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).

NOTE:&#160;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
<ol>
	<li>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.</li>
	<li>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.</li>
</ol>
2. Set the pixel size and framerate
<ol>
	<li>Fill in the appropriate frame rate and pixel size of the images in the appropriate &#34;Framerate&#34; or &#34;pixel size&#34; box (Figure 2A, green box). If no values are provided (they are set to the default &#34;0&#34;), 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. 
	&#8203;NOTE: In the example provided, the framerate is 100, and the pixel size is 0.08.</li>
</ol>
3. Choose or make masks
<ol>
	<li>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 &#34;Run indicator&#34; 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).</li>
	<li>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.</li>
	<li>As an alternative to using Mask Maker, if the signal to noise of images is insufficient, create masks manually in ImageJ.
	<ol>
		<li>First, open the raw image file in ImageJ (Figure 3A).</li>
		<li>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.</li>
		<li>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 &#34;_mask_file&#34;. For example, if a data file is named &#34;VAMP2_488_WT_1.tif&#34;, the corresponding mask file must be named &#34;VAMP2_488_WT_1_mask_file.tif&#34;.</li>
		<li>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. 
		&#8203;NOTE: It is important to have a mask for every data file before running the analysis.</li>
	</ol>
	</li>
</ol>
4. Analysis and feature extraction
<ol>
	<li>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 &#34;run indicator&#34; will change from green to yellow (Figure 4A, red box). After the analysis is finished, this will change back to green.</li>
	<li>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.</li>
</ol>
5. Classification of exocytic events
<ol>
	<li>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 &#62; 0.5. 
	&#8203;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.</li>
</ol>
6. Spatiotemporal analysis of exocytosis using Ripley&#39;s K values
<ol>
	<li>Create a separate &#34;neurite&#34; and &#34;soma&#34; 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.</li>
	<li>Open ImageJ/Fiji.</li>
	<li>Drag-and-drop the mask file or use File | Open and then select the mask file.</li>
	<li>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.</li>
	<li>Use the polygon-selection tool or freehand to draw an outline around the soma, separating it from the neurites. This requires manual decision-making.</li>
	<li>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.</li>
	<li>Without moving the drawn region, click on the header of the original mask file.</li>
	<li>Click Edit | Fill to fill in the circled soma so that only the neurites remain the mask.</li>
	<li>Once separate neurite and mask files are obtained, Save both the mask files.</li>
	<li>Open &#34;neurite_2D_network&#34; in Matlab.</li>
	<li>In MATLAB, navigate to directory with all analysis data.</li>
	<li>Change the path &#34;mask name&#34; to the name of the neurite mask, i.e., &#34;MaskFiles/VAMP2pHluorin_488_wt_4_mask_file_neurite.tif&#34;</li>
	<li>Change the &#34;csv_file_name&#34; to where fluorescent_traces.csv file is located, i.e., &#34;MaskFiles/VAMP2pHluorin_488_wt_4_mask_file_neurite.csv&#34;.</li>
	<li>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.</li>
	<li>Next, generate a CSV file for the soma. Open &#34;CSV_mask_creator.m&#34; in Matlab.</li>
	<li>Put in the path for the &#34;mask name&#34; to the name of the soma mask, i.e., &#34;MaskFiles/VAMP2phLuorin_488_wt_4_mask_file_soma.tif</li>
	<li>Change the &#34;writematrix&#34; to csv filename to be created, i.e., &#34;MaskFiles/VAMP2pHluorin_488_wt_4_mask_file_soma.csv&#34;</li>
	<li>Click Run. This creates the new VAMP2pHluorin_488_wt_4_mask_file_soma.csv file.</li>
	<li>Repeat 6.14 for every mask file.</li>
</ol>
7. RStudio setup
<ol>
	<li>Open Rstudio and open ripleys_k_analysis.R file.</li>
	<li>Install the package &#34;spatstat&#34; in RStudio by going to Tools | Install Packages and typing in &#34;spatstat&#34; followed by clicking Install. 
	NOTE: This only needs to be performed once per Rstudio Installation.</li>
	<li>Run the library spatstat at the beginning of each session.</li>
	<li>Pay attention to two main variables in this case: &#34;neuron_mask&#34; and &#34;neuron_datapoints.&#34; Neuron Mask points to the set of mask files to run, i.e., soma mask files.&#34; 
	NOTE: Run all soma mask files together, separately from Neurite mask files, and vice versa.</li>
	<li>Read in the .csv of the mask files for each of the neurons to be analyzed.</li>
	<li>Run multiple files at once by copying additional neuron_mask_n+1. This will allow to aggregate Ripley&#39;s analysis together, using the script described in section 8.</li>
	<li>Now check the second variable, &#34;neuron_datapoints&#34;.</li>
	<li>Read in the.&#34;X_fluorescent_traces.csv&#34; 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 &#34;neuron_datapoints&#34; position.</li>
	<li>In RStudio Select Code | Run Region | Run all. This generates several plots, including the grouped Ripley&#39;s K values as well as density plots.</li>
	<li>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 &#34;plot(density(soma_data,0.4))&#34;. The number &#34;0.4&#34; 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.</li>
	<li>Export or Save image from Rstudio. If a heatmap requires further editing, choose an appropriate file type (SVG or EPS).</li>
</ol>
8. Ripley&#39;s analysis
NOTE: The Ripleys_k_analysis.R file also automatically generated Ripley&#39;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.
<ol>
	<li>First, run the envelope function for each cell. This function simulated complete spatial randomness (CSR) to test the Ripley&#39;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)</li>
	<li>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&#39;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)</li>
	<li>Once complete, pool the Ripley&#39;s K values together and bootstrap their confidence intervals with the following command: 
	Data&#160;pool&#160;=&#160;pool(Data_ripleys_k_1,&#160;Data_ripleys_k_2,...)</li>
	<li>Bootstrap with the following command: 
	Final_Ripleys_K = varblock(fun = Kest, Data_pool)</li>
	<li>Plot the data.</li>
	<li>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).</li>
</ol>

