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Representative Results






Automated Detection and Analysis of Exocytosis

Published: September 11th, 2021



1UNC Neuroscience Center, Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill


Important: There has been an erratum issued for this article. Read more …

We developed automated computer vision software to detect exocytic events marked by pH-sensitive fluorescent probes. Here, we demonstrate the use of a graphical user interface and RStudio to detect fusion events, analyze and display spatiotemporal parameters of fusion, and classify events into distinct fusion modes.

Timelapse TIRF microscopy of pH-sensitive GFP (pHluorin) attached to vesicle SNARE proteins is an effective method to visualize single vesicle exocytic events in cell culture. To perform an unbiased, efficient identification and analysis of such events, a computer-vision based approach was developed and implemented in MATLAB. The analysis pipeline consists of a cell segmentation and exocytic-event identification algorithm. The computer-vision approach includes tools for investigating multiple parameters of single events, including the half-life of fluorescence decay and peak ΔF/F, as well as whole-cell analysis of the frequency of exocytosis. These and other parameters of fusion are used in a classification approach to distinguish distinct fusion modes. Here a newly built GUI performs the analysis pipeline from start to finish. Further adaptation of Ripley's K function in R Studio is used to distinguish between clustered, dispersed, or random occurrence of fusion events in both space and time.

VAMP-pHluorin constructs or transferrin receptor (TfR)-pHuji constructs are excellent markers of exocytic events, as these pH-sensitive fluorophores are quenched within the acid vesicle lumen and fluoresce immediately upon fusion pore opening between the vesicle and plasma membrane1. Following fusion pore opening, fluorescence decays exponentially, with some heterogeneity that reveals information about the fusion event. Here, a graphical user interface (GUI) application is described that automatically detects and analyzes exocytic events. This application allows the user to automatically detect exocytic events revealed by pH sensitive markers

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NOTE: The original Exocytosis Analysis GUI was written and compiled in Matlab version 9.10 (2021a). New versions of MATLAB have required updates to the GUI, which are available for download from our website:

1. Choose datasets and directory

  1. To select datasets for analysis, click the Find Datasets button (Figure 2A, red box 1) to navigate to the folder where data are deposited (e.......

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Here the GUI (Figure 2A) was utilized to analyze exocytic events from three VAMP2-pHluorin expressing neurons at 3 DIV using TIRF (total internal reflection fluorescence) microscopy. E15.5 cortical neurons were isolated, followed by transfection with VAMP2-pHluorin and plating using the protocols as outlined in Winkle et al., 2016 and Viesselmann et al., 201111,12. The methodology of imaging parameters is as outlined in Urbina et al........

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When using the exocytic detection and analysis software, please consider that the program only accepts lossless compression .tif files as input. The .tif image files may be 8-bit, 16-bit, or 32-bit grayscale (single channel) images. Other image formats must be converted into one of these types before input. For reference, examples used here are 16-bit grayscale images.

Inherent in the automated detection process, the timelapse image sets are processed for the automated background subtraction a.......

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We thank Dustin Revell and Reginald Edwards for testing code and the GUI. Funding was provided by the National Institutes of Health supported this research: including R01NS112326 (SLG), R35GM135160 (SLG), and F31NS103586 (FLU).


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Name Company Catalog Number Comments
MATLAB MathWorks
R R Core Team
Rstudio Rstudio, PBC

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Retraction: Automated Detection and Analysis of Exocytosis

The article Automated Detection and Analysis of Exocytosis has been retracted by the journal due to concerns regarding the validity of the presented data.

This article has been published

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