Automated Detection and Analysis of Exocytosis

Summary

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.

Abstract

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.

Introduction

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 markers² and generate features from each event that can be used for classification purposes³ (Figure 1A). In addition, analysis of exocytic event clustering using Ripley's K function is described.

The automated classification of exocytic events into different exocytic modes was recently reported³. Two modes of exocytosis, full-vesicle fusion (FVF) and kiss-and-run fusion (KNR) exocytosis have previously been described^4,5,6,7. During FVF, the fusion pore dilates, and the vesicle is incorporated into the plasma membrane. During KNR, the fusion pore transiently opens and then reseals^4,5,8,9,10. Four modes of exocytosis were identified in developing neurons, two related to FVF and two related to KNR. This work demonstrates that both FVF and KNR can be further subdivided into fusion events that proceed immediately to fluorescence decay (FVFi and KNRi) after fusion pore opening or exocytic events that exhibit a delay after fusion pore opening before fluorescence decay begins (FVFd and KNRd) (Figure 1B). The classifier identifies the mode of exocytosis for each fusion event. Here this analysis has been incorporated into a GUI that can be installed in MATLAB in Windows and Mac based operating systems. All analysis files can be found at https://drive.google.com/drive/folders/1VCiO-thMEd4jz-tYEL8I4N1Rf_zjnOgB?usp=sharing or
https://github.com/GuptonLab.

Protocol

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: https://guptonlab.web.unc.edu/code/

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.g., RawData folder). Datafiles will automatically populate the Data Files as a list. There can be more than one dataset in the folder.
2. Click the Choose Directory button and select the directory (e.g., Test) where analyzed files will be deposited (Figure 2A, red box 2) . A set of folders and finished analysis files as well as intermediate temporary images will be created in this directory when the Analysis button is pushed. Errors will be produced if a directory is not chosen.

2. Set the pixel size and framerate

1. Fill in the appropriate frame rate and pixel size of the images in the appropriate "Framerate" or "pixel size" box (Figure 2A, green box). If no values are provided (they are set to the default "0"), then the program will search the metadata associated with the file for the framerate and pixel size. If these values cannot be found, the program will default to per-pixel for measurement and per-frame for time points.
   ​NOTE: In the example provided, the framerate is 100, and the pixel size is 0.08.

3. Choose or make masks

1. Use the Mask Maker button to automatically create cell masks for the data in the file dataset list (Figure 2A, blue box). Upon using the Mask Maker button, a new folder in the chosen directory will be created called MaskFiles. The "Run indicator" will turn yellow while running and return to green when completed.
   NOTE: A mask for each file in the Data Files list will be created from the first 10 frames of the image file (or from all of the frames if less than 10 are in the image file) and deposited in the MaskFiles folder using the proper naming scheme (described below).
2. Ensure that the mask files automatically populate the Mask Files list. The user may proceed directly to the analysis.
   NOTE: Always visually check mask files and confirm they capture the entire region of interest. The first frame of the data file and the mask file are displayed on the UI when selected. (Figure 2B). The Mask Maker may produce errors in the case of low signal-to-noise, so validating that mask files are appropriate is critical for quality control.
3. As an alternative to using Mask Maker, if the signal to noise of images is insufficient, create masks manually in ImageJ.
   1. First, open the raw image file in ImageJ (Figure 3A).
   2. Click the Polygon Selection button and click to draw a mask around the cell. Once finished, double-click on the last point to complete the polygon.
   3. Once finished, navigate to Edit | Selection | Create Mask (Figure 3B). A new inverted mask will be created based on the polygon drawing. Save masks in a designated MaskFiles folder in the chosen directory. The mask file naming scheme must match the corresponding individual data files followed by "_mask_file". For example, if a data file is named "VAMP2_488_WT_1.tif", the corresponding mask file must be named "VAMP2_488_WT_1_mask_file.tif".
   4. Use the Find Maskfiles button to navigate to the chosen folder of deposited custom mask files. The masks will populate the Mask Files as a list.
      ​NOTE: It is important to have a mask for every data file before running the analysis.

4. Analysis and feature extraction

1. After the directory is chosen and the Data Files list and Mask files list are populated, click the Analysis button (Figure 4A). If the classification of exocytic events is required, move to step 5.
   NOTE: The Analysis button click will perform a series of automated tasks to analyze the data. It will create individual folders in the chosen directory to deposit analyzed data. While running, the "run indicator" will change from green to yellow (Figure 4A, red box). After the analysis is finished, this will change back to green.
2. Find a DataFiles folder (Figure 4B) with the complete set of analysis files (as well as feature extraction files, to be used in classification later) named according to each datafile (Figure 4C).
   NOTE: A description of these analysis files is included below in the Representative Results section.

5. Classification of exocytic events

1. To perform simultaneous classification of exocytic events with automated detection, check the Classification checkbox before clicking on the Analysis button. For each exocytic event, assign a probability score between 0-1 for each class. An exocytic event is considered classified as one of the four classes if the probability score for that class is > 0.5.
   ​NOTE: Once the analysis is complete, a new data file folder will appear within the chosen directory. The folder will contain analysis files corresponding to each image file.

