Easily acquire 3D and 4D T-cell datasets and analyze phenotypic evolution over time.

Despite the boom of CAR T-cell therapies, the discovery of novel immunotherapies that specifically enhance T-cell response against cancer cells remains a challenge limited by the absence of robust, in vitro models to evaluate these immunotherapies throughout their development.

In this application note, we describe a workflow for the generation of 3D tumor spheroids co-cultured with T-cells as a proof-of-concept model for CAR T assays. Further, we developed an image analysis approach that uses deep learning to accurately segment biological objects of interest and machine learning to quantify the phenotypic changes in the spheroids using only brightfield images.

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