Name: a graphical user interface for software-assisted tracking of protein concentration in dynamic cellular protrusions
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Description: Watch this Scientific Journal Video about A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions at JoVE.com

The authors acknowledge funding from the DFG (EXC-1003 to MG).

1. Cell Culture
<ol>
	<li>Culture HeLa or COS cells in Dulbecco&#39;s Modified Eagle Medium (DMEM) containing 4.5 g/L D-glucose, L-alanine-L-glutamine dipeptide, pyruvate, 10% fetal bovine serum, and 10 units/ml of penicillin/streptomycin. Culture neurons in culture-media without L-glutamine, glutamic acid or aspartic acid, supplemented with 0.5 mM L-alanine-L-glutamine dipeptide, serum-free neuronal supplements and 10 units/ml of penicillin/streptomycin.</li>
	<li>Once 40% confluency is reached, transfect cells with constructs of choice using a transfection reagent as per manufacturer&#39;s instructions. Keep transfected cells in incubator at 37 &#176;C and 5% CO2 for 15-18 h.</li>
	<li>To reduce changes in pH and osmolarity (due to evaporation) during image acquisition, fill the cell culture chambers (prior to imaging) up to 90 % with 37 &#176;C pre-warmed medium containing 20 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES). To seal the lid, apply a thin layer of vacuum grease on the inside of the lid and gently press it on the culture chamber containing the transfected cells.</li>
</ol>
2. Image Acquisition
NOTE: The length of filopodia varies from 2-10 &#181;m 13. Filopodia grow at an average velocity of 0.05-0.1 &#181;m/s 13,14.
<ol>
	<li>Acquire images using a microscope with 60X or 100X objective and no pixel binning (here, a spinning disc confocal microscope). Use acquisition-rates greater than 1 Hertz (Hz) to track filopodial dynamics. To minimize out-of-focus artifacts, image filopodia close to the basal membrane (i.e. substrate surface).</li>
	<li>To assure smooth tracking, adjust the exposure time of the camera and laser intensity such that the signal to noise ratio (SNR) is greater than 4. Avoid saturation of individual channels (i.e. pixel values of 255 for 8-bit images and 65,535 for 16-bit images), as this will preclude subsequent image analysis.</li>
	<li>To avoid bleed-through, only use fluorescence tags compatible with laser lines and filters of the microscope (for details see reference 15).</li>
</ol>
3. Image Pre-processing
NOTE: Use ImageJ or other available software to pre-process images 16,17.
<ol>
	<li>If the sample is moving, correct for lateral drift using available software (e.g. https://github.com/NMSchneider/fixTranslation-Macro_for_ImageJ) prior to analysis. Exclude movies with axial drift (i.e. movement in z-direction).</li>
	<li>Correct background (i.e. grey values of areas outside of the cell), bleaching (i.e. continuous loss if fluorescence intensity due to damage to fluorescence proteins) and possible bleed-trough (i.e. signal from one fluorescence probe in the both channels) using available software (e.g. http://imagej.net/Category:Plugins and 16,17). 
	Note: Fluorescence intensities of individual channels are not altered by the software.</li>
	<li>To ensure subsequent image analysis, save movies corresponding to particular protein channels in grey scale as &#39;.tiff&#39; stacked file format in the work folder. 
	NOTE: The dimensions (i.e. size, length) of the stacks must be same for all channels.</li>
</ol>
4. Image Analysis &#8211; Step 1: Load Images
NOTE: The software described here was written in Matlab (referred to as programming software) and will run only with this program.
<ol>
	<li>Download the zipped folder containing all required files for image analysis from the following site:&#160;https://campus.uni-muenster.de/en/einrichtungen/impb0/nanoscale-forces-in-cells/software/. Unzip and copy files into the work folder.</li>
	<li>Once installed, open the programming software and run &#39;filopodiaAnalysisM3.fig&#39;. GUI Window #1 will open as shown in Figure 2A.</li>
	<li>Load the saved stacked &#39;.tiff&#39; files corresponding to a particular protein of interest in GUI Window #1 using the buttons &#60;1b&#62; for protein A, &#60;1c&#62; for protein B, &#60;1d&#62; for protein C. See Figure 2 and Table 1 for details. 
	NOTE: Protein A shown in GUI Window #1 acts as the reference channel for the final ratiometric analysis.</li>
	<li>Create a superimposed image of the cell from the protein channels by clicking &#60;1a&#62;.</li>
	<li>Click on &#60;2a&#62; to assign the first frame and &#60;2b&#62; for the last frame used for analysis.</li>
	<li>Optionally, crop the region of interest (ROI) containing the filopodium of interest using button &#60;2h&#62;, rotate the image using button &#60;2i&#62;, or delete unwanted regions using the free-hand drawing tool &#60;2j&#62;. 
	NOTE: To streamline image analysis, it is recommended to isolate the ROI (i.e. crop, rotate, delete) together with the other preprocessing steps (background subtraction, bleach corrections, etc.) using ImageJ.</li>
	<li>Move the slider &#60;2c&#62; for each frame for quality control and to check whether the filopodium remains clearly visible throughout the whole movie.</li>
</ol>
5. Image Analysis &#8211; Step 2: Generate Trace
<ol>
	<li>Click on button &#60;3a&#62; in the GUI Window #1 to open GUI Window #2 (see Figure 2B).</li>
	<li>Click on button &#60;4&#62; in the GUI Window #2 to generate the mask of the superimposed cell body (generated in GUI Window #1 after clicking &#60;1a&#62;). The program also generates the boundary of the mask, where the convex hull is implemented to get the vertex points.</li>
	<li>Click button &#60;5&#62;; a cursor will appear. Use the cursor to select the base (from where the distance of the filopodial tip is measured) followed by the tip of the filopodia in the frame where it first appears. To do this, move the slider in Window #2). 
	NOTE: To minimize errors in data output, place the base point vertically below the filopodial tip along the axis. Positioning the base point on other places (e.g. laterally shifted) may introduce a directional bias in fluorescence values within the cell body.</li>
	<li>Select the threshold length (above which the filopodia will bend) using &#60;6c&#62;. 
	Note: This length is defined as the distance between the base point (selected using &#60;5&#62;) and the boundary of the cell body (i.e. the region from where the filopodia starts growing). The distance is measured is in pixels.</li>
	<li>Specify the number of segments used to approximate the shape of the filopodia in box &#60;6a&#62;. 
	NOTE: The minimum number of segments depends on the maximal length reached by the filopodium, but also how the filopodium bends. Do not select a number of segments larger than the number of pixel between the base and the tip (i.e. the threshold length). Selecting more segments than the threshold length defined in step 5.4. (i.e. number of pixels between base and tip) will result in an overestimation of the length of the filopodium.</li>
	<li>Specify the scan width, which acts as horizontal scanner to place the nodes, in &#60;6b&#62;. 
	NOTE: These nodes are used by the program to fit the line joining the base and the tip with the body of the filopodium (i.e. to create the backbone). As a starting point, put a value in pixels equal to the maximum length multiplied by a factor of <img alt="Equation" src="/files/ftp_upload/55653/55653eq1.jpg" />.</li>
	<li>Specify the scan radius (in pixels) in box &#60;6d&#62;. Use a value that is approximately 50% larger than the observed inter-frame tip displacement. 
	NOTE: Using a very large scan radius might grab unwanted convex-hull points in frames where no real filopodial tip is present (e.g. out of plane movement or low SNR).</li>
	<li>Specify the bending angle in box &#60;6e&#62;. 
	NOTE: The angle threshold is determined by the maximum angle the filopodium is bending during the whole analysis. Specifying the angle threshold helps the software to exclude tracking of unwanted structures growing from the side of the filopodia when the filopodia bends towards the cell body. The program works reliably for filopodia with tilting angles less than 45 degrees from the vertical axis.</li>
	<li>To start tracking, click the &#60;Track&#38;Analyze&#62; button in GUI Window #2. Click the &#39;History trace&#39; box in GUI Window #2 to save the whole tracking protocol for future reference. 
	NOTE: After the tracking procedure is complete, the length of the filopodium in each frame is stored in the sheet named &#39;length_vel&#39; of &#39;dynamics.xlsx&#39; for future references. Likewise, all other tracking parameters are stored in the sheet named &#39;parameters&#39; of &#39;dynamics.xlsx&#39;.</li>
	<li>Optionally, if the filopodia is not automatically detected in all frames, use the following steps to manually correct for it.
	<ol>
		<li>Visit the respective frame using the slider in Window #2 and manually select the tip of the filopodium. 
		Note: Frames, where no convex hull points are detected will be represented by a blue region in the tracking window of GUI Window #2. It is mandatory to check the &#39;History trace&#39; button before the tracking program was initiated for accessing the nodal coordinates in that frame.</li>
		<li>Select the reference points (base followed by tip) using &#60;6f&#62; in GUI Window #2. Specify the other parameters such as &#39;scan length&#39;, &#39;Accurate Measurement after&#39;, and &#39;maximum bending angle&#39; for that frame as described in steps 5.4-5.8.</li>
		<li>Click button &#60;6i&#62; to save the new parameters (specific to that one frame). The steps are to be repeated for all frames indicated by the blue region.</li>
		<li>When finished, re-initialize the tracking procedure using &#60;7&#62;. 
		NOTE: This correction can also be used if the program suddenly switches from one filopodial to another within a movie. As an alternative, consider cropping movies that contain multiple filopodia.</li>
	</ol>
	</li>
</ol>
6. Image Analysis &#8211; Step 3: Spatio-temporal Protein Analysis
<ol>
	<li>For spatio-temporal analysis, select the box represented by &#60;8b&#62; followed by the protein channel of interest (&#60;8c&#62; and/or &#60;8d&#62; and/or &#60;8e&#62;). 
	Note: It is mandatory to select Protein A prior to ratiometric analysis (&#60;9a&#62;, &#60;9b&#62; and/or &#60;9c&#62;) since Protein A is used as reference.</li>
	<li>Click on &#60;8a&#62; to initiate protein tracking along the filopodial length using the trace generated in step 5.9.</li>
</ol>
7. Image Analysis &#8211; Step 4: Ratio-metric Protein Analysis
<ol>
	<li>Check box &#60;9b&#62; or &#60;9c&#62; and click on the button &#60;9a&#62; to get the spatio-temporal ratiometric plot. 
	NOTE: To avoid misrepresentation of relative protein concentration, the ratiometric image is not plotted as X/Y but as log(X/Y). For future use, the ratiometric plot is exported in &#39;.png&#39; and &#39;.fig&#39; format files, and raw plot data is stored within the &#39;dynamics.xlsx&#39; file.</li>
</ol>
8. Image Analysis &#8211; Step 5: Filopodial Tip Analysis
<ol>
	<li>Check box &#60;8f&#62; and specify the tip length and threshold length from the base using &#60;8g&#62; and &#60;8h&#62;. Click on &#60;push button&#62; to save data to file &#39;dynamics.xlsx&#39;. Click on &#60;Analyze Protein Intensities&#62; to generate the trace of protein intensities of the filopodial tip and to save for ratiometric analysis. 
	NOTE: Tip length determines the number of pixels at the tip used for the analysis. The software will return the average intensity value of the pixels at the tip over each frame. The threshold length determines the minimum distance from the base to tip above which the filopodium (filopodial tip) starts growing (and when the program starts tracking the intensity value of the tip). So the tip length must be smaller than the threshold length.</li>
	<li>Click on the desired ratio to be analyzed using &#60;9d&#62; or &#60;9e&#62;.</li>
	<li>Click on the compare button &#60;9a&#62; to generate the ratiometric data.</li>
</ol>

<ol>
	<li>Dunaevsky, A., Tashiro, A., Majewska, A., Mason, C., Yuste, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Developmental+regulation+of+spine+motility+in+the+mammalian+central+nervous+system.">Developmental regulation of spine motility in the mammalian central nervous system.</a> Proc Natl Acad Sci U S A. 96 (23), 13438-13443 (1999).</li><li>Matus, A., Brinkhaus, H., Wagner, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Actin+dynamics+in+dendritic+spines:+a+form+of+regulated+plasticity+at+excitatory+synapses.">Actin dynamics in dendritic spines: a form of regulated plasticity at excitatory synapses.</a> Hippocampus. 10 (5), 555-560 (2000).</li><li>Galic, 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=Dynamic+recruitment+of+the+curvature-sensitive+protein+ArhGAP44+to+nanoscale+membrane+deformations+limits+exploratory+filopodia+initiation+in+neurons.">Dynamic recruitment of the curvature-sensitive protein ArhGAP44 to nanoscale membrane deformations limits exploratory filopodia initiation in neurons.</a> Elife. 3, e03116 (2014).</li><li>Hotulainen, P., 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=Defining+mechanisms+of+actin+polymerization+and+depolymerization+during+dendritic+spine+morphogenesis.">Defining mechanisms of actin polymerization and depolymerization during dendritic spine morphogenesis.</a> J Cell Biol. 185 (2), 323-339 (2009).</li><li>Leijnse, N., Oddershede, L. B., Bendix, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Helical+buckling+of+actin+inside+filopodia+generates+traction.">Helical buckling of actin inside filopodia generates traction.</a> Proc Natl Acad Sci U S A. 112 (1), 136-141 (2015).</li><li>Tsygankov, D., 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=CellGeo:+a+computational+platform+for+the+analysis+of+shape+changes+in+cells+with+complex+geometries.">CellGeo: a computational platform for the analysis of shape changes in cells with complex geometries.</a> J Cell Biol. 204 (3), 443-460 (2014).</li><li>Xiong, Y., 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=Automated+characterization+of+cell+shape+changes+during+amoeboid+motility+by+skeletonization.">Automated characterization of cell shape changes during amoeboid motility by skeletonization.</a> BMC Syst Biol. 4, 33 (2010).</li><li>Styner, M., Gerig, G., Lieberman, J., Jones, D., Weinberger, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Statistical+shape+analysis+of+neuroanatomical+structures+based+on+medial+models.">Statistical shape analysis of neuroanatomical structures based on medial models.</a> Med Image Anal. 7 (3), 207-220 (2003).</li><li>Blum, H., Wathen-Dunn, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+transformation+for+extracting+new+descriptors+of+shape.">A transformation for extracting new descriptors of shape.</a> Models for the Perception of Speech and Visual Form: Proceedings of a Symposium. , 362-380 (1967).</li><li>Barry, D. J., Durkin, C. H., Abella, J. V., Way, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Open+source+software+for+quantification+of+cell+migration,+protrusions,+and+fluorescence+intensities.">Open source software for quantification of cell migration, protrusions, and fluorescence intensities.</a> J Cell Biol. 209 (1), 163-180 (2015).</li><li>Machacek, 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=Coordination+of+Rho+GTPase+activities+during+cell+protrusion.">Coordination of Rho GTPase activities during cell protrusion.</a> Nature. 461 (7260), 99-103 (2009).</li><li>Saha, 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=Automated+analysis+of+filopodial+length+and+spatially+resolved+protein+concentration+via+adaptive+shape+tracking.">Automated analysis of filopodial length and spatially resolved protein concentration via adaptive shape tracking.</a> Mol Biol Cell. 27 (22), 3616-3626 (2016).</li><li>Argiro, V., Bunge, M. B., Johnson, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+study+of+growth+cone+filopodial+extension.">A quantitative study of growth cone filopodial extension.</a> J Neurosci Res. 13 (1-2), 149-162 (1985).</li><li>Mogilner, A., Rubinstein, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+physics+of+filopodial+protrusion.">The physics of filopodial protrusion.</a> Biophys J. 89 (2), 782-795 (2005).</li><li>Shaner, N. C., Steinbach, P. A., Tsien, R. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+guide+to+choosing+fluorescent+proteins.">A guide to choosing fluorescent proteins.</a> Nat Methods. 2 (12), 905-909 (2005).</li><li>Abramoff, M. D., Magalhaes, P. J., Ram, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+Processing+with+ImageJ.">Image Processing with ImageJ.</a> Biophotonics International. 11, 36-42 (2004).</li><li>Courtney, J., Woods, E., Scholz, D., Hall, W. W., Gautier, V. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MATtrack:+A+MATLAB-Based+Quantitative+Image+Analysis+Platform+for+Investigating+Real-Time+Photo-Converted+Fluorescent+Signals+in+Live+Cells.">MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells.</a> PLoS One. 10 (10), e0140209 (2015).</li><li>Schell, M. J., Erneux, C., Irvine, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inositol+1,4,5-trisphosphate+3-kinase+A+associates+with+F-actin+and+dendritic+spines+via+its+N+terminus.">Inositol 1,4,5-trisphosphate 3-kinase A associates with F-actin and dendritic spines via its N terminus.</a> J Biol Chem. 276 (40), 37537-37546 (2001).</li><li>Korobova, F., Svitkina, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+architecture+of+synaptic+actin+cytoskeleton+in+hippocampal+neurons+reveals+a+mechanism+of+dendritic+spine+morphogenesis.">Molecular architecture of synaptic actin cytoskeleton in hippocampal neurons reveals a mechanism of dendritic spine morphogenesis.</a> Mol Biol Cell. 21 (1), 165-176 (2010).</li><li>Cheadle, L., Biederer, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+novel+synaptogenic+protein+Farp1+links+postsynaptic+cytoskeletal+dynamics+and+transsynaptic+organization.">The novel synaptogenic protein Farp1 links postsynaptic cytoskeletal dynamics and transsynaptic organization.</a> J Cell Biol. 199 (6), 985-1001 (2012).</li><li>Tarnok, K., 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=A+new+tool+for+the+quantitative+analysis+of+dendritic+filopodial+motility.">A new tool for the quantitative analysis of dendritic filopodial motility.</a> Cytometry A. 87 (1), 89-96 (2015).</li><li>Hendricusdottir, R., Bergmann, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=F-dynamics:+automated+quantification+of+dendrite+filopodia+dynamics+in+living+neurons.">F-dynamics: automated quantification of dendrite filopodia dynamics in living neurons.</a> J Neurosci Methods. 236, 148-156 (2014).</li><li>Fanti, Z., Martinez-Perez, M. E., De-Miguel, F. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NeuronGrowth,+a+software+for+automatic+quantification+of+neurite+and+filopodial+dynamics+from+time-lapse+sequences+of+digital+images.">NeuronGrowth, a software for automatic quantification of neurite and filopodial dynamics from time-lapse sequences of digital images.</a> Dev Neurobiol. 71 (10), 870-881 (2011).</li><li>Costantino, S., 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=Semi-automated+quantification+of+filopodial+dynamics.">Semi-automated quantification of filopodial dynamics.</a> J Neurosci Methods. 171 (1), 165-173 (2008).</li><li>Nilufar, S., Morrow, A. A., Lee, J. M., Perkins, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FiloDetect:+automatic+detection+of+filopodia+from+fluorescence+microscopy+images.">FiloDetect: automatic detection of filopodia from fluorescence microscopy images.</a> BMC Syst Biol. 7, 66 (2013).</li></ol>

