Rapid and in-depth proteomic analysis of CHO cell bioprocesses

Chinese hamster ovary cells (CHO) are the main platform for monoclonal antibody production in the growing biopharmaceutical market. Compared to any other production platform, CHO cells, being mammalian cells, have the major benefit of providing appropriate protein folding and glycosylation patterns. However, further knowledge on the effect of protein burden and optimisations aimed at improving the viability and culture longevity for higher protein production are required. Programmed cell death – apoptosis, is the predominant form of response occurring from biochemical stresses in bioreactor culturing. Cell longevity, yield and product quality are thus negatively affected. However, apoptosis is operated through multinodal pathways, not yet comprehensively understood.

We used proteomics for global investigation of CHO cell bioprocesses and identification of pathways or markers associated with apoptosis that could be targets for cell process improvement. Due to the very large sample sets obtained per experiment, we introduced a short liquid chromatography gradient combined with a data-independent acquisition strategy, allowing for more sensitive and reproducible analysis of industrially relevant CHO cell lines. Additional utilisation of µPAC Neo HPLC columns reduced inter-column variability, increased the number of identified proteins and allowed increased number of samples processed per batch.

Investigation of different monoclonal antibody producing cell lines, with different productivities, has allowed the identification of stress-induced pathways connected with increased protein burden. Additional comparison of bioreactor-adapted producer cell line and non-adapted non-producer cell line suggested mechanisms through which cells adapt to the stress occurring when culturing non-adapted lines in bioreactor conditions. Our high throughput methodology could also be used in both industrial scale experiments and in other spheres such as large-sample clinical cohorts.