Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment.
Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment.
复制标题
肿瘤微环境中scRNA-Seq衍生的细胞间通讯网络的定位和验证。
DOI：
10.3389/fimmu.2022.885267
复制
发表时间：
2022
影响因子：
7.3
通讯作者：
中科院分区：
文献类型：
--
作者：
单细胞技术的最新进展，特别是单细胞rna测序（scRNA-seq），已经允许对各种生物系统进行高通量转录谱分析。由于scRNA-seq支持细胞间通讯的推断，这项技术已经并将继续为癌症治疗的新型免疫疗法的疗效和机制奠定开创性的研究。在这篇综述中，我们将重点介绍从scRNA-seq中推断细胞间和细胞内信号的方法，并讨论它们如何促进肿瘤微环境（TME）免疫治疗干预的研究。然而，一个核心挑战仍然是验证假设的细胞-细胞相互作用。因此，本综述还将涵盖将这些scrna -seq衍生的相互作用网络与现有实验和计算方法整合的策略。这些网络与成像、蛋白质分泌测量、网络分析和数学建模工具的整合解决了scRNA-seq仍然存在的挑战，以加强对TME中免疫抑制和免疫治疗改变信号的研究。
Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.
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影响因子：
14.9
作者：
wwPDB consortium
通讯作者：
wwPDB consortium
影响因子：
12.3
作者：
Abdelaal, Tamim;Michielsen, Lieke;Mahfouz, Ahmed
通讯作者：
Mahfouz, Ahmed
影响因子：
2.4
作者：
Barker, M;Rayens, W
通讯作者：
Rayens, W
影响因子：
48
作者：
Browaeys, Robin;Saelens, Wouter;Saeys, Yvan
通讯作者：
Saeys, Yvan
影响因子：
82.9
作者：
Auslander N;Zhang G;Lee JS;Frederick DT;Miao B;Moll T;Tian T;Wei Z;Madan S;Sullivan RJ;Boland G;Flaherty K;Herlyn M;Ruppin E
通讯作者：
Ruppin E