QuimP software for quantifying cellular morphodynamics

在过去的二十年里，活细胞显微镜在可视化活细胞内的动态过程方面取得了巨大的进步。要了解特定的细胞功能是如何正常调节或受疾病影响的，需要以定量的方式绘制细胞动力学图。这是一项艰巨的任务，因为细胞是高度可变形的，可以采用复杂的形状。因此，我们不能使用地标来绘制一个单元格内随时间变化的相应区域，或者更困难的是，从多个单元格聚合数据。到目前为止，还没有对这个问题的一般解决方案。我们已经率先绘制了细胞内肌动蛋白细胞骨架的调控动态图。肌动蛋白组装成紧邻细胞膜的致密细丝网络，推动细胞形状变化和迁移，例如当免疫细胞追逐细菌入侵者时。为此，我们开发了Quimp(膜蛋白定量成像)图像分析软件。Quimp的主要任务是1)自动跟踪图像时间序列中的细胞轮廓，2)匹配后续时间点上细胞边界上的相应区域，3)提取膜或细胞皮质中荧光标记的成分的空间分布。结果可以用许多不同的方式进行分析。可以很容易地计算出大量的全局参数，如细胞速度、运动的方向性、伸长率等。可以生成详细的时空地图，对荧光或膜曲率等测量数据进行统计分析，并询问它们之间的关系。一个主要特征是，图谱可以被处理以自动识别特定的事件，例如，驱动细胞运动的富含肌动蛋白的突起的形成。然后，这些事件可以作为里程碑，来自许多具有不同分子标记的细胞的多个事件可以组合在一起，以获得潜在调控动态的详细图景。Quimp已经被我们、英国和世界各地的组织用来研究细胞运动性的不同方面。用户密切了解了它的发展。它促进了许多重要的发现，例如，在转移性乳腺癌细胞中，膜突起和回缩在空间和时间上都高度同步，这可以解释为什么这些细胞比非转移性细胞移动更有效。这些结果表明，例如使用Quimp来分析癌细胞的侵袭力是可能的。由于其起源，Quimp目前被认为是细胞运动性研究社区的一种高度专业化的工具。然而，Quimp能够绘制细胞膜和细胞皮质的时空细胞动力学图，这使得Quimp成为研究一系列其他问题的明显选择。这些反应尤其包括细胞对外界刺激的反应，这些反应是通过细胞膜上的受体传递的。由膜受体激活触发的分子信号机制在很大程度上与膜密切相关，因此很容易通过quimp获得。在最近的一个例子中，Quimp被用来研究化学信号与膜受体的结合如何导致它们随后的内化，这对防止过长的细胞刺激很重要。我们在这里建议增强Quimp的可用性，使更广泛的用户群体能够使用它。这需要改变用户界面和改进文档，更好地处理大规模图像数据，并将受益于我们在其他领域的最新发展，这些领域涉及细胞检测和3D细胞表面重建。

Over the last twenty years live cell microscopy has made enormous progress in visualising dynamic processes inside living cells. To learn how specific cellular functions are normally regulated or affected by disease requires mapping cellular dynamics in a quantitative manner. This is a difficult task, because cells are highly deformable and can adopt complex shapes. Therefore we cannot use landmarks to map corresponding regions within one cell over time, or, what is even more difficult, aggregate data from multiple cells. To date no general solutions to this problem exist. We have pioneered mapping the regulatory dynamics of the actin cytoskeleton in cells. Assembly of actin into dense networks of filaments adjacent to the cell membrane drives cellular shape changes and migration, as needed for example when immune cells chase bacterial intruders. To this end we have developed QuimP (Quantitative Imaging of Membrane Proteins) image analysis software. The main tasks QuimP performs are 1) automated tracing of cell outlines in image time series, 2) matching corresponding regions on the cell boundary at subsequent time points, 3) extracting spatial distributions of fluorescently labelled constituents of the membrane or the cell cortex. The results can be analysed in many different ways. A large number of global parameters such as cell speed, directionality of movement, elongation and many more can be easily computed. Detailed spatio-temporal maps can be generated to perform statistical analyses of measurements such as fluorescence or membrane curvature, and ask how they are related. A main feature is that maps can be processed to automatically identify particular events, for example the formation of actin rich protrusions driving cell motility. These events can then serve as landmarks and multiple events from many cells with different molecular labels can be combined to obtain a detailed picture of the underlying regulatory dynamics. QuimP has been used by us, and groups in the UK and worldwide to study different aspects of cell motility. Users have closely informed its development. It has contributed to a number of important discoveries, for example that in metastatic breast cancers cells membrane protrusions and retractions are highly synchronized both in space and in time, which can explain why these cells move more efficiently than non-metastatic cells. These results suggest the possibility to use QuimP for example to assay the invasiveness of cancer cells. Because of its origin QuimP is currently regarded as a highly specialised tool for the cell motility research community. However, its ability to map spatio-temporal cellular dynamics at the membrane and in the cell cortex, makes QuimP an obvious choice for studying a host of other problems. These include in particular cellular responses to external stimuli, which are transmitted through receptors in the cell membrane. The molecular signalling machinery that is triggered by the activation of membrane receptors is in large parts closely associated with the membrane and therefore easily accessible through QuimP. In a recent example QuimP has been used to study how binding of chemical signals to membrane receptors results in their subsequent internalisation, important to prevent overly prolonged cell stimulation.We here propose to enhance the usability of QuimP and make it accessible to a broader user group. This requires changes to the user interface and improving documentation, better handling of large-scale image data, and will benefit from integration of our most recent developments in other areas, which concern cell detection and 3D cell surface reconstruction.