Neu Karlynn E, Tang Qingming, Wilson Patrick C, Khan Aly A
Committee on Immunology, University of Chicago, Chicago, IL 60637, USA.
Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.
Trends Immunol. 2017 Feb;38(2):140-149. doi: 10.1016/j.it.2016.12.001. Epub 2017 Jan 13.
Single-cell genomics offers powerful tools for studying immune cells, which make it possible to observe rare and intermediate cell states that cannot be resolved at the population level. Advances in computer science and single-cell sequencing technology have created a data-driven revolution in immunology. The challenge for immunologists is to harness computing and turn an avalanche of quantitative data into meaningful discovery of immunological principles, predictive models, and strategies for therapeutics. Here, we review the current literature on computational analysis of single-cell RNA-sequencing data and discuss underlying assumptions, methods, and applications in immunology, and highlight important directions for future research.
单细胞基因组学为研究免疫细胞提供了强大的工具,这使得观察在群体水平上无法分辨的罕见和中间细胞状态成为可能。计算机科学和单细胞测序技术的进步在免疫学领域引发了一场数据驱动的革命。免疫学家面临的挑战是利用计算技术,将大量的定量数据转化为对免疫学原理、预测模型和治疗策略的有意义的发现。在这里,我们综述了当前关于单细胞RNA测序数据计算分析的文献,并讨论了其在免疫学中的潜在假设、方法和应用,并强调了未来研究的重要方向。