The authors would like to thank Kathy Lowe at the Virginia-Maryland College of Veterinary Medicine for processing and staining TEM samples. 
Funding: 
National Institutes of Health R01-HL102298 
National Institutes of Health F31-HL140873-01

NOTE: Software required are ImageJ (or similar image-modifying software) and Matlab R2015. The user may encounter compatibility issues with other versions of Matlab.
1. Pre-Processing Images
<ol>
	<li>For any grayscale image, ensure the maximum intensity value of any given pixel is &#60;255. This is typically done by subtracting a value of 1 from the image in the custom Matlab program &#34;ImageSub.m&#34;, included in supplemental file S1.</li>
</ol>
2. Outlining the Perinexus
<ol>
	<li>Outline the perinexus in ImageJ or other image-processing software.</li>
	<li>Ensure that the outline is one pixel thick and is set to the highest intensity value in an image (255 in a grayscale image from 0 to 255).</li>
	<li>Identify the GJ by its pentalaminar structure11,12, and define the beginning of the perinexus as the point at which the two opposing cell membrane bilayers diverge, as shown in Figure 1A. Begin ~200 nm from the edge of the GJ, tracing along the inner membrane of the first cell and back along the inner membrane of the second cell. In ImageJ, release the pen to automatically close the outline. This artificial closing will be cropped out later. 
	NOTE: It is crucial to outline the perinexus with great care at as high a magnification as possible, as even small mis-applications of the outline can result in several nanometers of error in the final measurement.</li>
</ol>
3. Setting up Algorithm and Selecting Perinexus of Interest
NOTE: The pathfinding algorithm requires the AutoGraph, Edge, Graph, Node , and Pathfinding functions9 to be in the same directory as the MembraneSepDist m-file. All files can be found in supplemental file S1.
<ol>
	<li>Select save locations for data and figures. These are currently hard-coded into the m-file. 
	NOTE: The first line of the program is a function to clear all variables, close all windows and clear the command window. Save any desired variables or figures before running the m-file. 
	NOTE: Software screenshots are included in supplemental file S2 for all hard-coded values.</li>
	<li>Run the program &#34;MembraneSepDist.m&#34;.</li>
	<li>Set Parameters. 
	NOTE: A GUI will pop up with default parameters for the gradient threshold, scale, region of interest, and manual start. The default values can be changed in the m-file, or they can be changed for each individual image.
	<ol>
		<li>Set spatial derivative gradient threshold. 
		NOTE: Higher values result in more points selected in centerline isolation. Values that are too high or too low (outside of a range of approximately 3.0-7.9) may result in computational inefficiency or an imprecise selection of centerline points yielding imprecise centerline isolation (See Figure 2A-C).</li>
		<li>Set scale in pixels/scale units.</li>
		<li>Set the spatial lower and upper limits for the region of interest. 
		NOTE: By our laboratory&#39;s convention, the defined region of interest is between 30 and 105 nm from the edge of the GJ2,3,10.</li>
		<li>Set automatic/manual start. In the majority of cases, the algorithm accurately detects the starting point where the gap junction ends and perinexus begins. However, in some cases of irregularly-shaped perinexi, the user must identify the start point manually. Set this value to 0 for automatic, 1 for manual.</li>
	</ol>
	</li>
	<li>Select desired image. 
	NOTE: The file select folder can be changed in the m-file.</li>
	<li>Crop the image to select the perinexus of interest.
	<ol>
		<li>When the image comes up, the cursor will automatically change to a crosshair. Crop the image by dragging a box around the perinexus of interest (See Figure 3). The crop box can be adjusted by using the squares on the corners and sides to make it larger or smaller.</li>
		<li>When cropping, ensure the &#34;open&#34; end of the perinexus (furthest from the GJ, see Figure 3) is cropped so that the two membrane outlines reach the edge of the cropped image. 
		NOTE: It is recommended to make the image full-screen to more easily see the perinexus of interest and crop appropriately.</li>
	</ol>
	</li>
	<li>Select the final crop by double-clicking with the curser in between the opposing edges to be measured. 
	NOTE: It is critical that the double-click be performed inside the perinexus. If the program fails to identify a centerline, restart the program and make sure the click occurs within the perinexus.</li>
	<li>Observe the final centerline after all dilations and erosions pop up for a final user evaluation of the program&#39;s efficacy. 
	NOTE: A dialog box will appear on the screen while the program is running to inform the user that Matlab will be unable to process any additional commands until the program has finished. How long this process takes depends on array (image) size and computer processing power.</li>
	<li>If manual start point is enabled, observe the image of the centerline pop up over the original anatomical image, along with a cross-hair cursor (see Figure 2E). Select a point outside of the perinexus near the desired start point. 
	NOTE: The program will find the centerline point closest to the selected pixel and use that as the start point.</li>
	<li>Record data. 
	NOTE: Once the program has finished, the program will return a mapped centerline, plot of perinexal width as a function of distance from the edge of the GJ. Additionally, the program will return the average perinexal width up to 150 nm from the edge of the GJ as well as the average from within the defined region of interest at the Matlab command line. Wp values and distances from the GJ are stored in the variable &#34;WpList&#34; or the user can manually record them separately.</li>
</ol>
4. Algorithm Troubleshooting
<ol>
	<li>If the centerline is not properly identified (Figure 2A), open the figure &#34;Gmag&#34; and use the Index to identify an appropriate gradient threshold (Figure 2C).</li>
	<li>If the start point is not properly identified, set the start point manually (See Protocol 3.8).</li>
</ol>

