ImmunoTechnology Section, Vaccine Research Center, NIAID, National Institutes of Health, Bethesda, Maryland, USA.
Koch Institute for Integrative Cancer Research at Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Nat Immunol. 2014 Feb;15(2):128-35. doi: 10.1038/ni.2796.
The complex heterogeneity of cells, and their interconnectedness with each other, are major challenges to identifying clinically relevant measurements that reflect the state and capability of the immune system. Highly multiplexed, single-cell technologies may be critical for identifying correlates of disease or immunological interventions as well as for elucidating the underlying mechanisms of immunity. Here we review limitations of bulk measurements and explore advances in single-cell technologies that overcome these problems by expanding the depth and breadth of functional and phenotypic analysis in space and time. The geometric increases in complexity of data make formidable hurdles for exploring, analyzing and presenting results. We summarize recent approaches to making such computations tractable and discuss challenges for integrating heterogeneous data obtained using these single-cell technologies.
细胞的复杂异质性及其相互连接性是识别反映免疫系统状态和功能的临床相关测量指标的主要挑战。高度多重化的单细胞技术对于识别疾病的相关性或免疫干预措施以及阐明免疫的潜在机制可能至关重要。在这里，我们回顾了批量测量的局限性，并探讨了通过在空间和时间上扩展功能和表型分析的深度和广度来克服这些问题的单细胞技术的进展。数据复杂性的几何增长给探索、分析和呈现结果带来了巨大的障碍。我们总结了最近用于使这些计算变得可行的方法，并讨论了整合使用这些单细胞技术获得的异质数据的挑战。
