Regression Models of Viability and Apoptosis Can be Generated Leveraging Cluster Analysis From Ovizio iLineF PRO...

Area of Science:

• Biotechnology
• Cell Biology
• Bioprocess Engineering

Background:

• Chinese hamster ovary (CHO) cells are critical for large-scale monoclonal antibody (mAb) production.
• Fed-batch cultures are standard for growing CHO cells to high densities and producing mAbs.
• Cellular states during high productivity and apoptosis in CHO fed-batch cultures are not well understood.

Purpose of the Study:

• To characterize cellular subpopulations in CHO fed-batch cultures over time.
• To investigate the relationship between cellular states, specific productivity, and apoptosis.
• To evaluate in-line imaging as a process analytical technology for CHO cell cultures.

Main Methods:

• Utilized an Ovizio iLine F PRO system for in-line imaging of CHO cells in bioreactors.
• Applied cluster analysis to raw imaging data to identify distinct cellular subpopulations.
• Transformed imaging data into tank-level metrics for regression analysis with offline viability and apoptosis measurements.

Main Results:

• Cluster analysis revealed diverse cellular subpopulations within CHO fed-batch cultures.
• Regression analysis demonstrated a correlation between in-line imaging data and offline viability/apoptosis measurements.
• In-line imaging data, when averaged, effectively predicted offline measurements.

Conclusions:

• In-line imaging of CHO cells provides rich morphological and optical data.
• Cluster analysis of imaging data can identify critical cellular subpopulations.
• In-line imaging is a powerful process analytical tool for monitoring and controlling CHO fed-batch cultures.