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Cell Culture – Good Practice and Advanced Methods

Gloved hands hold an agar dish and a cotton swab. In the background other agar dishes and a test tube rack can be seen.
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Cell culture is an essential component of most biological and biomedical research. Many experiments are launched in vitro in culture before moving to animal models. Cell culture, and ever more complex adaptions, such as 3D cultures, may also take on more importance as efforts are implemented to encourage alternatives to animal research. Cell culture platforms are used in genetic screens, drug screens, imaging and sensing applications, and reporter assays, both in established cell lines and in animal- or patient-derived primary culture.


Cell cultures are also leveraged to gain insight into disease mechanisms, e.g., induced pluripotent stem cells (iPSCs) derived from patients. iPSCs can be differentiated into specific cell types, such as neurons, glia or muscle cells, which harbor the genetic background of the patient’s disease and serve as models to investigate pathophysiology. This is especially useful for sporadic diseases of uncertain genetic etiology, such as neurodegenerative illnesses, which frequently comprise both familial and sporadic forms, e.g., Alzheimer’s disease, amyotrophic lateral sclerosis.


In addition to their research applications, cell cultures are also a source of material in regenerative medicine, as cellular therapies, and as a workhorse of biotherapeutic production. Potentially, stem cells and iPSCs can be differentiated into cells of a specific organ, constructed into tissue, and used in transplants. However, most stem cell use in regenerative medicine remains in preclinical stages with only a few clinical uses, in large part due to a lack of quality control measures meeting regulatory standards.


In all aspects of cell culture, be it for research or medical applications, it is critical to implement appropriate quality control to ensure high standards. Quality control measures span widely applicable practices relevant to all cell culture, such as good laboratory practice to avoid contamination, to more particular practices for specialized applications, such as for stem cells in regenerative medicine.

The good “housekeeping” practices of cell culture

Given their importance to research, it is essential for laboratories to perform quality control and adopt best practices to avoid culture contamination. Best practices include working in dedicated culture rooms in laminar hoods, holding the sash at the appropriate position, sterilizing working surfaces, and wearing gloves and masks. Undetected contamination, such as by Mycoplasma or by contaminating cell types, can interfere with experiments, leading to misleading or biased results, which waste resources and time. “Laboratories or institutions can implement policies to mitigate contamination and prevent issues from misidentified cell cultures,” explained Matthew D. Hall, director of the Early Translation Branch in the Division of Preclinical Innovation at the National Center for Advancing Translational Sciences (NCATS), at the National Institutes of Health. In a recent paper, Hall shared NCATS’ success story for bringing down the percent of Mycoplasma infected cell line samples.


“The first year that we started testing, about 13% of cultures tested positive, which is a level commensurate with the literature contamination rates. However, after we implemented a regular testing schedule, positivity rates fell to around only 3% by the fifth year,” Hall summarized of NCATS’ protocol. “Our successful approach is multi-tiered. First, we only accept cell lines from collaborators that are certified free of Mycoplasma. We confirm this ourselves upon receipt of the incoming cell line. Second, we test all active cultures monthly, or upon thawing from cryovial stocks. Third, and this is extremely important for high-throughput screening, which is expensive, the intended cell line is tested immediately before launching the experiment. You want to be certain that the high-throughput screen will generate high-quality results, free from interference from Mycoplasma.”


Unfortunately, even when good surveillance protocols for Mycoplasma are implemented, contamination can still occur. In these instances, NCATS immediately destroys the positive cell line and tests the backup vials. If the backup vials also test positive, they are similarly destroyed. “We do have a remedial protocol for very valuable or rare lines if we can’t locate a non-contaminated stock. We quarantine these valuable contaminated cultures in a dedicated incubator outside the tissue culture room, and initiate plasmocin treatment,” Hall explained. “Once the plasmocin regimen is complete, we test the remediated culture a couple of times to ensure it is clean. We test twice because infection rebounds can occur. We also check that the cell behaves as it did before plasmocin, to verify treatment didn’t affect the culture’s properties. We also share the decontaminated and Mycoplasma free culture back with the original lab.”


In addition to Mycoplasma testing, NCATS also verifies cell line identity as part of their cell culture quality control measures. This is accomplished by short tandem repeat (STR) analysis, which serves as a fingerprint of cell origin. “There are many cautionary tales about switched cell lines, I think HeLa is among the most well-known contaminating lines,” Hall cautioned. “To avoid this scenario, we also decided to implement STR to validate most incoming cell lines. Since we started STR testing, we only found 5 misidentified cell lines out of the 186 that we examined.”


