We've updated our Privacy Policy to make it clearer how we use your personal data.

We use cookies to provide you with a better experience. You can read our Cookie Policy here.

Rectangle Image

Cell Culture Quality Control: Blissful Ignorance?

Rectangle Image

Cell Culture Quality Control: Blissful Ignorance?

Read time:

Complex 3D cellular models are gaining unprecedented momentum to fill the gap in pre-clinical models for drug discovery. The field is now getting ready to add a layer of complexity by using patient-derived cells to create personalized cellular models.

While much effort is devoted towards improving these cellular models, there has been less of an emphasis on standardizing and improving the control quality of the cell culture underlying these assays.

Cell culture quality control refers to measures taken to control the quality of the reagents and cells during the maintenance of said cellular models. Cell culture requires constant quality control as cells can evolve over time, require a sterile environment and are managed by scientists who can be prone to making mistakes.

Cell line authentication is one of the major issues in quality control: briefly, it refers to the control of the identity, purity and phenotype of the cells. In other words, ensuring cells have not been misidentified, are not contaminated with microbes and have not changed too much over time.

Understandably, cell culture quality control is a less appealing task to focus on than the cutting-edge research behind the development of such in vitro models. Unsurprisingly, it has therefore somewhat been neglected in basic research, and is not to this day systematically incorporated into cell culture protocols. Nonetheless, in the race towards ever more sophisticated models, ensuring the validity of the data though cell culture quality control will become increasingly important.

This begs the question: can we keep being as blissfully ignorant now that experiments are becoming more precious, expensive and potentially implemented in a clinical setting? 

Photo credit: Pixabay

Cell culture: a black art

As Dr. Derfogail Delcassian, a postdoctoral researcher at Massachusetts Institute of Technology, points out:

“Cell culture sometimes feels like a black art, with everyone having their own preferred method”.

Cell culture, the process by which scientists maintain and grow cells in vitro, has indeed been optimized for the last century in countless laboratories for a myriad of different cell types and applications. While this has significantly enriched our collective expertise, it also has meant that scientists have had to figure out protocols to make it work in their own environment.

Yet some problems are common to everyone, and still, not enough is done to acknowledge them and tackle them globally. One common problem is microbial contamination, which according to Dr. Delcassian, is “experienced at least once by almost everyone working in cell culture research labs”. Even though these issues are so predominant, little efforts have been made towards acknowledging them. Could it be because we are ignorant?

How ignorant are we really?

Often, scientists whose samples are contaminated do not realize it, as Dr. Delcassian points out:

“Most scientists are aware that contamination is bad news for your research, but it's not always easy to tell if your samples are contaminated with mycoplasma, for example”. More worryingly, she says that “Not everyone is aware that contamination with mycoplasma can affect results”. Therefore, not only can scientists be unaware that their samples are contaminated, but many do not even know the risks associated with contamination for their research.

It is hard to estimate the amplitude of the problem, as no research has been or can easily be pursued on a subject that scientists might not be eager to report as existing in their laboratories. In contrast, there have been recent efforts towards documenting the issues related to cell line misidentification, which can be used as an indicator of how such problems can spread, go unnoticed and affect research at an alarming scale. According to Horbach et al., more than 30,000 studies have reported research with misidentified cell lines.

Thus, it is safe to say that there is an awareness in the scientific community of the recurring problems that can affect cell culture, but maybe less of their impact on research. Often, these issues are tackled internally, by discarding contaminated or misidentified samples, and hopefully by also discarding any related data: yet expectations of integrity in the scientific community have sometimes not been met.

Discarding work is also easier said than done, especially if the issue is detected at the end of the study, as it can take an incredible amount of time and money to generate data from cell culture experiments. Often scientists do not have enough time to keep up with the pressure to publish: it might then be easier to ignore the possibility and consequences of wrong cell line authentication then to have to put extra time in controlling it.

Now that academic cell culture is growing ever closer to patients, time calls for change. Luckily, others have had to go through a similar increase in control quality to ensure the safety of patients and integrity of data, which we can learn from.