<ol>
	<li>Rhett, J. M., Gourdie, R. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+perinexus:+a+new+feature+of+Cx43+gap+junction+organization.">The perinexus: a new feature of Cx43 gap junction organization.</a> Heart Rhythm. 9 (4), 619-623 (2012).</li><li>Veeraraghavan, R., 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=Sodium+channels+in+the+Cx43+gap+junction+perinexus+may+constitute+a+cardiac+ephapse:+an+experimental+and+modeling+study.">Sodium channels in the Cx43 gap junction perinexus may constitute a cardiac ephapse: an experimental and modeling study.</a> Pflugers Archiv: European Journal of Physiology. , (2015).</li><li>George, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extracellular+sodium+dependence+of+the+conduction+velocity-calcium+relationship:+evidence+of+ephaptic+self-attenuation.">Extracellular sodium dependence of the conduction velocity-calcium relationship: evidence of ephaptic self-attenuation.</a> American Journal of Physiology - Heart and Circulatory Physiology. 310 (9), 1129-1139 (2016).</li><li>Veeraraghavan, R., 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=Potassium+channels+in+the+Cx43+gap+junction+perinexus+modulate+ephaptic+coupling:+an+experimental+and+modeling+study.">Potassium channels in the Cx43 gap junction perinexus modulate ephaptic coupling: an experimental and modeling study.</a> Pflugers Archiv: European Journal of Physiology. , (2016).</li><li>Rhett, J. 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=Cx43+associates+with+Na(v)1.5+in+the+cardiomyocyte+perinexus.">Cx43 associates with Na(v)1.5 in the cardiomyocyte perinexus.</a> Journal of Membrane Biology. 245 (7), 411-422 (2012).</li><li>Raisch, T. B., 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=Intercalated+Disc+Extracellular+Nanodomain+Expansion+in+Patients+with+Atrial+Fibrillation.">Intercalated Disc Extracellular Nanodomain Expansion in Patients with Atrial Fibrillation.</a> Frontiers in Physiology. , (2018).</li><li>Yan, J., 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=Novel+methods+of+automated+quantification+of+gap+junction+distribution+and+interstitial+collagen+quantity+from+animal+and+human+atrial+tissue+sections.">Novel methods of automated quantification of gap junction distribution and interstitial collagen quantity from animal and human atrial tissue sections.</a> PLoS One. 9 (8), 104357 (2014).</li><li>Papari, G., Petkov, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adaptive+pseudo+dilation+for+gestalt+edge+grouping+and+contour+detection.">Adaptive pseudo dilation for gestalt edge grouping and contour detection.</a> IEEE Transactions on Image Processing. 17 (10), 1950-1962 (2008).</li><li>George, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extracellular+sodium+and+potassium+levels+modulate+cardiac+conduction+in+mice+heterozygous+null+for+the+Connexin43+gene.">Extracellular sodium and potassium levels modulate cardiac conduction in mice heterozygous null for the Connexin43 gene.</a> Pflugers Archiv: European Journal of Physiology. , (2015).</li><li>Revel, J. P., Karnovsky, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hexagonal+array+of+subunits+in+intercellular+junctions+of+the+mouse+heart+and+liver.">Hexagonal array of subunits in intercellular junctions of the mouse heart and liver.</a> Journal of Cell Biology. 33 (3), 7-12 (1967).</li><li>Huttner, I., Boutet, M., More, R. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gap+junctions+in+arterial+endothelium.">Gap junctions in arterial endothelium.</a> Journal of Cell Biology. 57 (1), 247-252 (1973).</li><li>Makowski, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gap+junction+structures.+II.+Analysis+of+the+x-ray+diffraction+data.">Gap junction structures. II. Analysis of the x-ray diffraction data.</a> Journal of Cell Biology. 74 (2), 629-645 (1977).</li><li>Entz, 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=Heart+Rate+and+Extracellular+Sodium+and+Potassium+Modulation+of+Gap+Junction+Mediated+Conduction+in+Guinea+Pigs.">Heart Rate and Extracellular Sodium and Potassium Modulation of Gap Junction Mediated Conduction in Guinea Pigs.</a> Frontiers in Physiology. 7, 16 (2016).</li><li>Sild, M., Chatelain, R. P., Ruthazer, E. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+method+for+the+quantification+of+motility+in+glia+and+other+morphologically+complex+cells.">Improved method for the quantification of motility in glia and other morphologically complex cells.</a> Neural Plasticity. 2013, 853727 (2013).</li><li>Rhett, J. 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=The+perinexus:+Sign-post+on+the+path+to+a+new+model+of+cardiac+conduction.">The perinexus: Sign-post on the path to a new model of cardiac conduction.