Name: a graphical user interface for software-assisted tracking of protein concentration in dynamic cellular protrusions
Uploaded: 2017-07-11T14:00:00
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 c...

<ol>
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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...

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="http://cloudflare.jove.com/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="http://cloudflare.jove.com/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 border="0" cellpadding="0" cellspacing="0" width="816">
	<tbody>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">DMEM</td>
			<td style="width:204px;">Life Technologies</td>
			<td style="width:204px;">31966-021</td>
			<td style="width:204px;"></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">10% Fetal bovine serum</td>
			<td style="width:204px;">Biochrom AG</td>
			<td style="width:204px;">L11-044</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">Lipofectamine 2000</td>
			<td style="width:204px;">Life Technologies</td>
			<td style="width:204px;">11668-027</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">1% penicillin/streptomycin</td>
			<td style="width:204px;">Biochrom AG</td>
			<td style="width:204px;">12212</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">Neurobasal Medium</td>
			<td style="width:204px;">Life Technologies</td>
			<td style="width:204px;">21103-049</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">B27</td>
			<td style="width:204px;">Life Technologies</td>
			<td style="width:204px;">17504-044</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">HEPES (1M stock solution)</td>
			<td style="width:204px;">Life Technologies</td>
			<td style="width:204px;">15630</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">Citrine-N1</td>
			<td style="width:204px;">Addgene</td>
			<td style="width:204px;">54593</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;">Labtech</td>
			<td>Thermo</td>
			<td>155411</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">Glutamax-I</td>
			<td>Thermo</td>
			<td>35050-061</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">Hela</td>
			<td>Leibniz Institute DSMZ</td>
			<td>ACC-57</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">COS 7</td>
			<td>Leibniz Institute DSMZ</td>
			<td>ACC-60</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">3T3 cells</td>
			<td>Leibniz Institute DSMZ</td>
			<td>ACC-59</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">Microscope</td>
			<td>Nicon Eclipse</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">Camera</td>
			<td>Andor</td>
			<td>DU888 Ultra</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">Confocal Unit</td>
			<td>Yokagawa</td>
			<td>CSU-X1</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:204px;">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 a...

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 ...

The overall goal of this image analysis software is the parallel analysis of protrusion dynamics and relative protein concentration along the entire filopodial length. This method can really help us understand key questions in cell biology, especially the individual proteins involved in extension and retraction of filopodia. The main advantage of the technique is that the adaptive shape tracking algorithm provided, allows quantitative assessment of protrusion dynamics and spatially resolved protein localisation.To begin, culture neurons, HeLa or COS cells, in supplemented medium as detailed in the text protocol. Once 40%confluency is reached, transvect the cells with the constructs of choice, using a transvection reagent as per manufacturer's instructions. Keep the transvected cells in an incubator at 37 degree celsius and 5%CO2 for 15 to 18 hours.Following incubation, fill the cell culture chambers up to 90%with 37 degree celsius pre-warmed medium, containing 20 milli molar HEPES, to reduce changes in pH and osmolarity due to evaporation during image acquisition. 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 transvected cells. Possibly the most important part for image analysis is the image quality.One thing we have to take care is the signal to noise ratio, which has to be more than four and we can ensure that, by adjusting the exposure time of the camera and the laser intensity and force would tracking the frame rate has to be more than one hertz. Acquire the images using a microscope with a 60x or 100x objective and no pixel bending. Use acquisition rates not less than one hertz to track filopodial dynamics.To minimize out of focus artifacts, image filopodial close to the basil membrane. To ensure smooth tracking, adjust the exposure time of the camera and laser intensity such that the signal to noise ratio is greater than four. Once this is complete, start the image acquisition.Following image pre-processing, as described in the text protocol, load the images by downloading the zipped folder containing all required files for image analysis. Unzip and copy files into the work folder. Once installed, start the graphical user interface by opening the file named, filopodiaAnalysisM3.fig.Load the saved stack TIFF files for individual proteins in the graphical user interface or GUI. Using the buttons 1B for protein A, 1C for protein B and optionally, 1D for protein C.Create a superimposed image of the cell from the protein channels by clicking 1A. Then, click on 2A, to assign the first frame and click on 2B, to assign the last frame for the analysis.Optionally, crop the region of interest or ROI, containing the filopodium of interest using the 2H button. Rotate the image using the 2I button, or delete unwanted regions by using the free handed drawing tool, 2J. Move the slider, 2C for each frame for quality control and to check whether the filopodium remains clearly visible throughout the whole movie.To generate the trace, first, click on the 3A button in the GUI window one, to open GUI window two. Click on the four button in GUI window two, to generate the mass of the superimposed cell body that was generated in GUI window one, after clicking 1A. We recommend spending some time on optimizing parameters such as, the number of segments, the scan width and the scan radius for your experimental data set.Move the slider in window number two, to check where the filopodial tip first appears. Then, click button five and the 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. Next, select the threshold length above which the filopodia will bend, using 6C. Then, specify the number of segments used to approximate the shape of the filopodia in box 6A.Specify the scan width, which acts as a horizontal scanner, to place the nodes in 6B. Specify the scan radius in box 6D, use a value that is approximately 50%larger than the observed inter-frame tip displacement. Then, specify the bending angle in box 6E.Check the box's history trace in GUI window two, to save the whole tracking protocol for future reference and then click the track and analyze button to start tracking. For spatial temporal analysis, select the box represented by 8B, followed by the protein channel of interest. Click on 8A, to initiate protein tracking along the filopodial length, using the previously generated trace.For ratiometric protein analysis, check box 9B or 9C and click on the button, 9A to get the spatial temporal ratiometric plot. To perform the filopodial tip analysis, check box 8F and specify the tip length and threshold length from the base, using 8G and 8H. Click on the push button, to save the data to an excel file named, dynamics.xlsx.Then, click on analyze protein intensities to generate the trace of protein intensities of the filopodial tip and to save for ratiometric analysis. Click on the desired ratio to be analyzed, using 9D or 9E. Finally, click on the compare button, 9A to generate the ratiometric data.The analysis of COS cells transvected with a marker for filamentous actin in red, and a cytosolic reference in green reveal actin-rich filopodial protrusions. A time series shows actin-rich filopodial protrusions rapidly extend and retract. Using the image analysis software, the individual filopodia were traced.Image analysis software reliably track filopodial extension, in addition to growth and retraction rates of an individual filopodia, chemigraphs also depict concentration of actin normalized to the cytosolic reference. If used properly, this technique allows quantitative analysis of protrusion dynamics and spatially resolved protein concentration along the filopodia. When attempting the procedure, it is important to remember the acquisition speed and to keep the signal to noise ratio larger than four, while not saturating individual images.

