Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT, UK; London Centre for Nanotechnology, University College London, 17-19 Gordon Street, London, WC1H 0AH, UK.
London Centre for Nanotechnology, University College London, 17-19 Gordon Street, London, WC1H 0AH, UK; Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK.
Semin Cancer Biol. 2020 Jun;63:60-68. doi: 10.1016/j.semcancer.2019.05.007. Epub 2019 May 18.
Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either by biochemical signaling or by mechanical forces within the tissue. In both cases, competition has generally been characterized based on the elimination of loser cells at the population level, but significantly less attention has been focused on determining how single-cell dynamics and interactions regulate population-wide changes. In this review, we describe quantitative strategies and outline the outstanding challenges in understanding the single cell rules governing tissue-scale competition dynamics. We propose quantitative metrics to characterize single cell behaviors in competition and use them to distinguish the types and outcomes of competition. We describe how such metrics can be measured experimentally using a novel combination of high-throughput imaging and machine learning algorithms. We outline the experimental challenges to quantify cell fate dynamics with high-statistical precision, and describe the utility of computational modeling in testing hypotheses not easily accessible in experiments. In particular, cell-based modeling approaches that combine mechanical interaction of cells with decision-making rules for cell fate choices provide a powerful framework to understand and reverse-engineer the diverse rules of cell competition.
细胞竞争是组织中的一种质量控制机制，导致适应能力较低的细胞被消除。在过去的十年中，细胞竞争现象在许多生理和病理情况下都得到了确认，其驱动力要么是生化信号，要么是组织内的机械力。在这两种情况下，竞争通常是基于群体水平上淘汰失败者细胞来进行描述的，但对于如何确定单细胞动力学和相互作用如何调节整体种群变化，关注明显较少。在这篇综述中，我们描述了定量策略，并概述了理解控制组织规模竞争动力学的单细胞规则的突出挑战。我们提出了定量指标来描述竞争中的单细胞行为，并利用它们来区分竞争的类型和结果。我们描述了如何使用高通量成像和机器学习算法的新组合来从实验上测量这些指标。我们概述了量化具有高统计精度的细胞命运动力学的实验挑战，并描述了计算建模在测试实验中不易获得的假设方面的效用。特别是，将细胞间机械相互作用与细胞命运选择的决策规则相结合的基于细胞的建模方法，为理解和反向设计细胞竞争的多种规则提供了一个强大的框架。
