Animal-cell culture media: History, characteristics, and current issues

Abstract

Background: Cell culture technology has spread prolifically within a century, a variety of culture media has been designed. This review goes through the history, characteristics and current issues of animal-cell culture media.

Methods: A literature search was performed on PubMed and Google Scholar between 1880 and May 2016 using appropriate keywords.

Results: At the dawn of cell culture technology, the major components of media were naturally derived products such as serum. The field then gradually shifted to the use of chemical-based synthetic media because naturally derived ingredients have their disadvantages such as large batch-to-batch variation. Today, industrially important cells can be cultured in synthetic media. Nevertheless, the combinations and concentrations of the components in these media remain to be optimized. In addition, serum-containing media are still in general use in the field of basic research. In the fields of assisted reproductive technologies and regenerative medicine, some of the medium components are naturally derived in nearly all instances.

Conclusions: Further improvements of culture media are desirable, which will certainly contribute to a reduction in the experimental variation, enhance productivity among biopharmaceuticals, improve treatment outcomes of assisted reproductive technologies, and facilitate implementation and popularization of regenerative medicine.

Keywords: cell culture technique; cell proliferation; culture media; cultured cells; serum.

Figure 1

The pH control mechanism of culture media, based on the bicarbonate buffer system and the Henderson–Hasselbalch equation. When dissolved in water, sodium bicarbonate (NaHCO ₃) dissociates to form a sodium ion (Na⁺) and a bicarbonate ion (HCO ₃ ⁻). The latter reacts with H⁺ in solution to form carbonic acid (H₂ CO ₃), which dissociates into CO ₂ and H₂O. These two reactions attain their respective equilibria. The CO ₂ in solution also reaches equilibrium with CO ₂ in the gas phase. As a result, increasing the concentration of gas phase CO ₂ increases the amount of CO ₂ that is dissolved in the culture medium, in turn raising the H₂ CO ₃ concentration and lowering the pH. In contrast, if the concentration of the gas phase CO ₂ is lowered, then the pH rises due to the reverse reaction. The relationship between the culture medium pH and the concentrations of CO ₂ and NaHCO ₃ can be expressed by the Henderson–Hasselbalch equation: pH=pKa+log[HCO ₃ ⁻]/[CO ₂]_{Liquid phase}, where: pKa is the negative log of the acid dissociation constant.

Figure 2

An example of a concentration–response surface. When Component B's concentration is low (eg, 0mg/L), antibody production falls as Component A's concentration rises; however, when Component B's concentration is high (eg, 20mg/L), antibody production increases as Component A's concentration rises. Such a phenomenon—one component influencing the response of another component—is called a “two‐factor interaction.” The relationship between the concentration and response is not necessarily linear, as is the case for Component B. An analysis of the concentration by using at least a three‐level screening design is necessary to understand such responses

Figure 3

Concepts of the one‐factor‐at‐a‐time experiment and design of experiment (DoE). These figures show the difference in strategies between a one‐factor‐at‐a‐time experiment and a DoE for the same experimental runs. A, In the case of the one‐factor‐at‐a‐time experiment, the optimal concentration of one component (eg, component A) is determined at a fixed concentration of another component (eg, component B). Then, the optimal concentration of component B is determined at the optimal concentration of component A. This strategy, which usually has been used, has a big disadvantage of missing the optimal point because there are some unexamined areas in the range of parameters. B, In contrast, the DoE is a model‐based statistical method that can clarify the relationship between the response of the cells and the concentrations of the tested components in the range of settings. The process of the DoE is mainly composed of four steps. First, allocate the design points evenly throughout the area. Second, record the response of the cells for each run. Third, fit the collected data to an appropriate model (eg, a logistic regression model for a binomial response) and validate the relevance of the model to decide whether it is available for the next step. Finally, use the model to optimize the concentrations of the components or to predict a response of the cells