Quantifying cellular interaction dynamics in 3D fluorescence microscopy data
Quantifying cellular interaction dynamics in 3D fluorescence microscopy data
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DOI：
10.1038/nprot.2009.129
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发表时间：
2009-01-01
期刊：
影响因子：
14.8
通讯作者：
Meier-Schellersheim, Martin
中科院分区：
文献类型：
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作者：
Klauschen, Frederick;Ishii, Masaru;Meier-Schellersheim, Martin
用于分析具有高空间和时间分辨率的生物过程的先进荧光成像技术所提供的丰富信息要求高通量图像分析方法。在这里，我们描述了一种全自动的方法来分析细胞的相互作用行为在3D荧光显微镜图像。作为应用实例，我们分析了骨-破骨细胞相互作用中药物诱导的和S1 P（1）敲除相关的变化。此外，我们将我们的方法应用于图像显示树突状细胞与淋巴结内成纤维网状细胞网络的空间关联，以及关于T-B淋巴细胞突触形成的显微镜数据。产生关于决定细胞相互作用行为的分子机制的重要信息的这种分析将很难用依赖于手动/半自动化分析的方法来执行。该协议集成了自适应阈值分割，目标检测，自适应颜色通道合并，邻域分析，并允许快速，标准化，定量分析和比较的相关功能在大数据集。
The wealth of information available from advanced fluorescence imaging techniques used to analyze biological processes with high spatial and temporal resolution calls for high-throughput image analysis methods. Here, we describe a fully automated approach to analyzing cellular interaction behavior in 3D fluorescence microscopy images. As example application, we present the analysis of drug-induced and S1P(1)-knockout-related changes in bone-osteoclast interactions. Moreover, we apply our approach to images showing the spatial association of dendritic cells with the fibroblastic reticular cell network within lymph nodes and to microscopy data regarding T-B lymphocyte synapse formation. Such analyses that yield important information about the molecular mechanisms determining cellular interaction behavior would be very difficult to perform with approaches that rely on manual/semi-automated analyses. This protocol integrates adaptive threshold segmentation, object detection, adaptive color channel merging, and neighborhood analysis and permits rapid, standardized, quantitative analysis and comparison of the relevant features in large data sets.