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Imagej colocalization analysis5/28/2023 Accordingly, analyzing the data can represent a major bottleneck in the research pipeline. Advances in multi-fluorescence imaging, including automated whole section microscopy and three-dimensional imaging, have generated increasingly more complex data sets. With the exception of axonal tracing, the same techniques are also used to study molecular diversity in many non-neuronal tissues. Tissue samples can be processed with combinations of these techniques and subsequently imaged, allowing researchers to directly relate the different labeled components. RNA expression can also be visualized by fluorescence in-situ hybridization 11. Multi-fluorescence imaging involves the differential fluorescent labeling of neurons using a variety of techniques, including retrograde axonal tracing, immunolabeling, transgene-driven expression of fluorescent reporter proteins, and fluorescently tagged endogenous proteins 8, 9, 10. Multi-fluorescence imaging is used extensively to study the correlation of molecular, structural and functional features in neurons 3, 4, 5, 6, 7. However, the full molecular diversity of neuron types is far from completely described, even in intensively studied model organisms. Much progress has been made in relating distinct patterns of gene and protein expression to neuronal structure and function. The central nervous system (CNS) is characterized by the exceptional molecular, morphological and physiological diversity of its constituent neurons 1, 2. Here we demonstrate their utility in immunohistochemical analyses of the developing central nervous system, which is characterized by complexity in the number and distribution of cell types, and by high cell packing densities, which can both create challenging situations for OBCA. 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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