The tools are well suited for experiments involving complex and large image data sets, and can be used in combination or as individual components, allowing flexible, efficient and accurate OBCA. The Excel macro and the MATLAB script enable data organization and 3D visualization of object data across image series. One ImageJ plugin enables customizable processing of multichannel 3D images for enhanced visualization of features relevant to OBCA, and another enables semi-automatic colocalization quantification. Here, we present a novel set of tools for OBCA using a semi-automatic approach, consisting of two ImageJ plugins, a Microsoft Excel macro, and a MATLAB script. Object-based colocalization analysis (OBCA) tools can employ automatic approaches, which are sensitive to errors in cell segmentation, or manual approaches, which can be impractical and tedious. This is particularly important when investigating the biology of tissues with diverse cell types. Differential fluorescence labeling and multi-fluorescence imaging followed by colocalization analysis is commonly used to investigate cellular heterogeneity in situ.
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