Epstein Leo, Weiner Adam C, Verma Archit, Saedi Mozhgan, Carnevale Julia, Marson Alex, Engelhardt Barbara E
Gladstone Institutes, Institute of Data Science and Biotechnology, San Francisco, CA, USA.
Humboldt Universitat zu Berlin, Informatik, Unter den Linden 6, 10117 Berlin, Germany.
bioRxiv. 2025 Jun 7:2025.06.04.657687. doi: 10.1101/2025.06.04.657687.
Live-cell imaging (LCI) of modified T cells co-cultured with cancer cells is commonly used to quantify T cell anti-cancer function. Videos captured by LCI show complex multi-cell behavioral phenotypes that go beyond simple cancer cell fluorescence measurements. Here, we develop an unsupervised analysis workflow to characterize LCI data generated using the Incucyte imaging platform. Unlike most LCI analyses, we avoid cell segmentation due to the low spatiotemporal resolution of the LCI videos and high levels of cell-cell contact. Instead, we develop methods that identify global aggregation patterns and local cellular keypoints to characterize the multicellular interactions that determine cancer cell sensitivity to, or escape from, T cell surveillance. We demonstrate our segmentation-free live-cell behavioral analysis (SF-LCBA) methods on TCR T cells from four donors with varying proportions of cells with a beneficial RASA2 knockout and effector-to-target initial concentrations in a co-culture with A375 melanoma cells. We find that different T cell modifications affect the spatiotemporal dynamics of multicellular aggregate formation. In particular, we show that fewer and smaller cancer cell aggregates form at high ratios of effector T cells to target cancer cells and high titrations of T cells with RASA2 knockouts. Our SF-LCBA method identifies, characterizes, and tracks cellular aggregate formation in datasets that are unsuitable for cell segmentation and tracking, opening the door to more therapeutically-relevant measurements of modified T cell therapy cell behavioral phenotypes from LCI data.
与癌细胞共培养的修饰T细胞的活细胞成像（LCI）通常用于量化T细胞的抗癌功能。LCI捕获的视频显示了复杂的多细胞行为表型，这超出了简单的癌细胞荧光测量范围。在这里，我们开发了一种无监督分析工作流程，以表征使用Incucyte成像平台生成的LCI数据。与大多数LCI分析不同，由于LCI视频的时空分辨率低以及细胞间接触程度高，我们避免了细胞分割。相反，我们开发了一些方法，可识别全局聚集模式和局部细胞关键点，以表征决定癌细胞对T细胞监视的敏感性或逃避的多细胞相互作用。我们在来自四个供体的TCR T细胞上展示了我们的无分割活细胞行为分析（SF-LCBA）方法，这些供体在与A375黑色素瘤细胞共培养中具有不同比例的具有有益RASA2基因敲除的细胞以及效应细胞与靶细胞的初始浓度。我们发现不同的T细胞修饰会影响多细胞聚集体形成的时空动态。特别是，我们表明，在效应T细胞与靶癌细胞的高比例以及RASA2基因敲除的T细胞的高滴定度下，形成的癌细胞聚集体更少且更小。我们的SF-LCBA方法可识别、表征和跟踪不适用于细胞分割和跟踪的数据集中的细胞聚集体形成，为从LCI数据中对修饰T细胞治疗细胞行为表型进行更多与治疗相关的测量打开了大门。
