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Biotechnol Bioeng. 2015 Nov;112(11):2276-83. doi: 10.1002/bit.25646. Epub 2015 Sep 17.

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

Author information

1
Drug Substance Technologies, Process Development, Amgen, Inc., One Amgen Center Drive, Thousand Oaks, California, 91320.
2
Drug Substance Technologies, Process Development, Amgen, Inc., One Amgen Center Drive, Thousand Oaks, California, 91320. cgoudar@amgen.com.

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.

KEYWORDS:

CHO cells; biological variance; cell culture; metabolomics; technical variance; time-series

PMID:
25976859
DOI:
10.1002/bit.25646
[Indexed for MEDLINE]

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