</a> Trends in Cardiovascular Medicine. , (2013).</li><li>Lang, R. 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=Recommendations+for+cardiac+chamber+quantification+by+echocardiography+in+adults:+an+update+from+the+American+Society+of+Echocardiography+and+the+European+Association+of+Cardiovascular+Imaging.">Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.</a> Journal of the American Society of Echocardiography. 28 (1), 1-39 (2015).</li><li>Kawai, J., 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=Left+ventricular+volume+and+ejection+fraction+by+the+axius+auto+ejection+fraction+method:+comparison+with+manual+trace+method+and+visual+assessment+of+ejection+fraction.">Left ventricular volume and ejection fraction by the axius auto ejection fraction method: comparison with manual trace method and visual assessment of ejection fraction.</a> Journal of Cardiology. 49 (3), 125-134 (2007).</li><li>Frederiksen, C. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+utility+of+semi-automated+estimation+of+ejection+fraction+at+the+point-of-care.">Clinical utility of semi-automated estimation of ejection fraction at the point-of-care.</a> Heart, Lung and Vessels. 7 (3), 208-216 (2015).</li><li>Foster, F. 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=A+new+ultrasound+instrument+for+in+vivo+microimaging+of+mice.">A new ultrasound instrument for in vivo microimaging of mice.</a> Ultrasound in Medicine and Biology. 28 (9), 1165-1172 (2002).</li><li>Moran, C. 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=A+comparison+of+the+imaging+performance+of+high+resolution+ultrasound+scanners+for+preclinical+imaging.">A comparison of the imaging performance of high resolution ultrasound scanners for preclinical imaging.</a> Ultrasound in Medicine and Biology. 37 (3), 493-501 (2011).</li><li>Papademetris, X., 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=Estimation+of+3D+left+ventricular+deformation+from+echocardiography.">Estimation of 3D left ventricular deformation from echocardiography.</a> Medical Image Analysis. 5 (1), 17-28 (2001).</li><li>Hosny, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unlocking+vendor-specific+tags:+Three-dimensional+printing+of+echocardiographic+data+sets.">Unlocking vendor-specific tags: Three-dimensional printing of echocardiographic data sets.</a> Journal of Thoracic Cardiovascular Surgery. 155 (1), 143-145 (2018).</li><li>Cretoiu, 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=Human+cardiac+telocytes:+3D+imaging+by+FIB-SEM+tomography.">Human cardiac telocytes: 3D imaging by FIB-SEM tomography.</a> Journal of Cellular and Molecular Medicine. 18 (11), 2157-2164 (2014).</li><li>Risi, C., 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=Ca(2+)-induced+movement+of+tropomyosin+on+native+cardiac+thin+filaments+revealed+by+cryoelectron+microscopy.">Ca(2+)-induced movement of tropomyosin on native cardiac thin filaments revealed by cryoelectron microscopy.</a> Proceedings of the National Academy of Sciences of the United States of America. 114 (26), 6782-6787 (2017).</li><li>Dhindwal, 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=A+cryo-EM-based+model+of+phosphorylation-+and+FKBP12.6-mediated+allosterism+of+the+cardiac+ryanodine+receptor.">A cryo-EM-based model of phosphorylation- and FKBP12.6-mediated allosterism of the cardiac ryanodine receptor.</a> Science Signaling. 10 (480), (2017).</li><li>Reddy, V. 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=Integration+of+cardiac+magnetic+resonance+imaging+with+three-dimensional+electroanatomic+mapping+to+guide+left+ventricular+catheter+manipulation:+feasibility+in+a+porcine+model+of+healed+myocardial+infarction.">Integration of cardiac magnetic resonance imaging with three-dimensional electroanatomic mapping to guide left ventricular catheter manipulation: feasibility in a porcine model of healed myocardial infarction.</a> Journal of the American College of Cardiology. 44 (11), 2202-2213 (2004).</li><li>van Heeswijk, R. B., 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=Three-Dimensional+Self-Navigated+T2+Mapping+for+the+Detection+of+Acute+Cellular+Rejection+After+Orthotopic+Heart+Transplantation.">Three-Dimensional Self-Navigated T2 Mapping for the Detection of Acute Cellular Rejection After Orthotopic Heart Transplantation.</a> Transplant Direct. 3 (4), 149 (2017).</li><li>Valinoti, 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=3D+patient-specific+models+for+left+atrium+characterization+to+support+ablation+in+atrial+fibrillation+patients.">3D patient-specific models for left atrium characterization to support ablation in atrial fibrillation patients.</a> Magnetic Resonance Imaging. 45, 51-57 (2018).</li></ol>

