Name: creating a structurally realistic finite element geometric model of a cardiomyocyte to study the role of cellular architecture in cardiomyocyte systems biology
Uploaded: 2018-04-18T13:00:00
Description: Watch this Scientific Journal Video about Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biolog

This work was supported by the Royal Society of New Zealand Marsden Fast Start Grant 11-UOA-184, the Human Frontiers Science Program Research grant RGP0027/2013 and the Australian Research Council Discovery Project Grant DP170101358.

All methods described here have been approved by the University of Auckland Animal Ethics Committee and the University of California San Diego Institutional Animal Care and Use Committee, where the tissue protocol was originally developed.
1. Experimental Preparation
<ol>
	<li>Prepare stock solutions of 0.15 M and 0.3 M sodium cacodylate buffers at pH 7.4 according to Table 1.</li>
	<li>Prepare glutaraldehyde fixative (2% paraformaldehyde (PFA) + 2.5% glutaraldehyde ...

<ol>
	<li>Noble, D., Rudy, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Models+of+cardiac+ventricular+action+potentials:+iterative+interaction+between+experiment+and+simulation.">Models of cardiac ventricular action potentials: iterative interaction between experiment and simulation.</a> Phil. Trans. R. Soc. A: Math., Phys. and Eng. Sci. 359 (1783), 1127-1142 (2001).</li><li>Williams, G. S. B., Smith, G. D., Sobie, E. A., Jafri, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Models+of+cardiac+excitation-contraction+coupling+in+ventricular+myocytes.">Models of cardiac excitation-contraction coupling in ventricular myocytes.</a> Math. Biosci. 226 (1), 1-15 (2010).</li><li>Beard, D. A., Vendelin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systems+biology+of+the+mitochondrion.">Systems biology of the mitochondrion.</a> Am. J. Phys. - Cell Phys. 291 (6), C1101-C1103 (2006).</li><li>Crampin, E. J., Smith, N. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Dynamic+Model+of+Excitation-Contraction+Coupling+during+Acidosis+in+Cardiac+Ventricular+Myocytes.">A Dynamic Model of Excitation-Contraction Coupling during Acidosis in Cardiac Ventricular Myocytes.</a> Biophys. J. 90 (9), 3074-3090 (2006).</li><li>Li, L., Louch, W. E., 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=Calcium+Dynamics+in+the+Ventricular+Myocytes+of+SERCA2+Knockout+Mice:+A+Modeling+Study.">Calcium Dynamics in the Ventricular Myocytes of SERCA2 Knockout Mice: A Modeling Study.</a> Biophys. J. 100 (2), 322-331 (2011).</li><li>Shimizu, I., Minamino, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physiological+and+pathological+cardiac+hypertrophy.">Physiological and pathological cardiac hypertrophy.</a> J. Mol. Cell. Cardiol. 97, 245-262 (2016).</li><li>Wei, S., Guo, 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=T-tubule+remodeling+during+transition+from+hypertrophy+to+heart+failure.">T-tubule remodeling during transition from hypertrophy to heart failure.</a> Circ. Res. 107 (4), 520-531 (2010).</li><li>Jarosz, J., Ghosh, 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=Changes+in+mitochondrial+morphology+and+organization+can+enhance+energy+supply+from+mitochondrial+oxidative+phosphorylation+in+diabetic+cardiomyopathy.">Changes in mitochondrial morphology and organization can enhance energy supply from mitochondrial oxidative phosphorylation in diabetic cardiomyopathy.</a> Am. J. Phys. - Cell Phys. 312 (2), C190-C197 (2017).</li><li>Gonz&#225;lez, A., Ravassa, S., Beaumont, J., L&#243;pez, B., D&#237;ez, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+Targets+to+Treat+the+Structural+Remodeling+of+the+Myocardium.">New Targets to Treat the Structural Remodeling of the Myocardium.