The authors have nothing to disclose.

Here we present a detailed protocol for tracking filopodial growth dynamics and analysis of relative protein concentrations in these dynamic structures via the convex-hull algorithm. Using the software, up to 3 channels can be compared pair-wise in a single run, whereby the relative concentrations of two channels (i.e. proteins) is determined throughout the extension/retraction cycle and stored as image and data files in separate folders. In addition to the routine operations, the software also provides a number of parameters that can be modified to optimize analysis for the respective experiment. A detailed description of these modifications as well as troubleshooting can be found in the protocol section and in Table 1.
Image analysis is not limited to a particular microscope type, but rather by the image quality of the time series. Considering its relevance, image quality should therefore be optimized for each channel during acquisition. Our experiments show that the algorithm works best for filopodia with SNR &#62;4 acquired at 1 Hz using an objective with magnification above 40X and no pixel binning. Of course, this applies only as long as the fluorophores are compatible with the filter settings (i.e. no bleed-through; see protocol section 2 for details). Considering that relative intensities are compared against each other, the analysis is not sensitive to small differences in absolute fluorescence signal intensities between individual cells. Therefore, to deduce meaningful data, a well-defined reference channel (e.g. cytosolic marker) is of great importance. However, even if image quality is good there are some biological limitations. For instance, it is not well suited to analyze structures that are branching, tilting more than 45 degree from the axis orthogonal to the cell surface, or where other objects are crossing the structure that is being investigated.
In addition to these experimental limitations, there are critical steps within the protocol that need to be considered when operating the software, as this may otherwise trigger error messages. First, channels corresponding to a particular protein must be of same dimension and represent exactly the same ROI. Second, the stacked files must be in gray scale &#39;.tiff&#39; file format. Third, the stacked image files must be in the work folder. And finally, the names for data files in the work folder should not be changed, unless this is corrected for in the code.
How does the script compare to other existing methods? To probe our image analysis software, we downloaded a number of recently published software solutions that analyze filopodial features 6,10,21,22,23,24,25, and tested all for a number of selected criteria. A detailed comparison is presented in Table 2.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Tsygankov et al.</td>
			<td>Barry et al.</td>
			<td>Nilufar et al.</td>
			<td>Fanti et al.</td>
			<td>Constantino et al.</td>
			<td>Hendri- 
			cusdottir et al.</td>
			<td>Tarnok et al.</td>
			<td>Saha et al.</td>
		</tr>
		<tr>
			<td>Paper DOI</td>
			<td>10.1083/jcb.201306067</td>
			<td>10.1083/jcb.201501081</td>
			<td>10.1186/ 
			1752-0509-7-66</td>
			<td>10.1002/dneu.20866</td>
			<td>10.1016/j.jneumeth. 
			2008.02.009</td>
			<td>10.1016/j.jneumeth. 
			2014.08.016</td>
			<td>10.1002/cyto.a.22569</td>
			<td>10.1091/mbc. 
			E16-06-0406</td>
		</tr>
		<tr>
			<td>software (download link)</td>
			<td>http://www.hahnlab. 
			com/tools/ 
			THE 
			softwarepage. 
			html</td>
			<td>http://jcb-dataviewer. 
			rupress.org/jcb/ 
			browse/9059/</td>
			<td>http://www.perkinslab 
			.ca/pubs/ 
			NMLP20XX. 
			html</td>
			<td>http://www.ifc. 
			unam.mx/ 
			ffm/download.html</td>
			<td>http://fournierlab. 
			mcgill.ca/ 
			styled-7/fTracker.html</td>
			<td>https://fdynamics. 
			wordpress.com/</td>
			<td>http://cnblab. 
			elte.hu/dfma</td>
			<td>https://campus.uni-muenster.de/index.php?id=13794&#38;L=1</td>
		</tr>
		<tr>
			<td>program type</td>
			<td>Matlab</td>
			<td>ImageJ</td>
			<td>Matlab</td>
			<td>ImageJ</td>
			<td>Matlab</td>
			<td>Matlab</td>
			<td>ImageJ</td>
			<td>Matlab</td>
		</tr>
		<tr>
			<td>operation</td>
			<td>automated</td>
			<td>automated</td>
			<td>automated</td>
			<td>semi-automated</td>
			<td>semi-automated</td>
			<td>semi-automated</td>
			<td>semi-automated</td>
			<td>semi-automated</td>
		</tr>
		<tr>
			<td>single vs. batch analysis</td>
			<td>both</td>
			<td>both</td>
			<td>single</td>
			<td>single</td>
			<td>single</td>
			<td>single</td>
			<td>single</td>
			<td>single</td>
		</tr>
		<tr>
			<td>whole cell vs. subregion analysis</td>
			<td>whole</td>
			<td>whole</td>
			<td>both</td>
			<td>both</td>
			<td>both</td>
			<td>sub</td>
			<td>sub</td>
			<td>sub</td>
		</tr>
		<tr>
			<td>multiple vs. individual filopodia</td>
			<td>multiple</td>
			<td>multiple</td>
			<td>multiple</td>
			<td>multiple</td>
			<td>multiple</td>
			<td>ind</td>
			<td>ind</td>
			<td>ind</td>
		</tr>
		<tr>
			<td>filopodial ID and visual inspection possible</td>
			<td>yes</td>
			<td>yes</td>
			<td>no</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
		</tr>
		<tr>
			<td>Image processing possible</td>
			<td>no</td>
			<td>no</td>
			<td>no</td>
			<td>yes</td>
			<td>no</td>
			<td>no</td>
			<td>no</td>
			<td>yes</td>
		</tr>
		<tr>
			<td>filopodial length</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
		</tr>
		<tr>
			<td>filopodial growth dynamics</td>
			<td>yes</td>
			<td>yes</td>
			<td>no</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
		</tr>
		<tr>
			<td>filopodial lifetime</td>
			<td>yes</td>
			<td>yes</td>
			<td>no</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
			<td>yes</td>
		</tr>
		<tr>
			<td>ratiometric analysis in filopodia</td>
			<td>no</td>
			<td>no</td>
			<td>no</td>
			<td>no</td>
			<td>no</td>
			<td>no</td>
			<td>no</td>
			<td>yes</td>
		</tr>
		<tr>
			<td>output / storage format</td>
			<td>images / data file</td>
			<td>images / data file</td>
			<td>images / data file</td>
			<td>images / data file</td>
			<td>images / data file</td>
			<td>images / data file</td>
			<td>images / data file</td>
			<td>images / data file</td>
		</tr>
	</tbody>
</table>
Table 2: Overview of Software-assisted Image Analysis Solutions for Filopodia Quantification.
From the eight tested analysis software solutions, five were operated via Matlab, while the remaining three used ImageJ. Two software solutions (Table 2, to the left) were fully automated and optimized for batch analysis of filopodia length, lifetime and dynamics in whole cells. Of the remaining six software solutions, five required additional manual input (i.e. semi-automated), while one was fully automated. However, only the semi-automated solutions provide detailed information on filopodia growth dynamics, life time and identity. Among these remaining five, three were optimized for subcellular analysis of individual filopodia, while two could process multiple filopodia in parallel. Importantly, from all eight tested software solutions, only one approach allowed to combine information on filopodial growth dynamics with ratiometric protein localization within these finger-like structures (Table 2, to the right).
Although our image analysis software is not suitable for automated batch or whole cell analysis, quantification of an isolated filopodia takes only minutes. Thus, we envision that quantification of protein dynamics in 40-50 filopodia is feasible within a single day with the current software. In contrast, high-throughput screens may require further amendments to the code to streamline input and storage of thousands of individual filopodia in an automated manner. Based on the parameters that are required, other existing software solutions may thus be better suited for such an approach. Keeping these limitations in mind, the software provides a reliable platform for investigating spatio-temporal protein concentration in protrusion like structures in cells. Considering that filopodia are not the only fingerlike cellular structures, it is plausible to envision that the presented image analysis software may also be suitable to study protein dynamics in other biological processes (e.g. neurites, primary cilia).