Time to set priorities straight

In vitro fertilization has been operating for a while now under strict quality control3, to ensure the viability of the embryos and safety of patients. Similarly, cell therapy strives to follow a strict quality control set by governmental bodies such as the FDA4, but overall cell culture research in academia seems to lag behind. 

Clearly, the financial incentives and stakes are higher for therapeutic protocols where cells are eventually introduced in patients.

Yet, the problem is also important in basic research, as data based on wrong cell line authentication contaminates the literature which can affect future research that draws from this contaminated data. So even if measures are taken to avoid future problems, mistakes in the past will keep having an impact on research for some time.

Photo credit: Pixabay

Maybe then the problem is that there is little incentive in academia to systematically screen for contamination. According to Dr. Delcassian, “Researchers tend to be self-motivated to have good cell culture practice to ensure their data is reliable”. Yet, sometimes issues can go unnoticed for a long time despite genuine efforts from scientists.

Several options come to mind: we could improve screening standards but also try to decrease the risk of wrong cell line authentication, as Dr. Delcassian suggests: “Introducing regular contamination screening of cell cultures, and where possible, automation, can help”. Indeed, there is hope that automating tasks for cell culture can minimize the risk of error and contamination that is inherent to human manipulation. But, as Dr. Delcassian points out, “Being able to introduce these processes depends on both lab attitudes and also costs.”

Once appropriate tools become available, the incentive to use them might need to come from higher up, such as the research institutions themselves (as some already do), or independent committees such as the International Cell Line Authentication Committee who have been calling attention to this problem. As Dr. Delcassian suggests, the incentive for change could also be driven by journals that “could request statements or evidence from authors to describe what cell contamination screening was performed”.

Grants could allocate money to specifically address this issue, as lab budgets may not always allow for more costly experiments. But this in itself will not be enough: for example, even though established techniques exist to ensure cell line identification, such as short tandem profiling5, they are barely used. It has been argued that proper training would help, with particular care to not leave this education to the laboratory itself but at a higher level, so that training is consistent and held to common international standards.

Photo credit: Pexels

Biological research is already incredibly hard and time-consuming. Therefore, it is not enough to simply say that the responsibility lies solely with the scientists to perform additional cumbersome experiments to check the health and origin of their cells.

But, maybe it is time to set out our priorities and start discussing these issues more openly to stimulate a need for new tools and more importantly, new attitudes.

The current pressure to publish might be key in driving scientists to blissfully ignore the possibility of contamination or misidentification, as they do not have the time or means to cope with it. Efforts need to be made both by academics and private companies: the private sector needs to invent affordable new tools to automate cell culture and its quality control, and research institutions need to facilitate training and financial aids.

Finally, and perhaps more importantly, the entire community could rethink its priorities: that is, perhaps the goal could be to publish less but with more quality control. Acknowledging the issue will mean we cannot keep sweeping the problem under the rug. Instead, we can turn it on its head: we can use this challenge to improve workflows and standards in the research community, to ensure 3D cell culture can keep growing as a reliable solution for accelerating drug discovery.

References:1. Capes-Davis, A. & Neve, R. M. Authentication: A Standard Problem or a Problem of Standards? PLOS Biol. 14, e1002477 (2016).
2. Horbach, S. P. J. M. & Halffman, W. The ghosts of HeLa: How cell line misidentification contaminates the scientific literature. PLoS One 12, 1–16 (2017).
3 .Cutting, R., Pritchard, J., Clarke, H. & Martin, K. Establishing quality control in the new IVF laboratory. Hum. Fertil. (Camb). 7, 119–25 (2004).
4. Hanley, P., Luo, M., Bollard, C. & Loechelt, B. Quality control in academic cell therapy facilities: how much is too much? Cytotherapy 17, S27 (2015).
5. Masters, J. R. et al. Short tandem repeat profiling provides an international reference standard for human cell lines. Proc. Natl. Acad. Sci. U. S. A. 98, 8012–7 (2001).