系统免疫学：整合多组学数据以推断免疫调节网络和隐藏驱动因素。
Systems immunology: Integrating multi-omics data to infer regulatory networks and hidden drivers of immunity.
作者信息
Yu Jiyang, Peng Junmin, Chi Hongbo
机构信息
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
出版信息
Curr Opin Syst Biol. 2019 Jun;15:19-29. doi: 10.1016/j.coisb.2019.03.003. Epub 2019 Mar 12.
Abstract
The immune system is a highly complex and dynamic biological system. It operates through intracellular molecular networks and intercellular (cell-cell) interaction networks. Systems immunology is an emerging discipline that applies systems biology approaches of integrating high-throughput multi-omics measurements with computational network modeling to better understand immunity at various scales. In this review, we summarize key omics technologies and computational approaches used for immunological studies at both population and single-cell levels. We highlight the hidden driver analysis based on data-driven networks and comment on the potential of translating systems immunology discoveries to immunotherapy of cancer and other human diseases.
摘要
免疫系统是一个高度复杂且动态的生物系统。它通过细胞内分子网络和细胞间（细胞 - 细胞）相互作用网络运行。系统免疫学是一门新兴学科，它应用系统生物学方法，将高通量多组学测量与计算网络建模相结合，以在不同尺度上更好地理解免疫。在本综述中，我们总结了用于群体和单细胞水平免疫研究的关键组学技术和计算方法。我们重点介绍基于数据驱动网络的隐藏驱动因素分析，并对将系统免疫学发现转化为癌症和其他人类疾病免疫治疗的潜力进行评论。
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Cell Rep. 2019 Jul 16;28(3):819-831.e4. doi: 10.1016/j.celrep.2019.06.049.
2
Intratumoral Tcf1PD-1CD8 T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy.具有干性特征的肿瘤内 Tcf1PD-1CD8 T 细胞促进接种疫苗和检查点阻断免疫治疗后的肿瘤控制。
Immunity. 2019 Jan 15;50(1):195-211.e10. doi: 10.1016/j.immuni.2018.12.021. Epub 2019 Jan 8.
3
Checkpoint Blockade Immunotherapy Induces Dynamic Changes in PD-1CD8 Tumor-Infiltrating T Cells.检查点阻断免疫疗法诱导 PD-1CD8 肿瘤浸润 T 细胞的动态变化。
Immunity. 2019 Jan 15;50(1):181-194.e6. doi: 10.1016/j.immuni.2018.11.014. Epub 2019 Jan 8.
4
Challenges in unsupervised clustering of single-cell RNA-seq data.无监督单细胞 RNA-seq 数据聚类的挑战。
Nat Rev Genet. 2019 May;20(5):273-282. doi: 10.1038/s41576-018-0088-9.
5
Metabolic heterogeneity underlies reciprocal fates of T17 cell stemness and plasticity.代谢异质性是 T17 细胞干性和可塑性相互转化的基础。
Nature. 2019 Jan;565(7737):101-105. doi: 10.1038/s41586-018-0806-7. Epub 2018 Dec 19.
6
Single-cell mapping of lineage and identity in direct reprogramming.直接重编程中的谱系和身份的单细胞图谱。
Nature. 2018 Dec;564(7735):219-224. doi: 10.1038/s41586-018-0744-4. Epub 2018 Dec 5.
7
Understanding tumor ecosystems by single-cell sequencing: promises and limitations.单细胞测序解析肿瘤生态系统：机遇与挑战并存。
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Single-cell multiomics sequencing and analyses of human colorectal cancer.单细胞多组学测序和人类结直肠癌分析。
Science. 2018 Nov 30;362(6418):1060-1063. doi: 10.1126/science.aao3791.
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Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.谱系追踪揭示结直肠癌中 T 细胞的动态关系。
Nature. 2018 Dec;564(7735):268-272. doi: 10.1038/s41586-018-0694-x. Epub 2018 Oct 29.
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