We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan–Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.

中文翻译：

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人工智能辅助的集成、可扩展、单细胞表型转录组平台，用于阐明肿瘤内针对免疫反应的异质性

我们提出了一个新的框架，将单细胞表型数据与单细胞转录组分析相结合，以确定抗肿瘤免疫反应异质性的基础因素。我们在微流体细胞捕获平台上开发了自然杀伤 (NK-92MI) 细胞和患者来源的头颈鳞状细胞癌 (HNSCC) 细胞系之间的成对肿瘤免疫离散相互作用测定。此外，我们生成了一种深度学习计算机视觉算法，能够自动采集和分析跨越多个 HNSCC 的 24 小时内配对肿瘤免疫相互作用的大型活细胞成像数据集（>100 万）行（n  = 10）。最后，我们将 Kaplan-Meier 生存分析针对 NK 介导的杀伤测量的反应数据与下游单细胞转录组分析相结合，以询问与 NK 效应反应相关的分子特征。作为所提出框架的概念验证，我们有效地确定了 MHC I 类驱动的细胞毒性抵抗是无反应者免疫逃避的关键机制，同时发现细胞粘附分子表达的增强与对 NK 介导的细胞毒性的敏感性相关。我们的结论是，这种集成的、数据驱动的表型方法在推进快速识别与免疫逃避和反应相关的新机制和治疗靶点方面具有巨大的前景。