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. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+ryanodine+receptor+clusters+in+rat+and+human+cardiac+myocytes.">Analysis of ryanodine receptor clusters in rat and human cardiac myocytes.</a> Proc. Natl. Acad. Sci. 104 (38), 14958-14963 (2007).</li><li>Soeller, C., Baddeley, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Super-resolution+imaging+of+EC+coupling+protein+distribution+in+the+heart.">Super-resolution imaging of EC coupling protein distribution in the heart.</a> J. Mol. Cell. Cardiol. 58 (1), 32-40 (2013).</li><li>Yu, Z., Holst, M. 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. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+effects+of+L-type+Ca2++current+and+Na+-Ca2++exchanger+on+Ca2++trigger+flux+in+rabbit+myocytes+with+realistic+t-tubule+geometries.">Modeling effects of L-type Ca2+ current and Na+-Ca2+ exchanger on Ca2+ trigger flux in rabbit myocytes with realistic t-tubule geometries.</a> Front. in Physiol. 3, 1-14 (2012).</li><li>Zienkiewicz, O. C., Taylor, R. 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. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Induction+and+assessment+of+ischemia-reperfusion+injury+in+langendorff+perfused+rat+hearts.">Induction and assessment of ischemia-reperfusion injury in langendorff perfused rat hearts.</a> J. Vis. Exp. (101), e52908 (2015).</li><li>Judd, J., Lovas, J., Huang, G. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation,+culture+and+transduction+of+adult+mouse+cardiomyocytes.">Isolation, culture and transduction of adult mouse cardiomyocytes.</a> J. Vis. Exp. (114), (2016).</li><li>Hagler, H. 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. P. 39 (5), 469-476 (2012).</li><li>Bradley, C., Bowery, 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=OpenCMISS:+a+multi-physics+&amp;+multi-scale+computational+infrastructure+for+the+VPH/Physiome+project.">OpenCMISS: a multi-physics &amp; multi-scale computational infrastructure for the VPH/Physiome project.</a> Prog. Biophys. Mol. