Accelerating Cell Line Development: The CellShepherd Advantage
In the competitive landscape of biologics manufacturing, time-to-market and production efficiency are critical factors that directly impact costs and competitive advantage. While over 100 million cell lines have been created for various applications, Cell Line Development (CLD) remains a lengthy and complex process. A key challenge in this process is identifying and isolating cells with optimal characteristics – particularly those with faster proliferation rates that can significantly reduce development and manufacturing timelines.
Uncovering Cell Heterogeneity
Our recent experiment using the CellShepherd platform has revealed insights into the natural heterogeneity of cell populations that typically remains hidden in traditional bulk culture methods. By dispensing 92 individual CHO-K1 cells into glass nanowells and monitoring their growth over 96 hours, we observed substantial variations in proliferation rates within this seemingly uniform population.
The results showed:
Growth Rate Variation: Population doubling times (PDT) ranged from over 56 hours to as little as 14 hours within the same CHO cell line
Validated Culture Environment: The average growth rate matched classical CHO culture parameters, confirming that the CellShepherd's dispensing process is gentle and its culturing conditions are reliable
Performance Differences: The fastest-growing cells produced approximately 79 cells after 90 hours, while the average was only 17 cells
The graph shows the monoclonal expansion of CHO-K1 cells in glass nanowells. In total 92 nanowells were charged with single cells and cultured in the CellShepherd platform, for a duration of 96 hours. Images were taken every 6 hours to monitor proliferation. In green is shown the corridor of expected cell growth based on reported population doubling times (PDTs) for CHO-K1 cells between 18 — 24 hours. Big blue dots represent average cell number in nanowells, small blue dots represent cell number of individual wells and the blue line traces cell count of the well with the highest number of cells at end of experiment.
The Impact on Biopharmaceutical Development Timelines
The practical implications of identifying and isolating fast-growing cells become clear when considering a standard workflow for expanding an individual cell into a larger population
Our simulation of a typical scale-up process from single cell to shaker flask revealed significant differences based on cell growth rates. Fast-growing cells with a population doubling time (PDT) of 14 hours completed the entire process in just 15.2 days. In contrast, average-performing cells with a 21-hour PDT required 22.8 days to reach the same endpoint, while slow-growing cells with a 56-hour PDT needed approximately two months to complete the scale-up process.
These findings demonstrate that selecting cells with optimal growth characteristics can reduce development time by 33% compared to using average cells, and by 75% when compared to slow-growing cells.
Implications for Monoclonal Antibody Production
For researchers and manufacturers working with biologics such as monoclonal antibodies, the ability to identify and isolate fast-growing cells offers several key advantages:
Accelerated Bioprocess Development: Compress timelines from initial cell isolation to production
Reduced Manufacturing Costs: Faster-growing cells mean more efficient use of resources
Enhanced Productivity: Optimize cell line selection based on actual performance data
Improved Process Control: Better understanding of cell population dynamics
The CellShepherd Approach to High-Throughput Clone Selection
What makes these insights possible is the CellShepherd's integrated approach to cell line development. The CellShepherd combines precise single cell dispensing, high-resolution imaging, gentle cell isolation as well as a sterile, climate-controlled environment.
This integration allows for continuous, hands-off monitoring throughout the experiment, providing detailed insights into growth onset, consistency within individual wells and enabling the selection of optimal performers.