The authors have nothing to disclose.

The algorithm uses serial image dilations to count the number of pixels between two opposing 2D edges in a binary image, which in this case is the inter-membrane separation of the perinexus2,3,14. A spatial derivative and a pathfinding algorithm are then used to isolate the centerline, followed by a secondary dilation and erosion sequence to fill gaps in the centerline, similar to what has been done before15. The centerline is then combined with the final dilation-count image to represent perinexal width as a function of distance from the beginning of edge separation, in this case the end of the GJ and the beginning of the perinexus16.
Four primary parameters are user-defined in a GUI at the start of the program:
<ol>
	<li>Gradient threshold</li>
	<li>Scale</li>
	<li>Region of interest range</li>
	<li>Start point selection method (automatic or manual)</li>
</ol>
The most common mechanism of failure for the algorithm is the failure of the centerline to reach the edge of the image, which is how the endpoint is determined for the pathfinding algorithm. In order to fix such an issue, the user can increase the gradient threshold described in step 3.3.1, which will cause the program to select more points out of the spatial derivative image, which will increase the computation time required by the pathfinding algorithm. Therefore, this algorithm requires a compromise between computation speed and centerline integrity. It is important to note that so long as all the points of the centerline are identified from the spatial derivative, along with an appropriate start point, the spatial derivative threshold will have no effect on the edge separation measurement.
Image orientation appears to affect dilation values, because the kernel dilates in 90 degree steps, which can introduce an error if the majority of the region of interest is at an angle 45&#176; to the axes of dilation matrices. Therefore, the dilation count may not always be an accurate representation of the space between the edges. This limitation has been addressed by a trigonometric correction factor, but could potentially be ignored if all images in a dataset are aligned at the same orientation. Furthermore, caution should be used in interpreting results, as it is possible that section planes are not perfectly perpendicular to the two membranes. In Figure 9B, we use GJW to suggest that our perinexus images were in-plane. Still, it is imperative that sample sizes be sufficiently large to account for any sectioning variations between images. Additionally, our perinexal width measurements should not be interpreted to reflect in vivo spaces, but this approach is used to measure mean differences in perinexal width relative to some intervention or disease state.
The current algorithm also requires a manually-traced outline of the edges as an input. It is important to note that so long as the scale is set correctly, spatial resolution has no effect on the algorithm&#39;s measurements, as demonstrated by the varying resolutions of images in Figure 6 and an additional low-resolution image in supplemental file S6. The next step in improving the algorithm is removing human intervention from outline generation along with a tool that can select the area of interest. These features would likely enhance the precision of the measurement and reduce user bias.
This computationally efficient algorithm provides a faster method, requiring approximately one fifth the man-hours, of quantifying the perinexus with no detectable penalty to reproducibility when compared to the manual segmentation process. Additionally, the manual segmentation process utilizes one measurement every 15 nanometers to quantify perinexal width, which can lead to under sampling as the membrane separation of the perinexus can change substantially within that 15 nm range. In contrast, the automated program has a spatial resolution equal to that of the imaging modality, in this case 2.9 pixels per nanometer along the length of the perinexus, therefore delivering a more finely resolved average of perinexal width.
While the applications in the field of cardiac structural biology are promising and exciting, this algorithm&#39;s uses are not confined to TEM images. Any field requiring a precise, high-resolution measurement of two quasi-parallel 2D edges can make use of this algorithm. The algorithm could be used to track anything from riverbank erosion and flood patterns from satellite images to vascular development with brightfield or fluorescent microscopy. One of the most promising potential applications is in the field of cardiology and measuring ventricular ejection fraction (EF) with point-of-care cardiac echocardiography. Currently, the standard technique is the biplane method of disks17, though a newer algorithm, AutoEF, is currently the cutting edge EF-quantifying method18,19. For biplane method of disks, the chamber in question is manually traced and quantified using a modified Simpson&#39;s method, whereby overall volume is automatically calculated by the summation of stacked elliptical disks. The main limitation with this method is that it can only return the total cross-sectional area of the desired chamber, with no resolution to identify specific regions of interest, and also requires substantial human input and expertise. The newer method, AutoEF, identifies and outlines the edge of the ventricle using a 2D speckling algorithm and then computes the ventricular cross-sectional area. This process, while precise and efficient for measuring gross ventricular area, also has a similar inherent limitation of only measuring total cross-sectional area. This primary drawback limits clinicians&#39; diagnostic and treatment abilities. In contrast, the algorithm presented in this manuscript can identify a midline and has a resolution equal to the resolution of the imaging modality to pinpoint specific regions of interest. This is important because ultrasound scanners with micrometer spatial resolution are commercially available20,21, implying that this algorithm could detect localized wall motion abnormalities at the resolution of micrometers instead of centimeters. While this application needs to be experimentally validated, it is one of the most immediately promising applications of this algorithm. In fact, it could easily be combined with the speckle tracking capabilities of AutoEF or the manual traces utilized in manual planimetry to provide higher-resolution information in parallel with conventional EF data.
As versatile and applicable as the current algorithm is, it was developed for 2D images. However, as imaging technologies continue to improve, there is an increasing demand for 3 and 4D quantification technologies. Therefore, the next iteration of the algorithm is to adapt the same approach, serially dilating a binary image, to a 3-dimensional object, where automatically defining a centerline is currently beyond the capabilities of current imaging programs. Such an algorithm would have wide applications both clinically and experimentally in the cardiac field alone, including 3D cardiac echocardiograms22,23, 3D electron microscopy24,25,26, and 3D magnetic resonance imaging27,28,29.

