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>

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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. 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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.

<table><tbody><tr><td>Touchscreen Monitor</td><td>Dell</td><td>S2240T</td><td>Needs soft-tipped stylus</td></tr><tr><td>Desktop</td><td>Dell</td><td>Precision T1650</td><td>8GB RAM</td></tr><tr><td>Operating System</td><td>Microsoft</td><td>Windows 7 Enterprise</td><td>64-bit OS</td></tr><tr><td>Program platform</td><td>Mathworks</td><td>Matlab R2015b</td><td>Program may be incompatible with newer/older versions of Matlab</td></tr></tbody></table>

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

Research

JoVE Journal

Bioengineering

6.4K Views.  Virginia Tech. Deze methode kan helpen bij het beantwoorden van belangrijke vragen in het hart elektrofysiologische veld, over de structuur van de extracellulaire ruimte en cellulaire communicatiemechanismen. De belangrijkste voordelen van deze techniek zijn dat het een hoge doorvoercapaciteit heeft en een verbeterde ruimtelijke bemonsteringsfrequentie. In wezen kunnen we sneller en met meer vertrouwen metingen krijgen dan voorheen.Perinexus identificatie en het oplossen van programma's zijn moeilijk...

Video: Quantifying Intermembrane Distances with Serial Image Dilations

Displacement Analysis of Myocardial Mechanical Deformation (DIAMOND) Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Zebrafish are increasingly utilized as a model organism for cardiomyopathies and regeneration. Current methods evaluating cardiac function fail to reliably detect segmental mechanics and are not readily feasible in zebrafish. Here we present a semiautomated, open-source method for the quantitative assessment of four-dimensional (4D) segmental cardiac function: displacement analysis of myocardial mechanical deformation (DIAMOND). Transgenic embryonic zebrafish were imaged in vivo using a light-sheet fluorescence microscopy system with 4D cardiac motion synchronization. Acquired 3D digital hearts were reconstructed at end-systole and end-diastole, and the ventricle was manually segmented into binary datasets. Then, the heart was reoriented and isotropically resampled along the true short axis, and the ventricle was evenly divided into eight portions (I&#8211;VIII) along the short axis. Due to the different resampling planes and matrices at end-systole and end-diastole, a transformation matrix was applied for image registration to restore the original spatial relationship between the resampled systolic and diastolic image matrices. After image registration, the displacement vector of each segment from end-systole to end-diastole was calculated based on the displacement of mass centroids in three dimensions (3D). DIAMOND shows that basal myocardial segments adjacent to the atrioventricular canal undergo the highest mechanical deformation and are the most susceptible to doxorubicin-induced cardiac injury. Overall, DIAMOND provides novel insights into segmental cardiac mechanics in zebrafish embryos beyond traditional ejection fraction (EF) under both physiological and pathological conditions.

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

The goal of this protocol is to detail a novel method for the assessment of segmental cardiac function in embryonic zebrafish under both physiological and pathological conditions.

Zebrafish are increasingly utilized as a model organism for cardiomyopathies and regeneration. Current methods evaluating cardiac function fail to ...

Developmental Biology

Simultaneous Interference Reflection and Total Internal Reflection Fluorescence Microscopy for Imaging Dynamic Microtubules and Associated Proteins

Several techniques have been employed for the direct visualization of cytoskeletal filaments and their associated proteins. Total-internal-reflection-fluorescence (TIRF) microscopy has a high signal-to-background ratio, but it suffers from photobleaching and photodamage of the fluorescent proteins. Label-free techniques such as interference reflection microscopy (IRM) and interferometric scattering microscopy (iSCAT) circumvent the problem of photobleaching but cannot readily visualize single molecules. This paper presents a protocol for combining IRM with a commercial TIRF microscope for the simultaneous imaging of microtubule-associated proteins (MAPs) and dynamic microtubules in vitro. This protocol allows for high-speed observation of MAPs interacting with dynamic microtubules. This improves on existing two-color TIRF setups by eliminating both the need for microtubule labeling and the need for several additional optical components, such as a second excitation laser. Both channels are imaged on the same camera chip to avoid image registration and frame synchronization problems. This setup is demonstrated by visualizing single kinesin molecules walking on dynamic microtubules.

We present a protocol for implementing interference-reflection microscopy and total-internal-reflection-fluorescence microscopy for the simultaneous imaging of dynamic microtubules and fluorescently labeled microtubule-associated proteins.

Several techniques have been employed for the direct visualization of cytoskeletal filaments and their associated proteins. ...

