Culture Medium Analysis for a Metabolic Analysis of Antibody-Producing Cells Using LC-MS/MS and ICP-MS

Significance of the Topic

Understanding both organic and inorganic components in culture medium is critical for optimizing host cell metabolism and improving antibody production quality.

Objectives and Study Overview

This study aimed to profile metabolic changes in a novel CHL-YN antibody-producing cell line by quantifying 144 organic metabolites and nine metal ions in culture medium and supernatant over a 96-hour cultivation.

Methodology and Instrumentation

Cell Culture Conditions:

• Seeding density: 1.0 × 10^5 cells/mL
• Agitation: 90 rpm
• Temperature/humidity/CO2: 37 °C, 80 % RH, 5 % CO2

Sample Preparation:

• Organic analysis: protein removal and filtration for LC-MS/MS
• Inorganic analysis: dilution in 1 % HNO3 for ICP-MS

Analytical Systems:

• LC-MS/MS: Nexera X3 with LCMS-8060NX using a cell culture profiling method for 144 metabolites
• ICP-MS: ICPMS-2030 targeting Co, Cu, Fe, Mg, Mn, Mo, Ni, Se, and Zn

Key Results and Discussion

• Time-course data revealed depletion of key amino acids (e.g., asparagine, methionine) in later stages and dynamic shifts between secretion and uptake for metabolites such as lactic and succinic acids.
• ICP-MS monitoring showed selective uptake patterns for trace metals, with distinct timing for each element.
• Correlation analysis (|r|>0.5, FDR<0.05) linked specific organic and inorganic components to the antibody production rate.
• Enrichment analysis identified glutathione metabolism and the urea cycle as significant pathways associated with higher antibody productivity.

Benefits and Practical Applications

The combined LC-MS/MS and ICP-MS workflow enables comprehensive metabolic profiling with minimal pretreatment, informing feed condition optimization, quality control, and rational host cell line development in biopharmaceutical manufacturing.

Future Trends and Potential Applications

• Integration of multi-omics data and real-time process monitoring for adaptive bioprocess control.
• Expansion to other production cell lines and high-throughput screening formats.
• Application of machine learning to predict metabolic shifts and optimize culture strategies.

Conclusion

This dual-platform analytical approach provides detailed insights into cell culture metabolism by linking specific metabolites and metal ions to antibody productivity, paving the way for enhanced process optimization and cell line engineering.

References

1. Yamano-Adachi N. et al. (2020) Establishment of Fast-Growing Serum-Free Immortalised Cells from Chinese Hamster Lung Tissues for Biopharmaceutical Production. Sci Rep 10:17612.
2. Graham RJ, Bhatia H, Yoon S. (2019) Consequences of Trace Metal Variability and Supplementation on CHO Cell Culture Performance: Review of Key Mechanisms and Considerations. Biotechnol Bioeng 116(12):3446–3456.
3. Pang Z. et al. (2021) MetaboAnalyst 5.0: Narrowing the Gap between Raw Spectra and Functional Insights. Nucleic Acids Res 49(W1):W388–W396.