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Tutorials : Sep 1, 2009 ( )
Using Metabolomics to Optimize Bioprocessing
Systems Biology Tool Strives to Help Scientists Increase Knowledge of Cellular Changes!--h2>
Cells can be grown in cultured media for a variety of scientific applications. One common use is the production of recombinant proteins in cell-based bioreactors. Determining the optimal growth conditions in order for these cells to achieve the highest product quantity and quality involves a complex series of experiments that modify the hundreds of components in the media, the feeding schedule, and a wide variety of other variables such as temperature and dissolved oxygen.
New tools are emerging to better characterize cellular growth and production of recombinant proteins. These tools provide insight into the mechanisms involved in growth and production, as well as uncover biomarkers that can be used to monitor the key mechanisms involved. Given the complex nature of cells growing in a cultured environment, systems biology tools (Figure 1) are well-adapted for understanding cell growth on a comprehensive basis.
Understanding Cellular Phenotype
Metabolomics, a systems biology tool, is defined as the comprehensive profiling of all the biochemicals and metabolites in a biological sample. It is nonhypothesis driven so that no specific class of biochemicals or particular pathways is targeted. The majority of metabolomics studies are done using mass spectrometry, often coupled with another chromatographic method for better resolution of components. This analysis reveals changes in biochemicals and their associated pathways that are related to the experimental design, including drug, disease, diet, or environmental effects. Because these changes are closely related to the biological phenotype (Figure 1), a metabolomic analysis can provide insight into mechanism (e.g., drug toxicity) as well as markers related to the phenotype (e.g., diagnostic markers of disease).
It is estimated that the number of biochemicals and metabolites in a cell is on the order of several thousand. For media-optimization studies, only a handful of biochemical markers are analyzed (e.g., glucose, lactate, acetate, glutamate, glutamine, ammonia). For some situations, these markers may not be relevant to the phenotype, and additional biochemical and metabolic measurements will increase insight into optimization.
Applying metabolomics to bioprocessing involves measuring the biochemicals and metabolites from both the media and from within the cells. An overview of a typical metabolomics experiment for bioprocessing utilizing Metabolon’s analytical platform is shown in Figure 2.
The experiment begins with the collection of samples from a shake flask or bioreactor over time of interest. For CHO cell experiments, for example, this can include sampling each day over a 14-day period. The cells are separated from spent media and flash frozen. Each sample is extracted to isolate the biochemicals and metabolites (typically <1,500 MW) and divided into three portions. Each aliquot is analyzed using a different analytical platform: two UHPLC-MS/MS ESI platforms (positive ESI and negative ESI) and one GS-MS platform.
The software processes the mass spectral data, detecting and integrating chromatographic peaks. Each peak is composed of a number of mass spectra (nominal mass and MS/MS fragmentation pattern). By comparing the retention time of the peak and the mass spectral information to a database of biochemical standards, the software can rapidly identify hundreds of analytes in a single sample.
Then the data is statistically analyzed to determine significant changes at each time point compared to the baseline sample. These metabolic changes are grouped by pathway and color-coded to allow rapid determination of pathways altered. Because both cells and spent media are analyzed, changes in the media can be compared to changes in cellular metabolism. This allows researchers to monitor the impact of biochemicals depleted in the media and their impact on the metabolism of the cell. Likewise, accumulation of toxic metabolites and their effect can be determined.
Metabolomics technology can be applied to bioprocessing operations in two ways. The first application is in finding targets for metabolic engineering, formulating growth media, and identifying areas for potential process improvement. For example, metabolomics can uncover novel metabolism, find blind spots in media requirements, and help to develop feeding strategies. The knowledge gained can also generate hypotheses for further testing.
The other general application for metabolomics is biomarker discovery. By using the rich global information from this analysis, new markers can be discovered. These new markers can be employed at any point in cell culture development work, much like the way lactate or ammonia are currently used. This may include selection criteria for clone selection or media development, process development monitoring and other potential downstream uses (CQA for PAT and QbD).
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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