Traditionally, monoclonality identification involves manual screening and selection of individual hybridoma cells, which can be a time-consuming and labor-intensive process. However, with the advancements in automation and high-throughput technologies, the identification of monoclonal cell lines can now be streamlined and expedited.
Automatic monoclonality identification systems utilize various techniques to analyze and characterize individual cells, allowing for rapid screening and selection of monoclonal cell lines. Here are some ways in which automatic monoclonality identification accelerates the cell line development process:
High-throughput screening: Automated systems can process a large number of cells simultaneously, enabling high-throughput screening. This eliminates the need for manual handling of individual cells and significantly speeds up the screening process.
Imaging and image analysis: Automated imaging systems capture images of individual cells and analyze them to identify monoclonal cell lines. Sophisticated image analysis algorithms can accurately detect and classify cells based on specific criteria, such as morphology, size, and fluorescence intensity.
Data integration and analysis: Automatic monoclonality identification systems can integrate data from multiple sources, such as images, fluorescence signals, and cell growth data. This enables comprehensive analysis and characterization of monoclonal cell lines, facilitating efficient decision-making in the cell line development process.
By automating the monoclonality identification process, the overall timeline for cell line development can be significantly reduced. This acceleration allows for faster production of monoclonal antibodies, expedites research and development activities, and enables quicker translation of discoveries into diagnostic and therapeutic applications.
It’s important to note that while automatic monoclonality identification systems offer numerous advantages, manual confirmation and quality control steps are still necessary to ensure the reliability and accuracy of the identified monoclonal cell lines.
The Countstar Castor X1 is the ideal platform for monoclonal identification. It is equipped with advanced optical technologies and Artificial Intelligence (AI) based image recognition algorithms, ensuring high quality images and accurate image processing for confident results. Sophisticated software with intuitive user interface provides comprehensive suite of data analysis tools, simplifying data review of large data set.
The number of cells in each well is counted and tracked automatically throughout the cell culture duration. The AI powered algorithm ensures accurate identification of monoclonality for both adherent and suspended cells, reduces manual verification time by 65%.
