Proteomics Reveals Distinctive Host Cell Protein Expression Patterns in Fed-Batch and Perfusion Cell Culture Processes

Abstract

Chinese hamster ovary (CHO) cells are widely used to produce recombinant proteins, including monoclonal antibodies (mAbs), through various process modes. While fed-batch (FB) processes have been the standard, a shift toward high-density perfusion processes is being driven by increased productivity, flexible facility footprints, and lower costs. Ensuring the clearance of process-related impurities, such as host cell proteins (HCPs), is crucial in biologics manufacturing. Although purification processes remove most impurities, integrated strategies are being developed to enhance clearance of some high-risk HCPs. Current understanding of HCP expression dynamics in cell culture is limited. This study utilized data-independent acquisition (DIA) proteomics to compare the proteomic profiles of cell culture supernatants from 14 FB clones and three perfusion clones, all expressing the same mAb from the same host cell line. Results showed that perfusion processes enhance cell growth and productivity, exhibiting distinct proteomic profiles compared to FB processes. Perfusion processes also maintain a more comparable HCP abundance profile across clones, especially for 46 problematic HCPs monitored. Cluster analysis of FB proteomics revealed distinct abundance patterns and correlations with process parameters. Differential abundance analysis identified significant protein differences between the two processes. This is the first extensive study characterizing HCPs expressed by clones under different process modes. Further research could lead to strategies for preventing or managing problematic HCPs in biologics manufacturing.

Keywords: fed‐batch; host cell proteins; perfusion; proteomics.

FIGURE 1

Cell culture process profiles of viability (%), integrated viable cell density (IVCD), titer (g/L), and specific productivity (Qp) for clones run in fed‐batch and perfusion modes. Red symbols indicate clones run in both fed‐batch and perfusion modes. All the clones in fed‐batch run were harvested on day 14 except for clone C10 which was harvested on day 13.

FIGURE 2

Proteomics profile of HCCF fluid in fed‐batch mode. (A) Heatmap of high‐risk and frequently seen HCPs for 14 clones with LFQ values (log2 scale) from Day 14. Clones C9, C13, and C14 were run at least three times and each profile displayed individually. (B) Heatmap of the entire HCCF proteomics profile, where each protein is scaled across the clones and hierarchical clustering was performed on the pairwise correlation scores (Pearson). standardized protein abundance LFQ values were labeled as z‐scores and dense colors of each process parameter represents higher values. (C) Density plots of rho values (spearman correlation scores) between various parameters and HCPs by cluster. (D) Correlation plot between process parameters and selected HCP abundances, with significant correlations (p value < 0.05) marked by *. (E) Cellular localization of HCPs in each cluster by enrichment p values. HCCF, harvested cell culture fluid; HCPs, host cell proteins.

FIGURE 3

Proteomics profile of HCCF fluid in perfusion mode. (A) Time course abundance trends of high‐risk and frequently seen HCPs stratified by clone and HCP, fitted using a generalized additive model. (B) Heatmap of proteomics profiles of clones by day, displaying LFQ values (log2 scale) with hierarchical clustering. HCPs are labeled near their clusters. HCCF, harvested cell culture fluid; HCPs, host cell proteins.

FIGURE 4

Comparative proteomics between fed‐batch and perfusion processes. (A) Volcano plot showing differential protein abundance of perfusion versus fed‐batch processes, highlighting selected HCPs with log2 fold change > 0.5 and adjusted p value < 0.01 in red. The horizontal dashed line indicates an adjusted p value < 0.05. (B) GSEA analysis of fold change abundance values of perfusion over fed‐batch for positive enrichment scores, displaying top GO terms for BP, MF, and CC on the left, with GSEA enrichment plots for selected GO terms on the right. (C) Average death marker percentage calculated as the fraction of death markers relative to total LFQ value for each sample. Each dot represents a sample within specific time points or conditions (perfusion and fed‐batch), with horizontal lines showing mean values. (D) The bar graph illustrates the average log fold change of PSDE (Polysorbate degradation enzyme) HCPs in perfusion compared to fed‐batch, with error bars representing the standard error of the mean for each protein. BP, biological processes; CC, cellular components; GO, gene ontology; HCPs, host cell proteins; MF, molecular functions.