![]() This is like a rehearsal before recording your actions. Please first try to reproduce the workflow using mouse and the graphical user interface (GUI). We recommend you NOT to launch the command recorder from the beginning. In the following we record these steps as macro commands using the Command Recorder ( ). Subtract the eroded from the dilated (◘ Fig. Remove other Nuclei: At the right-bottom corner of the image, a small part of different nucleus is present. Nucleus segmentation: Binarize the image by intensity thresholding (◘ Fig. In principle, similar post-processing strategy is also applicable to 3D volumes by using 3D morphology filters.īlur the image to attenuate noise (◘ Fig. This chapter should be a good guide not only limited to study the intensity changes occurring at the nuclear envelope, but also in general for segmenting the edge (perimeter) of biological compartments such as the edge of organelle, plasma membrane and tissue boundaries. Especially for the analysis of time-lapse sequence, programming is highly recommended to iterate the measurement for each time point. We just need to loop the same process for each time point. When we are successful in determining the area of nuclear envelope, the measurement of intensity in that region over time is rather trivial. DAPI) is a popular image analysis technique used in many biological research projects, but to measure more specific location-in our case nuclear envelope-we need to add several more steps to refine the region-of-interest. Segmentation of nucleus using its marker (e.g. Second, we use that segmented nuclear rim as a mask to measure the intensity changes over time in the second channel. The workflow involves two steps: First, we segment the rim of nucleus-nuclear membrane-using the first channel (histone). We construct a workflow that measures this accumulation process by writing an ImageJ macro. The green signal broadly distributed in the cytoplasmic area at time point 1 becomes accumulated at the periphery of nuclei (red) at time point 15-between these image frames, the signal changed its localization from ER to the nuclear envelope. Footnote 2 Compare these images carefully. 2.1 are from the first and the last time points of a time-lapse sequence. Those codes and image data used in his study, which might be interesting for you after going through this chapter, are accessible through the supplementary data section in the journal website. His work, with more advanced bioimage analysis workflows for analyzing the protein targeting dynamics, is published in The Journal of Cell Biology (Boni et al. The data was acquired by Andreas Boni (Jan Ellenberg lab, EMBL Heidelberg) and have been used in many training workshops in EMBL as a great example for learning bioimage analysis. We analyze a two-channel time-lapse image stack, a sequence of the process of the protein re-localization that causes increases in the protein density at the nuclear envelope. The protein changes its location from the cytoplasmic area (Endoplasmic Reticulum, ER) to the nuclear envelope (Boni et al. ![]() Here, we pick up an example analysis of the Lamin B receptor protein density targeting inner nuclear membrane. In some biological research projects, we encounter problems that should be studied by measuring fluorescence intensity at the boundary between two different compartments. ![]()
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