Cell Culture – Good Practice and Advanced Methods
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Published: February 19, 2024
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Cell culture is an essential component of most biological and biomedical research and is also a source of material in regenerative medicine. In all aspects of cell culture, it is critical to implement appropriate quality control to ensure high standards and avoid contamination.
This article explores how quality control can improve protocol standardization, enhance scalability and ensure that cultures meet regulatory criteria.
Download this article to learn more about:
- Cell culture applications in research and medicine
- Best practices to ensure quality control
- Innovative automated technologies to monitor quality control
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Cell Culture – Good Practice and
Advanced Methods
Published: January 24, 2023| Masha Savelieff, PhD
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.
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“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 highthroughput 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.
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, noninvasive 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.
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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.
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