</a> J. Am. Coll. Cardiol. 58 (18), 1833-1843 (2011).</li><li>Hayashi, T., Martone, M. E., Yu, Z., Thor, A., Doi, M. <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+electron+microscopy+reveals+new+details+of+membrane+systems+for+Ca2++signaling+in+the+heart.">Three-dimensional electron microscopy reveals new details of membrane systems for Ca2+ signaling in the heart.</a> J. Cell Sci. , (2009).</li><li>Soeller, C., Crossman, D., Gilbert, R., Cannell, M. 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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=Three-dimensional+geometric+modeling+of+membrane-bound+organelles+in+ventricular+myocytes:+bridging+the+gap+between+microscopic+imaging+and+mathematical+simulation.">Three-dimensional geometric modeling of membrane-bound organelles in ventricular myocytes: bridging the gap between microscopic imaging and mathematical simulation.</a> J. Struct. Biol. 164 (3), 304-313 (2008).</li><li>Hake, J., Edwards, A. G., 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=Modelling+cardiac+calcium+sparks+in+a+three-dimensional+reconstruction+of+a+calcium+release+unit.">Modelling cardiac calcium sparks in a three-dimensional reconstruction of a calcium release unit.</a> J. Physiol. 590 (18), 4403-4422 (2012).</li><li>Soeller, C., Jayasinghe, I. D., Li, P., Holden, A. V., Cannell, M. B. <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+high-resolution+imaging+of+cardiac+proteins+to+construct+models+of+intracellular+Ca2++signalling+in+rat+ventricular+myocytes.">Three-dimensional high-resolution imaging of cardiac proteins to construct models of intracellular Ca2+ signalling in rat ventricular myocytes.</a> Exp. Physiol. 94 (5), 496-508 (2009).</li><li>Kekenes-Huskey, P. M., Cheng, Y., Hake, J. 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L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The finite element method. 1, (2000).</li><li>Rajagopal, V., Bass, G., 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=Examination+of+the+effects+of+heterogeneous+organization+of+RyR+clusters,+myofibrils+and+mitochondria+on+Ca2++release+patterns+in+cardiomyocytes.">Examination of the effects of heterogeneous organization of RyR clusters, myofibrils and mitochondria on Ca2+ release patterns in cardiomyocytes.</a> PLoS Comp. Biol. 11 (9), e1004417 (2015).</li><li>Illian, J., Penttinen, A., Stoyan, H., Stoyan, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Statistical Analysis and Modelling of Spatial Point Patterns. Statistical Analysis and Modelling of Spatial Point Patterns. , 1-534 (2008).</li><li>Kremer, J. R., Mastronarde, D. N., McIntosh, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computer+Visualization+of+Three-Dimensional+Image+Data+Using+IMOD.">Computer Visualization of Three-Dimensional Image Data Using IMOD.</a> J. Struct. Biol. 116, 71-76 (1996).</li><li>Fang, Q., Boas, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tetrahedral+mesh+generation+from+volumetric+binary+and+grayscale+images.">Tetrahedral mesh generation from volumetric binary and grayscale images.</a> Proc. ISBI. , 1142-1145 (2009).</li><li>Aune, D. J., Herr, S. E., Menick, D. 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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultramicrotomy+for+biological+electron+microscopy.">Ultramicrotomy for biological electron microscopy.</a> Electron Microscopy: Methods and Protocols. 369 (Chapter 5), 67-96 (2007).