Complex Systems and Signal Processing Group, Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom.
PLoS One. 2012;7(4):e35182. doi: 10.1371/journal.pone.0035182. Epub 2012 Apr 26.
As we begin to understand the signals that drive chemotaxis in vivo, it is becoming clear that there is a complex interplay of chemotactic factors, which changes over time as the inflammatory response evolves. New animal models such as transgenic lines of zebrafish, which are near transparent and where the neutrophils express a green fluorescent protein, have the potential to greatly increase our understanding of the chemotactic process under conditions of wounding and infection from video microscopy data. Measurement of the chemoattractants over space (and their evolution over time) is a key objective for understanding the signals driving neutrophil chemotaxis. However, it is not possible to measure and visualise the most important contributors to in vivo chemotaxis, and in fact the understanding of the main contributors at any particular time is incomplete. The key insight that we make in this investigation is that the neutrophils themselves are sensing the underlying field that is driving their action and we can use the observations of neutrophil movement to infer the hidden net chemoattractant field by use of a novel computational framework. We apply the methodology to multiple in vivo neutrophil recruitment data sets to demonstrate this new technique and find that the method provides consistent estimates of the chemoattractant field across the majority of experiments. The framework that we derive represents an important new methodology for cell biologists investigating the signalling processes driving cell chemotaxis, which we label the neutrophils eye-view of the chemoattractant field.
当我们开始理解体内趋化作用的信号时，很明显，趋化因子之间存在着复杂的相互作用，随着炎症反应的发展，这种相互作用会随时间而变化。新的动物模型，如具有近透明性且中性粒细胞表达绿色荧光蛋白的转基因斑马鱼系，有可能通过视频显微镜数据极大地增加我们对创伤和感染条件下趋化过程的理解。测量趋化因子在空间中的分布（及其随时间的演变）是理解驱动中性粒细胞趋化作用的信号的关键目标。然而，不可能测量和可视化体内趋化作用的最重要贡献者，实际上，在任何特定时间对主要贡献者的理解都是不完整的。我们在这项研究中得出的关键见解是，中性粒细胞本身正在感知驱动其运动的基础场，我们可以使用中性粒细胞运动的观察结果来推断隐藏的净趋化场，这是通过一种新的计算框架来实现的。我们将该方法应用于多个体内中性粒细胞募集数据集，以证明这种新技术，并发现该方法在大多数实验中都提供了趋化场的一致估计。我们得出的框架代表了细胞生物学家研究驱动细胞趋化作用的信号过程的一种重要的新方法，我们将其标记为趋化因子场的中性粒细胞视野。