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Research

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Biology

Application of Light-cured Dental Adhesive Resin for Mounting Electrodes or Microdialysis Probes in Chronic Experiments

In chronic recording experiments, self-curing dental acrylic resins have been used as a mounting base of electrodes or microdialysis-probes. Since these acrylics do not bond to the bone, screws have been used as anchors. However, in small experimental animals like finches or mouse, their craniums are very fragile and can not successfully hold the anchors. In this report, we propose a new application of light-curing dental resins for mounting base of electrodes or microdialysis probes in chronic experiments. This material allows direct bonding to the cranium. Therefore, anchor screws are not required and surgical field can be reduced considerably. Past experiences show that the bonding effect maintains more than 2 months. Conventional resin's window of time when the materials are pliable and workable is a few minutes. However, the window of working time for these dental adhesives is significantly wider and adjustable.

In this report, we propose a new application of light-curing dental resins for mounting base of electrodes or microdialysis probes in chronic experiments. This material allows direct bonding to the cranium.

In chronic recording experiments, self-curing dental acrylic resins have been used as a mounting base of electrodes or microdialysis-probes. Since these ...

Microvolume Protein Concentration Determination using the NanoDrop 2000c Spectrophotometer

Traditional spectrophotometry requires placing samples into cuvettes or capillaries. This is often impractical due to the limited sample volumes often used for protein analysis. The Thermo Scientific NanoDrop 2000c Spectrophotometer solves this issue with an innovative sample retention system that holds microvolume samples between two measurement surfaces using the surface tension properties of liquids, enabling the quantification of samples in volumes as low as 0.5-2 &mu;L. The elimination of cuvettes or capillaries allows real time changes in path length, which reduces the measurement time while greatly increasing the dynamic range of protein concentrations that can be measured. The need for dilutions is also eliminated, and preparations for sample quantification are relatively easy as the measurement surfaces can be simply wiped with laboratory wipe. This video article presents modifications to traditional protein concentration determination methods for quantification of microvolume amounts of protein using A280 absorbance readings or the BCA colorimetric assay.

Microvolume samples are quantified by a spectrophotometer system that uses natural surface tension to retain samples without the use of cuvettes or capillaries. The dynamic range of protein concentrations and speed by which they can be measured are greatly increased with this method.

Traditional spectrophotometry requires placing samples into cuvettes or capillaries. This is often impractical due to the limited sample volumes often ...

