Christopher P. Tostado, Lucas Xian Da Ong, Joel Jia Wei Heng, Carlo Miccolis, Shumei Chia, Justine Jia Wen Seow, Yi-Chin Toh, Ramanuj DasGupta
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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.</p>","PeriodicalId":9263,"journal":{"name":"Bioengineering & Translational Medicine","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/btm2.10628","citationCount":"0","resultStr":"{\"title\":\"An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response\",\"authors\":\"Christopher P. 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引用次数: 0
摘要
我们提出了一个结合单细胞表型数据和单细胞转录组分析的新框架,以确定抗肿瘤免疫反应异质性的基础因素。我们在微流控细胞捕获平台上开发了自然杀伤(NK-92MI)细胞与患者来源的头颈部鳞状细胞癌(HNSCC)细胞系之间的成对、肿瘤免疫离散化相互作用测定。此外,我们还生成了一种深度学习计算机视觉算法,该算法能够自动获取和分析大型活细胞成像数据集(>100 万),这些数据集记录了多个 HNSCC 细胞系(n = 10)在 24 小时内肿瘤与免疫相互作用的配对情况。最后,我们将通过 Kaplan-Meier 生存分析测得的 NK 介导杀伤反应数据与下游单细胞转录组分析相结合,研究与 NK 效应相关的分子特征。作为拟议框架的概念验证,我们有效地确定了 MHC I 类驱动的细胞毒性抵抗是无应答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,这种数据驱动的综合表型方法在推动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前景广阔。
An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response
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.
期刊介绍:
Bioengineering & Translational Medicine, an official, peer-reviewed online open-access journal of the American Institute of Chemical Engineers (AIChE) and the Society for Biological Engineering (SBE), focuses on how chemical and biological engineering approaches drive innovative technologies and solutions that impact clinical practice and commercial healthcare products.