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September 01, 2009 (Vol. 29, No. 15)

Using Metabolomics to Optimize Bioprocessing

Systems Biology Tool Strives to Help Scientists Increase Knowledge of Cellular Changes

  • Case Study

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    Figure 3. Condensed heat maps of cell and media metabolites over time, with a focus on lipids (red = increase; green = decrease)

    In a recent study, investigators suspected that cells were in an energy-deficient state and were not using glucose efficiently as the culture progressed. Glucose levels in the media were marginally informative of this suspicion. One goal of the study was to discover markers that are more robust and descriptive than glucose.

    Figure 3 shows the results of heat mapping generated through metabolomic analysis and the relevant changes discovered. Each cell of the heat map represents a single measurement of either cells or media and is colored to represent the fold change from the initial time (red areas reflect an increase over time; green areas depict a decrease). The expanded center section of the heat map shows critical changes, demonstrating that the sorbitol pathway of glucose utilization changed during the run.

    Sorbitol can be induced in times of osmotic stress; at high levels, it can induce apoptosis in some cell types. Sorbitol, however, is generally thought to be produced in the presence of elevated glucose levels. Sorbitol may be a marker of reduced glucose utilization by glycolytic pathways. In contrast to glucose measurement in the experimental media, the sorbitol signal is more pronounced with time.

    Thus, whether a marker of reduced glucose utilization by glycolysis or an indicator of osmotic changes, this metabolomic analysis demonstrated that sorbitol can be used as a robust marker of cellular changes. Sorbitol could possibly also be used in conjunction with glucose and lactate—as a measure of glucose utilization efficiency. Clearly, measuring only standard markers would not have provided a conclusive view of what was actually occurring in the experiment.

    Historically, bioprocessing and cell culture development have been difficult because of limited knowledge about the components of an experimental system. These systems involve hundreds of metabolites that constantly change during growth and in response to feeding and other environment modifications.

    Traditionally, monitoring of these processes has involved a handful of metabolites. In some cases, these metabolites give insight into metabolic changes. More often than not, however, other metabolites are more closely tied to the phenotypical changes of interest (cell viability, protein expression levels, product quality). Using metabolomics, the metabolic underpinnings of cellular changes can be rapidly pinpointed, directing scientists to key areas for optimization. 

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