The following algorithm was developed to measure the intermembrane distance between two structurally coupled cardiomyocytes at the point they separate from each other at the edge of a gap junction plaque in a nanodomain called the perinexus1, which has been implicated in ephaptic coupling2,3,4,5. In the process of analyzing hundreds of transmission electron microscopy (TEM) images of perinexi using a manual segmentation method in a previous study6, the need was identified for a higher-throughput method that sampled perinexal width in higher spatial resolution while preserving the accuracy of the previous manual segmentation process During manual segmentation, lines are drawn at 15 nm intervals, approximately orthogonal to the centerline, in order to measure perinexal width.The new algorithm takes a one pixel thick binary outline of two parallel lines and uses serial image dilations to count the number of pixels between the two membranes.While image dilations have commonly been used in a myriad of image processing applications, including contour or edge detection7,8, this algorithm uses dilations as a counting mechanism. The centerline is then isolated using a pathfinding algorithm9 and perinexal width is then measured at a resolution along the length of perinexus equal to the resolution of the image. The difference in resolution in this case is 1 measurement per 15 nm for manual segmentation and 1 measurement per 0.34 nm with the new algorithm, a 44-fold increase in spatial sampling frequency.Furthermore, this increased sampling frequency is accomplished in approximately 1/5th the time needed for manual segmentation.
This algorithm will be used in its current form to measure perinexal width at the conventional 0-150 nm from the edge of a gap junction plaque5 (GJ) as well as within a specified region of interest, where the perinexus plateaus between 30 and 105 nm2,3,10. The increased sampling frequency reduces the variability in individual perinexus measurements compared to manual segmentation and significantly shortens analysis time, allowing for efficient processing of large data sets.However, this program is not limited to nanoscale TEM images of cardiac intercalated disks.The same approach could be used to quantify vascular diameter, ventricular ejection fraction, or even non-biological phenomena such as river erosion or flooding. This algorithm is appropriate for quantifying the distance between any two quasi-parallel edges.

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		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Touchscreen Monitor</td>
			<td style="width:79px;">Dell</td>
			<td style="width:119px;">S2240T</td>
			<td style="width:453px;">Needs soft-tipped stylus</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Desktop</td>
			<td style="width:79px;">Dell</td>
			<td style="width:119px;">Precision T1650</td>
			<td>8GB RAM</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Operating System</td>
			<td style="width:79px;">Microsoft</td>
			<td style="width:119px;">Windows 7 Enterprise</td>
			<td>64-bit OS</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Program platform</td>
			<td style="width:79px;">Mathworks</td>
			<td style="width:119px;">Matlab R2015b</td>
			<td>Program may be incompatible with newer/older versions of Matlab</td>
		</tr>
	</tbody>
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quantifying intermembrane distances with serial image dilations

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for electrical conduction between cardiomyocytes. The current method for quantifying this space by manual segmentation is slow and has low spatial resolution.We developed an algorithm that uses serial image dilations of a binary outline to count the number of pixels between two opposing 2 dimensional edges.This algorithm requires fewer man hours and has a higher spatial resolution than the manual method while preserving the reproducibility of the manual process.In fact, experienced and novice investigators were able to recapitulate the results of a previous study with this new algorithm.The algorithm is limited by the human input needed to manually outline the perinexus and computational power mainly encumbered by a pre-existing pathfinding algorithm.However, the algorithm&#39;s high-throughput capabilities, high spatial resolution and reproducibility make it a versatile and robust measurement tool for use across a variety of applications requiring the measurement of the distance between any 2-dimensional (2D) edges.

Watch this Scientific Journal Video about Quantifying Intermembrane Distances with Serial Image Dilations at JoVE.com

Quantifying Intermembrane Distances with Serial Image Dilations

The purpose of this algorithm is to continuously measure the distance between two 2-dimensional edges using serial image dilations and pathfinding. This algorithm can be applied to a variety of fields such as cardiac structural biology, vascular biology, and civil engineering.

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for ...

quantifying-intermembrane-distances-with-serial-image-dilations

Universidad de Cartagena UDEC

Research

JoVE Journal

Bioengineering

6.3K Views.  Virginia Tech. The purpose of this algorithm is to continuously measure the distance between two 2-dimensional edges using serial image dilations and pathfinding. This algorithm can be applied to a variety of fields such as cardiac structural biology, vascular biology, and civil engineering.