The NCATS experience demonstrates the feasibility and effectiveness of a good surveillance protocol for lowering the number of Mycoplasma contaminated cultures and preventing the use of misidentified cell lines. “Our overriding principle at NCATS is simple. We think it is worthwhile to spend a relatively small amount of effort routinely and frequently if it will prevent a massive error from using a contaminated or misidentified culture. A large error can ultimately incur far more time and effort, and can even potentially mislead research directions,” concluded Hall.

Cell Culture Resource Guide

Maintaining healthy cell cultures is vital to obtaining reliable, high-quality data. However, a lot can go wrong when you are manipulating living cells. Download this eBook to learn more about cell culture seeding, expanding and harvesting, helpful calculations and references, and top considerations for custom cell culture media.

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Deep learning monitors cell culture quality control

Stem cells have potential use in regenerative medicine, but advances are hampered by a lack of standardization and difficulties scaling up. Quality control can improve protocol standardization, enhance scalability, and ensure cultures meet regulatory criteria. One clinical application is of primary human epidermal keratinocytes, which are used to treat skin burns and skin loss from genetic disease. Human keratinocytes are amenable to ex vivo expansion by culturing on a feeder layer of mouse 3T3 embryonic fibroblasts. Currently, human keratinocyte stem cells are selected by clonal analysis, which assesses stemness and proliferative capacity. Ideally, clones with a high proliferative capacity, called holoclones, which constitute less than 5% of cells in culture, need to be identified and selected for further expansion.


“Although serviceable, clonal analysis is time-, cost- and labor-intensive and requires judgment by an expert, which limits standardization and scalability,” explained Jun'ichi Kotoku, professor at the Graduate School of Medical Care and Technology, Teikyo University. “To overcome these issues, we developed an automated, non-invasive method based on phase-contrast imaging to identify human keratinocyte clones. This technology, which we called deep learning-based automated cell tracking, or DeepACT, was created in collaboration with Daisuke Nanba, professor at the Department of Stem Cell Biology, Medical Research Institute, Tokyo Medical and Dental University,” elaborated Kotoku of the technique.


DeepACT was inspired by previous work, which found that stem cells with high proliferative capacity exhibited a characteristic cell motion. Importantly, stem cell velocity correlated positively with proliferative capacity. “We realized we could leverage this characteristic motion to non-invasively identify stem cells with high proliferative capacity from microscopy images. However, our earlier work was slow because stem cell tracking had to be achieved manually or through motion analysis, which is less accurate,” explained Nanba. “We recognized if we could automate cell tracking using computational approaches, such as deep learning, we could identify keratinocyte stem cells with the largest capacity for proliferation, and hence, with the most promise for skin transplants.”


Put to the test, deep learning identified human keratinocyte nuclei with 77% accuracy, most of whose motion could be tracked, even in the presence of cell debris. Automated tracking performed similarly to manual tracking but recorded more cells within a higher velocity bracket. “We also found that DeepACT could assess culture conditions. Keratinocyte stem cells moved with greater velocity when they were fed or supplemented with epidermal growth factor,” noted Kotoku. “Therefore, DeepACT can be used to optimize cell culturing by identifying the conditions that maximize motion.”


Lastly, DeepACT was tested for its ability to detect the most prized holoclones. “We observed that the motion index, a metric of individual cell motion dynamics, was a good predictor of stemness. A motion index larger than one, as assessed by DeepACT, indicated a colony with keratinocyte stem cells moving faster at the periphery than within the colony center,” explained Nanba. “These colonies had a higher probability of yielding holoclones. Thus, DeepACT automatically performed quality control by pinpointing the colonies most likely to yield stem cell holoclones that would be most suitable for transplant.”


Kotoku and Nanba foresee further uses for automated technologies, such as DeepACT, in quality control. “Deep learning algorithms can be trained to identify other cells in addition to human keratinocytes. So, we may be able to apply our system to other stem cell cultures, including iPSCs. Further, the technologies may be expanded to beyond stem cell cultures to assess stem cell-based products in regenerative medicine,” they concluded. 

Meet the Author
Masha Savelieff, PhD
Masha Savelieff, PhD
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