Functional proteomic analysis of GS-NS0 murine myeloma cell lines with varying recombinant monoclonal antibody production rate.

We previously compared changes in individual protein abundance between the proteomes of GS-NS0 cell lines with varying rates of cell-specific recombinant monoclonal antibody production (qMab). Here we extend analyses of our proteomic dataset to statistically determine if particular cell lines have distinct functional capabilities that facilitate production of secreted recombinant Mab. We categorized 79 proteins identified by mass spectrometry according to their biological function or location in the cell and statistically compared the relative abundance of proteins in each category between GS-NS0 cell lines with varying qMab. We found that the relative abundance of proteins in ER chaperone, non-ER chaperone, cytoskeletal, cell signaling, metabolic, and mitochondrial categories were significantly increased with qMab. As the GS-NS0 cell line with highest qMab also had an increased intracellular abundance of unassembled Mab heavy chain (HC), we tested the hypothesis that the increased ER chaperone content was caused by induction of an unfolded protein response (UPR) signaling pathway. Immunoblot analyses revealed that spliced X-box binding protein 1 (XBP1), a marker for UPR induction, was not detectable in the GS-NS0 cells with elevated qMab, although it was induced by chemical inhibitors of protein folding. These data suggest that qMab is functionally related to the abundance of specific categories of proteins that together facilitate recombinant protein production. We infer that individual cells within parental populations are more functionally equipped for high-level recombinant protein production than others and that this bias could be used to select cells that are more likely to achieve high qMab.

Evaluation of individual protein errors in silver-stained two-dimensional gels.

The relationship between spot volume and variation for all protein spots observed on large format 2D gels when utilising silver stain technology and a model system based on mammalian NSO cell extracts is reported. By running multiple gels we have shown that the reproducibility of data generated in this way is dependent on individual protein spot volumes, which in turn are directly correlated with the coefficient of variation. The coefficients of variation across all observed protein spots were highest for low abundant proteins which are the primary contributors to process error, and lowest for more abundant proteins. Using the relationship between spot volume and coefficient of variation we show it is necessary to calculate variation for individual protein spot volumes. The inherent limitations of silver staining therefore mean that errors in individual protein spot volumes must be considered when assessing significant changes in protein spot volume and not global error.