</li><li>He, W., He, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electron+tomography+for+organelles,+cells,+and+tissues.">Electron tomography for organelles, cells, and tissues.</a> Electron Microscopy: Methods and Protocols. 1117 (20), 445-483 (2014).</li><li>Diggle, P., Marron, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Equivalence+of+smoothing+parameter+selectors+in+density+and+intensity+estimation.">Equivalence of smoothing parameter selectors in density and intensity estimation.</a> J. Am. Stat. Assoc. 83 (403), 793-800 (1988).</li><li>Ghosh, S., Crampin, E. J., Hanssen, E., Rajagopal, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+computational+study+of+the+role+of+mitochondrial+organization+on+cardiac+bioenergetics.">A computational study of the role of mitochondrial organization on cardiac bioenergetics.</a> Proc. EMBC. , 2696-2699 (2017).</li><li>Pinali, C., Kitmitto, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Serial+block+face+scanning+electron+microscopy+for+the+study+of+cardiac+muscle+ultrastructure+at+nanoscale+resolutions.">Serial block face scanning electron microscopy for the study of cardiac muscle ultrastructure at nanoscale resolutions.</a> J. Mol. Cell. Cardiol. 76, 1-11 (2014).</li><li>Hussain, A., Hanssen, E., Rajagopal, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Semi-Automated+Workflow+for+Segmenting+Contents+of+Single+Cardiac+Cells+from+Serial-Block-Face+Scanning+Electron+Microscopy+Data.">A Semi-Automated Workflow for Segmenting Contents of Single Cardiac Cells from Serial-Block-Face Scanning Electron Microscopy Data.</a> Microsc Microanal. 23 (S1), 240-241 (2017).</li><li>Pinali, C., Bennett, H., Davenport, J. B., Trafford, A. W., Kitmitto, A. <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+reconstruction+of+cardiac+sarcoplasmic+reticulum+reveals+a+continuous+network+linking+transverse-tubules:+this+organization+is+perturbed+in+heart+failure.">Three-dimensional reconstruction of cardiac sarcoplasmic reticulum reveals a continuous network linking transverse-tubules: this organization is perturbed in heart failure.</a> Circ. Res. 113 (11), 1219-1230 (2013).</li><li>LeGrice, I. J., Hunter, P. J., Smaill, B. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laminar+structure+of+the+heart:+a+mathematical+model.">Laminar structure of the heart: a mathematical model.</a> Am. J. Physiol. 272 (5 Pt 2), H2466-H2476 (1997).</li><li>Jayasinghe, I. D., Cannell, M. B., Soeller, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organization+of+ryanodine+receptors,+transverse+tubules,+and+sodium-calcium+exchanger+in+rat+myocytes.">Organization of ryanodine receptors, transverse tubules, and sodium-calcium exchanger in rat myocytes.</a> Biophys. J. 97 (10), 2664-2673 (2009).</li><li>Jayasinghe, I. D., Crossman, D. J., Soeller, C., Cannell, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+the+organization+of+t-tubules,+sarcoplasmic+reticulum+and+ryanodine+receptors+in+rat+and+human+ventricular+myocardium.">Comparison of the organization of t-tubules, sarcoplasmic reticulum and ryanodine receptors in rat and human ventricular myocardium.</a> Clinic. Exp. Pharmacol. 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The above protocol outlines key steps to generate a novel finite element geometric model of cardiomyocyte ultrastructure. The method enables computational fusion of different microscopy (or, in principle, other data) modalities to develop a more comprehensive computational model of cardiomyocyte dynamics that includes details of spatial cell architecture. There is currently no other protocol available to create such a model of a cardiomyocyte.
Step 1 outlines a protocol for perfusion fixation....

