Abstract

The CRISPR/Cas 9 gene editing technology has become one of the favoured methods to generate cell lines with targeted gene knock-ins or knock-outs. This method is highly precise and efficient, and has been adopted by many research labs within both industry and academia. In general, research experiments involving the CRISPR/Cas 9 protocol will generate many cell lines for characterisation, thus requiring a high-throughput method to measure the efficiency of the gene editing, the proliferative and healthiness of the generated cell lines, and verify cell line monoclonality. In recent years, the use of standard microwell plates for image cytometry has become increasingly popular due to the need for higher throughput. Specifically, the Celigo Image Cytometer can perform high-throughput, whole-well, cell-based assays, utilising special optics for rapid capture and analysis of brightfield and fluorescent images. Performing a 96-well, whole-well, cell-based assay using brightfield and one fluorescence channel typically requires less than 8 min. In this work, we demonstrate the employment of the Celigo Image Cytometer to perform rapid high-throughput imaging and analysis of CRISPR/Cas 9 gene-edited cell lines. There are three major assays where speed and efficiency can be greatly improved by using the image cytometry method. Firstly, cells may be directly imaged and analysed in brightfield and fluorescence to measure gene editing efficiency. This assay may be performed without the need to trypsinise cells, as otherwise required for flow cytometry, thus disturbing their natural state. Secondly, it is critical to monitor the proliferative capability of the generated cell lines as well as their viability. Many of the generated cell lines may not proliferate due to cell death, therefore it is important to be able to perform high-throughput cell proliferation assays to characterise them. Traditionally, MTT or CellTiter-Glo® assays are performed, but can introduce uncertainties without actual cell images. In addition, enzymatic activities may be disrupted by treatments, thus generating inaccurate endpoint viability results. Finally, for therapeutic cell line generation, it is important to ensure monoclonality, therefore image cytometry was used to capture whole well images, identify single cells, and monitor outgrowth over time. This method is a significant improvement over the current method of manually inspecting every well to identify single colony outgrowth to validate monoclonality, which may have a high operator-dependent error rate. In conclusion, the use of image cytometry may alleviate many issues from traditional detection methods. The ability to quickly characterise the CRISPR-generated therapeutic cell lines can improve the efficiency in identifying potential candidates for treatment.