Split-Ubiquitin Based Membrane Yeast Two-Hybrid (MYTH) System: A Powerful Tool For Identifying Protein-Protein Interactions

The fundamental biological and clinical importance of integral membrane proteins prompted the development of a yeast-based system for the high-throughput identification of protein-protein interactions (PPI) for full-length transmembrane proteins. To this end, our lab developed the split-ubiquitin based Membrane Yeast Two-Hybrid (MYTH) system. This technology allows for the sensitive detection of transient and stable protein interactions using Saccharomyces cerevisiae as a host organism. MYTH takes advantage of the observation that ubiquitin can be separated into two stable moieties: the C-terminal half of yeast ubiquitin (Cub) and the N-terminal half of the ubiquitin moiety (Nub). In MYTH, this principle is adapted for use as a 'sensor' of protein-protein interactions. Briefly, the integral membrane bait protein is fused to Cub which is linked to an artificial transcription factor. Prey proteins, either in individual or library format, are fused to the Nub moiety. Protein interaction between the bait and prey leads to reconstitution of the ubiquitin moieties, forming a full-length 'pseudo-ubiquitin' molecule. This molecule is in turn recognized by cytosolic deubiquitinating enzymes, resulting in cleavage of the transcription factor, and subsequent induction of reporter gene expression. The system is highly adaptable, and is particularly well-suited to high-throughput screening. It has been successfully employed to investigate interactions using integral membrane proteins from both yeast and other organisms.

Split-Ubiquitin Based Membrane Yeast Two-Hybrid MYTH System: A Powerful Tool For Identifying Protein-Protein Interactions

MYTH allows the sensitive detection of transient and stable interactions between proteins that are expressed in the model organism Saccharomyces cerevisiae. It has been successfully applied to study exogenous and yeast integral membrane proteins in order to identify their interacting partners in a high throughput manner.

The fundamental biological and clinical importance of integral membrane proteins prompted the development of a yeast-based system for the high-throughput ...

The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis 

In this study, we explore the interaction between the bovine cysteine protease inhibitor cystatin B and a catalytically inactive form of papain (Fig. 1), a plant cysteine protease, by real-time label-free analysis using Biacore X100. Several cystatin B variants with point mutations in areas of interaction with papain, are produced. For each cystatin B variant we determine its specific binding concentration using calibration-free concentration analysis (CFCA) and compare the values obtained with total protein concentration as determined by A280. After that, the kinetics of each cystatin B variant binding to papain is measured using single-cycle kinetics (SCK). We show that one of the four cystatin B variants we examine is only partially active for binding. This partial activity, revealed by CFCA, translates to a significant difference in the association rate constant (ka) and affinity (KD), compared to the values calculated using total protein concentration. Using CFCA in combination with kinetic analysis in a structure-function study contributes to obtaining reliable results, and helps to make the right interpretation of the interaction mechanism.

The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis

We apply label-free protein interaction analysis using Biacore X100 for structure-function analysis of the binding of several cystatin B mutants to papain through kinetic characterization. Calibration-free concentration analysis (CFCA) measures the concentration of protein with retained binding activity without the need for a standard curve. We show that confirmation of concentrations using CFCA increases the reliability of the kinetic analysis and that kinetic constants can reliably be determined even if the activity of a recombinant protein is reduced.

In this study, we explore the interaction between the bovine cysteine protease inhibitor cystatin B and a catalytically inactive form of papain (Fig. 1), ...

A Microfluidic Device for Quantifying Bacterial Chemotaxis in Stable Concentration Gradients

Chemotaxis allows bacteria to approach sources of attractant chemicals or to avoid sources of repellent chemicals. Bacteria constantly monitor the concentration of specific chemoeffectors by comparing the current concentration to the concentration detected a few seconds earlier. This comparison determines the net direction of movement. Although multiple, competing gradients often coexist in nature, conventional approaches for investigating bacterial chemotaxis are suboptimal for quantifying migration in response to concentration gradients of attractants and repellents. Here, we describe the development of a microfluidic chemotaxis model for presenting precise and stable concentration gradients of chemoeffectors to bacteria and quantitatively investigating their response to the applied gradient. The device is versatile in that concentration gradients of any desired absolute concentration and gradient strength can be easily generated by diffusive mixing. The device is demonstrated using the response of Escherichia coli RP437 to gradients of amino acids and nickel ions.

This protocol describes the development of a microfluidic device for investigating bacterial chemotaxis in stable concentration gradients of chemoeffectors.