Excitation-contraction coupling (ECC) in the heart refers to the important and intricate coupling between electrical excitation of the cardiomyocyte membrane and the subsequent mechanical contraction of the cell during each heartbeat. Mathematical models have played a key role in developing a quantitative understanding of the interlinked biochemical processes that regulate the action potential1, cytosolic calcium signaling2, bioenergetics3, and subsequent contractile force generation. Such models have also successfully predicted changes to the heartbeat when one or several of these biochemical processes undergo alterations4,5. The highly-organized ultrastructure of the cardiomyocyte has increasingly been recognized to play a critical role in the normal contractile function of the cell and the whole heart. Indeed, changes to the morphology and organization of components of cardiac ultrastructure occur in parallel to biochemical changes in disease conditions such as hypertrophy6, heart failure7, and diabetic cardiomyopathy8. Whether these structural changes are minor, adaptive, or pathological responses to the changing biochemical conditions is still largely unknown9. The inherently tight coupling between form and function in biology means that experimental studies alone cannot provide deeper insights than correlations between structural remodeling and cardiomyocyte function. A new generation of mathematical models that can incorporate the structural assembly of the sub-cellular components, along with the well-studied biochemical processes, are necessary to develop a comprehensive, quantitative understanding of the relationship between structure, biochemistry, and contractile force in cardiomyocytes. This protocol describes methods that can be used to generate structurally accurate finite element models of cardiomyocytes that can be used for such investigations.
The last decade has seen significant advances in 3D electron microscopy10, confocal11, and super-resolution microscopy12 that provide unprecedented, high-resolution insights into the nano-scale and micro-scale assembly of the sub-cellular components of the cardiomyocyte. Recently, these datasets have been used to generate computational models of cardiomyocyte ultrastructure13,14,15,16. These models use a well-established engineering simulation method, called the finite element method17, to create finite element computational meshes over which biochemical processes and cardiomyocyte contractions can be simulated. However, these models are limited by the resolution and detail that a microscopy method can provide in an image dataset. For example, electron microscopy can generate nanometer-level detail of cell structure, but it is difficult to identify specific proteins within the image that would be necessary to create a model. On the other hand, super-resolution optical microscopy can provide high contrast images at resolutions on the order of 50 nm of only a select few molecular components of the cell. Only by integrating complementary information from these imaging modalities can one realistically explore the sensitivity of function to changes in structure. Correlative light and electron microscopy is still not a routine procedure and it would still suffer the limitation that only a limited number of components could be stained in the immunofluorescence view and correlated with the electron microscopy view.
This protocol presents a novel approach18 that uses statistical methods19 to analyze and computationally fuse light microscopy information on the spatial distribution of ion-channels with electron microscopy information on other cardiac ultrastructure components, such as myofibrils and mitochondria. This produces a finite element model that can be used with biophysical models of biochemical processes to study the role of cardiomyocyte sub-cellular organization on the biochemical processes that regulate cardiomyocyte contraction. For example, this protocol could be used to create models from healthy and streptozotocin-induced diabetic cardiac myocytes to study the effect of structural remodeling on cardiac cell function that is observed in diabetic animal models8. An additional advantage of the statistical nature of the presented method is also illustrated in the protocol: the method can generate multiple instances of finite element geometries that closely mimic the experimentally observed variations in cell structure.
As an overview, the protocol steps include: (i) preparation of cardiac tissue for electron microscopy to generate 3D images with sufficient resolution and contrast; (ii) reconstruction and segmentation of 3D image stacks from electron microscopy data using a 3D electron microscopy reconstruction and image analysis software called IMOD20; (iii) using iso2mesh21 to generate a finite element mesh using the segmented data as input; (iv) using the novel algorithm and codes to map the distribution of ion channels onto the finite element mesh.
The premise of the approach to each step is outlined within the protocol, and representative results are provided in the accompanying figures. An overview is outlined specifying how the generated spatially detailed models can be used to study the spatial dynamics of calcium during ECC, as well as mitochondrial bioenergetics. Some of the current limitations of the protocol are discussed, as well as new developments that are underway to overcome them and further advance a quantitative understanding of the role of cell structure to cardiac systems biology. How these methods could be generalized to create finite element models of other cell types is also addressed.
Users of this protocol may skip step 1 and the reconstruction part of step 2 if they have access to a pre-existing electron microscopy image stack. Users who intend to acquire their data in collaboration with more experienced electron microscopists may wish to discuss and compare the fixation and staining procedures in step 1 with the expert to determine an optimal protocol for acquisition.