Spatio-temporal control of actin regulatory proteins is associated with filopodium dynamics 1,2. Tracking spatially resolved protein concentration along the whole filopodial length through time is thus crucial to advance our understanding of the mechanisms underlying initiation, elongation, stabilization or collapse of these dynamic structures 3,4. Unlike protein analysis in the cytosol, where many cell shape changes occur at a larger scale, filopodia are dynamic micro structures that constantly buckle 5 and bend, thus precluding analysis using a simple approach such as a line-scan.
Different software solutions for tracking filopodial shape are available 6,7,8,9. Likewise, software for ratiometric tracking of protein dynamics within the cell body has been developed 10,11. To combine automated tracking of filopodial shape and spatio-temporal protein analysis, we recently developed an image analysis software based on the convex-hull algorithm 12. This novel analysis method, which is operated via a graphical user interface (GUI), combines for the first time, relative protein concentration along the filopodial length and growth velocity, thus allowing the accurate measurement of spatio-temporal protein distribution independent of movement of these dynamic structures 12.
The idea behind the software (source code is freely available, see below) is that one of the vertices of the convex hull will coincide with the tip of the filopodium (Figure 1A). By looking in the subsequent frame for the nearest vertex of the convex-hull, the moving tip can be tracked throughout the whole movie. Once the tip is detected in each frame, its position is used to draw an axis by joining the tip with a reference point at the base of the filopodium (Figure 1B). Finally, using equidistant nodal points, whose positions are determined by the median pixel with maximum intensity along the line orthogonal to the axis, are used to determine a backbone that follows the filopodial shape. Taking advantage of this adaptive backbone, a kymograph is generated to trace filopodial growth and protein concentrations for up to three channels along the filopodial length (Figure 1C).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55653/55653fig1.jpg" /> 
Figure 1: Working Principle of the Image Analysis Software. (A) The algorithm behind the software. In Step1 the user specifies the reference (base) and the vertex (the tip) of the filopodium. In Step 2-1 the backbone of the filopodium is obtained using the median pixel with maximum intensity value. In Step 3 the backbone is used for spatial protein intensity profile. In Step 2-2 the software automatically tracks the tip in the subsequent frame. The whole procedure iterates. (B) Snapshot of the algorithm with real filopodium introducing important elements such as the convex hull that is being used for tracking. (C) Overview of parameters that can be measured with the algorithm. This figure has been modified from reference 12. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55653/55653fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The image analysis software is operated in Matlab (referred to as programming software) via a graphical user interface. To maximize flexibility and robustness for the particular experimental setting, the user can adjust a series of tracking parameters (e.g. permitted bending angle and inter-frame movement) and also make some corrections to the movies (e.g. cropping, rotation, removal of unwanted objects) (Figure 2A and Table 1).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>GUI</td>
			<td>No.</td>
			<td>Mandatory</td>
			<td colspan="5">Description</td>
			<td>Name (in GUI)</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>1a</td>
			<td>Y</td>
			<td colspan="5">Loading stacked .tiff file representing cell body (with box checked in) or create superimposed cell body from channels</td>
			<td>CellBody</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>1b</td>
			<td>Y</td>
			<td colspan="5">Loading stacked image file corresponding to protein 1</td>
			<td>Protein 1</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>1c</td>
			<td>Y</td>
			<td colspan="5">Loading stacked image file corresponding to protein 2</td>
			<td>Protein 2</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>1d</td>
			<td>Y</td>
			<td colspan="5">Loading stacked image file corresponding to protein 3</td>
			<td>Protein 3</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>1e</td>
			<td>N</td>
			<td colspan="5">Resets everything to preloaded stacked image files</td>
			<td>Reset</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2a</td>
			<td>Y</td>
			<td colspan="5">Scroll bar to determine the initial frame for analysis in GUI window #2</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2b</td>
			<td>Y</td>
			<td colspan="5">Scroll bar to determine the final frame for analysis in GUI window #2</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2c</td>
			<td>Y</td>
			<td colspan="5">Scroll bar representing current frame</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2d</td>
			<td>N</td>
			<td colspan="5">The grey value of the pixels below which all pixels will be set to zero</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2e</td>
			<td>N</td>
			<td colspan="5">The grey value of the pixels above which all pixels will be set to maximum values</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2f</td>
			<td>N</td>
			<td colspan="5">Set the intensity values of the pixels specified by &#60;2e&#62; &#38; &#60;2f&#62;</td>
			<td>Set</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2g</td>
			<td>N</td>
			<td colspan="5">Play the intensity-adjusted movie</td>
			<td>Play</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2h</td>
			<td>N</td>
			<td colspan="5">Crop Image</td>
			<td>Crop</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2i</td>
			<td>N</td>
			<td colspan="5">Rotate Image</td>
			<td>Rotate</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>2j</td>
			<td>N</td>
			<td colspan="5">Delete regions in the whole stack</td>
			<td>Delete regions</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>3a</td>
			<td>Y</td>
			<td colspan="5">Click to open the &#8216;Analysis Window&#8217; (GUI window #2)</td>
			<td>Tracking Window</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>3b</td>
			<td>Y</td>
			<td colspan="5">Enter the size of a pixel in microns</td>
			<td>Enter Pixel size</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>4</td>
			<td>Y</td>
			<td colspan="5">Click to generate the boundary/edge image of the superimposed cell body</td>
			<td>Boundary</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>5</td>
			<td>Y</td>
			<td colspan="5">Click to select the base and tip of the filopodia</td>
			<td>Referernce</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6a</td>
			<td>Y</td>
			<td colspan="5">Enter the number of segments or nodes</td>
			<td>No of segments</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6b</td>
			<td>Y</td>
			<td colspan="5">Enter the scan length (perpendicular to axis)</td>
			<td>Scan Width</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6c</td>
			<td>Y</td>
			<td colspan="5">Enter the length above which filopodia starts bending</td>
			<td>Accurate Meas after</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6d</td>
			<td>Y</td>
			<td colspan="5">Enter the radius of the tip detection circle (i.e. area where the vertex can be localized in the next frame)</td>
			<td>Radius of tip detection</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6e</td>
			<td>Y</td>
			<td colspan="5">Enter the maximum angle the filopodium can bend from the vertical axis</td>
			<td>Angle Threshold</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6f</td>
			<td>N</td>
			<td colspan="5">Add reference points for base and tip for that specific frame</td>
			<td>Select reference</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6g</td>
			<td>N</td>
			<td colspan="5">Enter the length above which filopodia starts bending for that specific frame</td>
			<td>Accurate Meas after</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6h</td>
			<td>N</td>
			<td colspan="5">Enter the radius of the detection circle for that specific frame</td>
			<td>Radius of tip detection</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6i</td>
			<td>N</td>
			<td colspan="5">After entering all the parameters for the specific frame click to store the values to memory and file for further reference</td>
			<td>Add</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6j</td>
			<td>N</td>
			<td colspan="5">Click to delete the set manually parameters for that frame</td>
			<td>Delete</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6k</td>
			<td>N</td>
			<td colspan="5">Click to delete all parameters stored manually using the &#8216;optional features panel&#8217; for all frames</td>
			<td>Reset</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>6l</td>
			<td>N</td>
			<td colspan="5">Check in before tracking to store all tracking results in memory for future reference</td>
			<td>History trace</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>7</td>
			<td>Y</td>
			<td colspan="5">Click to start tracking</td>
			<td>Track&#38;Analyze</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8a</td>
			<td>N</td>
			<td colspan="5">Click to start tracking protein channel intensity</td>
			<td>Analyze Protein Intensities</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8b</td>
			<td>N</td>
			<td colspan="5">Check in to track protein channel intensity along the filopodial length</td>
			<td>Whole filopodia</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8c</td>
			<td>N</td>
			<td colspan="5">Check in for tracking the reference protein or protein A</td>
			<td>ProteinA</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8d</td>
			<td>N</td>
			<td colspan="5">Check in for tracking the protein B</td>
			<td>ProteinB</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8e</td>
			<td>N</td>
			<td colspan="5">Check in for tracking the protein C</td>
			<td>ProteinC</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8f</td>
			<td>N</td>
			<td colspan="5">Check in to track average protein intensity in the tip</td>
			<td>Leading tip</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8g</td>
			<td>N</td>
			<td colspan="5">Enter the length of the tip</td>
			<td>Tip Length</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8h</td>
			<td>N</td>
			<td colspan="5">Enter the minimum distance from base above which the tip starts forming</td>
			<td>threshold</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>8i</td>
			<td>N</td>
			<td colspan="5">Click to save the leading tip analysis results to file</td>
			<td>Push Button</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>9a</td>
			<td>N</td>
			<td colspan="5">Click to initiate ratiometric protein analysis</td>
			<td>Compare</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>9b</td>
			<td>N</td>
			<td colspan="5">Check in to compare protein B with respect to A</td>
			<td>log10(B/A)</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>9c</td>
			<td>N</td>
			<td colspan="5">Check in to compare protein C with respect to A</td>
			<td>log10(C/A)</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>9d</td>
			<td>N</td>
			<td colspan="5">Check in to compare protein B with respect to A at the tip</td>
			<td>log10(B/A)</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>9e</td>
			<td>N</td>
			<td colspan="5">Check in to compare protein C with respect to A at the tip</td>
			<td>log10(C/A)</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>10a</td>
			<td>N</td>
			<td colspan="5">Choose other color-map (default: Jetplot)</td>
			<td>Color Map</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>10b</td>
			<td>N</td>
			<td colspan="5">Edit the colormap</td>
			<td>Edit colormap</td>
		</tr>
	</tbody>
</table>
Table 1: Overview of All Functions Present in the GUI Windows #1 and #2.
Once this is accomplished, the program creates a convex hull and automatically tracks the tip throughout the movie. Parameters extracted from the movie, such as a ratiometric kymograph, growth velocity, and filopodial length are displayed and also stored in the work folder as images and as data files. Other parameters such as filopodial lifetime, growth rate and retraction rate can then be extracted and further analyzed from the stored data files (Figure 2B).
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55653/55653fig2.jpg" /> 
Figure 2: Graphical User Interface for using the Image Analysis Software. (A) GUI Window #1 is used for loading and processing images. The program can load up to 3 protein channels, whereby 2 channels are compared pair-wise. The window comes with mandatory (blue) and optional features (green) for pre-processing the images prior to tracking (B) GUI Window #2 is used for tracking the filopodium as well as spatio-temporal and ratio-metric protein analysis. Again, optional features are marked in green. This figure has been modified from reference 12. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55653/55653fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Here, we present a detailed protocol for sample preparation and software handling. We start with detailed instructions on culturing cells and acquiring movies optimized for image analysis. This section on data acquisition is followed by a detailed description for operating the image analysis software. Throughout the protocol, we introduce critical steps and optional features that should be considered when collecting and processing data. Finally, we analyze filopodia from different model systems with the image analysis software, before closing with a comparison of the described image analysis software with other programs available for filopodial quantification and a discussion on limitations and future direction.