Biochemistry

Mammalian Cell Division in 3D Matrices via Quantitative Confocal Reflection Microscopy

The study of how mammalian cell division is regulated in a 3D environment remains largely unexplored despite its physiological relevance and therapeutic significance. Possible reasons for the lack of exploration are the experimental limitations and technical challenges that render the study of cell division in 3D culture inefficient. Here, we describe an imaging-based method to efficiently study mammalian cell division and cell-matrix interactions in 3D collagen matrices. Cells labeled with fluorescent H2B are synchronized using the combination of thymidine blocking and nocodazole treatment, followed by a mechanical shake-off technique. Synchronized cells are then embedded into a 3D collagen matrix. Cell division is monitored using live-cell microscopy. The deformation of collagen fibers during and after cell division, which is an indicator of cell-matrix interaction, can be monitored and quantified using quantitative confocal reflection microscopy. The method provides an efficient and general approach to study mammalian cell division and cell-matrix interactions in a physiologically relevant 3D environment. This approach not only provides novel insights into the molecular basis of the development of normal tissue and diseases, but also allows for the design of novel diagnostic and therapeutic approaches.

This protocol efficiently studies mammalian cell division in 3D collagen matrices by integrating synchronization of cell division, monitoring of division events in 3D matrices using live-cell imaging technique, time-resolved confocal reflection microscopy and quantitative imaging analysis.

The study of how mammalian cell division is regulated in a 3D environment remains largely unexplored despite its physiological relevance and therapeutic ...

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction

Most studies on morphogenesis rely on qualitative descriptions of how anatomical traits are affected by the disruption of specific genes and genetic pathways. Quantitative descriptions are rarely performed, although genetic manipulations produce a range of phenotypic effects and variations are observed even among individuals within control groups.&#160;Emerging evidence shows that morphology, size and location of organelles play a previously underappreciated, yet fundamental role in cell function and survival. Here we provide step-by-step instructions for performing quantitative analyses of phenotypes at the Drosophila larval neuromuscular junction (NMJ). We use several reliable immuno-histochemical markers combined with bio-imaging techniques and morphometric analyses to examine the effects of genetic mutations on specific cellular processes. In particular, we focus on the quantitative analysis of phenotypes affecting morphology, size and position of nuclei within the striated muscles of Drosophila larvae. The Drosophila larval NMJ is a valuable experimental model to investigate the molecular mechanisms underlying the structure and the function of the neuromuscular system, both in health and disease. However, the methodologies we describe here can be extended to other systems as well.

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction

Morphology, size and location of intracellular organelles are evolutionarily conserved and appear to directly affect their function. Understanding the molecular mechanisms underlying these processes has become an important goal of modern biology. Here we show how these studies can be facilitated by the application of quantitative techniques.

Most studies on morphogenesis rely on qualitative descriptions of how anatomical traits are affected by the disruption of specific genes and genetic ...

Neuroscience

Controlled Strain of 3D Hydrogels under Live Microscopy Imaging

External forces are an important factor in tissue formation, development, and maintenance. The effects of these forces are often studied using specialized in vitro stretching methods. Various available systems use 2D substrate-based stretchers, while the accessibility of 3D techniques to strain soft hydrogels, is more restricted. Here, we describe a method that allows external stretching of soft hydrogels from their circumference, using an elastic silicone strip as the sample carrier. The stretching system utilized in this protocol is constructed from 3D-printed parts and low-cost electronics, making it simple and easy to replicate in other labs. The experimental process begins with polymerizing thick (&#62;100 &#956;m) soft fibrin hydrogels (Elastic Modulus of ~100 Pa) in a cut-out at the center of a silicone strip. Silicone-gel constructs are then attached to the printed-stretching device and placed on the confocal microscope stage. Under live microscopy the stretching device is activated, and the gels are imaged at various stretch magnitudes. Image processing is then used to quantify the resulting gel deformations, demonstrating relatively homogenous strains and fiber alignment throughout the gel&#8217;s 3D thickness (Z-axis). Advantages of this method include the ability to strain extremely soft hydrogels in 3D while executing in situ microscopy, and the freedom to manipulate the geometry and size of the sample according to the user&#8217;s needs. Additionally, with proper adaptation, this method can be used to stretch other types of hydrogels (e.g., collagen, polyacrylamide or polyethylene glycol) and can allow for analysis of cells and tissue response to external forces under more biomimetic 3D conditions.

The presented method involves uniaxial stretching of 3D soft hydrogels embedded in silicone rubber while allowing live confocal microscopy. Characterization of the external and internal hydrogel strains as well as fiber alignment are demonstrated. The device and protocol developed can assess the response of cells to various strain regimes.

External forces are an important factor in tissue formation, development, and maintenance. The effects of these forces are often studied using specialized ...

