Identifying key signatures of highly productive CHO cells from transcriptome and proteome profiles
© Sanny et al; licensee BioMed Central Ltd. 2006
Published: 10 October 2006
One of the key challenges in biotherapeutics production is the selection of a high-producing animal cell line to maximize protein yield in cell culture. Clone selection is often a tedious process, involving rounds of selection and single cell cloning which is costly in both money and time. In an effort to increase the throughput of clone selection, we seek to identify key signatures of a highly productive cell line using an integrated genomic and proteomic platform. In our study, we analysed microarray and proteomics data generated from a characterization of two populations of CHO cells stably expressing high and low levels of green fluorescent protein (GFP). The high producer cells (HP) make 6x more GFP than the low producer cells (LP) as determined by ELISA. Comparison of transcript levels between HP and LP in the mid-exponential phase was performed using a proprietary 15k CHO cDNA microarray chip, of which 7559 genes are unique , while proteomic analysis on samples in the mid-exponential and stationary phases was performed using iTRAQ quantitative protein profiling technique . Although there was a general lack of correlation between mRNA levels and quantitated protein abundance, results from both datasets concurred on groups of proteins/genes based on functional categorization.
Our results show that an integrated approach using microarray and proteomics platform can be effectively utilized as tools to monitor transcriptional and post-transcriptional events of mammalian cells in culture, enabling us to identify distinctive changes in cells caused by recombinant protein expression. This information, together with changes in other important cellular processes, would be valuable in a rational approach for engineering cell-lines as well as for the designing of media and cell culture parameters to enhance product yield in CHO cells.
We thank the support of A*Star, Agency for Science, Technology and Research, Singapore for funding the project. Angie Chang, Lu Wei Da, Toh Poh Choo and Wong Chun Loong and members of the proteomics group for their excellent technical assistance.
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