<table><tbody><tr><td>DMEM</td><td>Life Technologies</td><td>31966-021</td><td></td></tr><tr><td>10% Fetal bovine serum</td><td>Biochrom AG</td><td>L11-044</td><td></td></tr><tr><td>Lipofectamine 2000</td><td>Life Technologies</td><td>11668-027</td><td></td></tr><tr><td>1% penicillin/streptomycin</td><td>Biochrom AG</td><td>12212</td><td></td></tr><tr><td>Neurobasal Medium</td><td>Life Technologies</td><td>21103-049</td><td></td></tr><tr><td>B27</td><td>Life Technologies</td><td>17504-044</td><td></td></tr><tr><td>HEPES (1M stock solution)</td><td>Life Technologies</td><td>15630</td><td></td></tr><tr><td>Citrine-N1</td><td>Addgene</td><td>54593</td><td></td></tr><tr><td>Labtech</td><td>Thermo</td><td>155411</td><td></td></tr><tr><td>Glutamax-I</td><td>Thermo</td><td>35050-061</td><td></td></tr><tr><td>Hela</td><td>Leibniz Institute DSMZ</td><td>ACC-57</td><td></td></tr><tr><td>COS 7</td><td>Leibniz Institute DSMZ</td><td>ACC-60</td><td></td></tr><tr><td>3T3 cells</td><td>Leibniz Institute DSMZ</td><td>ACC-59</td><td></td></tr><tr><td>Microscope</td><td>Nicon Eclipse</td><td></td><td></td></tr><tr><td>Camera</td><td>Andor</td><td>DU888 Ultra</td><td></td></tr><tr><td>Confocal Unit</td><td>Yokagawa</td><td>CSU-X1</td><td></td></tr><tr><td>Pyruvate</td><td>Gibco</td><td>31966-021</td><td></td></tr></tbody></table>

a graphical user interface for software-assisted tracking of protein concentration in dynamic cellular protrusions

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex signaling mechanisms underlying filopodial initiation, elongation and subsequent stabilization or retraction, it is crucial to determine the spatio-temporal protein activity in these dynamic structures. To analyze protein function in filopodia, we recently developed a semi-automated tracking algorithm that adapts to filopodial shape-changes, thus allowing parallel analysis of protrusion dynamics and relative protein concentration along the whole filopodial length. Here, we present a detailed step-by-step protocol for optimized cell handling, image acquisition and software analysis. We further provide instructions for the use of optional features during image analysis and data representation, as well as troubleshooting guidelines for all critical steps along the way. Finally, we also include a comparison of the described image analysis software with other programs available for filopodia quantification. Together, the presented protocol provides a framework for accurate analysis of protein dynamics in filopodial protrusions using image analysis software.

Using COS cells transfected with a marker for filamentous actin (f-tractin18, red) and a cytosolic reference (green), we found actin-rich filopodial protrusions (Figure 3A, top panel). Time series showed that filopodia rapidly extend and retract (Figure 3A, middle panel). Using the image analysis software, we then traced individual filopodia. Comparison of filopodial length measured by hand vs. the image analysis software showed a Pearson cross-correlation value of 0.947 arguing that the software reliably tracked filopodial extension 12. In addition to growth and retraction rates of an individual filopodia, kymographs at the bottom of Figure 3A also depicts fluorescence intensities of actin (top), the cytosolic reference (middle), and the relative concentration of actin normalized to the cytosolic reference (bottom). Together, these experiments provide evidence that the program reliably measures growth dynamics as well as spatially resolved relative protein concentration throughout the full life-cycle of an individual filopodium in COS cells.
Next, we investigated filopodia dynamics in cultured mouse hippocampal neurons (Figure 3B, top panel). For this, neurons were transfected at day in vitro (DIV) 8 with f-tractin (red) and a cytosolic reference (green) (Figure 3B, middle panel). As in the previous experiment, kymographs depicting fluorescence intensities of actin and cytosolic reference showed that relative enrichment of actin preceded formation of exploratory dendritic filopodia (Figure 3B, bottom panel). These findings are in line with published work, describing formation of actin-rich patches prior to filopodial elongation 3,19,20.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55653/55653fig3.jpg" /> 
Figure 3: Examples of Filopodia Analyzed by the Software. (A) Overview image (top panel) and time series (middle panel) of COS cells transfected with f-tractin (red) and cytosolic marker (green). Below, plots depicting analysis of filopodial growth dynamics and relative concentration of f-tractin along the filopodium (bottom panels) are shown. (B) Overview image of hippocampal neuron transfected with cytosolic marker (top) and time series of neuron transfected with f-tractin (red) and cytosolic marker (green) (middle panel), as well as plot depicting analysis of dendritic filopodial growth dynamics and relative concentration of f-tractin along the protrusion (bottom panels). (C) SEM image (left panel) and time series of HeLa cells transfected with f-tractin (right panel). Note fluctuations in fluorescence intensity due to filopodial buckling. Scale bars = 20 &#181;m (A, top), 50 &#181;m (B, top), 10 &#181;m (C, top). This figure has been modified from reference 12. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55653/55653fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Finally, we aimed to challenge the software by monitoring filopodia in HeLa cells. Filopodia in HeLa cells are long (Figure 3C, left panel) and highly dynamic, often leaving the plane of acquisition. Intriguingly, when focusing on the basal membrane, we observed a sub-fraction of filopodia to undergo helical twisting (Figure 3C, top right panel), whereby parts of the filopodium transiently left the focal plane. Accordingly, kymographs depicting the absolute fluorescence intensity of f-tractin showed wave-like changes in fluorescence intensity through time (Figure 3C, bottom right panel). This behavior is reminiscent of filopodial buckling, a mechanism recently described to be used by filopodia to exert pulling forces 5.
In summary, these experiments provide evidence that the software reliably tracks protein concentration and growth dynamics of filopodial from various origins.

Watch this Scientific Journal Video about A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions at JoVE.com

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

We present a software solution for semi-automated tracking of relative protein concentration along the length of dynamic cellular protrusions.

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex ...

a-graphical-user-interface-for-software-assisted-tracking-protein

Universidad San Sebastian

Research

JoVE Journal

Biology

7.3K Views.  University of Muenster. We present a software solution for semi-automated tracking of relative protein concentration along the length of dynamic cellular protrusions.

Video: A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring cells. Of interest, only few assays are capable of specifically probing such interactions directly in living cells. Here, we present an assay to measure the binding of proteins expressed at the surfaces of neighboring cells, at cell-cell contacts. This assay consists of two steps: mixing of cells expressing the proteins of interest fused to different fluorescent proteins, followed by fluorescence fluctuation spectroscopy measurements at cell-cell contacts using a confocal laser scanning microscope. We demonstrate the feasibility of this assay in a biologically relevant context by measuring the interactions of the amyloid precursor-like protein 1 (APLP1) across cell-cell junctions. We provide detailed protocols on the data acquisition using fluorescence-based techniques (scanning fluorescence cross-correlation spectroscopy, cross-correlation number and brightness analysis) and the required instrument calibrations. Further, we discuss critical steps in the data analysis and how to identify and correct external, spurious signal variations, such as those due to photobleaching or cell movement.
In general, the presented assay is applicable to any homo- or heterotypic protein-protein interaction at cell-cell contacts, between cells of the same or different types and can be implemented on a commercial confocal laser scanning microscope. An important requirement is the stability of the system, which needs to be sufficient to probe diffusive dynamics of the proteins of interest over several minutes.

This protocol describes a fluorescence fluctuation spectroscopy-based approach to investigate interactions among proteins mediating cell-cell interactions, i.e. proteins localized in cell junctions, directly in living cells. We provide detailed guidelines on instrument calibration, data acquisition and analysis, including corrections to possible artefact sources.

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring ...

Biochemistry

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and millisecond temporal resolution. These capabilities make single-molecule localization microscopy ideally suited to study molecular level biological functions in physiologically relevant environments. Here, we demonstrate an integrated protocol for both acquisition and processing/analysis of single-molecule tracking data to extract the different diffusive states a protein of interest may exhibit. This information can be used to quantify molecular complex formation in living cells. We provide a detailed description of a camera-based 3D single-molecule localization experiment, as well as the subsequent data processing steps that yield the trajectories of individual molecules. These trajectories are then analyzed using a numerical analysis framework to extract the prevalent diffusive states of the fluorescently labeled molecules and the relative abundance of these states. The analysis framework is based on stochastic simulations of intracellular Brownian diffusion trajectories that are spatially confined by an arbitrary cell geometry. Based on the simulated trajectories, raw single-molecule images are generated and analyzed in the same way as experimental images. In this way, experimental precision and accuracy limitations, which are difficult to calibrate experimentally, are explicitly incorporated into the analysis workflow. The diffusion coefficient and relative population fractions of the prevalent diffusive states are determined by fitting the distributions of experimental values using linear combinations of simulated distributions. We demonstrate the utility of our protocol by resolving the diffusive states of a protein that exhibits different diffusive states upon forming homo- and hetero-oligomeric complexes in the cytosol of a bacterial pathogen.

3D single-molecule localization microscopy is utilized to probe the spatial positions and motion trajectories of fluorescently labeled proteins in living bacterial cells. The experimental and data analysis protocol described herein determines the prevalent diffusive behaviors of cytosolic proteins based on pooled single-molecule trajectories.

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and ...

High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., kinetics, coexistence of different states and populations, transient interactions), which are typically hidden in ensemble measurements, such as those obtained with standard biochemical or microscopy methods. Thus, dynamic events, such as receptor-receptor interactions, can be followed in real time in a living cell with high spatiotemporal resolution. This protocol describes a method based on labeling with small and bright organic fluorophores and total internal reflection fluorescence (TIRF) microscopy to directly visualize single receptors on the surface of living cells. This approach allows one to precisely localize receptors, measure the size of receptor complexes, and capture dynamic events such as transient receptor-receptor interactions. The protocol provides a detailed description of how to perform a single-molecule experiment, including sample preparation, image acquisition and image analysis. As an example, the application of this method to analyze two G-protein-coupled receptors, i.e., &#946;2-adrenergic and &#947;-aminobutyric acid type B (GABAB) receptor, is reported. The protocol can be adapted to other membrane proteins and different cell models, transfection methods and labeling strategies.

This protocol describes how to use total internal reflection fluorescence microscopy to visualize and track single receptors on the surface of living cells and thereby analyze receptor lateral mobility, size of receptor complexes as well as to visualize transient receptor-receptor interactions. This protocol can be extended to other membrane proteins.

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., ...

Bioengineering

Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking

Protein-DNA interactions are at the heart of many fundamental cellular processes. For example, DNA replication, transcription, repair, and chromosome organization are governed by DNA-binding proteins that recognize specific DNA structures or sequences. In vitro experiments have helped to generate detailed models for the function of many types of DNA-binding proteins, yet, the exact mechanisms of these processes and their organization in the complex environment of the living cell remain far less understood. We recently introduced a method for quantifying DNA-repair activities in live Escherichia coli cells using Photoactivated Localization Microscopy (PALM) combined with single-molecule tracking. Our general approach identifies individual DNA-binding events by the change in the mobility of a single protein upon association with the chromosome. The fraction of bound molecules provides a direct quantitative measure for the protein activity and abundance of substrates or binding sites at the single-cell level. Here, we describe the concept of the method and demonstrate sample preparation, data acquisition, and data analysis procedures.

Photoactivated localization microscopy (PALM) combined with single-molecule tracking allows direct observation and quantification of protein-DNA interactions in live Escherichia coli cells.

Protein-DNA interactions are at the heart of many fundamental cellular processes. For example, DNA replication, transcription, repair, and chromosome ...

Immunology and Infection

Label-Free Imaging of Single Proteins Secreted from Living Cells via iSCAT Microscopy

We demonstrate interferometric scattering (iSCAT) microscopy, a method capable of detecting single unlabeled proteins secreted from individual living cells in real time. In this protocol, we cover the fundamental steps to realize an iSCAT microscope and complement it with additional imaging channels to monitor the viability of a cell under study. Following this, we use the method for real-time detection of single proteins as they are secreted from a living cell which we demonstrate with an immortalized B-cell line (Laz388). Necessary steps concerning the preparation of microscope and sample as well as the analysis of the recorded data are discussed. The video protocol demonstrates that iSCAT microscopy offers a straightforward method to study secretion at the single-molecule level.

We present a protocol for the real-time optical detection of single unlabeled proteins as they are secreted from living cells. This is based on interferometric scattering (iSCAT) microscopy, which can be applied to a variety of different biological systems and configurations.

