Exocytosis is a fundamental biological process where the intracellular vesicle eventually fuses to the plasma membrane, releasing its content into the extracellular space. To report exocytosis, we take advantage of pH-sensitive probes which are quenched in acidic environment and fluorescently bursts when exposed to a neutral pH environment (e.g. extracellular space).<\/p>\n\n\n\n
We developed ExoJ, a computer-vision assisted tool implemented as ImageJ\/Fiji plugin to detect and record exocytic features on single-cell basis. Built-in options allow end-users to immediately review each detected event and further the entire population within one cell.<\/p>\n\n\n\n
We define a bona fide exocytosis event as a round-shaped object displaying a sudden burst of fluorescence followed by a decayed signal. Additional details are translated into a set of thresholding parameters to fully account of end-user experimental condition and type of vesicle.<\/p>\n\n\n\n
Installation<\/strong><\/p>\n\n\n\n The plugin ExoJ was successfully tested on computers (MAC OS Catalina and newer releases, Windows) with ImageJ2\/Fiji 1.53s or newer versions and Java 8 installed.<\/p>\n\n\n\n It requires the prior installation of the plugin Bio-Formats<\/a> (Linkert et al.<\/em>, J. Cell Biol.<\/em> 2010) which handles multiple image formats.<\/p>\n\n\n\n Download and copy the .jar file in ImageJ2\/Fiji plugin folder. Restart ImageJ2\/Fiji, and the plugin ExoJ should appear in your plugin list under Plugin\/Projet-ExoJ\/ExoJ.<\/p>\n\n\n\n Recommendation<\/strong><\/p>\n\n\n\n The plugin was designed to automatically detect and record exocytosis from fluorescent time series. It can\u2019t handle 3D time series. We strongly advise to avoid time series displaying saturated pixels.<\/p>\n\n\n\n In case of lateral drift during live cell imaging, we recommend performing registration with available online plugins.<\/p>\n\n\n\n Procedure<\/strong><\/p>\n\n\n\n The workflow comprises three main steps: (1) Detecting the bright spots seen as vesicles, (2) building 1D time-lapse trajectories and (3) identifying candidate exocytic events according to one\u2019s definition. At any points during the procedure, users can save and load detection settings (.dat file).<\/p>\n\n\n\n A pop-up window lists opened files in ImageJ2\/Fiji. For newly-imported files, press Refresh to update the list.<\/p>\n\n\n\n Upon file selection (Open), the plugin reads the metadata to fill the required information in the prompt-up window. The spot detection algorithm is based on Olivo-Marin\u2019s work (Olivo-Marin, Pattern Recognition<\/em> 2002). Users are asked to set the range of wavelet scales, and the Median Absolute Deviation (*<\/sup>MAD) threshold value on wavelet coefficients \u03c3wavelet<\/sub>. The calculation is done on individual frames, and the result can be assessed by pressing Preview. Upon activation, the resulting lowpass image is generated for further assessment.<\/p>\n\n\n\n We have implemented two additional options: (1) if the wavelet detection button is deactivated, the spot detection relies on the Maximum Finder<\/a> tool implemented on ImageJ\/Fiji, (2) Live cell imaging inherently faces photobleaching effect. To account for this, a correction can be performed upon clicking on the dedicated box. The correction is immediately applied and can be reversed (v1.09).<\/p>\n\n\n\n When set, users can press Run to move to the next step.<\/p>\n\n\n\n The next step consists in building 1D time lapse trajectories using previously detected spots. The spatial range a well as the gap tolerance are the two required user-inputs in order to connect spots.<\/p>\n\n\n\n Spatial searching range<\/strong>: spots within the spatial range will be linked.<\/p>\n\n\n\n Temporal searching depth (Gap closing)<\/strong>: This corresponds to the number of gaps allowed in the spot trajectories. Note that by default a gap tolerance of 1 frame forces the algorithm to look for spots from one frame to the other.<\/p>\n\n\n\n Minimal event size<\/strong>: An additional threshold on the minimal number of spots within each trajectory is implemented.<\/p>\n\n\n\n Tracking results can be previewed by pressing Preview, and the list of reconstructed trajectories can be displayed if Show Tracking List is activated.<\/p>\n\n\n\n <\/p>\n\n\n\n To detect exocytosis events, the last step is made fully customizable by users. These parameters\/thresholds can be de-\/activated to refine the identification of candidate exocytic events:<\/p>\n\n\n\n Min. points for fitting procedure<\/strong>: this corresponds to the number of points used to derive the signal duration.<\/p>\n\n\n\n Extended frames (pre\/post peak)<\/strong>: additional frames are added before (resp. after) for each detected trajectory. Increasing the number of pre-peak frames influences the calculation of MAD of dF and the background intensity F0<\/sub>. Beware that candidate events within the first frames of the time series might be excluded. Increasing the number of post-peak frames refines the estimation of mean lifetime (decay). This could, however, impair the detection of successive event at the same location.<\/p>\n\n\n\n Detection threshold<\/strong>: this relates to the MAD of **<\/sup>dF . Only candidate events above the detection threshold value will be considered.<\/p>\n\n\n\n Upper and Lowed decay limit<\/strong>: upper and lower boundaries of the exocytosis signal mean lifetime duration derived from the intensity profile by fitting an exponential decay function<\/p>\n\n\n\n Upper and Lower estimated radius limit<\/strong>: upper and lower boundaries of the apparent size derived from the intensity profile (at the onset of the event) by fitting a 2D gaussian function. The Full Width Half Maximum is used as a proxy for the event apparent size.<\/p>\n\n\n\n Max displacement<\/strong>: Maximal displacement allowed throughout the spot trajectory.<\/p>\n\n\n\n Min<\/strong>. R<\/strong>2<\/sup><\/strong>: Minimal goodness-of-fit imposed while deriving the signal duration (decay) and the apparent size (radius)<\/p>\n\n\n\n * MAD: Median Absolute Deviation is a measure of dispersion based on the calculation of the median of the absolute deviation from the data\u2019s median.<\/p>\n\n\n\n ** dF: 1st<\/sup> order differential peak intensity profile<\/p>\n\n\n\n References<\/strong><\/p>\n\n\n\n Olivo-Marin, J.-C. (2002). Extraction of spots in biological images using multiscale products. Pattern Recognition<\/em>, 35<\/em>(9), 1989\u20131996. https:\/\/doi.org\/10.1016\/S0031-3203(01)00127-3<\/a><\/p>\n\n\n\n Linkert, M., Rueden, C. T., Allan, C., Burel, J.-M., Moore, W., Patterson, A., Loranger, B., Moore, J., Neves, C., MacDonald, D., Tarkowska, A., Sticco, C., Hill, E., Rossner, M., Eliceiri, K. W., & Swedlow, J. R. (2010). Metadata matters: access to image data in the real world. Journal of Cell Biology<\/em>, 189<\/em>(5), 777\u2013782. https:\/\/doi.org\/10.1083\/jcb.201004104<\/a><\/p>\n\n\n\n\n
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