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 a...

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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....

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

Research

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Biology

3.4K Views.  University of North Carolina at Chapel Hill. 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.

Video: Automated Detection and Analysis of Exocytosis

Measuring Exocytosis in Neurons Using FM Labeling

The ability to measure the kinetics of vesicle release can help provide insight into some of the basics of neurotransmission.  Here we used real-time imaging of vesicles labeled with FM dye to monitor the rate of presynaptic vesicle release.  FM4-64 is a red fluorescent amphiphilic styryl dye that embeds into the membranes of synaptic vesicles as endocytosis is stimulated.  Lipophilic interactions cause the dye to greatly increase in fluorescence, thus emitting a bright signal when associated with vesicles and a nominal one when in the extracellular fluid. After a wash step is used to help remove external dye within the plasma membrane, the remaining FM is concentrated within the vesicles and is then expelled when exocytosis is induced by another round of electrical stimulation. The rate of vesicles release is measured from the resulting decrease in fluorescence.  Since FM dye can be applied external and transiently, it is a useful tool for determining rates of exocytosis in neuronal cultures, especially when comparing the rates between transfected synapses and neighboring control boutons.



The ability to measure the kinetics of vesicle release can help provide insight into some of the basics of neurotransmission. Here we used real-time imaging of vesicles labeled with the red fluorescent dye FM 4-64 to measure the rate of presynaptic vesicle release in hippocampal neuronal cultures.

The ability to measure the kinetics of vesicle release can help provide insight into some of the basics of neurotransmission.  Here we used real-time ...

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Imaging Exocytosis in Retinal Bipolar Cells with TIRF Microscopy

Total internal reflectance fluorescence (TIRF) microscopy is a technique that allows the study of events happening at the cell membrane, by selective imaging of fluorescent molecules that are closest to a high refractive index substance such as glass1. In this article, we apply this technique to image exocytosis of synaptic vesicles in retinal bipolar cells isolated from the goldfish retina. These neurons are very suitable for this kind of study due to their large axon terminals. By simultaneously patch clamping the bipolar cells, it is possible to investigate the relationship between pre-synaptic voltage and synaptic release2,3. Synaptic vesicles inside the bipolar cell terminals are loaded with a fluorescent dye (FM 1-43&reg;) by co-puffing the dye and a ringer solution containing a high K+ concentration onto the synaptic terminals. This depolarizes the cells and stimulates endocytosis and consequent dye uptake into the glutamatergic vesicles. After washing the excess dye away for around 30 minutes, cells are ready for being patch clamped and imaged simultaneously with a 488 nm laser. The patch pipette solution contains a rhodamine-based peptide that binds selectively to the synaptic ribbon protein RIBEYE4, thereby labeling ribbons specifically when terminals are imaged with a 561 nm laser. This allows the precise localization of active zones and the separation of synaptic from extra-synaptic events.

In this video, we demonstrate how to label and visualize single synaptic vesicle exocytosis and trafficking in goldfish retinal bipolar cells using total internal reflectance fluorescence (TIRF) microscopy.

Total internal reflectance fluorescence (TIRF) microscopy is a technique that allows the study of events happening at the cell membrane, by selective ...

Semi-automated Optical Heartbeat Analysis of Small Hearts

We have developed a method for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts (Fink, et. al., 2009). Our Semi-automatic Optical Heartbeat Analysis (SOHA) uses a novel movement detection algorithm that is able to detect cardiac movements associated with individual contractile and relaxation events. The program provides a host of physiologically relevant readouts including systolic and diastolic intervals, heart rate, as well as qualitative and quantitative measures of heartbeat arrhythmicity. The program also calculates heart diameter measurements during both diastole and systole from which fractional shortening and fractional area changes are calculated. Output is provided as a digital file compatible with most spreadsheet programs. Measurements are made for every heartbeat in a record increasing the statistical power of the output. We demonstrate each of the steps where user input is required and show the application of our methodology to the analysis of heart function in all three genetically tractable heart models.

