By Joanna Hawryluk, PhD, Product Manager, Olympus Life Science

Since the early 20th century, cell culture has been considered the gold standard in various research, drug development, and diagnostic labs. Within drug discovery, cell cultures are used to develop and test compounds to determine compound efficiency and efficacy.

Before you begin growing cell cultures, you must first select the cell line that most appropriately complements the compound. The reason is that the activity may be specific to a certain cell type or protein expressed by the cells. Then you must outline the optimal conditions for both the cell culture media and the drug solvent, as well as other reagents that will be used in the process. A variation in each of these factors could affect the robustness of the downstream cell-based assay by influencing the live cells and, therefore, the assay’s results.

This brings up some important questions. How do you make sure your procedures are well suited for the specific cell line of choice? And how will the process be followed consistently to achieve stable and accurate results?

Where the Traditional Cell Culture Workflow Falls Short

Although traditional cell culture workflows have been used for years, lab personnel face many challenges to grow and maintain healthy cultures for downstream processes. Just consider the guidelines from the Assay Guidance Manual produced by the National Center for Advancing Translational Sciences (NCATS).

The manual reads, “Standardizing primary cell culture conditions is essential for robust assay performance. For experiments where cells are used from a new source (patient or animal) for each experiment, responsiveness will vary, and separate normalizations will be required for each experiment.”

Another section states, “Particular attention to cell culture conditions is critical for the success of the assay. Cells should be confluent, but not overgrown, for the assay to work properly. The operator needs to identify cell growth conditions and cell plating density that are appropriate for providing a good assay signal.”

Following this guidance makes the cell culture process time consuming and can lead to inconsistencies across workers.

Typically, lab personnel must check cultures every few days. This task involves removing a cell flask from the incubator, counting cells, and checking their quality to determine if the cells are ready for propagation or plating for assays. This is mostly done by checking cell morphology (i.e., cell shape) and confluency (i.e., the percentage of the surface of a culture dish that is covered by the adherent cells). Confluency is a key factor that can determine whether you are ready to further propagate your cell line by doing a passage or subculture. If cells are overconfluent, cells can become inactive or die.

Historically, researchers checked cell morphology and confluency using visual observation under a cell culture microscope. But human eyes are prone to optical illusions. Our eyes process the perceived size and shape of a cell rather than the actual size.

Automating the Cell Culture Workflow with Artificial Intelligence

To reduce user bias, some labs are turning to automated cell culture solutions such as the Olympus™ Provi CM20 incubation monitoring system. This monitor uses artificial intelligence (AI) technology to automatically measure the health of cell cultures using consistent parameters.

Yoshihito Tachi of Olympus RMS Corporation recounts, “I previously used a microscope to observe the conditions of cultured cells, but I can only make a qualitative evaluation with this method. Another challenge is that the analysis results vary depending on the experience and subjectivity of the worker. In contrast, the Olympus CM20 system can count cells and measure confluency with the same constant analysis parameters for highly reproducible and consistent analysis results. We can compare data between experiments when changing culture conditions, compare it with past data, and easily share it within the team, all of which can help us improve development efficiency.”

Since the AI technology uses stable and standardized parameters, it offers a more robust and quantitative method than visual evaluation to determine confluency and count cells and colonies.

The CM20 monitor will automatically scan multiple points in a vessel, providing periodic quantitative data about the health and confluency of cultures. It uses AI technology to measure cell conditions using constant analysis parameters, eliminating variability in results introduced by different users. The monitor will automatically record the data, which can be easily stored, reused, and transferred to help reduce training time and make sure different individuals are using the same parameters.

By comparing current and past data, users can detect an abnormal cell culture earlier in the process. This information saves time, minimizes the use of expensive lab consumables on unusable cell cultures, and delivers more consistent results for downstream processes.

“Because the CM20 monitor collects quantitative data on the cell growth process, data from different cell lines can be measured and compared using the same standards,” explains Dr. Takanori Takebe of the Tokyo Medical and Dental University Institute of Research. “The monitor makes it easy to collect data from multiple strains maintained by a single researcher under the same process, as well as from multiple researchers. This makes it easy to check if the process was done correctly by comparing cell images and growth. In the past, when cell quality varied, it was difficult to determine whether it was caused by differences in the properties of individual cell lines or an error in the culture process.”

Today, the CM20 monitor is an example of how artificial intelligence can help standardize and quantify the cell culture process.

Olympus RMS CORP. is a joint venture between the Olympus CORP. and SEWON CELLONTECH CORP. of South Korea.

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