How to overcome the biggest challenges in cell line development

The advancement of cell line engineering and microfluidic technologies offers a range of benefits in terms of reducing the duration of cell line development (CLD).

Optimizing CLD processes can also present new difficulties and uncertainties. Four of the most crucial challenges in this regard are:

1) The potential for selection bias in the cell line

2) Fluctuations in gene expression

3) Difficulties with automated cloning methods

d) Varying cell growth rates.

This article offers an overview of these four challenges, as well as practical solutions for addressing each scenario.

Host cell line selection bias

Preventing selection bias in the cell line is a critical aspect of cell line development. Selecting clones based on factors such as stability, antibody secretion, or other metrics can result in a limited and highly-selected subset of cells that have not undergone a comprehensive evaluation.

This can be problematic, as early selection criteria may prove to be unreliable later in the development process.

For instance, selecting clones for high productivity in the early stages may be misleading, as these cells may not necessarily maintain their productivity, production, or growth stability in the long term. This can lead to reduced viability and cell death.

Therefore, selection bias is a critical consideration when predicting the commercial viability of a cell line. It is advisable to be cautious and select cells based on their future potential rather than relying on early metrics.

A common approach to minimize selection bias in CLD is to initially choose a diverse range of high- and medium-producing clones for further evaluation. This enables the assessment of variations in product quality, stability, and growth.

By observing the clones during expansion and conducting additional validation, it is possible to identify cell lines with improved attributes.

Gene expression

Despite efforts to optimize the host cell line, such as utilizing directed evolution or utilizing chemically-defined, animal-free media, difficulties with gene expression can still occur at the initial stage.

Various techniques have been employed to enhance protein yields in CLD to regulate and improve gene expression.

One such approach is the use of site-specific integration and the careful design of expression vectors. These techniques are crucial in engineering the host cell line and maximizing the amount of active product produced.

Site-specific integration

The expression of the gene of interest (GOI) can be hindered by relying on the method of random integration, which can result in unpredictable and inconsistent gene expression due to the transgene’s integration location.

This method requires the screening of numerous clones to identify cell lines with the desired expression levels.

In contrast, targeted integration addresses this issue by specifying a specific region in the cell genome, known as a “landing pad,” for gene insertion during transfection. This reduces the impact of position effects on gene expression.

Targeted integration has gained significant attention with the advancements in genome engineering and the introduction of engineered nucleases, including CRISPR/Cas9, TALENS, and ZFNs. These nucleases have greatly facilitated the process of targeted integration, making it a promising area of development in the field.

Using these new methods, researchers may concentrate on managing the position of insertion and, as a result, recombinant gene expression, resulting in less clonal diversity.

When using targeted integration, CLD researchers can achieve even more dependable expression by utilizing ‘safe harbor’ places within the cell’s DNA. These particular sites prevent the transgene from being dampened or silenced by the host cell genome or the environment of gene insertion.

Hipp11 (11) and Rosa26 have been investigated for usage in mammalian cell lines as common ‘safe harbor’ locations.

By properly inserting a GOI into these specified loci of HEK293 and CHO cells, studies have demonstrated a rise in gene copy number integration and steady gene expression, which may result in higher and consistent protein output.1,2

By boosting the dependability of gene expression for optimum gene expression with high precision, targeted integration can assist in increasing product titer. However, there are potential drawbacks to adopting technologies like CRISPR since they might cause off-target effects and mutations in the host DNA.

When it comes to using the more dependable approach of DNA repair, homology-directed repair, CHO cells, for example, are recalcitrant (HDR). CHO cells are more receptive to non-homologous end-joining (NHEJ) DNA repair, which is less stable and can result in indel mutations.3

However, as CRISPR technology progresses, further enhancements are being made to expand functional capabilities, minimize off-target impacts, and lessen mutation occurrences.

Targeted integration allows for a more iterative and predictive method of producing cell lines that adhere to a much more engineering design-build-test approach. Although this method has been employed in clinical and commercial manufacturing, it has not been broadly embraced.

Expression vector design

Transfection of a transgene seldom results in a high-producing cell line, and CLD researchers frequently use gene amplification to boost protein output. As a result, careful design of the expression vector might raise the specific productivity of the cell line, resulting in higher product yields.

Traditionally, the expression vector will include the antibody gene, selection marker genes, and genes that allow expression in the chosen cells. The insertion of a selection marker allows for the easy selection of high-producing clones from the cell population.

Traditional selection marker systems for CHO cells (the most often used cell line in CLD) include glutamine synthetase (GS) and dihydrofolate reductase (DHFR).

To allow for selection, these metabolic enzymes are suppressed by particular medications. GS is inhibited by methionine sulfoximine (MSX), which prevents glutamine formation, while DHFR is inhibited by methotrexate (MTX), which blocks RNA and DNA synthesis.

To accompany these selection procedures, CHO cell lines defective in key metabolic enzymes have been created and are readily accessible commercially.

When transfected cells are cultivated in a single concentration of the specified medication, either MTX or MSX depending on the method employed, and the GOI is located adjacent to either GS or DHFR on the expression vector, selection occurs. Amplification is based on a gradual rise in medication concentration.

As a result, cells with greater levels of GS or DHFR gene amplification, which can operate in higher concentrations of MTX or MSX, are chosen as having more copies of the gene. These cells are then cloned as single cells to further characterize expression and production.

The selection markers may be used to create expression vectors with a weaker promoter for the selection marker gene, resulting in a more robust type of cell selection known as selection marker attenuation.

The stringency of selection will be increased by lowering the expression of the selection marker, resulting in just a few high-producing clones moving forward for further characterization.

Increasing the concentration of the MSX or MTX can also result in increased stringency. However, these drugs slow cell proliferation, making selection marker attenuation more appealing than ramping up toxic drug concentrations and extending timelines .4

The GOI is usually expressed by a promoter, which is either viral or endogenous. However, viral and endogenous promoters can produce unpredictability and uncontrollability, which is suboptimal for industrial-scale operations.

Several corporations and academic organizations are developing fully synthetic promoters to help address the difficulties that endogenous and viral promoters face.5

An enhanced expression can be achieved by matching the promoter sequence to the host cell’s transcription machinery. Synthetic promoters outperform viral promoters in terms of predictability and enable geographic and temporal control over transgene expression, making biopharmaceutical development easier.

Single-cell subcloning from the cultured population can reduce the heterogeneity generated by random integration, gene amplification, and the intrinsic genetic instability of cell lines, which can occur from engineering the host cell line to modify gene expression.

Subcloning eliminates a substantial percentage of the diversity, although cell productivity and growth rate can still vary dramatically.

Due to the high metabolic requirement of protein creation, high-producing cells have a delayed growth profile. With heterogeneity still present in the subcloned population, the lower-producing cells can overrun and overwhelm the population of the slower-growing high-producers.

In order to retain the majority of high-producers, the number of subcloning events and duration in culture should be restricted so that the lower-producing cells do not overrun and dominate the population.6

The use of automated technology in CLD operations has aided in reducing overall timeframes and speeding up subcloning.


  1. Zboray, K., Sommeregger, W., Bogner, E., Gili, A., Sterovsky, T., Fauland, K., Grabner, B., Stiedl, P., Moll, HP., Bauer, A., Kunert, R., Casanova, E. 2015. Heterologous protein production using euchromatin-containing expression vectors in mammalian cells. Nucleic Acids Res 43(16):e102.
  2. Chi, X., Zheng, Q., Jiang, Chen-Tsai, R.Y.R., Kong, L.J. 2019. A system for site-specific integration of transgenes in mammalian cells. PLoS One, 14(7): e0219842. pone.0219842.
  3. Lee, J. S., Kallehauge, T. B., Pedersen, L. E., Kildegaard, H. F. 2015. Site-specific integration in CHO cells mediated by CRISPR/Cas9 and homology-directed DNA repair pathway. Scientific Reports 5:8572.
  4. Lai, T., Yang, Y., Ng, S, K. 2013. Advances in Mammalian cell line development technologies for recombinant protein production. Pharmaceuticals, 6(5), 579-603.
  5. Yohari, Y.B., Brown, A, J., Alves C, S., Zhou, Y., Wright, C, M., Estes, S, D., Kshirsagar, R & James, D, C (2019). CHO Genome Mining for Synthetic Promoter Design. Journal of Biotechnology, 294, 1-13.
  6. Browne,S,M., and Al-Rubeai, M., 2007 Selection methods for high-producing mammalian cell lines. TRENDS in Biotechnology, 25 (9) 425-432.

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Last updated: May 23, 2023 at 8:48 AM


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