Super-resolution Imaging of the Bacterial Division Machinery

Bacterial cell division requires the coordinated assembly of more than ten essential proteins at midcell1,2. Central to this process is the formation of a ring-like suprastructure (Z-ring) by the FtsZ protein at the division plan3,4. The Z-ring consists of multiple single-stranded FtsZ protofilaments, and understanding the arrangement of the protofilaments inside the Z-ring will provide insight into the mechanism of Z-ring assembly and its function as a force generator5,6. This information has remained elusive due to current limitations in conventional fluorescence microscopy and electron microscopy. Conventional fluorescence microscopy is unable to provide a high-resolution image of the Z-ring due to the diffraction limit of light (~200 nm). Electron cryotomographic imaging has detected scattered FtsZ protofilaments in small C. crescentus cells7, but is difficult to apply to larger cells such as E. coli or B. subtilis. Here we describe the application of a super-resolution fluorescence microscopy method, Photoactivated Localization Microscopy (PALM), to quantitatively characterize the structural organization of the E. coli Z-ring8.
	PALM imaging offers both high spatial resolution (~35 nm) and specific labeling to enable unambiguous identification of target proteins. We labeled FtsZ with the photoactivatable fluorescent protein mEos2, which switches from green fluorescence (excitation = 488 nm) to red fluorescence (excitation = 561 nm) upon activation at 405 nm9. During a PALM experiment, single FtsZ-mEos2 molecules are stochastically activated and the corresponding centroid positions of the single molecules are determined with &lt;20 nm precision. A super-resolution image of the Z-ring is then reconstructed by superimposing the centroid positions of all detected FtsZ-mEos2 molecules.
	Using this method, we found that the Z-ring has a fixed width of ~100 nm and is composed of a loose bundle of FtsZ protofilaments that overlap with each other in three dimensions. These data provide a springboard for further investigations of the cell cycle dependent changes of the Z-ring10 and can be applied to other proteins of interest.

We describe a super-resolution imaging method to probe the structural organization of the bacterial FtsZ-ring, an essential apparatus for cell division. This method is based on quantitative analyses of photoactivated localization microscopy (PALM) images and can be applied to other bacterial cytoskeletal proteins.

Bacterial cell division requires the coordinated  assembly of more than ten essential proteins at midcell1,2. Central  to this process is the formation of ...

Biology

Measuring Properties of the Membrane Periodic Skeleton of the Axon Initial Segment using 3D-Structured Illumination Microscopy (3D-SIM)

The axon initial segment (AIS) is the site at which action potentials initiate and constitutes a transport filter and diffusion barrier that contribute to the maintenance of neuronal polarity by sorting somato-dendritic cargo. A membrane periodic skeleton (MPS) comprising periodic actin rings provides a scaffold for anchoring various AIS proteins, including structural proteins and different ion channels. Although recent proteomic approaches have identified a considerable number of novel AIS components, details of the structure of the MPS and the roles of its individual components are lacking. The distance between individual actin rings in the MPS (~190 nm) necessitates the employment of super-resolution microscopy techniques to resolve the structural details of the MPS. This protocol describes a method for using cultured rat hippocampal neurons to examine the precise localization of an AIS protein in the MPS relative to sub-membranous actin rings using 3D-structured illumination microscopy (3D-SIM). In addition, an analytical approach to quantitively assess the periodicity of individual components and their position relative to actin rings is also described.

Measuring Properties of the Membrane Periodic Skeleton of the Axon Initial Segment using 3D-Structured Illumination Microscopy 3D-SIM

The present protocol describes a method to visualize and measure actin rings and other components of the membrane periodic skeleton of the axon initial segment using cultured rat hippocampal neurons and 3D-structured illumination microscopy (3D-SIM).

The axon initial segment (AIS) is the site at which action potentials initiate and constitutes a transport filter and diffusion barrier that contribute to ...

Membrane Transport Processes Analyzed by a Highly Parallel Nanopore Chip System at Single Protein Resolution

