Characterization of intrinsic variability in time-series metabolomic data of cultured mammalian cells

• Published in: Biotechnology and bioengineering 2015-11, Vol.112 (11), p.2276-2283
• Main Authors: Le, Huong, Jerums, Matthew, Goudar, Chetan T.
• Format: Article
• Language: English
• Subjects: Animals; Biological; biological variance; Bioreactors; Biotechnology; cell culture; CHO Cells; Chromatography; Chromatography, Liquid; Cricetulus; Culture Media - chemistry; Cytoplasm - chemistry; Failure; Gas Chromatography-Mass Spectrometry; Genetic engineering; Mammals; Mass Spectrometry; Metabolism; Metabolites; Metabolome; metabolomics; Pathways; Physiology; Reproduction; Rodents; technical variance; Time Factors; time-series
• ISSN: 0006-3592; 1097-0290
• DOI: 10.1002/bit.25646

ABSTRACT In an attempt to rigorously characterize the intrinsic variability associated with Chinese Hamster Ovary (CHO) cell metabolomics studies, supernatant and intracellular samples taken at 5 time points from duplicate lab‐scale bioreactors were analyzed using a combination of gas chromatography (GC)‐ and liquid chromatography–mass spectrometry (LC–MS) based metabolomics. The intrinsic variability between them was quantified using the relative standard deviation (RSD), and the median RSD was 9.4% and 12.4% for supernatant and intracellular samples, respectively. When exploring metabolic changes between lab‐ and pilot‐scale bioreactors, a high number of metabolites (65–105) were significantly different when no corrections were made for this intrinsic variability. This distinction also extended to principal component and metabolic pathway analysis. However, when intrinsic variability was taken into account, the number of metabolite with significant changes reduced substantially (20–25) as did the separation in principal component and metabolic pathway analysis, suggesting a much smaller change in physiology across bioreactor scale. Our results also suggested the contribution of biological variability to the total variability across replicates (∼0.4%) was significantly lower than that from technical variability (∼9–12%). Our study highlights the need for understanding and accounting for intrinsic variability in CHO cell metabolomics studies. Failure to do so can result in incorrect biological interpretation of the observations which could ultimately lead to the identification of a suboptimal set of targets for genetic engineering or process development considerations. Biotechnol. Bioeng. 2015;112: 2276–2283. © 2015 Wiley Periodicals, Inc. Comparative metabolomics studies across experiments that do not account for the intrinsic error can lead to incorrect biological interpretation and misidentification of targets for cell line engineering and bioprocess optimization. To address this limitation, Le and coauthors have rigorously characterized the intrinsic error in replicate CHO cell bioreactor cultures and have demonstrated that failure to account for such variability suggests more differences between lab‐ and pilot‐scale bioreactors, a conclusion that is not true when the intrinsic variability is accounted for.