Chemotaxis allows bacteria to approach sources of attractant chemicals or to avoid sources of repellent chemicals. Bacteria constantly monitor the ...

Isolation of Labile Multi-protein Complexes by in vivo Controlled Cellular Cross-Linking and Immuno-magnetic Affinity Chromatography

The dynamic nature of cellular machineries is frequently built on transient and/or weak protein associations. These low affinity interactions preclude stringent methods for the isolation and identification of protein networks around a protein of interest. The use of chemical crosslinkers allows the selective stabilization of labile interactions, thus bypassing biochemical limitations for purification. Here we present a protocol amenable for cells in culture that uses a homobifunctional crosslinker with a spacer arm of 12 &Aring;, dithiobis-(succinimidyl proprionate) (DSP). DSP is cleaved by reduction of a disulphide bond present in the molecule. Cross-linking combined with immunoaffinity chromatography of proteins of interest with magnetic beads allows the isolation of protein complexes that otherwise would not withstand purification. This protocol is compatible with regular western blot techniques and it can be scaled up for protein identification by mass spectrometry1.
Stephanie A. Zlatic and Pearl V. Ryder contributed equally to this work.

Isolation of Labile Multi-protein Complexes by in vivo Controlled Cellular Cross-Linking and Immuno-magnetic Affinity Chromatography

The cell permeable crosslinker DSP [dithiobis-(succinimidyl propionate)] stabilizes transient and labile interactions in vivo, which allows their isolation using stringent protein complex purification techniques. Here we present a technique for crosslinking cells grown in culture followed by isolation of protein complexes by immunoprecipitation.

The dynamic nature of cellular machineries is frequently built on transient and/or weak protein associations. These low affinity interactions preclude ...

Analysis of the Development of a Morphological Phenotype as a Function of Protein Concentration in Budding Yeast

Gene deletion and protein overexpression are common methods for studying functions of proteins. In this article, we describe a protocol for analysis of phenotype development as a function of protein concentration at population and single-cell levels in Saccharomyces cerevisiae. Although this protocol is based on the overexpression of a protein, it can easily be adapted for morphological phenotypes dependent on suppression of protein expression. Our lab is interested in studying the signaling properties of the endocytic adaptor protein epsin. To that purpose we used a dominant negative approach in which we over-expressed the conserved Epsin N-Terminal Homology (ENTH) domain in order to interfere with the functions of endogenous epsin-2 (Ent2 or YLR206W). We observed that overexpression of the ENTH domain of Ent2 (ENTH2) in wild type cells led to a cell division defect that is dependent on the mislocalization of a family of scaffolding proteins, septins.

Gene deletion and protein overexpression are common methods for studying functions of proteins. In this article, we describe a protocol for analysis of phenotype development as a function of protein concentration at population and single-cell levels in Saccharomyces cerevisiae.

Gene deletion and protein overexpression are common methods for studying functions of proteins. In this article, we describe a protocol for analysis of ...

Collecting Variable-concentration Isothermal Titration Calorimetry Datasets in Order to Determine Binding Mechanisms

Isothermal titration calorimetry (ITC) is commonly used to determine the thermodynamic parameters associated with the binding of a ligand to a host macromolecule. ITC has some advantages over common spectroscopic approaches for studying host/ligand interactions. For example, the heat released or absorbed when the two components interact is directly measured and does not require any exogenous reporters. Thus the binding enthalpy and the association constant (Ka) are directly obtained from ITC data, and can be used to compute the entropic contribution. Moreover, the shape of the isotherm is dependent on the c-value and the mechanistic model involved. The c-value is defined as c = n[P]tKa, where [P]t is the protein concentration, and n is the number of ligand binding sites within the host. In many cases, multiple binding sites for a given ligand are non-equivalent and ITC allows the characterization of the thermodynamic binding parameters for each individual binding site. This however requires that the correct binding model be used. This choice can be problematic if different models can fit the same experimental data. We have previously shown that this problem can be circumvented by performing experiments at several c-values. The multiple isotherms obtained at different c-values are fit simultaneously to separate models. The correct model is next identified based on the goodness of fit across the entire variable-c dataset. This process is applied here to the aminoglycoside resistance-causing enzyme aminoglycoside N-6'-acetyltransferase-Ii (AAC(6')-Ii). Although our methodology is applicable to any system, the necessity of this strategy is better demonstrated with a macromolecule-ligand system showing allostery or cooperativity, and when different binding models provide essentially identical fits to the same data. To our knowledge, there are no such systems commercially available. AAC(6')-Ii, is a homo-dimer containing two active sites, showing cooperativity between the two subunits. However ITC data obtained at a single c-value can be fit equally well to at least two different models a two-sets-of-sites independent model and a two-site sequential (cooperative) model. Through varying the c-value as explained above, it was established that the correct binding model for AAC(6')-Ii is a two-site sequential binding model. Herein, we describe the steps that must be taken when performing ITC experiments in order to obtain datasets suitable for variable-c analyses.

ITC is a powerful tool for studying the binding of a ligand to its host. In complex systems however, several models may fit the data equally well. The method described here provides a means to elucidate the appropriate binding model for complex systems and extract the corresponding thermodynamic parameters.

Isothermal titration calorimetry (ITC) is commonly used to determine the thermodynamic parameters associated with the binding of a ligand to a host ...

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo

Embryonic epithelia undergo complex deformations (e.g. bending, twisting, folding, and stretching) to form the primitive organs of the early embryo. Tracking fiducial markers on the surfaces of these cellular sheets is a well-established method for estimating morphogenetic quantities such as growth, contraction, and shear. However, not all surface labeling techniques are readily adaptable to conventional imaging modalities and possess different advantages and limitations. Here, we describe two labeling methods and illustrate the utility of each technique. In the first method, hundreds of fluorescent labels are applied simultaneously to the embryo using magnetic iron particles. These labels are then used to quantity 2-D tissue deformations during morphogenesis. In the second method, polystyrene microspheres are used as contrast agents in non-invasive optical coherence tomography (OCT) imaging to track 3-D tissue deformations. These techniques have been successfully implemented in our lab to studythe physical mechanisms of early head fold, heart, and brain development, and should be adaptable to a wide range morphogenetic processes.

This article describes surface labeling and ex ovo tissue culture in the early chick embryo. Techniques amenable to time-lapse bright field, fluorescence, and optical coherence tomography imaging are presented. Tracking surface labels with high spatiotemporal resolution enables kinematic quantities such as morphogenetic strains (deformations) to be calculated in both two and three dimensions.

Embryonic epithelia undergo complex deformations (e.g. bending, twisting, folding, and stretching) to form the primitive organs of the early embryo. ...

Studying Mitotic Checkpoint by Illustrating Dynamic Kinetochore Protein Behavior and Chromosome Motion in Living Drosophila Syncytial Embryos

The spindle assembly checkpoint (SAC) mechanism is an active signal, which monitors the interaction between chromosome kinetochores and spindle microtubules to prevent anaphase onset until the chromosomes are properly connected. Cells use this mechanism to prevent aneuploidy or genomic instability, and hence cancers and other human diseases like birth defects and Alzheimer's1. A number of the SAC components such as Mad1, Mad2, Bub1, BubR1, Bub3, Mps1, Zw10, Rod and Aurora B kinase have been identified and they are all kinetochore dynamic proteins2. Evidence suggests that the kinetochore is where the SAC signal is initiated. The SAC prime regulatory target is Cdc20. Cdc20 is one of the essential APC/C (Anaphase Promoting Complex or Cyclosome) activators3 and is also a kinetochore dynamic protein4-6. When activated, the SAC inhibits the activity of the APC/C to prevent the destruction of two key substrates, cyclin B and securin, thereby preventing the metaphase to anaphase transition7,8. Exactly how the SAC signal is initiated and assembled on the kinetochores and relayed onto the APC/C to inhibit its function still remains elusive.
Drosophila is an extremely tractable experimental system; a much simpler and better-understood organism compared to the human but one that shares fundamental processes in common. It is, perhaps, one of the best organisms to use for bio-imaging studies in living cells, especially for visualization of the mitotic events in space and time, as the early embryo goes through 13 rapid nuclear division cycles synchronously (8-10 minutes for each cycle at 25 &deg;C) and gradually organizes the nuclei in a single monolayer just underneath the cortex9.
Here, I present a bio-imaging method using transgenic Drosophila expressing GFP (Green Fluorescent Protein) or its variant-targeted proteins of interest and a Leica TCS SP2 confocal laser scanning microscope system to study the SAC function in flies, by showing images of GFP fusion proteins of some of the SAC components, Cdc20 and Mad2, as the example.

Studying Mitotic Checkpoint by Illustrating Dynamic Kinetochore Protein Behavior and Chromosome Motion in Living Drosophila Syncytial Embryos

The kinetochore is where the SAC initiates its signal monitoring the mitotic segregation of the sister chromatids. A method is described to visualize the recruitment and turnover of one of the kinetochore proteins and its coordination with the chromosome motion in Drosophila embryos using a Leica laser scanning confocal system.