<table>
	<tbody>
		<tr>
			<td>Materials</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium chloride</td>
			<td>Sigma-Aldrich</td>
			<td>746398</td>
			<td></td>
		</tr>
		<tr>
			<td>Calcium chloride</td>
			<td>Sigma-Aldrich</td>
			<td>C8106</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnesium chloride</td>
			<td>Sigma-Aldrich</td>
			<td>M2393</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium bicarbonate</td>
			<td>Sigma-Aldrich</td>
			<td>S5761</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium chloride</td>
			<td>Sigma-Aldrich</td>
			<td>P5405</td>
			<td></td>
		</tr>
		<tr>
			<td>Dextrose</td>
			<td>Sigma-Aldrich</td>
			<td>D9434</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium hydroxide</td>
			<td>Sigma-Aldrich</td>
			<td>S8045</td>
			<td></td>
		</tr>
		<tr>
			<td>Probenecid</td>
			<td>Sigma-Aldrich</td>
			<td>P8761</td>
			<td></td>
		</tr>
		<tr>
			<td>2,3-Butanedione monoxime</td>
			<td>Sigma-Aldrich</td>
			<td>B0753</td>
			<td></td>
		</tr>
		<tr>
			<td>25% Glutaraldehyde EM Grade (500 ml bottle)</td>
			<td>Merck</td>
			<td>354400-500ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Paraformaldehyde</td>
			<td>Sigma-Aldrich</td>
			<td>P6148</td>
			<td></td>
		</tr>
		<tr>
			<td>Tannic Acid</td>
			<td>Sigma-Aldrich</td>
			<td>403040-500G</td>
			<td>100g EM grade</td>
		</tr>
		<tr>
			<td>Sodium cacodylate</td>
			<td>Sigma-Aldrich</td>
			<td>C0250</td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphate-buffered saline (PBS)</td>
			<td>Sigma-Aldrich</td>
			<td>P4593</td>
			<td></td>
		</tr>
		<tr>
			<td>Osmium Tetroxide</td>
			<td>Sigma-Aldrich</td>
			<td>75632-10ML</td>
			<td>4% in water, 5 ml bottle (or 10 ml bottle also available)</td>
		</tr>
		<tr>
			<td>Uranyl Acetate</td>
			<td>EM Sciences</td>
			<td>22400</td>
			<td>25g bottle</td>
		</tr>
		<tr>
			<td>Potassium Ferrocyanide</td>
			<td>Merck Millipore</td>
			<td>104973</td>
			<td></td>
		</tr>
		<tr>
			<td>Toluene blue</td>
			<td>Sigma-Aldrich</td>
			<td>T3260</td>
			<td></td>
		</tr>
		<tr>
			<td>Borax</td>
			<td>Sigma-Aldrich</td>
			<td>S9640</td>
			<td>also termed sodium borate</td>
		</tr>
		<tr>
			<td>Ethanol</td>
			<td>Sigma-Aldrich</td>
			<td>792780</td>
			<td>Diluted to different percentages with pure water</td>
		</tr>
		<tr>
			<td>Acetone</td>
			<td>EM Sciences</td>
			<td>RT10017</td>
			<td></td>
		</tr>
		<tr>
			<td>Resin kit</td>
			<td>EM Sciences</td>
			<td>14040</td>
			<td>ACM Durcupan works well</td>
		</tr>
		<tr>
			<td>Hydrochloric acid</td>
			<td>Sigma-Aldrich</td>
			<td>H9892</td>
			<td>1Normal solution</td>
		</tr>
		<tr>
			<td>Equipment</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Ultramicrotome</td>
			<td>Leica</td>
			<td>EM UC7</td>
			<td></td>
		</tr>
		<tr>
			<td>Transmission electron microscope</td>
			<td>ThermoFisher Scientific</td>
			<td>Tecnai F30</td>
			<td>http://www.leica-microsystems.com/</td>
		</tr>
		<tr>
			<td>Retort stand</td>
			<td>Proscitech</td>
			<td>T752</td>
			<td></td>
		</tr>
		<tr>
			<td>Tubing</td>
			<td>BioStrategy</td>
			<td>75831-346</td>
			<td>for langendorff perfusion apparatus, 3 mm diameter is recommended but not essential</td>
		</tr>
		<tr>
			<td>Stopcocks</td>
			<td>SDR</td>
			<td>QP13813</td>
			<td>for langendorff tubing; product is only an example, user can select any</td>
		</tr>
		<tr>
			<td>retort stand clamps</td>
			<td>Proscitech</td>
			<td>T715</td>
			<td></td>
		</tr>
		<tr>
			<td>Plastic syringes</td>
			<td>SDR</td>
			<td>QPC1108</td>
			<td>for solutions on langendorff apparatus</td>
		</tr>
		<tr>
			<td>Cannulation silk suture, 7-0</td>
			<td>TeleFlex</td>
			<td>15B051000</td>
