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).
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.
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.
Installation
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.
It requires the prior installation of the plugin Bio-Formats (Linkert et al., J. Cell Biol. 2010) which handles multiple image formats.
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.
Recommendation
The plugin was designed to automatically detect and record exocytosis from fluorescent time series. It can’t handle 3D time series. We strongly advise to avoid time series displaying saturated pixels.
In case of lateral drift during live cell imaging, we recommend performing registration with available online plugins.
Procedure
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’s definition. At any points during the procedure, users can save and load detection settings (.dat file).
A pop-up window lists opened files in ImageJ2/Fiji. For newly-imported files, press Refresh to update the list.
- Spot detection
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’s work (Olivo-Marin, Pattern Recognition 2002). Users are asked to set the range of wavelet scales, and the Median Absolute Deviation (*MAD) threshold value on wavelet coefficients σwavelet. 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.
We have implemented two additional options: (1) if the wavelet detection button is deactivated, the spot detection relies on the Maximum Finder 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).
When set, users can press Run to move to the next step.
- Spot tracking
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.
Spatial searching range: spots within the spatial range will be linked.
Temporal searching depth (Gap closing): 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.
Minimal event size: An additional threshold on the minimal number of spots within each trajectory is implemented.
Tracking results can be previewed by pressing Preview, and the list of reconstructed trajectories can be displayed if Show Tracking List is activated.
- Event Identification
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:
Min. points for fitting procedure: this corresponds to the number of points used to derive the signal duration.
Extended frames (pre/post peak): 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. 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.
Detection threshold: this relates to the MAD of **dF . Only candidate events above the detection threshold value will be considered.
Upper and Lowed decay limit: upper and lower boundaries of the exocytosis signal mean lifetime duration derived from the intensity profile by fitting an exponential decay function
Upper and Lower estimated radius limit: 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.
Max displacement: Maximal displacement allowed throughout the spot trajectory.
Min. R2: Minimal goodness-of-fit imposed while deriving the signal duration (decay) and the apparent size (radius)
* MAD: Median Absolute Deviation is a measure of dispersion based on the calculation of the median of the absolute deviation from the data’s median.
** dF: 1st order differential peak intensity profile
References
Olivo-Marin, J.-C. (2002). Extraction of spots in biological images using multiscale products. Pattern Recognition, 35(9), 1989–1996. https://doi.org/10.1016/S0031-3203(01)00127-3
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, 189(5), 777–782. https://doi.org/10.1083/jcb.201004104
Copyright (C) 2022 – LIU Junjun, BUN Philippe
ExoJ is an Image/Fiji plugin to automate the detection and the analysis of exocytosis in fluorescent time series.
This program is a free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.
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