Quantifying cellular interaction dynamics in 3D fluorescence microscopy data

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 S1P1-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.