Eldrid Charles, Hawke Ellie, Cain Kathleen M, Meeson Kate, Watson Joanne, Spiess Reynard, Johnston Luke, Smith William, Russell Matthew, Hoare Robyn, Raven John, Schwartz Jean-Marc, Rattray Magnus, Pybus Leon, Dickson Alan, Pitt Andrew, Barran Perdita
Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Mol Cell Proteomics. 2025 Jun 4;24(7):101011. doi: 10.1016/j.mcpro.2025.101011.
Chinese hamster ovary (CHO) cells are the industrial workhorse for manufacturing biopharmaceuticals, including monoclonal antibodies. CHO cell line development requires a more data-driven approach for the accelerated identification of hyperproductive cell lines. Traditional methods, which rely on time-consuming hierarchical screening, often fail to elucidate the underlying cellular mechanisms driving optimal bioreactor performance. Big data analytics, coupled with advancements in "omics" technologies, are revolutionizing the study of industrial cell lines. Translating this knowledge into practical methods widely utilized in industrial biomanufacturing remains a significant challenge. This study leverages discovery proteomics to characterize dynamic changes within the CHO cell proteome during a 14-day fed-batch bioreactor cultivation. Utilizing a global untargeted proteomics workflow on both a ZenoTOF 7600 and a Cyclic IMS QToF, we identify 3358 proteins and present a comprehensive data set that describes the molecular changes that occur within a well-characterized host chassis. By mapping relative abundances to key cellular processes, eight protein targets were selected as potential biomarkers. The abundance of these proteins through the production run is quantified using a 15-min targeted triple quadrupole (MRM) assay, which provides a molecular-level QC for cell viability. This discovery to target workflow has the potential to assist engineering of new chassis and provide simple readouts of successful bioreactor batches.
中国仓鼠卵巢(CHO)细胞是生产生物制药(包括单克隆抗体)的工业主力军。CHO细胞系的开发需要一种更数据驱动的方法来加速鉴定高产细胞系。传统方法依赖耗时的分级筛选,往往无法阐明驱动最佳生物反应器性能的潜在细胞机制。大数据分析与“组学”技术的进步相结合,正在彻底改变工业细胞系的研究。将这些知识转化为工业生物制造中广泛使用的实用方法仍然是一项重大挑战。本研究利用发现蛋白质组学来表征在14天的补料分批生物反应器培养过程中CHO细胞蛋白质组内的动态变化。在ZenoTOF 7600和Cyclic IMS QToF上使用全球非靶向蛋白质组学工作流程,我们鉴定出3358种蛋白质,并提供了一个全面的数据集,描述了在一个特征明确的宿主底盘内发生的分子变化。通过将相对丰度映射到关键细胞过程,选择了8个蛋白质靶点作为潜在的生物标志物。使用15分钟的靶向三重四极杆(MRM)分析对这些蛋白质在生产过程中的丰度进行定量,该分析为细胞活力提供分子水平的质量控制。这种从发现到靶向的工作流程有可能协助新底盘的工程设计,并为成功的生物反应器批次提供简单的读数。