<ol>
	<li>Miesenb&#246;ck, G., De Angelis, D. A., Rothman, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualizing+secretion+and+synaptic+transmission+with+pH-sensitive+green+fluorescent+proteins.">Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins.</a> Nature. 394 (6689), 192-195 (1998).</li><li>Urbina, F. L., Gomez, S. M., Gupton, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatiotemporal+organization+of+exocytosis+emerges+during+neuronal+shape+change.">Spatiotemporal organization of exocytosis emerges during neuronal shape change.</a> Journal of Cell Biology. 217 (3), 1113-1128 (2018).</li><li>Urbina, F. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TRIM67+regulates+exocytic+mode+and+neuronal+morphogenesis+via+SNAP47.">TRIM67 regulates exocytic mode and neuronal morphogenesis via SNAP47.</a> Cell Reports. 34 (6), 108743 (2021).</li><li>Alabi, A. A., Tsien, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Perspectives+on+kiss-and-run:+Role+in+exocytosis,+endocytosis,+and+neurotransmission.">Perspectives on kiss-and-run: Role in exocytosis, endocytosis, and neurotransmission.</a> Annual Review of Physiology. 75, 393-422 (2013).</li><li>Albillos, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+exocytotic+event+in+chromaffin+cells+revealed+by+patch+amperometry.">The exocytotic event in chromaffin cells revealed by patch amperometry.</a> Nature. 389 (6650), 509-512 (1997).</li><li>He, L., Wu, L. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+debate+on+the+kiss-and-run+fusion+at+synapses.">The debate on the kiss-and-run fusion at synapses.</a> Trends in Neuroscience. 30 (9), 447-455 (2007).</li><li>Elhamdani, A., Azizi, F., Artalejo, C. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Double+patch+clamp+reveals+that+transient+fusion+(kiss-and-run)+is+a+major+mechanism+of+secretion+in+calf+adrenal+chromaffin+cells:+high+calcium+shifts+the+mechanism+from+kiss-and-run+to+complete+fusion.">Double patch clamp reveals that transient fusion (kiss-and-run) is a major mechanism of secretion in calf adrenal chromaffin cells: high calcium shifts the mechanism from kiss-and-run to complete fusion.</a> Journal of Neuroscience. 26 (11), 3030 (2006).</li><li>Bowser, D. N., Khakh, B. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two+forms+of+single-vesicle+astrocyte+exocytosis+imaged+with+total+internal+reflection+fluorescence+microscopy.">Two forms of single-vesicle astrocyte exocytosis imaged with total internal reflection fluorescence microscopy.</a> Proceedings of the National Academy of Sciences of the United States of America. 104 (10), 4212-4217 (2007).</li><li>Holroyd, P., Lang, T., Wenzel, D., De Camilli, P., Jahn, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+direct,+dynamin-dependent+recapture+of+fusing+secretory+granules+on+plasma+membrane+lawns+from+PC12+cells.">Imaging direct, dynamin-dependent recapture of fusing secretory granules on plasma membrane lawns from PC12 cells.</a> Proceedings of the National Academy of Sciences of the United States of America. 99 (26), 16806-16811 (2002).</li><li>Wang, C. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Different+domains+of+synaptotagmin+control+the+choice+between+kiss-and-run+and+full+fusion.">Different domains of synaptotagmin control the choice between kiss-and-run and full fusion.</a> Nature. 424 (6951), 943-947 (2003).</li><li>Winkle, C. C., Hanlin, C. C., Gupton, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Utilizing+combined+methodologies+to+define+the+role+of+plasma+membrane+delivery+during+axon+branching+and+neuronal+morphogenesis.">Utilizing combined methodologies to define the role of plasma membrane delivery during axon branching and neuronal morphogenesis.</a> Journal of Visualized Experiments. (109), e53743 (2016).</li><li>Viesselmann, C., Ballweg, J., Lumbard, D., Dent, E. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nucleofection+and+primary+culture+of+embryonic+mouse+hippocampal+and+cortical+neurons.">Nucleofection and primary culture of embryonic mouse hippocampal and cortical neurons.</a> Journal of Visualized Experiments. (47), e2373 (2011).</li><li>Plooster, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TRIM9-dependent+ubiquitination+of+DCC+constrains+kinase+signaling,+exocytosis,+and+axon+branching.">TRIM9-dependent ubiquitination of DCC constrains kinase signaling, exocytosis, and axon branching.</a> Molecular Biology of the Cell. 28 (18), 2374-2385 (2017).</li><li>Urbina, F. L., Gupton, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SNARE-mediated+exocytosis+in+neuronal+development.">SNARE-mediated exocytosis in neuronal development.</a> Frontiers in Molecular Neuroscience. 13, 133 (2020).</li><li>Ripley, B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+second-order+analysis+of+stationary+point+processes.">The second-order analysis of stationary point processes.</a> Journal of Applied Probability. 13 (2), 255-266 (1976).</li><li>Liu, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=pHmScarlet+is+a+pH-sensitive+red+fluorescent+protein+to+monitor+exocytosis+docking+and+fusion+steps.">pHmScarlet is a pH-sensitive red fluorescent protein to monitor exocytosis docking and fusion steps.</a> Nature Communication. 12 (1), 1413 (2021).</li></ol>

The authors declare nothing to disclose.

