人工智能辅助的集成、可扩展的单细胞表观-转录组学平台,用于阐明肿瘤内异质性对免疫反应的影响

IF 6.1 2区 医学 Q1 ENGINEERING, BIOMEDICAL Bioengineering & Translational Medicine Pub Date : 2024-01-02 DOI:10.1002/btm2.10628
Christopher P. Tostado, Lucas Xian Da Ong, Joel Jia Wei Heng, Carlo Miccolis, Shumei Chia, Justine Jia Wen Seow, Yi-Chin Toh, Ramanuj DasGupta
{"title":"人工智能辅助的集成、可扩展的单细胞表观-转录组学平台,用于阐明肿瘤内异质性对免疫反应的影响","authors":"Christopher P. Tostado,&nbsp;Lucas Xian Da Ong,&nbsp;Joel Jia Wei Heng,&nbsp;Carlo Miccolis,&nbsp;Shumei Chia,&nbsp;Justine Jia Wen Seow,&nbsp;Yi-Chin Toh,&nbsp;Ramanuj DasGupta","doi":"10.1002/btm2.10628","DOIUrl":null,"url":null,"abstract":"<p>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 (&gt;1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (<i>n</i> = 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.</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. Tostado,&nbsp;Lucas Xian Da Ong,&nbsp;Joel Jia Wei Heng,&nbsp;Carlo Miccolis,&nbsp;Shumei Chia,&nbsp;Justine Jia Wen Seow,&nbsp;Yi-Chin Toh,&nbsp;Ramanuj DasGupta\",\"doi\":\"10.1002/btm2.10628\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 (&gt;1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (<i>n</i> = 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.</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\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioengineering & Translational Medicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/btm2.10628\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioengineering & Translational Medicine","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/btm2.10628","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Bioengineering & Translational Medicine
Bioengineering & Translational Medicine Pharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science
CiteScore
8.40
自引率
4.10%
发文量
150
审稿时长
12 weeks
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.
期刊最新文献
Fecal microbiota transplantation for the treatment of intestinal and extra‐intestinal diseases: Mechanism basis, clinical application, and potential prospect ColMA‐based bioprinted 3D scaffold allowed to study tenogenic events in human tendon stem cells Facile minocycline deployment in gingiva using a dissolvable microneedle patch for the adjunctive treatment of periodontal disease Temperature‐sensitive sodium beta‐glycerophosphate/chitosan hydrogel loaded with all‐trans retinoic acid regulates Pin1 to inhibit the formation of spinal cord injury‐induced rat glial scar Recent regulatory developments in EU Medical Device Regulation and their impact on biomaterials translation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1