Video: Quantifying Intermembrane Distances with Serial Image Dilations

Preparation of Complaint Matrices for Quantifying Cellular Contraction

The regulation of cellular adhesion to the extracellular matrix (ECM) is essential for cell migration and ECM remodeling. Focal adhesions are macromolecular assemblies that couple the contractile F-actin cytoskeleton to the ECM. This connection allows for the transmission of intracellular mechanical forces across the cell membrane to the underlying substrate. Recent work has shown the mechanical properties of the ECM regulate focal adhesion and F-actin morphology as well as numerous physiological processes, including cell differentiation, division, proliferation and migration. Thus, the use of cell culture substrates has become an increasingly prevalent method to precisely control and modulate ECM mechanical properties.
To quantify traction forces at focal adhesions in an adherent cell, compliant substrates are used in conjunction with high-resolution imaging and computational techniques in a method termed traction force microscopy (TFM). This technique relies on measurements of the local magnitude and direction of substrate deformations induced by cellular contraction. In combination with high-resolution fluorescence microscopy of fluorescently tagged proteins, it is possible to correlate cytoskeletal organization and remodeling with traction forces.
Here we present a detailed experimental protocol for the preparation of two-dimensional, compliant matrices for the purpose of creating a cell culture substrate with a well-characterized, tunable mechanical stiffness, which is suitable for measuring cellular contraction. These protocols include the fabrication of polyacrylamide hydrogels, coating of ECM proteins on such gels, plating cells on gels, and high-resolution confocal microscopy using a perfusion chamber. Additionally, we provide a representative sample of data demonstrating location and magnitude of cellular forces using cited TFM protocols.

In this video, we demonstrate the experimental techniques used to fabricate compliant, extracellular matrix (ECM) coated substrates suitable for cell culture, and which are amenable to traction force microscopy and observing effects of ECM stiffness on cell behavior.

The regulation of cellular adhesion to the extracellular matrix (ECM) is essential for cell migration and ECM remodeling.  Focal adhesions are ...

Quantifying the Mechanical Properties of the Endothelial Glycocalyx with Atomic Force Microscopy

Our understanding of the interaction of leukocytes and the vessel wall during leukocyte capture is limited by an incomplete understanding of the mechanical properties of the endothelial surface layer. It is known that adhesion molecules on leukocytes are distributed non-uniformly relative to surface topography 3, that topography limits adhesive bond formation with other surfaces 9, and that physiological contact forces (&asymp; 5.0 &minus; 10.0 pN per microvillus) can compress the microvilli to as little as a third of their resting length, increasing the accessibility of molecules to the opposing surface 3, 7. We consider the endothelium as a two-layered structure, the relatively rigid cell body, plus the glycocalyx, a soft protective sugar coating on the luminal surface 6. It has been shown that the glycocalyx can act as a barrier to reduce adhesion of leukocytes to the endothelial surface 4. In this report we begin to address the deformability of endothelial surfaces to understand how the endothelial mechanical stiffness might affect bond formation. Endothelial cells grown in static culture do not express a robust glycocalyx, but cells grown under physiological flow conditions begin to approximate the glycocalyx observed in vivo 2. The modulus of the endothelial cell body has been measured using atomic force microscopy (AFM) to be approximately 5 to 20 kPa 5. The thickness and structure of the glycocalyx have been studied using electron microscopy 8, and the modulus of the glycocalyx has been approximated using indirect methods, but to our knowledge, there have been no published reports of a direct measurement of the glycocalyx modulus in living cells. In this study, we present indentation experiments made with a novel AFM probe on cells that have been cultured in conditions to maximize their glycocalyx expression to make direct measurements of the modulus and thickness of the endothelial glycocalyx.

The mechanical characteristics of endothelial glycocalyx were measured by indentation using micron sized spheres on AFM cantilevers. Endothelial cells were cultured in a custom chamber under physiological flow conditions to induce glycocalyx expression. Data were analyzed using a thin film model to determine the glycocalyx thickness and modulus.

Our  understanding of the interaction of leukocytes and the vessel wall during  leukocyte capture is limited by an incomplete understanding of the ...

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Cells embedded in collagen and fibrin gels attach and exert traction forces on the fibers of the gel. These forces can lead to local and global reorganization and realignment of the gel microstructure. This process proceeds in a complex manner that is dependent in part on the interplay between the location of the cells, the geometry of the gel, and the mechanical constraints on the gel. To better understand how these variables produce global fiber alignment patterns, we use time-lapse differential interference contrast (DIC) microscopy coupled with an environmentally controlled bioreactor to observe the compaction process between geometrically spaced explants (clusters of fibroblasts). The images are then analyzed with a custom image processing algorithm to obtain maps of the strain. The information obtained from this technique can be used to probe the mechanobiology of various cell-matrix interactions, which has important implications for understanding processes in wound healing, disease development, and tissue engineering applications.

Time-lapse microscopy and image processing techniques were used to observe and analyze fibroblast-mediated gel compaction and fibrin fiber realignment in an environmentally controlled bioreactor over a 48 hr&#160;period.

Cells embedded in collagen and fibrin gels attach and exert traction forces on the fibers of the gel. These forces can lead to local and global ...