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 and photobleaching correction. For background subtraction, the pixels outside of the masked region of the mask file are averaged over the timelapse of the whole image, and the average value is subtracted from the image set. For photobleaching correction, a mono-exponential decay fit is applied to the average fluorescence of pixels in the mask over the course of the video, with the corrected intensity adjusted as follows:
Corrected intensity = (Intensity at time t) &#247; exp-k&#215;t where k = decay constant
Therefore, no pre-processing is necessary prior to input. These processes, however, critically rely on the image mask to effectively separate the cell from the background, and thus a proper cell mask is necessary for good results.
The automated cell mask creator requires a uniform signal with a sufficient signal-to-noise ratio (ideally, at least 2x the standard deviation of the average background signal) to perform well. Empirically, the CAAX box tagged with a fluorescent protein, which inserts into the plasma membrane upon prenylation, works well. There is no requirement that the signal can be maintained throughout the timelapse imaging of exocytosis, as a suitable mask can be created from a high signal in the first 10 frames of the sequence. However, if the cell morphology changes significantly during the imaging paradigm, care should be taken.
When using the automated detection software, include the framerate and pixel size for accurate temporal and spatial outputs. If no framerate or pixel size is declared, the output will be per-pixel and per-frame. As a rule-of-thumb, vesicle diameters in developing neurons are on the scale of ~100 nm13, and thus the automated detection of events may work for vesicles similar in size. As it stands, there is no hard limit on the size of vesicles that can be detected (by pixel area), as automated detection relies on Gaussian-shaped intensity over a large range of Gaussian widths. Fusion of vesicles smaller than the width of a pixel can be detected accurately if the intensity of the event meets the signal-to-noise criteria of 2x the standard deviation of the average background signal as the fluorescence diffraction expands over multiple pixels.
This program was developed for detecting exocytic events in developing neurons. However, the software has been exploited to successfully detect exocytic events in other cell lines2, indicating the detection algorithm is robust. Although we have used the software for detecting exocytic events in non-neuronal cell types, differences in exocytic event mode between cell types14 indicate that the classification algorithm may not be suitable for other cell types. Exocytic events in developing neurons were originally classified using three different methods: Hierarchical clustering, Dynamic Time Warping, and Principal Component Analysis (PCA), revealing four different classes3. Here all three classifiers are employed in the GUI to categorize exocytic events into one of these established four classes. For each exocytic event, a probability score between 0-1 is assigned for each of the four possible classes. Any class with a probability score &#62; 0.5 is considered to be of that class. This classification has only been used in developing neurons to date. Whether these classes exist in other cell types or at later developmental time points in neurons is not known. A large number of events with probability scores of &#60;0.5 for any of the classes would suggest the classification procedure may not be appropriate for the exocytic events currently being evaluated. If low probability scores are assigned to exocytic events of different cell types, this would suggest that de novo classification is needed as new or alternate modes of exocytosis may exist. The same classification methods used here would need to be applied to the automatically detected exocytic events.
To explore the spatial and temporal clustering of exocytic events, Ripley&#39;s K analysis15 is exploited. Analyzing clustering for neurons involves three separate analyses: One for the soma, one for the neurites, and one for time. The reason for splitting the soma and neurites is to account for the extreme morphology of neurites, which are often thin enough that they can be treated as a 2D network and apply a 2D variant of Ripley&#39;s K analysis. For time, a 1D Ripley&#39;s K analysis is implemented for temporal clustering. Ripley&#39;s K analysis is a robust method for detecting clustering of point processes and colocalization. The graph of Ripley&#39;s K can be interpreted as such: the Ripley&#39;s K value + confidence interval has a 5% chance to fall outside of the line of complete spatial randomness at any point, analogous to a p-value of 0.05. Where the value falls outside of the line of complete spatial randomness, exocytic events are clustered (above CSR) or are separated at regular intervals (below CSR) at those distances (x-axis).
The creation of the mask file relies on a high initial signal-to-noise of the cell. pH sensitive markers may not always perform well in illuminating a cell, depending on what protein the probe is attached to. Another option for making mask files if the signal to noise of the exocytic marker insufficiently highlights the cell border is to employ a second fluorescent channel/image for mask creation. If using a different fluorescence marker (tagRFP-CAAX, for example) to make masks, when choosing a dataset, first navigate to the folder with the images to make masks from (for example, a folder containing the tagRFP-CAAX images). Use the Mask Maker button here. Importantly, remember to rename the mask files to match the exocytic data file to be analyzed with the above naming scheme (following the example above, the mask files named &#34;CAAX_1_mask_file.tif&#34; will need to be renamed to &#34;VAMP2_488_WT_1_mask_file.tif&#34; to match the VAMP2 image set to be analyzed). Once mask files are appropriately named, return to the Choose Dataset button again to navigate to exocytic dataset files to analyze.
The detection of exocytosis is remarkably sensitive, and exocytic events were accurately detected at signal-to-noise ratios as low as a &#916;F/F of 0.01. The sensitivity of the detection depends partially on the variance of the background fluorescence, and events less than 4 standard deviations above the background signal will not be detected.
The detection of exocytic events relies on their transient nature. As a feature of the analysis of transient events, our detection code explores a 20-second window around the exocytic event. In some cell types, kiss-and-stay exocytosis may &#34;stay&#34; for a much longer temporal window than in developing neurons, and the automated detection may not robustly capture these less transient events. This feature is an easily modifiable variable for those comfortable with MATLAB. Similar to the limitation of the 20 second ROI, exocytic events that appear but do not disappear before the last time frame of the video may not be counted as true exocytosis, which may influence the frequency, which is calculated based on the entire length of the time series.
The mask maker included is automated by design and performs well on segmenting complex shapes from the background; however, the fully automated design limits the range of signal-to-noise and signal type that can be used to generate a mask automatically. If the user cannot include a fluorescent cell marker that is uniform and of a significant signal-to-noise ratio, the cell mask will need to be drawn manually.
User based analysis of exocytosis is a time-consuming process and subject to personal bias, as events are not always clearly separated from other fluorescence. The use of an automated analysis program to correctly identify and analyze exocytic events in an unbiased manner increases analysis efficiency and improves reproducibility and rigor.
Not only does this exocytic event detection work for accurately capturing pH sensitive fluorescence in developing neurons but other cell types as well (Urbina et al., 2018). Whether classification works for other cell types will require determination. Future applications of this technique may be useful at synapses, in non-neuronal cell types, or with novel markers of exocytic vesicle docking and fusion, such as recently described pHmScarlet16.

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&#39;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 described4,5,6,7. During FVF, the fusion pore dilates, and the vesicle is incorporated into the plasma membrane. During KNR, the fusion pore transiently opens and then reseals4,5,8,9,10. 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&#160;https://drive.google.com/drive/folders/1VCiO-thMEd4jz-tYEL8I4N1Rf_zjnOgB?usp=sharing or 
https://github.com/GuptonLab.

<table><tbody><tr><td>MATLAB</td><td>MathWorks</td><td></td><td>https://www.mathworks.com/products/matlab.html</td></tr><tr><td>R</td><td>R Core Team</td><td></td><td>https://www.r-project.org/</td></tr><tr><td>Rstudio</td><td>Rstudio, PBC</td><td></td><td>https://rstudio.com/</td></tr></tbody></table>

automated detection and analysis of exocytosis

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 &#916;F/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&#39;s K function in R Studio is used to distinguish between clustered, dispersed, or random occurrence of fusion events in both space and time.

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., 20182. Briefly, TIRF microscopy was used to image the basal plasma membrane of neurons every 100 ms for 2 min. Figure 2, Figure 3, Figure 4 show a step-by-step guide to analyze exocytic events. The folder where the neuron images are located is selected, and a directory to deposit the final analysis datafiles is chosen (Figure 2A). Using the MaskMaker function, a mask is generated for the neurons, which is inspected in the GUI (Figure 2B). In this instance, the cell mask is of good quality, and the analysis can proceed. Should a mask be insufficient, a mask can be created in ImageJ (Figure 3). After using the MaskMaker function or creating a mask in ImageJ and selecting the directory where the mask files are located, the analysis is performed (Figure 4A). Results are generated in the DataFiles folder when the analysis is finished (the yellow indicator changes back to green) (Figure 4B).
Datafiles are automatically generated and named according to the raw data files provided.
Assuming the datafile is named X:
X_tracking: This file includes x,y position and frame number of each event as well as bounding boxes which can be used to draw boxes around each event. Age indicates the number of frames past the initial detection where an event is a distinct gaussian puncta. If classification is checked, the classification results will appear in this file, which indicates the probability of an exocytic event belonging to one of four classes. If the probability is greater than .5, and greater than other probabilities, then an exocytic class was chosen.
X_fluorescent traces: This file includes x,y position and frame number of each event. In addition, it includes fluorescent intensity measures in a region of interest around each event 2 seconds before and 10 seconds following the peak &#916;F/F for each event (indicated by the Timepoint columns).
X_cell_statistics: This file includes the cell area, total image time, and automatically calculated frequency of exocytic events for each cell (in events/mm2/minute).
Feature extraction files include:
X_contrast: Contrast.&#160;A measure of the intensity contrast between a pixel and its neighbor over the whole image.
X_correlation: Correlation. A measure of how correlated a pixel is to its neighbor over the whole image.
X_energy Total energy. Defined as the squared sum of the pixel intensity.
X_homogeneity measures the closeness of the distribution of elements in the ROI to the ROI diagonal.
X_ring_fluorescence: the average fluorescence of border pixels.
X_SD: Standard Deviation. This is defined as the standard deviation of the ROI.
Examples of average fluorescence traces &#177; SEM from each exocytic class were plotted from X_fluorescent traces file (Figure 5A). Using the Cell_statistics file, the frequency of exocytosis for each class was plotted for each neuron (Figure 5B). With the Classification checkbox clicked, the program assigns each exocytic event to a class, plotted in Figure 5C. Following classification, the Ripley&#39;s K analysis code was used to determine if exocytic events are random, clustered, or dispersed in space and time. Density heatmaps of the localization of exocytic events (Figure 5D) were generated. These reveal expected clustered &#34;hotspots&#34; in distinct regions of the neuron. Next, Ripley&#39;s K analysis was performed for the soma, neurite, and clustering over time (Figure 5E). The Ripley&#39;s K value and SEM (black line and blue shaded region, respectively) rise above&#160;the line of complete spatial randomness (red dotted line), suggesting statistically significant clustering. 
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62400/62400fig01.jpg" /> 
Figure 1: Representation of exocytic analysis and classification. (A) Outline of the analysis pipeline for the GUI. Cells are segmented from the background before exocytic events are identified and tracked. Parameters such as the peak &#916;F/F and t1/2 are calculated from fluorescent traces of exocytosis in a ROI around the event pre and post-fusion. (B) illustration of the four modes of exocytosis and example image montages. After fusion, events may proceed instantaneous to FVF or KNR (FVFi and KNRi), or a delay may be present before the onset of fusion fate to FVF or KNR (FVFd and KNRd). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62400/62400fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62400/62400fig02.jpg" /> 
Figure 2: Step by step example analysis. (A) First, datasets are chosen (1., red box), and a directory is chosen to place analysis files (2., red box). Next, the framerate and pixel size are specified (3., green box). Here, 0.08 &#181;m pixel size and 100 ms framerate were used. The MaskMaker function button is then pushed (4., blue box). A folder titled &#34;MaskFiles&#34; is automatically created in the chosen directory containing a mask file for each image file in the dataset. (B) When datasets are loaded, selecting a data file and/or mask file will display the first frame of the images for ease of comparison (Green box). Mask files may not be completely correct; this mask file can be corrected for errors, or a new mask file may be made manually <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62400/62400fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62400/62400fig03.jpg" /> 
Figure 3: Creating a mask file manually in ImageJ. (A) First, open the file for making a mask. The &#34;Polygon Selections&#34; button is outlined in red. By clicking around the edge of the cell, a polygon outline is created. (B) How to create a mask from the polygon outline. By selecting Edit&#160;|&#160;Selection&#160;|&#160;Create Mask, a black and white mask will be created from the polygon (right image). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62400/62400fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62400/62400fig04.jpg" /> 
Figure 4: Analysis of exocytic events. (A) Demonstration of the analysis button (orange box) and a running analysis. Notice the run indicator turns yellow while an analysis is being performed. (B) Example analysis files that are created in the chosen directory when the analysis is complete. DataFiles contain all analysis files of the exocytic event. (C) Analysis files generated in the DataFiles Folder. Color boxes represent the open files in subsequent images. (D) Three open files, &#34;X_fluorescent_traces.csv&#34; (red) and &#34;X_Cell_statistics&#34;(green), and &#34;X_tracking&#34;(blue). Fluorescent traces contain the x,y position and frame number of each event, as well as fluorescent intensity at each ROI. Cell_statistics contains summary information of whole-cell exocytic statistics such as frequency of exocytosis. X_tracking contains position and time information for each exocytic event as well as the probability of each class of exocytosis for each event, represented as a number between 0-1 (&#62;0.5 indicates an event belongs to a particular class). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62400/62400fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62400/62400fig05.jpg" /> 
Figure 5: Representative results. (A)&#160;Average fluorescence traces +/- SEM from each exocytic class. (B) Frequency of events plotted for three murine cortical neurons for each exocytic class. These data values were plotted from &#34;X_Cell_statistics&#34;, using the classes assigned in &#34;X_tracking&#34;. (C) Distribution of the classes of exocytosis for the same three cells used in A). Here, the ratio of each mode is plotted. (D) Density plot of where exocytic events are occurring as generated in the Ripley&#39;s K Analysis portion of the protocol. This can be interpreted as a &#34;heat map&#34; of the spatial likelihood of where events are occurring. (E) Ripley&#39;s K analysis of three cells used for A) and B). The red line indicates what value completely spatially random distribution of exocytic events would be. The black line indicates the aggregate Ripley&#39;s K value for the three cells in this example, and the blue shaded region represents the confidence interval. Here, the shaded region notably falls outside of the line of complete spatial randomness between ~0.25-1 &#181;m, suggesting exocytic events are clustered at those distances. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62400/62400fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Automated Detection and Analysis of Exocytosis at JoVE.com

Automated Detection and Analysis of Exocytosis

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 ...

automated-detection-and-analysis-of-exocytosis

Nanyang Technological University

Research

JoVE Journal

Biology

3.4K Views.  University of North Carolina at Chapel Hill. De geautomatiseerde detectie en analyse van exocytose software zal de gebruiker in staat stellen om automatisch exocytische gebeurtenissen en TIRF beeldsequenties van pH-gevoelige fluoroforen te detecteren. Het zal ook automatisch kenmerken van exocytose uitvoeren, zoals de ruimtelijke verdeling of de frequentie, evenals individuele eigenschappen van exocytische gebeurtenissen, zoals de halfwaardetijd of verandering in fluorescentie over de achtergrond. Daarnaast is een optie opgenomen om exo...

Video: Automated Detection and Analysis of Exocytosis

Investigating Mast Cell Secretory Granules; from Biosynthesis to Exocytosis

Mast Cells (MC) are secretory cells of the immune system that accomplish their physiological and pathological functions by releasing pre-formed and newly synthesized allergic, inflammatory and immunoregulatory mediators. MCs&#8217; mediators affect multiple tissues and organs culminating in allergic and immune responses. The synthesis, storage and release of the MC mediators are highly regulated. The pre-formed mediators are packed in cytoplasmic secretory granules&#160;(SG) that fuse with the plasma membrane and release their content by regulated exocytosis. We present a protocol, based on the co-expression of a gene of interest with a reporter gene that is targeted to the SGs and is released in a regulated fashion alongside the endogenous SG mediators. The protocol enables high resolution four dimensional confocal analyses of the MC SGs and monitoring their timeline from biogenesis to triggered exocytosis. Thus, using this protocol for screening genes of interest for their phenotypic and functional impact allows deciphering the molecular mechanisms that govern the biogenesis and exocytosis of the MC SGs and identifying the regulators involved. Thereby, further insights into the cellular mechanisms that account for MCs function in health and disease should be provided.