Membrane protein transport on the single protein level still evades detailed analysis, if the substrate translocated is non-electrogenic. Considerable efforts have been made in this field, but techniques enabling automated high-throughput transport analysis in combination with solvent-free lipid bilayer techniques required for the analysis of membrane transporters are rare. This class of transporters however is crucial in cell homeostasis and therefore a key target in drug development and methodologies to gain new insights desperately needed.
The here presented manuscript describes the establishment and handling of a novel biochip for the analysis of membrane protein mediated transport processes at single transporter resolution. The biochip is composed of microcavities enclosed by nanopores that is highly parallel in its design and can be produced in industrial grade and quantity. Protein-harboring liposomes can directly be applied to the chip surface forming self-assembled pore-spanning lipid bilayers using SSM-techniques (solid supported lipid membranes). Pore-spanning parts of the membrane are freestanding, providing the interface for substrate translocation into or out of the cavity space, which can be followed by multi-spectral fluorescent readout in real-time. The establishment of standard operating procedures (SOPs) allows the straightforward establishment of protein-harboring lipid bilayers on the chip surface of virtually every membrane protein that can be reconstituted functionally. The sole prerequisite is the establishment of a fluorescent read-out system for non-electrogenic transport substrates.
High-content screening applications are accomplishable by the use of automated inverted fluorescent microscopes recording multiple chips in parallel. Large data sets can be analyzed using the freely available custom-designed analysis software. Three-color multi spectral fluorescent read-out furthermore allows for unbiased data discrimination into different event classes, eliminating false positive results.
The chip technology is currently based on SiO2 surfaces, but further functionalization using gold-coated chip surfaces is also possible.

The presented protocol describes the analysis of membrane protein mediated transport on the single transporter level using pore-spanning solvent-free lipid bilayers. This is achieved by the creation of bulk produced nanopore array chips, combined with highly parallel data acquisition and analysis, enabling the future establishment of membrane protein effector screenings.

Membrane protein transport on the single protein level still evades detailed analysis, if the substrate translocated is non-electrogenic. Considerable ...

Three-dimensional Characterization of Interorganelle Contact Sites in Hepatocytes using Serial Section Electron Microscopy

Transmission electron microscopy has been long considered to be the gold standard for the visualization of cellular ultrastructure. However, analysis is often limited to two dimensions, hampering the ability to fully describe the three-dimensional (3D) ultrastructure and functional relationship between organelles. Volume electron microscopy (vEM) describes a collection of techniques that enable the interrogation of cellular ultrastructure in 3D at mesoscale, microscale, and nanoscale resolutions. 
This protocol provides an accessible and robust method to acquire vEM data using serial section transmission EM (TEM) and covers the technical aspects of sample processing through to digital 3D reconstruction in a single, straightforward workflow. To demonstrate the usefulness of this technique, the 3D ultrastructural relationship between the endoplasmic reticulum and mitochondria and their contact sites in liver hepatocytes is presented. Interorganelle contacts serve vital roles in the transfer of ions, lipids, nutrients, and other small molecules between organelles. However, despite their initial discovery in hepatocytes, there is still much to learn about their physical features, dynamics, and functions. 
Interorganelle contacts can display a range of morphologies, varying in the proximity of the two organelles to one another (typically ~10-30 nm) and the extent of the contact site (from punctate contacts to larger 3D cisternal-like contacts). The examination of close contacts requires high-resolution imaging, and serial section TEM is well suited to visualize the 3D ultrastructural of interorganelle contacts during hepatocyte differentiation, as well as alterations in hepatocyte architecture associated with metabolic diseases.

A simple and comprehensive protocol to acquire three-dimensional details of membrane contact sites between organelles in hepatocytes from the liver or cells in other tissues.

Transmission electron microscopy has been long considered to be the gold standard for the visualization of cellular ultrastructure. However, analysis is ...

Reconstitution of Membrane-Tethered Minimal Actin Cortices on Supported Lipid Bilayers

The surface of a living cell provides a versatile active platform for numerous cellular processes, which arise from the interplay of the plasma membrane with the underlying actin cortex. In the past decades, reconstituted, minimal systems based on supported lipid bilayers in combination with actin filament networks have proven to be very instrumental in unraveling basic mechanisms and consequences of membrane-tethered actin networks, as well as in studying the functions of individual membrane-associated proteins. Here, we describe how to reconstitute such active composite systems in vitro that consist of fluid supported lipid bilayers coupled via membrane-associated actin-binding proteins to dynamic actin filaments and myosin motors that can be readily observed via total internal reflection fluorescence microscopy. An open-chamber design allows one to assemble the system in a step-by-step manner and to systematically control many parameters such as linker protein concentration, actin concentration, actin filament length, actin/myosin ratio, as well as ATP levels. Finally, we discuss how to control the quality of the system, how to detect and troubleshoot commonly occurring problems, and some limitations of this system in comparison with the living cell surface.

This protocol describes the formation of supported lipid bilayers and the addition of cytoskeletal filaments and motor proteins to study the dynamics of reconstituted, membrane-tethered cytoskeletal networks using fluorescence microscopy.

The surface of a living cell provides a versatile active platform for numerous cellular processes, which arise from the interplay of the plasma membrane ...

Quantifying Intermembrane Distances with Serial Image Dilations

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