Manufacture of Concentrated, Lipid-based Oxygen Microbubble Emulsions by High Shear Homogenization and Serial Concentration

Gas-filled microbubbles have been developed as ultrasound contrast and drug delivery agents. Microbubbles can be produced by processing surfactants using sonication, mechanical agitation, microfluidic devices, or homogenization. Recently, lipid-based oxygen microbubbles (LOMs) have been designed to deliver oxygen intravenously during medical emergencies, reversing life-threatening hypoxemia, and preventing subsequent organ injury, cardiac arrest, and death. We present methods for scaled-up production of highly oxygenated microbubbles using a closed-loop high-shear homogenizer. The process can produce 2 L of concentrated LOMs (90% by volume) in 90 min. Resulting bubbles have a mean diameter of ~2 &#956;m, and a rheologic profile consistent with that of blood when diluted to 60 volume %. This technique produces LOMs in high capacity and with high oxygen purity, suggesting that this technique may be useful for translational research labs.

We describe methods for the manufacture of large volumes of lipid-based oxygen microbubbles (LOMs) designed for intravenous oxygen delivery using high-shear homogenization and serial concentration.

Gas-filled microbubbles have been developed as ultrasound contrast and drug delivery agents. Microbubbles can be produced by processing surfactants using ...

Hybrid &#181;CT-FMT imaging and image analysis

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (&#181;CT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal &#181;CT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints.

Hybrid  &#181;CT-FMT imaging and image analysis

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

Custom-designed Laser-based Heating Apparatus for Triggered Release of Cisplatin from Thermosensitive Liposomes with Magnetic Resonance Image Guidance

Liposomes have been employed as drug delivery systems to target solid tumors through exploitation of the enhanced permeability and retention (EPR) effect resulting in significant reductions in systemic toxicity. Nonetheless, insufficient release of encapsulated drug from liposomes has limited their clinical efficacy. Temperature-sensitive liposomes have been engineered to provide site-specific release of drug in order to overcome the problem of limited tumor drug bioavailability. Our lab has designed and developed a heat-activated thermosensitive liposome formulation of cisplatin (CDDP), known as HTLC, to provide triggered release of CDDP at solid tumors. Heat-activated delivery in vivo was achieved in murine models using a custom-built laser-based heating apparatus that provides a conformal heating pattern at the tumor site as confirmed by MR thermometry (MRT). A fiber optic temperature monitoring device was used to measure the temperature in real-time during the entire heating period with online adjustment of heat delivery by alternating the laser power. Drug delivery was optimized under magnetic resonance (MR) image guidance by co-encapsulation of an MR contrast agent (i.e.,&#160;gadoteridol) along with CDDP into the thermosensitive liposomes as a means to validate the heating protocol and to assess tumor accumulation. The heating protocol consisted of a preheating period of 5 min prior to administration of HTLC and 20 min heating post-injection. This heating protocol resulted in effective release of the encapsulated agents with the highest MR signal change observed in the heated tumor in comparison to the unheated tumor and muscle. This study demonstrated the successful application of the laser-based heating apparatus for preclinical thermosensitive liposome development and the importance of MR-guided validation of the heating protocol for optimization of drug delivery.

AN MRI-compatible&#160;custom-designed laser-based heating apparatus has been developed to provide local heating of subcutaneous tumors in order to activate release of agents from thermosensitive liposomes specifically at the tumor region.

Liposomes have been employed as drug delivery systems to target solid tumors through exploitation of the enhanced permeability and retention (EPR) effect ...

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

This detailed protocol outlines the implementation of image-guided, laser-based hydrogel degradation for the fabrication of vascular-derived microfluidic networks embedded in PEGDA hydrogels. Here, we describe the creation of virtual masks that allow for image-guided laser control; the photopolymerization of a micromolded PEGDA hydrogel, suitable for microfluidic network fabrication and pressure head-driven flow; the setup and use of a commercially available laser scanning confocal microscope paired with a femtosecond pulsed Ti:S laser to induce hydrogel degradation; and the imaging of fabricated microfluidic networks using fluorescent species and confocal microscopy. Much of the protocol is focused on the proper setup and implementation of the microscope software and microscope macro, as these are crucial steps in using a commercial microscope for microfluidic fabrication purposes that contain a number of intricacies. The image-guided component of this technique allows for the implementation of 3D image stacks or user-generated 3D models, thereby allowing for creative microfluidic design and for the fabrication of complex microfluidic systems of virtually any configuration. With an expected impact in tissue engineering, the methods outlined in this protocol could aid in the fabrication of advanced biomimetic microtissue constructs for organ- and human-on-a-chip devices. By mimicking the complex architecture, tortuosity, size, and density of in vivo vasculature, essential biological transport processes can be replicated in these constructs, leading to more accurate in vitro modeling of drug pharmacokinetics and disease.

This protocol outlines the implementation of image-guided, laser-based hydrogel degradation to fabricate vascular-derived, biomimetic microfluidic networks embedded in poly(ethylene glycol) diacrylate (PEGDA) hydrogels. These biomimetic microfluidic systems may be useful for tissue engineering applications, generation of in vitro disease models, and fabrication of advanced "on-a-chip" devices.

This detailed protocol outlines the implementation of image-guided, laser-based hydrogel degradation for the fabrication of vascular-derived microfluidic ...