We have developed a Semi-automated Optical Heartbeat Analysis method (SOHA) for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts. We demonstrate the application of our methodology to the analysis of heart function in fruit fly and embryonic mouse hearts.

We have developed a method for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts (Fink, et. al., 2009). Our ...

Development of automated imaging and analysis for zebrafish chemical screens.

We demonstrate the application of image-based high-content screening (HCS) methodology to identify small molecules that can modulate the FGF/RAS/MAPK pathway in zebrafish embryos. The zebrafish embryo is an ideal system for in vivo high-content chemical screens. The 1-day old embryo is approximately 1mm in diameter and can be easily arrayed into 96-well plates, a standard format for high throughput screening. During the first day of development, embryos are transparent with most of the major organs present, thus enabling visualization of tissue formation during embryogenesis. The complete automation of zebrafish chemical screens is still a challenge, however, particularly in the development of automated image acquisition and analysis. We previously generated a transgenic reporter line that expresses green fluorescent protein (GFP) under the control of FGF activity and demonstrated their utility in chemical screens 1. To establish methodology for high throughput whole organism screens, we developed a system for automated imaging and analysis of zebrafish embryos at 24-48 hours post fertilization (hpf) in 96-well plates 2. In this video we highlight the procedures for arraying transgenic embryos into multiwell plates at 24hpf and the addition of a small molecule (BCI) that hyperactivates FGF signaling 3. The plates are incubated for 6 hours followed by the addition of tricaine to anesthetize larvae prior to automated imaging on a Molecular Devices ImageXpress Ultra laser scanning confocal HCS reader. Images are processed by Definiens Developer software using a Cognition Network Technology algorithm that we developed to detect and quantify expression of GFP in the heads of transgenic embryos. In this example we highlight the ability of the algorithm to measure dose-dependent effects of BCI on GFP reporter gene expression in treated embryos.

We report the development of a system for automated imaging and analysis of zebrafish transgenic embryos in multiwell plates. This demonstrates the ability to measure dose dependent effects of a small molecule, BCI, on Fibroblast Growth Factor reporter gene expression and provide technology for establishing high-throughput zebrafish chemical screens.

We demonstrate the application of image-based high-content screening (HCS) methodology to identify small molecules that can modulate the FGF/RAS/MAPK ...

Rapid and Efficient Zebrafish Genotyping Using PCR with High-resolution Melt Analysis

Zebrafish is a powerful vertebrate model system for studying development, modeling disease, and performing drug screening. Recently a variety of genetic tools have been introduced, including multiple strategies for inducing mutations and generating transgenic lines. However, large-scale screening is limited by traditional genotyping methods, which are time-consuming and labor-intensive. Here we describe a technique to analyze zebrafish genotypes by PCR combined with high-resolution melting analysis (HRMA).&#160;This approach is rapid, sensitive, and inexpensive, with lower risk of contamination artifacts.&#160;Genotyping by PCR with HRMA can be used for embryos or adult fish, including in high-throughput screening protocols.

PCR combined with high-resolution melt analysis (HRMA) is demonstrated as a rapid and efficient method to genotype zebrafish.

Zebrafish is a powerful vertebrate model system for studying development, modeling disease, and performing drug screening. Recently a variety of genetic ...

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ imaging data is time consuming and subject to bias. Automated signal analysis algorithms based on region of interest (ROI) detection have been implemented for one-dimensional line scan measurements, but there is no current algorithm which integrates optimized identification and analysis of ROIs in two-dimensional image sequences. Here an algorithm for rapid acquisition and analysis of ROIs in image sequences is described. It utilizes ellipses fit to noise filtered signals in order to determine optimal ROI placement, and computes Ca2+ signal parameters of amplitude, duration and spatial spread. This algorithm was implemented as a freely available plugin for ImageJ (NIH) software. Together with analysis scripts written for the open source statistical processing software R, this approach provides a high-capacity pipeline for performing quick statistical analysis of experimental output. The authors suggest that use of this analysis protocol will lead to a more complete and unbiased characterization of physiologic Ca2+ signaling.

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca2+ signals with minimal user input and bias.

Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ ...

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis algorithms which can be used effectively to count various biological particles. When counting large numbers of cell samples, the hemocytometer presents a bottleneck with regards to time. Likewise, counting membranes from migration/invasion assays with the ImageJ plugin Cell Counter, although accurate, is exceptionally labor intensive, subjective, and infamous for causing wrist pain. To address this need, we developed two plugins within ImageJ for the sole task of automated hemocytometer (or known volume) and migration/invasion cell counting. Both plugins rely on the ability to acquire high quality micrographs with minimal background. They are easy to use and optimized for quick counting and analysis of large sample sizes with built-in analysis tools to help calibration of counts. By combining the core principles of Cell Counter with an automated counting algorithm and post-counting analysis, this greatly increases the ease with which migration assays can be processed without any loss of accuracy.

This paper describes the quantification of hemocytometer and migration/invasion micrographs through two new open-source ImageJ plugins Cell Concentration Calculator and migration assay Counter. Furthermore, it describes image acquisition and calibration protocols as well as discusses in detail the input requirements of the plugins.

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis ...

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts of fixed or live cells in multiwell plates. This provides a means to screen for cell signaling processes read out using intramolecular FRET biosensors or intermolecular FRET of protein interactions such as oligomerization or heterodimerization, which can be used to identify binding partners. We describe here the functionality of this automated multiwell plate FLIM instrumentation and present exemplar data from our studies of HIV Gag protein oligomerization and a time course of a FRET biosensor in live cells. A detailed description of the practical implementation is then provided with reference to a list of hardware components and a description of the open source data acquisition software written in &#181;Manager. The application of FLIMfit, an open source MATLAB-based client for the OMERO platform, to analyze arrays of multiwell plate FLIM data is also presented. The protocols for imaging fixed and live cells are outlined and a demonstration of an automated multiwell plate FLIM experiment using cells expressing fluorescent protein-based FRET constructs is presented. This is complemented by a walk-through of the data analysis for this specific FLIM FRET data set.

We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in &#181;Manager and data analysis is undertaken in FLIMfit.

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions ...

In Vivo Detection and Analysis of Rb Protein SUMOylation in Human Cells

The post-translational modifications of proteins are critical for the proper regulation of intracellular signal transduction. Among these modifications, small ubiquitin-related modifier (SUMO) is a ubiquitin-like protein that is covalently attached to the lysine residues of a variety of target proteins to regulate cellular processes, such as gene transcription, DNA repair, protein interaction and degradation, subcellular transport, and signal transduction. The most common approach to detecting protein SUMOylation is based on the expression and purification of recombinant tagged proteins in bacteria, allowing for an in vitro biochemical reaction which is simple and suitable for addressing mechanistic questions. However, due to the complexity of the process of SUMOylation in vivo, it is more challenging to detect and analyze protein SUMOylation in cells, especially when under endogenous conditions. A recent study by the authors of this paper revealed that endogenous retinoblastoma (Rb) protein, a tumor suppressor that is vital to the negative regulation of the cell cycle progression, is specifically SUMOylated at the early G1 phase. This paper describes a protocol for the detection and analysis of Rb SUMOylation under both endogenous and exogenous conditions in human cells. This protocol is appropriate for the phenotypical and functional investigation of the SUMO-modification of Rb, as well as many other SUMO-targeted proteins, in human cells.

In Vivo Detection and Analysis of Rb Protein SUMOylation in Human Cells

Small ubiquitin-related modifier (SUMO) family proteins are conjugated to the lysine residues of target proteins to regulate various cellular processes. This paper describes a protocol for the detection of retinoblastoma (Rb) protein SUMOylation under endogenous and exogenous conditions in human cells.

The post-translational modifications of proteins are critical for the proper regulation of intracellular signal transduction. Among these modifications, ...