Blennemann Marcus, Verma Archit, Bachl Stefanie, Carnevale Julia, Engelhardt Barbara E
Gladstone Institutes, San Francisco, CA 94158, USA,
University of California, San Francisco, San Francisco, CA 94143, USA,
Pac Symp Biocomput. 2025;30:382-393. doi: 10.1142/9789819807024_0028.
Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T cell performance to create more powerful cancer immunotherapies, but understanding the changes to T cell behavior induced by genetic perturbations remains a challenge. Prior studies have evaluated the effect of different genetic modifications with cytokine production and metabolic activity assays. Live-cell imaging is an inexpensive and robust approach to capture TCR T cell responses to cancer. Most methods to quantify T cell responses in live-cell imaging data use simple approaches to count T cells and cancer cells across time, effectively quantifying how much space in the 2D well each cell type covers, leaving actionable information unexplored. In this study, we characterize changes in TCR T cell's interactions with cancer cells from live-cell imaging data using explainable artificial intelligence (AI). We train convolutional neural networks to distinguish behaviors in TCR T cell with CRISPR knock outs of CUL5, RASA2, and a safe harbor control knockout. We use explainable AI to identify specific interaction types that define different knock-out conditions. We find that T cell and cancer cell coverage is a strong marker of TCR T cell modification when comparing similar experimental time points, but differences in cell aggregation characterize CUL5KO and RASA2KO behavior across all time points. Our pipeline for discovery in live-cell imaging data can be used for characterizing complex behaviors in arbitrary live-cell imaging datasets, and we describe best practices for this goal.
对T细胞受体（TCR）T细胞进行基因扰动是一种很有前景的方法，有望提升TCR T细胞性能，从而开发出更强大的癌症免疫疗法，但了解基因扰动所诱导的T细胞行为变化仍是一项挑战。此前的研究已通过细胞因子产生和代谢活性检测评估了不同基因修饰的效果。活细胞成像技术是一种低成本且可靠的方法，可用于捕捉TCR T细胞对癌症的反应。在活细胞成像数据中，大多数量化T细胞反应的方法都采用简单方式，即随时间对T细胞和癌细胞进行计数，从而有效量化每种细胞类型在二维孔板中所覆盖的空间大小，而未对可采取行动的信息进行深入探究。在本研究中，我们利用可解释人工智能（AI），根据活细胞成像数据来表征TCR T细胞与癌细胞相互作用的变化。我们训练卷积神经网络，以区分在CUL5、RASA2基因经CRISPR敲除的TCR T细胞以及安全港对照敲除细胞中的行为。我们使用可解释人工智能来识别定义不同敲除条件的特定相互作用类型。我们发现，在比较相似的实验时间点时，T细胞和癌细胞的覆盖面积是TCR T细胞修饰的一个有力标志，但在所有时间点上，细胞聚集的差异则表征了CUL5基因敲除（CUL5KO）和RASA2基因敲除（RASA2KO）细胞的行为。我们用于在活细胞成像数据中进行发现的流程可用于表征任意活细胞成像数据集中的复杂行为，并且我们描述了实现这一目标的最佳实践方法。