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy (DHM)

Accurately detecting and counting sparse bacterial samples has many applications in the food, beverage, and pharmaceutical processing industries, in medical diagnostics, and for life detection by robotic missions to other planets and moons of the solar system. Currently, sparse bacterial samples are counted by culture plating or epifluorescence microscopy. Culture plates require long incubation times (days to weeks), and epifluorescence microscopy requires extensive staining and concentration of the sample. Here, we demonstrate how to use off-axis digital holographic microscopy (DHM) to enumerate bacteria in very dilute cultures (100-104 cells/mL). First, the construction of the custom DHM is discussed, along with detailed instructions on building a low-cost instrument. The principles of holography are discussed, and a statistical model is used to estimate how long videos should be to detect cells, based on the optical performance characteristics of the instrument and the concentration of the bacterial solution (Table 2). Video detection of cells at 105, 104, 103, and 100 cells/mL is demonstrated in real time using un-reconstructed holograms. Reconstruction of amplitude and phase images is demonstrated using an open-source software package.

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy DHM

Digital holographic microscopy (DHM) is a volumetric technique that allows imaging samples 50-100X thicker than brightfield microscopy at comparable resolution, with focusing performed post-processing. Here DHM is used for identifying, counting, and tracking microorganisms at very low densities and compared with optical density measurements, plate count, and direct count.

Accurately detecting and counting sparse bacterial samples has many applications in the food, beverage, and pharmaceutical processing industries, in ...

A Rapid Image-based Bacterial Virulence Assay Using Amoeba

Traditional bacterial virulence assays involve prolonged exposure of bacteria over the course of several hours to host cells. During this time, bacteria can undergo changes in the physiology due to the exposure to host growth environment and the presence of the host cells. We developed an assay to rapidly measure the virulence state of bacteria that minimize the extent to which bacteria grow in the presence of host cells. Bacteria and amoebae are mixed together and immobilized on a single imaging plane using an agar pad. The procedure uses single-cell fluorescence imaging with calcein-acetoxymethyl ester (calcein-AM) as an indicator of host cell health. The fluorescence of host cells is analyzed after 1 h of exposure of host cells to bacteria using epifluorescence microscopy. Image analysis software is used to compute a host killing index. This method has been used to measure virulence within planktonic and surface-attached Pseudomonas aeruginosa sub-populations during the initial stage of biofilm formation and may be adapted to other bacteria and other stages of biofilm growth. This protocol provides a rapid and robust method of measuring virulence and avoids many of the complexities associated with the growth and maintenance of mammalian cell lines. Virulence phenotypes measured here using amoebae have also been validated using mouse macrophages. In particular, this assay was used to establish that surface attachment upregulates virulence in P. aeruginosa.

Here, we present a protocol to measure the virulence of planktonic or surface-attached bacteria using D. discoideum (amoeba) as a host. Virulence is measured over a period of 1 h and host killing is quantified using fluorescence microscopy and image analysis. We demonstrate this protocol using the bacterium P. aeruginosa.

Traditional bacterial virulence assays involve prolonged exposure of bacteria over the course of several hours to host cells. During this time, bacteria ...

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Particle image velocimetry (PIV) is used in a wide variety of fields, due to the opportunity it provides for precisely visualizing and quantifying flows across a large spatiotemporal range. However, its implementation typically requires the use of expensive and specialized instrumentation, which limits its broader utility. Moreover, within the field of bioengineering, in vitro flow visualization studies are also often further limited by the high cost of commercially sourced tissue phantoms that recapitulate desired anatomical structures, particularly for those that span the mesoscale regime (i.e., submillimeter to millimeter length scales). Herein, we present a simplified experimental protocol developed to address these limitations, the key elements of which include 1) a relatively low-cost method for fabricating mesoscale tissue phantoms using 3-D printing and silicone casting, and 2) an open-source image analysis and processing framework that reduces the demand upon the instrumentation for measuring mesoscale flows (i.e., velocities up to tens of millimeters/second). Collectively, this lowers the barrier to entry for nonexperts, by leveraging resources already at the disposal of many bioengineering researchers. We demonstratethe applicability of this protocol within the context of neurovascular flow characterization; however, it is expected to be relevant to a broader range of mesoscale applications in bioengineering and beyond.

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Here we present simplified methods for fabricating transparent neurovascular phantoms and characterizing the flow therein. We highlight several important parameters and demonstrate their relationship to field accuracy.

Particle image velocimetry (PIV) is used in a wide variety of fields, due to the opportunity it provides for precisely visualizing and quantifying flows ...

Quantifying Intermembrane Distances with Serial Image Dilations

Summary

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Chapters in this video

Preparation of Complaint Matrices for Quantifying Cellular Contraction

Quantifying the Mechanical Properties of the Endothelial Glycocalyx with Atomic Force Microscopy

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Manufacture of Concentrated, Lipid-based Oxygen Microbubble Emulsions by High Shear Homogenization and Serial Concentration

Hybrid µCT-FMT imaging and image analysis

Custom-designed Laser-based Heating Apparatus for Triggered Release of Cisplatin from Thermosensitive Liposomes with Magnetic Resonance Image Guidance

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy (DHM)

A Rapid Image-based Bacterial Virulence Assay Using Amoeba

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro