Table of Contents

  1. The Importance of Cell Line Screening
  2. Challenges with Cell Line Screening
  3. Best Practices for Cell Line Screening
  4. How Cell Line Screening Works at Culture

The Importance of Cell Line Screening

Cell line screening for cell culture applications is important to identify clones with desirable attributes for production. Stable transfection of cells for production can result in individual clones with distinct performance characteristics, especially with regard to productivity, product quality, and stability. In order to identify clones of interest for further process development, scale-up and production, the performance of multiple stably transfected, product-expressing cell lines are tested in a single screening process. Once cell lines are screened in a platform process, the best performers can be further developed to achieve maximum productivity and the desired product quality profile.

Challenges with Cell Line Screening


Performance can be variable between different clones, and it is optimal to assess strain performance in the context of the controlled environment of a bioreactor. Process parameters such as dissolved oxygen and pH can only be adequately controlled in a manner that resembles manufacturing conditions in the context of a bioreactor. While cell lines can be screened in less controlled environments, such as shake flasks, the rankings obtained may be different than those in bioreactors and yield results that will not be replicated in the path towards manufacturing. However, many bioprocessing facilities have insufficient capacity to screen the number of desired clones, particularly if clone performance is observed to be variable and requires screening of a larger number of clones.   

Product generation

In some cases, a small amount of product may be required in early stage screening for product quality testing. Smaller scale platforms may not yield the volume required for testing, but 250mL may often be sufficient. Thus, Culture’s platform can be leveraged for small amounts of product generation for testing across multiple clones.

Best Practices for Cell Line Screening

There are a number of best practices for cell line screening. Performance of a clone in a cell screening process will be impacted by the environment throughout the culture. Therefore, when screening clones, a screening process that is as close as possible to that anticipated or already implemented at manufacturing scale will increase the likelihood of successful scaling. Similarly, cell line screening should be approached with the expectation that some clones will give false positive signals that will not be representative of performance at manufacturing scale.  Therefore, ideal platform screening processes can be constructed to highlight both desired and negative cell line attributes. Finally, there are 'nuisance factors' that can impact cell line performance, such as the media lot of the experimental run. Structuring experiments with the appropriate controls can avoid nuisance factors impacting experimental conclusions.

How Cell Line Screening Works at Culture

Cell line screening at Culture works in a similar manner to any other experimental work. Experimental plans are submitted to Culture and the experiments are executed on a planned schedule. There are several benefits to performing cell line screening at Culture. 


Culture’s bioreactor capacity allows for screening larger numbers of cell lines. This is important as one of the most reasonable ways to reach an adequate starting point for developing a viable manufacturing process is to screen many initial cell lines for desirable characteristics such as acceptable growth rate and high product titer and quality. Culture’s capacity enables screening of a sufficient number of cell lines with sufficient replication and controls to be in a strong position for reaching manufacturability requirements in future process development. Additionally, with sufficient capacity, this work can be performed rapidly, enabling cell line screening to be completed within allocated timelines. 

Data quality

Culture provides rich data sets with each experiment in real time. These include online and offline measurements that can give a detailed assessment of differences between cell lines based on growth rates, product titer, and cell line viability in order to make informed decisions around the entire performance profile of a cell line, beyond simple endpoint metrics.