T Cell-Induced Morphological Change Analysis in Colorectal Cancer Organoids

This technical report presents a workflow for analyzing T cell-induced morphological changes in colorectal cancer organoids using artificial intelligence. The study highlights the challenges of applying CAR T-cell therapy in solid tumors, particularly due to the tumor microenvironment's suppressive effects on T-cell efficacy. It details the advantages of using three-dimensional patient-derived organoids (PDOs) over traditional two-dimensional cultures, emphasizing their ability to mimic the tumor microenvironment more accurately. The report outlines the development of a semi-automated bioreactor for the large-scale expansion of organoids and describes a method for quantifying T-cell efficacy through high-content imaging. The methodology includes the segmentation of organoids using a deep-learning model and classification of morphological changes via a Random Forest algorithm. Results indicate that stimulated T-cells significantly alter organoid morphology, demonstrating the potential of this approach for live T-cell efficacy assessment in drug discovery. The report concludes with insights into the successful implementation of this novel workflow.