首页 | 本学科首页   官方微博 | 高级检索  
     


Transcriptomics and cell painting analysis reveals molecular and morphological features associated with fed-batch production performance in CHO recombinant clones
Authors:Luke Nelson  Mike Veling  Fatemeh Farhangdoust  Xuezhu Cai  Steve Huhn  Veronica Soloveva  Meiping Chang
Affiliation:Merck & Co., Inc., Rahway, New Jersey, USA
Abstract:Stable, highly productive mammalian cells are critical for manufacturing affordable and effective biological medicines. Establishing a rational design of optimal biotherapeutic expression systems requires understanding how cells support the high demand for efficient biologics production. To that end, we performed transcriptomics and high-throughput imaging studies to identify putative genes and morphological features that underpin differences in antibody productivity among clones from a Chinese hamster ovary cell line. During log phase growth, we found that the expression of genes involved in biological processes related to cellular morphology varied significantly between clones with high specific productivity (qP > 35 pg/cell/day) and low specific productivity (qP < 20 pg/cell/day). At Day 10 of a fed-batch production run, near peak viable cell density, differences in gene expression related to metabolism, epigenetic regulation, and proliferation became prominent. Furthermore, we identified a subset of genes whose expression predicted overall productivity, including glutathione synthetase (Gss) and lactate dehydrogenase A (LDHA). Finally, we demonstrated the feasibility of cell painting coupled with high-throughput imaging to assess the morphological properties of intracellular organelles in relation to growth and productivity in fed-batch production. Our efforts lay the groundwork for systematic elucidation of clone performance using a multiomics approach that can guide future process design strategies.
Keywords:biologics  cell line development  cell painting  CHO  morphological profiling  transcriptomics
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号