In my career as a stem cell scientist, I noticed a common theme in the lab: when an experiment failed, it could almost always be traced back to a cell population that wasn’t as healthy as we thought it was. My lab was by no means unique. Suboptimal cell cultures are one of the biggest contributors to the lack of reproducibility in biological experiments.
All scientists worth their salt know that keeping cells healthy is essential to good research. But how to verify that health is another matter. Even though cultured cells are critical for biomedical research, there has been minimal innovation to support better ways of maintaining healthy cell stocks. The best technique available today depends on precisely counting cells at each passage to calculate their proliferation rate—a task so tedious and time consuming that even the most methodical scientists often skip it in favor of estimates based on logging the days between cell passages and time to confluency. This has consequences. When experiments failed in my lab over the years, we could often identify the source of the problem as a slightly slower time to confluency in our cell stocks. If we hadn’t been tracking those numbers carefully, it would have been easy to miss the declining health of our cells in culture.
Even scientists who take the time to count cells precisely often do so on replicates of a cell culture to avoid disrupting the culture that will be used in an experiment. In some ways, that’s a better approach, but it still introduces the potential for error by involving the use of a proxy to infer the health of a cell population. Without a direct and accurate count of the cells that will be used in an experiment, there’s no way to generate reliable proliferation rates.
Fortunately, there have been some recent advances to help scientists generate more precise data on cell health. ASTM International, an organization that develops and issues technical standards for global use, released a new standard aimed at improving the methods used to calculate cell proliferation rates in serially maintained cultures of animal or human cells. The new standard, known as F3716, covers techniques for measuring and comparing cell proliferation rates and is relevant to research performed in academia, biopharma, biomanufacturing, cultured food, and regenerative medicine, among many other areas. It’s a significant step forward in establishing reliable methods that will help all scientists ensure greater reproducibility and higher-confidence results in their research. It should also improve consistency across operators and laboratories.
ASTM International is now developing a follow-up standard, designed specifically for differential stem cell counting. If you work in this area and could use a reliable standard to enhance your work—or if you’re just committed to the idea of reproducibility in research—I encourage you to consider participating in the development of this standard.
In addition to standards development, freely available online calculators are also available to help scientists calculate cell proliferation rates, such as population doubling time or cumulative population doublings, with accuracy. These can be used for general cell counting or for differential tissue stem cell counting, and they allow users to verify the health of their cell populations before proceeding with an experiment. The online stem cell calculators do not use biomarkers or physical imaging; instead, they incorporate a computer simulation method based on the kinetics of cell division.
In the future, I believe that more of the cell counting and stock maintenance tasks will be handled by automation. Until then, these recent developments should help scientists hone their own best practices for ensuring the health of their cell populations.