We demonstrate interferometric scattering (iSCAT) microscopy, a method capable of detecting single unlabeled proteins secreted from individual living ...

Engineering

Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics

Short-lived or transient interactions of macromolecules at and with lipid membranes, an interface where a multitude of essential biological reactions take place, are inherently difficult to assess with standard biophysical methods. The introduction of mass-sensitive particle tracking (MSPT) constitutes an important step toward a thorough quantitative characterization of such processes. Technically, this was made possible through the advent of interferometric scattering microscopy (iSCAT)-based mass photometry (MP). When the background removal strategy is optimized to reveal the two-dimensional motion of membrane-associated particles, this technique allows the real-time analysis of both diffusion and molecular mass of unlabeled macromolecules on biological membranes. Here, a detailed protocol to perform and analyze mass-sensitive particle tracking of membrane-associated systems is described. Measurements performed on a commercial mass photometer achieve time resolution in the millisecond regime and, depending on the MP system, a mass detection limit down to 50 kDa. To showcase the potential of MSPT for the in-depth analysis of membrane-catalyzed macromolecule dynamics in general, results obtained for exemplary protein systems such as the native membrane interactor annexin V are presented.

This protocol describes an iSCAT-based image processing and single-particle tracking approach that enables the simultaneous investigation of the molecular mass and the diffusive behavior of macromolecules interacting with lipid membranes. Step-by-step instructions for sample preparation, mass-to-contrast conversion, movie acquisition, and post-processing are provided alongside directions to prevent potential pitfalls.

Short-lived or transient interactions of macromolecules at and with lipid membranes, an interface where a multitude of essential biological reactions take ...

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using the analysis of cell membrane receptor clusters as a model, we present a detailed protocol for this image analysis task using Fiji (ImageJ) and Matlab routines to: 1) define regions of interest and design masks adapted to these regions; 2) track the particles in fluorescence microscopy videos; 3) analyze the diffusion and intensity characteristics of selected tracks. The quantitative analysis of the diffusion coefficients, types of motion, and cluster size obtained by fluorescence microscopy and image processing provides a valuable tool to objectively determine particle dynamics and the consequences of modifying environmental conditions. In this article we present detailed protocols for the analysis of these features. The method described here not only allows single-molecule tracking detection, but also automates the estimation of lateral diffusion parameters at the cell membrane, classifies the type of trajectory and allows complete analysis thus overcoming the difficulties in quantifying spot size over its entire trajectory at the cell membrane.

Here, we present a protocol for single particle tracking image analysis that allows quantitative evaluation of diffusion coefficients, types of motion and cluster sizes of single particles detected by fluorescence microscopy.

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using ...

Micromanipulation Techniques Allowing Analysis of Morphogenetic Dynamics and Turnover of Cytoskeletal Regulators

Examining the spatiotemporal dynamics of proteins can reveal their functional importance in various contexts. In this article, it is discussed how fluorescent recovery after photobleaching (FRAP) and photoactivation techniques can be used to study the spatiotemporal dynamics of proteins in subcellular locations. We also show how these techniques enable straightforward determination of various parameters linked to actin cytoskeletal regulation and cell motility. Moreover, the microinjection of cells is additionally described as an alternative treatment (potentially preceding or complementing the aforementioned photomanipulation techniques) to trigger instantaneous effects of translocated proteins on cell morphology and function. Micromanipulation such as protein injection or local application of plasma membrane-permeable drugs or cytoskeletal inhibitors can serve as powerful tool to record immediate consequences of a given treatment on cell behavior at the single cell and subcellular level. This is exemplified here by immediate induction of lamellipodial cell edge protrusion by the injection of recombinant Rac1 protein, as established a quarter-century ago. In addition, we provide a protocol for determining the turnover of enhanced green fluorescent protein (EGFP)-VASP, an actin filament polymerase prominently accumulating at lamellipodial tips of B16-F1 cells, employing FRAP and including associated data analysis and curve fitting. We also present guidelines for estimating the rates of lamellipodial actin network polymerization, as exemplified by cells expressing EGFP-tagged &#946;-actin. Finally, instructions are given for how to investigate the rates of actin monomer mobility within the cell cytoplasm, followed by actin incorporation at sites of rapid filament assembly, such as the tips of protruding lamellipodia, using photoactivation approaches. None of these protocols is&#160;restricted to components or regulators of the actin cytoskeleton, but can easily be extended to explore in analogous fashion the spatiotemporal dynamics and function of proteins in various different subcellular structures or functional contexts.

We describe how micro- and photomanipulation techniques such as FRAP and photoactivation enable the determination of motility parameters and the spatiotemporal dynamics of proteins within migrating cells. Experimental readouts include subcellular dynamics and turnover of motility regulators or of the underlying actin cytoskeleton.

Examining the spatiotemporal dynamics of proteins can reveal their functional importance in various contexts. In this article, it is discussed how ...

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Imaging-derived mean square displacement (iMSD) is used to address the structural and dynamic properties of subcellular nanostructures, such as vesicles involved in the endo/exocytotic trafficking of solutes and biomolecules. iMSD relies on standard time-lapse imaging, is compatible with any optical setup, and does not need to dwell on single objects to extract trajectories. From each iMSD trace, a unique triplet of average structural and dynamic parameters (i.e., size, local diffusivity, anomalous coefficient) is calculated and combined to build the "iMSD signature" of the nanostructure under study. 
The potency of this approach is proved here with the exemplary case of macropinosomes. These vesicles evolve in time, changing their average size, number, and dynamic properties passing from early to late stages of intracellular trafficking. As a control, insulin secretory granules (ISGs) are used as a reference for subcellular structures that live in a stationary state in which the average structural and dynamic properties of the whole population of objects are invariant in time. The iMSD analysis highlights these peculiar features quantitatively and paves the way to similar applications at the sub-cellular level, both in the physiological and pathological states.

Imaging-derived mean square displacement (iMSD) analysis is applied to macropinosomes to highlight their intrinsic time-evolving nature in terms of structural and dynamic properties. Macropinosomes are then compared to insulin secretory granules (ISGs) as a reference for subcellular structures with time-invariant average structural/dynamic properties.

Imaging-derived mean square displacement (iMSD) is used to address the structural and dynamic properties of subcellular nanostructures, such as vesicles ...

Measuring Cell-Edge Protrusion Dynamics during Spreading using Live-Cell Microscopy

The development and homeostasis of multicellular organisms rely on coordinated regulation of cell migration. Cell migration is an essential event in the construction and regeneration of tissues, and is critical in embryonic development, immunological responses, and wound healing. Dysregulation of cell motility contributes to pathological disorders, such as chronic inflammation and cancer metastasis. Cell migration, tissue invasion, axon, and dendrite outgrowth all initiate with actin polymerization-mediated cell-edge protrusions. Here, we describe a simple, efficient, time-saving method for the imaging and quantitative analysis of cell-edge protrusion dynamics during spreading. This method measures discrete features of cell-edge membrane dynamics, such as protrusions, retractions, and ruffles, and can be used to assess how manipulations of key actin regulators impact cell-edge protrusions in diverse contexts.

This protocol aims to measure the dynamic parameters (protrusions, retractions, ruffles) of protrusions at the edge of spreading cells.

The development and homeostasis of multicellular organisms rely on coordinated regulation of cell migration. Cell migration is an essential event in the ...