			<td>for tieing heart on langedorff apparatus</td>
		</tr>
		<tr>
			<td>Cannula</td>
			<td></td>
			<td></td>
			<td>Made from 3 mm outer-diameter steel needle</td>
		</tr>
		<tr>
			<td>Rubber petri dish mat</td>
			<td>Proscitech</td>
			<td>H068</td>
			<td>for use as cutting board during fixed-heart dissection</td>
		</tr>
		<tr>
			<td>Razor blades</td>
			<td>Proscitech</td>
			<td>L056</td>
			<td>for cutting fixed-heart into small blocks for EM processing</td>
		</tr>
		<tr>
			<td>Glass bottles</td>
			<td>BioStrategy</td>
			<td>89000-236</td>
			<td>for storing solutions during tissue fixation and processing for EM</td>
		</tr>
		<tr>
			<td>Beakers</td>
			<td>BioStrategy</td>
			<td>213-0477</td>
			<td>for storing solutions temporarily and during perfusion</td>
		</tr>
		<tr>
			<td>Scintillation vials</td>
			<td>BioStrategy</td>
			<td>548-2170</td>
			<td>for tissue samples during EM processing</td>
		</tr>
		<tr>
			<td>Dissection kit</td>
			<td>Proscitech</td>
			<td>T161</td>
			<td>for animal dissection</td>
		</tr>
		<tr>
			<td>Syringe Filters</td>
			<td>Proscitech</td>
			<td>WS3-02225S</td>
			<td>for purification of Uranyl Acetate</td>
		</tr>
		<tr>
			<td>Aluminium/silver foil baking cups</td>
			<td></td>
			<td></td>
			<td>From any baking products store</td>
		</tr>
		<tr>
			<td>Dupont Diamond knife</td>
			<td>BioStrategy</td>
			<td>102680-780</td>
			<td>35 degree angle version produces best sections.</td>
		</tr>
		<tr>
			<td>Colloidal Gold</td>
			<td>BBI Solutions</td>
			<td>EM. GC15</td>
			<td>15 nm colloidal gold</td>
		</tr>
		<tr>
			<td>EM mesh grids</td>
			<td>Proscitech</td>
			<td>GCU150</td>
			<td>a variety of sizes can be tested: GCU150h, GCU200h for example</td>
		</tr>
		<tr>
			<td>Plastic disposal pippettes</td>
			<td>Proscitech</td>
			<td>LCH20</td>
			<td>best to use plastic disposables especially when working with resin</td>
		</tr>
		<tr>
			<td>Software</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SerialEM</td>
			<td>University of Boulder</td>
			<td></td>
			<td>tomography acquisition</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td></td>
			<td>https://www.mathworks.com/products/matlab.html</td>
		</tr>
		<tr>
			<td>IMOD</td>
			<td>University of Boulder</td>
			<td></td>
			<td>image alignment and segmentation</td>
		</tr>
		<tr>
			<td>iso2mesh</td>
			<td></td>
			<td></td>
			<td>available at http://iso2mesh.sourceforge.net</td>
		</tr>
		<tr>
			<td>Fiji or similar image processing software</td>
			<td>ImageJ</td>
			<td>Fiji is Just Image J</td>
			<td>available at https://fiji.sc for manipulation of binary image stacks</td>
		</tr>
		<tr>
			<td>RyR-Simulator codes/data</td>
			<td>CellSMB group</td>
			<td></td>
			<td>available at https://github.com/CellSMB/RyR-simulator</td>
		</tr>
		<tr>
			<td>CardiacCellMeshGenerator</td>
			<td>CellSMB group</td>
			<td></td>
			<td>comes with RyR-Simulator under folder &#34;gui-version&#34;</td>
		</tr>
		<tr>
			<td>R-statistics software</td>
			<td>R-project</td>
			<td></td>
			<td>Download from https://www.r-project.org</td>
		</tr>
		<tr>
			<td>spatstat</td>
			<td>R-project</td>
			<td></td>
			<td>install via R program</td>
		</tr>
		<tr>
			<td>rgl</td>
			<td>R-project</td>
			<td></td>
			<td>install via R program</td>
		</tr>
		<tr>
			<td>doparallel</td>
			<td>R-project</td>
			<td></td>
			<td>install via R program</td>
		</tr>
		<tr>
			<td>foreach</td>
			<td>R-project</td>
			<td></td>
			<td>install via R program</td>
		</tr>
		<tr>
			<td>doSNOW</td>
			<td>R-project</td>
			<td></td>
			<td>install via R program</td>
		</tr>
		<tr>
			<td>iterators</td>
			<td>R-project</td>
			<td></td>
			<td>install via R program</td>
		</tr>
	</tbody>
</table>

