QuimP software for quantifying cellular morphodynamics

用于量化细胞形态动力学的 QuimP 软件

• 批准号: BB/M01150X/1
• 负责人: Till Bretschneider
• 金额: $ 42.56万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM01150X%2F1
• 关键词: QuimP; software; quantifying; cellular; morphodynamics

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.

在过去的二十年里，活细胞显微镜在活细胞内部动态过程的可视化方面取得了巨大进步。要了解特定细胞功能通常如何受疾病调节或影响，需要以定量方式绘制细胞动态图。这是一项艰巨的任务，因为细胞具有高度可变形性并且可以呈现复杂的形状。因此，我们不能使用地标来绘制一个细胞内随时间变化的相应区域，或者更困难的是，聚合来自多个细胞的数据。迄今为止，尚不存在针对该问题的通用解决方案。我们率先绘制了细胞中肌动蛋白细胞骨架的调控动力学图谱。将肌动蛋白组装成与细胞膜相邻的致密丝状网络，可驱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所需要的。为此我们开发了QuimP（膜蛋白定量成像）图像分析软件。 QuimP 执行的主要任务是 1) 在图像时间序列中自动追踪细胞轮廓，2) 在后续时间点匹配细胞边界上的相应区域，3) 提取膜或细胞皮层荧光标记成分的空间分布。可以通过多种不同的方式分析结果。可以轻松计算大量全局参数，例如细胞速度、运动方向、伸长率等。可以生成详细的时空图来对荧光或膜曲率等测量结果进行统计分析，并询问它们之间的关系。一个主要特征是可以处理图谱以自动识别特定事件，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，并且可以将来自具有不同分子标记的许多细胞的多个事件组合起来，以获得潜在调控动态的详细信息。 QuimP 已被我们以及英国和世界各地的团体用来研究细胞运动的不同方面。用户密切了解其发展情况。它促成了许多重要的发现，例如，在转移性乳腺癌中，细胞膜的突出和缩回在空间和时间上高度同步，这可以解释为什么这些细胞比非转移性细胞移动更有效。这些结果表明可以使用 QuimP 来测定癌细胞的侵袭性。由于其起源，QuimP 目前被视为细胞运动研究界的高度专业化工具。然而，QuimP 能够绘制膜和细胞皮层的时空细胞动态图，使其成为研究许多其他问题的明显选择。这些特别包括细胞对外部刺激的反应，这些反应通过细胞膜上的受体传递。由膜受体激活触发的分子信号传导机制在很大程度上与膜密切相关，因此可以通过 QuimP 轻松访问。在最近的一个例子中，QuimP 已被用来研究化学信号与膜受体的结合如何导致其随后的内化，这对于防止过度长时间的细胞刺激很重要。我们在此建议增强 QuimP 的可用性并使其可供更广泛的用户群体使用。这需要改变用户界面、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（涉及细胞检测和 3D 细胞表面重建）的最新进展的集成。

项目成果

Generative Adversarial Networks for Augmenting Training Data of Microscopic Cell Images

• DOI: 10.3389/fcomp.2019.00010
• 发表时间: 2019-11-26
• 期刊: FRONTIERS IN COMPUTER SCIENCE
• 影响因子: 2.6
• 作者: Baniukiewicz, Piotr;Lutton, E. Josiah;Bretschneider, Till
• 通讯作者: Bretschneider, Till

Parameter Estimation in an SPDE Model for Cell Repolarization

细胞复极化 SPDE 模型中的参数估计

• DOI: 10.1137/20m1373347
• 发表时间: 2022
• 期刊: SIAM/ASA Journal on Uncertainty Quantification
• 作者: Altmeyer R

Strategies for structuring interdisciplinary education in Systems Biology: an European perspective.

• DOI: 10.1038/npjsba.2016.11
• 发表时间: 2016
• 期刊: NPJ systems biology and applications
• 影响因子: 4
• 作者: Cvijovic M;Höfer T;Aćimović J;Alberghina L;Almaas E;Besozzi D;Blomberg A;Bretschneider T;Cascante M;Collin O;de Atauri P;Depner C;Dickinson R;Dobrzynski M;Fleck C;Garcia-Ojalvo J;Gonze D;Hahn J;Hess HM;Hollmann S;Krantz M;Kummer U;Lundh T;Martial G;Dos Santos VM;Mauer-Oberthür A;Regierer B;Skene B;Stalidzans E;Stelling J;Teusink B;Workman CT;Hohmann S
• 通讯作者: Hohmann S

Image based validation of dynamical models for cell reorientation.

• DOI: 10.1002/cyto.a.22600
• 发表时间: 2015-06
• 期刊: Cytometry. Part A : the journal of the International Society for Analytical Cytology
• 作者: Lockley R;Ladds G;Bretschneider T
• 通讯作者: Bretschneider T

Image based modeling of bleb site selection.

基于图像的BLEB站点选择的建模。

• DOI: 10.1038/s41598-017-06875-9
• 发表时间: 2017-07-27
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Collier S;Paschke P;Kay RR;Bretschneider T
• 通讯作者: Bretschneider T