Case Study 2: Platform Process Evaluation
Figure 5. In this example, elevated glycerol and monoacylglycerols (possibly from lipid storage droplets) caused triacylglycerol
lipolysis.
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In an effort to improve a developing platform process, the investigator sought greater knowledge into uncharted metabolic
deficiencies that could be targets for cell culture performance. Figure 4 shows changes over time in the heat map for data
from the cells and the media (again, red reflects an increase over time, and green reflects a decrease). Again, only a few
of the biochemicals detected in the study are shown. The biochemical pathway on the right demonstrates that many lipid metabolites
changed over time.
Figure 6. In contrast to what is seen in Figure 5, in this case, membrane phospholipid catabolites were reduced over time.
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Figure 5 illustrates that elevated glycerol and monoacylglycerols (possibly from lipid storage droplets) caused triacylglycerol
lipolysis. By contrast, membrane phospholipid catabolites were reduced over time (Figure 6). Figure 7 shows that the changes
caused loss of membrane integrity, demonstrated by the progressive accumulation of glucose-6-phosphate in the media instead
of being sequestered in the cell. The large changes in lipids indicated some manner of lipid imbalance, which resulted in
reduced cell membrane integrity and viability.
Figure 7. Changes caused a loss of membrane integrity, demonstrated by the progressive accumulation of glucose-6-phosphate
in the media instead of being sequestered in the cell. The large changes in lipids indicated some manner of lipid imbalance,
which resulted in reduced cell membrane integrity and viability.
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Because choline is a major head-group of membrane phospholipids, it is possible that choline depletion in this experiment
had an effect on membrane integrity and viability. Although there are other possibilities for the altered lipid metabolism,
the use of global metabolic profiling uncovered an imbalance in lipid metabolism, proposed a possible source of the imbalance
(i.e., choline reduction), and (at a minimum) reduced the design space for tackling optimization of this process.
Conclusion
Bioprocessing and cell culture development have historically been difficult because of the limitations on how much is known
about the components of an experimental system. These systems involve hundreds of metabolites constantly changing 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.
MICHAEL MILBURN is the chief scientific officer at Metabolon, Inc., Research Triangle Park, NC, 919.572.1711, info@metabolon.com
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