creating a structurally realistic finite element geometric model of a cardiomyocyte to study the role of cellular architecture in cardiomyocyte systems biology

With the advent of three-dimensional (3D) imaging technologies such as electron tomography, serial-block-face scanning electron microscopy and confocal microscopy, the scientific community has unprecedented access to large datasets at sub-micrometer resolution that characterize the architectural remodeling that accompanies changes in cardiomyocyte function in health and disease. However, these datasets have been under-utilized for&#160;investigating the role of cellular architecture remodeling in cardiomyocyte function. The purpose of this protocol is to outline how to create an accurate finite element model of a cardiomyocyte using high resolution electron microscopy and confocal microscopy images. A detailed and accurate model of cellular architecture has significant potential to provide new insights into cardiomyocyte biology, more than experiments alone can garner. The power of this method lies in its ability to computationally fuse information from two disparate imaging modalities of cardiomyocyte ultrastructure to develop one unified and detailed model of the cardiomyocyte. This protocol outlines steps to integrate electron tomography and confocal microscopy images of adult male Wistar (name for a specific breed of albino rat) rat cardiomyocytes to develop a half-sarcomere finite element model of the cardiomyocyte. The procedure generates a 3D finite element model that contains an accurate, high-resolution depiction (on the order of ~35 nm) of the distribution of mitochondria, myofibrils and ryanodine receptor clusters that release the necessary calcium for cardiomyocyte contraction from the sarcoplasmic reticular network (SR) into the myofibril and cytosolic compartment. The model generated here as an illustration does not incorporate details of the transverse-tubule architecture or the sarcoplasmic reticular network and is therefore a minimal model of the cardiomyocyte. Nevertheless, the model can already be applied in simulation-based investigations into the role of cell structure in calcium signaling and mitochondrial bioenergetics, which is illustrated and discussed using two case studies that are presented following the detailed protocol.

