. 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, ).…”

Section: Introductionmentioning

confidence: 99%

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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“…Especially the long list of approved therapeutics manufactured in CHO and processes that were established for this cell line during the last decades are emphasizing why CHO is the working horse of biopharma (Walsh, 2014). Nevertheless, genetic instability and intensive cell line generation, as well as process development timelines, are the major bottleneck of this system (Vcelar, Jadhav et al, 2018;Wurm, 2004).…”

mentioning

confidence: 99%

Genetic engineering approaches to improve posttranslational modification of biopharmaceuticals in different production platforms

Amann

Schmieder

Kildegaard

et al. 2019

Biotech & Bioengineering

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The number of approved biopharmaceuticals, where product quality attributes remain of major importance, is increasing steadily. Within the available variety of expression hosts, the production of biopharmaceuticals faces diverse limitations with respect to posttranslational modifications (PTM), while different biopharmaceuticals demand different forms and specifications of PTMs for proper functionality. With the growing toolbox of genetic engineering technologies, it is now possible to address general as well as host-or biopharmaceutical-specific product quality obstacles.In this review, we present diverse expression systems derived from mammalians, bacteria, yeast, plants, and insects as well as available genetic engineering tools.

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Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting

Cited by 74 publications

References 28 publications

Changes in Chromosome Counts and Patterns in CHO Cell Lines upon Generation of Recombinant Cell Lines and Subcloning

Changes in Chromosome Counts and Patterns in CHO Cell Lines upon Generation of Recombinant Cell Lines and Subcloning

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

Genetic engineering approaches to improve posttranslational modification of biopharmaceuticals in different production platforms

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