6. Spatiotemporal analysis of exocytosis using Ripley's K values

1. Create a separate "neurite" and "soma" mask file. First, segment the soma from the neurite. There is no unbiased method for segmenting the soma from the neurites, so the user should be blinded to the conditioning experiment and use the best judgment; an ellipsoid with no obvious neurite extensions is suggested.
2. Open ImageJ/Fiji.
3. Drag-and-drop the mask file or use File | Open and then select the mask file.
4. Use the color-picker tool and click on any of the black pixels in the background of the mask to set the color to black.
5. Use the polygon-selection tool or freehand to draw an outline around the soma, separating it from the neurites. This requires manual decision-making.
6. Click Edit | Selection | Make Mask. A new image will open with the circled soma segmented from the rest of the image. This will create a soma mask file.
7. Without moving the drawn region, click on the header of the original mask file.
8. Click Edit | Fill to fill in the circled soma so that only the neurites remain the mask.
9. Once separate neurite and mask files are obtained, Save both the mask files.
10. Open "neurite_2D_network" in Matlab.
11. In MATLAB, navigate to directory with all analysis data.
12. Change the path "mask name" to the name of the neurite mask, i.e., "MaskFiles/VAMP2pHluorin_488_wt_4_mask_file_neurite.tif"
13. Change the "csv_file_name" to where fluorescent_traces.csv file is located, i.e., "MaskFiles/VAMP2pHluorin_488_wt_4_mask_file_neurite.csv".
14. Click Run to skeletonize the neurite mask file. This creates a skeletonized version of the neurite mask file and deposits it as a CSV file under the maskfiles folder.
15. Next, generate a CSV file for the soma. Open "CSV_mask_creator.m" in Matlab.
16. Put in the path for the "mask name" to the name of the soma mask, i.e., "MaskFiles/VAMP2phLuorin_488_wt_4_mask_file_soma.tif
17. Change the "writematrix" to csv filename to be created, i.e., "MaskFiles/VAMP2pHluorin_488_wt_4_mask_file_soma.csv"
18. Click Run. This creates the new VAMP2pHluorin_488_wt_4_mask_file_soma.csv file.
19. Repeat 6.14 for every mask file.

7. RStudio setup

1. Open Rstudio and open ripleys_k_analysis.R file.
2. Install the package "spatstat" in RStudio by going to Tools | Install Packages and typing in "spatstat" followed by clicking Install.
   NOTE: This only needs to be performed once per Rstudio Installation.
3. Run the library spatstat at the beginning of each session.
4. Pay attention to two main variables in this case: "neuron_mask" and "neuron_datapoints." Neuron Mask points to the set of mask files to run, i.e., soma mask files."
   NOTE: Run all soma mask files together, separately from Neurite mask files, and vice versa.
5. Read in the .csv of the mask files for each of the neurons to be analyzed.
6. Run multiple files at once by copying additional neuron_mask_n+1. This will allow to aggregate Ripley's analysis together, using the script described in section 8.
7. Now check the second variable, "neuron_datapoints".
8. Read in the."X_fluorescent_traces.csv" file generated by the analysis program (by features all extracted_R file) with the x,y,t positions of the exocytic events, as well as the neurite-specific file of the x,y positions for the 2D network. This goes in the "neuron_datapoints" position.
9. In RStudio Select Code | Run Region | Run all. This generates several plots, including the grouped Ripley's K values as well as density plots.
10. Save plots by going to Export | Save Image As | Select appropriate image format and directory, and input appropriate file name, then click Save.
    NOTE: The plotting function for the heatmaps is called in the script using "plot(density(soma_data,0.4))". The number "0.4" here represents how smoothed-out the density function should be. It can be changed to fit user data in a meaningful way, but if comparisons are to be performed between different heatmaps, the number must be the same between them.
11. Export or Save image from Rstudio. If a heatmap requires further editing, choose an appropriate file type (SVG or EPS).

8. Ripley's analysis

NOTE: The Ripleys_k_analysis.R file also automatically generated Ripley's k value plots. Running the entire script will automatically run the functions mentioned below, but it is included in detail if one wishes to run each portion of the script individually or make changes to the analysis.

1. First, run the envelope function for each cell. This function simulated complete spatial randomness (CSR) to test the Ripley's K value of the exocytic event point pattern against.
   Data_envelope_1 = envelope(soma_data_1, Kest, nsim = 19, savefuns = TRUE)
   Data_envelope_2 = envelope(soma_data_2, Kest, nsim = 19, savefuns = TRUE)
2. Next, pool these envelopes together and create one estimate of CSR for the group:
   Pool_csr = pool(Data_envelope_1, Data_envelop_2,...)
   Next, run the Ripley's K function for all data points.
   Data_ripleys_k_1 = Kest(soma_data_1, ratio = TRUE)
   Data_ripleys_k_2 = Kest(soma_data_2, ratio = TRUE)
3. Once complete, pool the Ripley's K values together and bootstrap their confidence intervals with the following command:
   Data pool = pool(Data_ripleys_k_1, Data_ripleys_k_2,...)
4. Bootstrap with the following command:
   Final_Ripleys_K = varblock(fun = Kest, Data_pool)
5. Plot the data.
6. If a hard-statistical difference is required, Studentised Permutation Test is included in the spatstat package to test for a difference between groups of point patterns:
   Test_difference = studpermu.test(all_points_to_test, exocytic_events ~ group, nperm = np).

Results

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., 2011^11,12. The methodology of imaging parameters is as outlined in Urbina et al....

Discussion

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...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
MATLAB	MathWorks		https://www.mathworks.com/products/matlab.html
R	R Core Team		https://www.r-project.org/
Rstudio	Rstudio, PBC		https://rstudio.com/