Figure 2 through Figure 7 provide representative results of several key steps in this protocol: (i) visualizing and reorienting tissue blocks for cross-sectional electron microscopy views; (ii) generating a 3D electron microscopy image stack; (iii) segmenting sub-cellular ultrastructure for organelles of interest; (iv) generation of a finite element mesh using iso2mesh; (v) simulating a realistic distribution of RyR clusters on t...

Watch this Scientific Journal Video about Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biolog

Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology

This protocol outlines a novel method to create a spatially detailed finite element model of the intracellular architecture of cardiomyocytes from electron microscopy and confocal microscopy images. The power of this spatially detailed model is demonstrated using case studies in calcium signaling and bioenergetics.

With the advent of three-dimensional (3D) imaging technologies such as electron tomography, serial-block-face scanning electron microscopy and confocal ...

The overall goal of this procedure is to create a spatially detailed finite element model of the intracellular architecture of a heart cell. This model is created from electron and confocal microscopy data. Such models can be used to study the role of cellular architecture on cardiac cell function.This method can help answer key questions about how change to internal organization of the cell and health and disease conditions can affect cardiac cell systems'biology. The main advantage of this method is that it allows you to integrate complementary information from electron microscopy and confocal microscopy in order to create a spatially detailed model of the cellular architecture. Though this method can provide insight into cardiac cell system biology, but this general overall approach, it can also be used on other cells where we need to understand the effect of the cellular structure on the function of the cell.Visual demonstration of this method is critical, as the steps to simulate and map the ion channels onto the finer element mesh is so new. After preparing left ventricular free wall tissue sections and imaging them using electron tomography according to the text protocol, execute the IMOD program 3dmod. Within the graphical user interface, enter the address of the rec or mrc file that contains the 3D reconstructed image data set of the cell into the entry box labeled Image Files, then press Okay.Next, under the file menu, select New Model. Then, using the Save Model As menu item, save the file with an appropriate name. Under the Special menu, select Drawing Tools, then choose the Sculpt option and move the mouse over to the image window.A circular contour centered on the mouse pointer will appear. While holding the middle mouse button down over a mitochondrion, which is a darker region of the image files, drag the perimeter of the circle contour into the shape of its boundary. Once contouring of the mitochondrion boundary is complete, release the middle button and repeat this process for each mitochondrion in the data.Each contour will automatically be recognized by IMOD as a new contour within the same object. Under the Edit Object menu, select New to create a new object. This will automatically increment the total number of objects by one and assign this number to the new object.Use the Sculpt option to segment and save myofibril contours. Also segment the cell boundary, then under the File menu save the model file. After downloading iso2mesh according to the text protocol, download the source codes and data to simulate RyR clusters on the mesh from the GitHub website shown here.Start the Cardiac Cell Mesh Generator MATLAB application. Then, using the three Push buttons on the upper left hand side of the GUI, load the different organelle component masks into MATLAB. To create another binary image stack that demarcates gaps between myofibrils and mitochondria, open Image J, then, using the File Open dialogue, load the myofibrils and mitochondria TIFF stacks into the program.Initiate the Image Addition plug-in by selecting Process Calculator Plus. Select the myofibril image stack as I1, the mitochondria image stack as I2, and choose the Add Operator, then click Okay. After a new image stack representing the result appears, select Edit Invert to produce an image stack.Load the file containing the binary image stack of the gaps between myofibrils and mitochondria by pushing the RyR Gaps file button on the Cardiac Cell Mesh Generator program, then push Generate Mesh on the GUI. Generate the necessary inputs for the RyR-Simulator by pushing the button labeled Generate RyR-Simulator Inputs on the GUI. After the function executes, check that the following files have been created within the directory specified as the OUT_DIR path, the d_axial_micron.txt file, which represents the axial distance between the position of the Z-disc and the remainder of the pixels in the image stack, the d_radial_micron. txt file, which represents the Euclidean distance excluding the axial component from each pixel in the set of possible RyR cluster locations to the pixels on the Z-disc plane, the W_micro. txt file, which represents the list of spatial coordinates of all the available positions for RyR clusters to be present.The remaining three files in the folder contain the suffix pixel rather than micron to denote that the values within these files have been written out in pixel-coordinate form. To simulate RyR cluster distributions on the binary image stack of myofibrils, push the button labeled Open RyR-Simulator in R to initiate the R program. On the R GUI, select File Open and within the RyR-Simulator package, find the file settings.R.Refer to the text protocol for detailed explanations of the parameters in this field and the default values. Users should examine the simulated distribution of RyR clusters in the R window and adjust the settings to ensure that the clusters are located near the Z-discs and evenly spread across the entire cross-section of the cell. To map points as spatial densities onto a computational model using the Cardiac Cell Mesh Generator, select the button labeled Select RyR Points File and choose a simulated RyR cluster distribution text file from those that were output by the RyR-Simulator.Finally, on the GUI in MATLAB, execute the RyR Density Mapper. Users should check visually that the density values are distributed such that the neighborhoods of high density values are similar to the size of RyR clusters in experimental data. These bright field images reveal how cells appear when oriented longitudinally, obliquely, and cross-sectionally with respect to the cutting plane.Oblique and longitudinal samples exhibit striations, while cross-sectional samples do not. The capillaries also appear more circular in cross-sectional views than in oblique views. This panel represents a good quality tomogram stack that can be acquired when the tissue preparation portion of this protocol is followed.Care and experience are necessary to ensure that the tissue blocks are sufficiently stained and that there is an even distribution of colloidal gold particles through the tissue volume. Shown here is a segmented 3D model of myofibrils and mitochondria. This 3D rendering of the tetrahedral mesh is produced by iso2mesh.As long as the original image stack and segmentation tasks are of good quality, this step is fairly robust. Finally, these panels show three examples of simulated RyR clusters on the same mesh topology after using the spherical kernel intensity estimator algorithm. Notice the variation in organization of the RyR clusters.Once mastered and if it's performed properly, a structurally realistic finite element model of a heart cell, it can be generated within a couple of hours by using our technique. Following this procedure, finite element simulations of calcium signaling, cardiac bioenergetics, and cellular mechanics, it can be performed to answer questions about the role of cellular architecture on the functioning of cardiomyocytes.

Research

JoVE Journal

Bioengineering

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