The goal of the present&#160;protocol was to develop a method that will allow functional genomic analyses of mast cell secretion. The protocol is based on quantitative assessment of the release of a fluorescent reporter gene cotrasfected with the gene of interest and real time&#160;analyses of the secretory granule&#39;s morphology.

Mast Cells (MC) are secretory cells of the immune system that accomplish their physiological and pathological functions by releasing pre-formed and newly ...

Immunology and Infection

Measuring Synaptic Vesicle Endocytosis in Cultured Hippocampal Neurons

During endocytosis, fused synaptic vesicles are retrieved at nerve terminals, allowing for vesicle recycling and thus the maintenance of synaptic transmission during repetitive nerve firing. Impaired endocytosis in pathological conditions leads to decreases in synaptic strength and brain functions. Here, we describe methods used to measure synaptic vesicle endocytosis at the mammalian hippocampal synapse in neuronal culture. We monitored synaptic vesicle protein endocytosis by fusing a synaptic vesicular membrane protein, including synaptophysin and VAMP2/synaptobrevin, at the vesicular lumenal side, with pHluorin, a pH-sensitive green fluorescent protein that increases its fluorescence intensity as the pH increases. During exocytosis, vesicular lumen pH increases, whereas during endocytosis vesicular lumen pH is re-acidified. Thus, an increase of pHluorin fluorescence intensity indicates fusion, whereas a decrease indicates endocytosis of the labelled synaptic vesicle protein. In addition to using the pHluorin imaging method to record endocytosis, we monitored vesicular membrane endocytosis by electron microscopy (EM) measurements of Horseradish peroxidase (HRP) uptake by vesicles. Finally, we monitored the formation of nerve terminal membrane pits at various times after high potassium-induced depolarization. The time course of HRP uptake and membrane pit formation indicates the time course of endocytosis.

Synaptic vesicle endocytosis is detected by light microscopy of pHluorin fused with synaptic vesicle protein and by electron microscopy of vesicle uptake.

During endocytosis, fused synaptic vesicles are retrieved at nerve terminals, allowing for vesicle recycling and thus the maintenance of synaptic ...

Neuroscience

Quantification of Efferocytosis by Single-cell Fluorescence Microscopy

Studying the regulation of efferocytosis requires methods that are able to accurately quantify the uptake of apoptotic cells and to probe the signaling and cellular processes that control efferocytosis. This quantification can be difficult to perform as apoptotic cells are often efferocytosed piecemeal, thus necessitating methods which can accurately delineate between the efferocytosed portion of an apoptotic target versus residual unengulfed cellular fragments. The approach outlined herein utilizes dual-labeling approaches to accurately quantify the dynamics of efferocytosis and efferocytic capacity of efferocytes such as macrophages. The cytosol of the apoptotic cell is labeled with a cell-tracking dye to enable monitoring of all apoptotic cell-derived materials, while surface biotinylation of the apoptotic cell allows for differentiation between internalized and non-internalized apoptotic cell fractions. The efferocytic capacity of efferocytes is determined by taking fluorescent images of live or fixed cells and quantifying the amount of bound versus internalized targets, as differentiated by streptavidin staining. This approach offers several advantages over methods such as flow cytometry, namely the accurate delineation of non-efferocytosed versus efferocytosed apoptotic cell fractions, the ability to measure efferocytic dynamics by live-cell microscopy, and the capacity to perform studies of cellular signaling in cells expressing fluorescently-labeled transgenes. Combined, the methods outlined in this protocol serve as the basis for a flexible experimental approach that can be used to accurately quantify efferocytic activity and interrogate cellular signaling pathways active during efferocytosis.

Efferocytosis, the phagocytic removal of apoptotic cells, is required to maintain homeostasis and is facilitated by receptors and signaling pathways that allow for the recognition, engulfment, and internalization of apoptotic cells. Herein, we present a fluorescence microscopy protocol for the quantification of efferocytosis and the activity of efferocytic signaling pathways.

Studying the regulation of efferocytosis requires methods that are able to accurately quantify the uptake of apoptotic cells and to probe the signaling ...

Monitoring the Effect of Osmotic Stress on Secretory Vesicles and Exocytosis

Amperometry recording of cells subjected to osmotic shock show that secretory cells respond to this physical stress by reducing the exocytosis activity and the amount of neurotransmitter released from vesicles in single exocytosis events. It has been suggested that the reduction in neurotransmitters expelled is due to alterations in membrane biophysical properties when cells shrink in response to osmotic stress and with assumptions made that secretory vesicles in the cell cytoplasm are not affected by extracellular osmotic stress. Amperometry recording of exocytosis monitors what is released from cells the moment a vesicle fuses with the plasma membrane, but does not provide information on the vesicle content before the vesicle fusion is triggered. Therefore, by combining amperometry recording with other complementary analytical methods that are capable of characterizing the secretory vesicles before exocytosis at cells is triggered offers a broader overview for examining how secretory vesicles and the exocytosis process are affected by osmotic shock. We here describe how complementing amperometry recording with intracellular electrochemical cytometry and transmission electron microscopy (TEM) imaging can be used to characterize alterations in secretory vesicles size and neurotransmitter content at chromaffin cells in relation to exocytosis activity before and after exposure to osmotic stress. By linking the quantitative information gained from experiments using all three analytical methods, conclusions were previously made that secretory vesicles respond to extracellular osmotic stress by shrinking in size and reducing the vesicle quantal size to maintain a constant vesicle neurotransmitter concentration. Hence, this gives some clarification regarding why vesicles, in response to osmotic stress, reduce the amount neurotransmitters released during exocytosis release. The amperometric recordings here indicate this is a reversible process and that vesicle after osmotic shock are refilled with neurotransmitters when placed cells are reverted into an isotonic environment.

Osmotic stress affects exocytosis and the amount of neurotransmitter released during this process. We demonstrate how combining electrochemical methods together with transmission electron microscopy can be used to study the effect of extracellular osmotic pressure on exocytosis activity, vesicle quantal size, and the amount of neurotransmitter released during exocytosis.

Amperometry recording of cells subjected to osmotic shock show that secretory cells respond to this physical stress by reducing the exocytosis activity ...

Quantitative Approaches for Scoring in vivo Neuronal Aggregate and Organelle Extrusion in Large Exopher Vesicles in C. elegans

Toxicity of misfolded proteins and mitochondrial dysfunction are pivotal factors that promote age-associated functional neuronal decline and neurodegenerative disease across species. Although these neurotoxic challenges have long been considered to be cell-intrinsic, considerable evidence now supports that misfolded human disease proteins originating in one neuron can appear in neighboring cells, a phenomenon proposed to promote pathology spread in human neurodegenerative disease.
C. elegans adult neurons that express aggregating proteins can extrude large (~4 &#181;m) membrane-surrounded vesicles that can include the aggregated protein, mitochondria, and lysosomes. These large vesicles are called &#8220;exophers&#8221; and are distinct from exosomes (which are about 100x smaller and have different biogenesis). Throwing out cellular debris in exophers may occur by a conserved mechanism that constitutes a fundamental, but formerly unrecognized, branch of neuronal proteostasis and mitochondrial quality control, relevant to processes by which aggregates spread in human neurodegenerative diseases.
While exophers have been mostly studied in animals that express high copy transgenic mCherry within touch neurons, these protocols are equally useful in the study of exophergenesis using fluorescently tagged organelles or other proteins of interest in various classes of neurons.
Described here are the physical features of C. elegans exophers, strategies for their detection, identification criteria, optimal timing for quantitation, and animal growth protocols that control for stresses that can modulate exopher production levels. Together, details of protocols outlined here should serve to establish a standard for quantitative analysis of exophers across laboratories. This document seeks to serve as a resource in the field for laboratories seeking to elaborate molecular mechanisms by which exophers are produced and by which exophers are reacted to by neighboring and distant cells.

Quantitative Approaches for Scoring in vivo Neuronal Aggregate and Organelle Extrusion in Large Exopher Vesicles in C. elegans

This protocol describes approaches for detection and quantitation of large aggregate and/or organelle extrusions (~4 &#181;m) produced by C. elegans cells in the form of membrane-bound exophers. We describe strains, growth conditions, scoring criteria, timing, and microscopy considerations needed to facilitate dissection of this debris expulsion mechanism.

Toxicity of misfolded proteins and mitochondrial dysfunction are pivotal factors that promote age-associated functional neuronal decline and ...

Visualizing Clathrin-mediated Endocytosis of G Protein-coupled Receptors at Single-event Resolution via TIRF Microscopy

Many important signaling receptors are internalized through the well-studied process of clathrin-mediated endocytosis (CME). Traditional cell biological assays, measuring global changes in endocytosis, have identified over 30 known components participating in CME, and biochemical studies have generated an interaction map of many of these components. It is becoming increasingly clear, however, that CME is a highly dynamic process whose regulation is complex and delicate. In this manuscript, we describe the use of Total Internal Reflection Fluorescence (TIRF) microscopy to directly visualize the dynamics of components of the clathrin-mediated endocytic machinery, in real time in living cells, at the level of individual events that mediate this process. This approach is essential to elucidate the subtle changes that can alter endocytosis without globally blocking it, as is seen with physiological regulation. We will focus on using this technique to analyze an area of emerging interest, the role of cargo composition in modulating the dynamics of distinct clathrin-coated pits (CCPs). This protocol is compatible with a variety of widely available fluorescence probes, and may be applied to visualizing the dynamics of many cargo molecules that are internalized from the cell surface.

Clathrin-mediated endocytosis, a rapid and highly dynamic process internalizes many proteins, including signaling receptors. The protocol described here directly visualizes the kinetics of individual endocytic events. This is essential for understanding how core members of the endocytic machinery coordinate with each other, and how protein cargo influence this process.

Many important signaling receptors are internalized through the well-studied process of clathrin-mediated endocytosis (CME). Traditional cell biological ...

Techniques for the Analysis of Extracellular Vesicles Using Flow Cytometry

Extracellular Vesicles (EVs) are small, membrane-derived vesicles found in bodily fluids that are highly involved in cell-cell communication and help regulate a diverse range of biological processes. Analysis of EVs using flow cytometry (FCM) has been notoriously difficult due to their small size and lack of discrete populations positive for markers of interest. Methods for EV analysis, while considerably improved over the last decade, are still a work in progress. Unfortunately, there is no one-size-fits-all protocol, and several aspects must be considered when determining the most appropriate method to use. Presented here are several different techniques for processing EVs and two protocols for analyzing EVs using either individual detection or a bead-based approach. The methods described here will assist with eliminating the antibody aggregates commonly found in commercial preparations, increasing signal&#8211;to-noise ratio, and setting gates in a rational fashion that minimizes detection of background fluorescence. The first protocol uses an individual detection method that is especially well suited for analyzing a high volume of clinical samples, while the second protocol uses a bead-based approach to capture and detect smaller EVs and exosomes.

Many different methods exist for the measurement of extracellular vesicles (EVs) using flow cytometry (FCM). Several aspects should be considered when determining the most appropriate method to use. Two protocols for measuring EVs are presented, using either individual detection or a bead-based approach.

Extracellular Vesicles (EVs) are small, membrane-derived vesicles found in bodily fluids that are highly involved in cell-cell communication and help ...

Imaging FITC-dextran as a Reporter for Regulated Exocytosis

Regulated exocytosis is a process by which cargo, which is stored in secretory granules (SGs), is released in response to a secretory trigger. Regulated exocytosis is fundamental for intercellular communication and is a key mechanism for the secretion of neurotransmitters, hormones, inflammatory mediators, and other compounds, by a variety of cells. At least three distinct mechanisms are known for regulated exocytosis: full exocytosis, where a single SG fully fuses with the plasma membrane, kiss-and-run exocytosis, where a single SG transiently fuses with the plasma membrane, and compound exocytosis, where several SGs fuse with each other, prior to or after SG fusion with the plasma membrane. The type of regulated exocytosis undertaken by a cell is often dictated by the type of secretory trigger. However, in many cells, a single secretory trigger can activate multiple modes of regulated exocytosis simultaneously. Despite their abundance and importance across cell types and species, the mechanisms that determine the different modes of secretion are largely unresolved. One of the main challenges in investigating the different modes of regulated exocytosis, is the difficulty in distinguishing between them as well as exploring them separately. Here we describe the use of fluorescein isothiocyanate (FITC)-dextran as an exocytosis reporter, and live cell imaging, to differentiate between the different pathways of regulated exocytosis, focusing on compound exocytosis, based on the robustness and duration of the exocytic events.

Here we detail a method for live cell imaging of regulated exocytosis. This method utilizes FITC-dextran, which accumulates in lysosome-related organelles, as a reporter. This simple method also allows distinguishing between different modes of regulated exocytosis in cells that are difficult to manipulate genetically.

Regulated exocytosis is a process by which cargo, which is stored in secretory granules (SGs), is released in response to a secretory trigger. Regulated ...

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Live imaging of the pHluorin tagged Soluble N-ethylmaleimide-sensitive-factor Attachment protein REceptor (v-SNARE) Vesicle-associated membrane protein 7 (VAMP7) by total internal reflection fluorescence microscopy (TIRFM) is a straightforward way to explore secretion from the lysosomal compartment. Taking advantage of cell culture on micropatterned surfaces to normalize cell shape, a variety of statistical tools were employed to perform a spatial analysis of secretory patterns. Using Ripley&#8217;s K function and a statistical test based on the nearest neighbor distance (NND), we confirmed that secretion from lysosomes is not a random process but shows significant clustering. Of note, our analysis revealed that exocytosis events are also clustered in nonadhesion areas, indicating that adhesion molecules are not the only structures that can induce secretory hot spots at the plasma membrane. Still, we found that cell adhesion enhances clustering. In addition to precisely defined adhesive and nonadhesive areas, the circular geometry of these micropatterns allows the use of polar coordinates, simplifying analyses. We used Kernel Density Estimation (KDE) and the cumulative distribution function on polar coordinates of exocytosis events to identify enriched areas of exocytosis. In ring-shaped micropattern cells, clustering occurred at the border between the adhesive and nonadhesive areas. Our analysis illustrates how statistical tools can be employed to investigate spatial distributions of diverse biological processes.

Live imaging of lysosomal exocytosis on micropatterned cells allows a spatial quantification of this process. Morphology normalization using micropatterns is an outstanding tool to uncover general rules about the spatial distribution of cellular processes.

Live imaging of the pHluorin tagged Soluble N-ethylmaleimide-sensitive-factor Attachment protein REceptor (v-SNARE) Vesicle-associated membrane protein 7 ...

An Introduction to Endocytosis and Exocytosis

Cells can take in substances from the extracellular environment by endocytosis and actively release molecules into it by exocytosis. Such processes involve lipid membrane-bound sacs called vesicles. Knowledge of the molecular architecture and mechanisms of both is key to understanding normal cell physiology, as well as the disease states that arise when they become defective. This video will first briefly review a few pivotal discoveries in the history of endo- and exocytosis research. Next, some key questions will be examined, followed by a discussion of the prominent methods used to investigate these problems, including cell labeling, fusion assays, and fluorescence imaging. Finally, it will explore current research being conducted by scientists in the field today.

Endocytosis and Exocytosis; Clathrin Coated Pits and SNAREs

Cells can take in substances from the extracellular environment by endocytosis and actively release molecules into it by exocytosis. Such processes ...

Automated Detection and Analysis of Exocytosis

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