(228ct) Elucidating Cellular Metabolism through Untargeted ‘Omics Methods Towards Improving Process Robustness | AIChE

(228ct) Elucidating Cellular Metabolism through Untargeted ‘Omics Methods Towards Improving Process Robustness


Raju, R. - Presenter, University of Minnesota
Gilbert, A., Biogen
Alden, N., Tufts University
Lee, K., Tufts University
Kshirsagar, R., Biogen
In this work, an untargeted metabolomics approach was applied to an industrially relevant system wherein high peak densities of CHO cells were attained, using a fed-batch process with chemically defined media. This study was used to extend the scope of the analysis beyond well-known metabolic byproducts such as lactate and ammonia, which inhibit growth upon accumulation. Cell supernatant samples were collected from the exponential growth phase and at peak density from multiple cell lines, spanning two parental origins, varying growth rates and monoclonal antibodies produced. An untargeted metabolomics workflow using a combination of liquid chromatography-mass spectrometry (LC-MS) methods was used in conjunction with a novel, biological context-driven data processing and annotation pipeline to characterize extracellular metabolites resulting cellular metabolic activity. About 60 metabolites were identified, with approximately 40% being confirmed against the METLIN database. The metabolite profiles across all samples were then systematically compared to identify those that correlate with cell growth arrest. Comparison of trends from the exponential and peak samples as well as low and high growing cells, within and across both parental cell lines was performed. About 8 metabolites accumulating at higher peak densities compared to the growth phase and in low growing cell lines were of potential interest. These represent metabolic byproducts that could be toxic or growth inhibitory and warrant further investigation. Toxicity dosing studies were subsequently performed to validate the detrimental effects of these compounds on cell growth. A survey of the metabolic pathways involved resulted in design of strategies to mitigate their generation. Overall, using an untargeted â??omics approach provided important insights into cellular metabolism and allowed for further process optimization and robustness.