Using Metabolic Profiling Technology to Advance Cell Culture Development - Through metabolomics, the metabolic underpinnings of cellular changes can be rapidly pinpointed, directing process

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Using Metabolic Profiling Technology to Advance Cell Culture Development
Through metabolomics, the metabolic underpinnings of cellular changes can be rapidly pinpointed, directing process development scientists to key areas for cell culture optimization.


BioPharm International Supplements


Using Metabolomics for Bioprocessing Studies


Figure 1. An overview of a typical metabolomics experiment for bioprocessing. The experiment begins with the collection of samples from a shake flask or bioreactor. The cells are separated from spent media and flash frozen. Each sample is extracted to isolate the biochemicals and metabolites, then split into three portions and run across three mass spectrometry (MS) platforms. Software processes the MS data. 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 are 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 effect of a media-depleted biochemical on the metabolism of the cell. Likewise, the effect of accumulation of toxic metabolites can be determined.
An overview of a typical metabolomics experiment for bioprocessing is shown in Figure 1. The experiment begins with the collection of samples from a shake flask or bioreactor over a period of time. For Chinese hamster ovary (CHO) cell experiments, this may mean 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 with a molecular weight <1,500), then split into three portions and run across three mass spectrometry platforms (UHPLC–MS/MS + ESI, UHPLC–MS/MS - ESI, and GC–MS + EI) in which the collected data are analyzed by proprietary software.

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 are 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 altered pathways. 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 effect of a media-depleted biochemical on the metabolism of the cell. Likewise, the effect of accumulation of toxic metabolites can be determined.

Two general classes of applications have emerged using metabolomics technology in bioprocessing. The first is in finding targets for metabolic engineering, formulating growth media, and identifying areas for potential process development and improvement. For example, metabolomics can uncover a novel metabolism. It also can find blind spots in media requirements and help develop a rationale for a feeding strategy. The knowledge gained also can 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 used the way lactate or ammonia are currently used—at any point in cell culture development work. For example, the data may be used to determine selection criteria for clone selection or media development, process development monitoring, and other potential downstream uses (for example, to determine critical quality attributes for a Quality-by-Design approach to process development, and for later application of process analytical technology for ongoing process monitoring.).


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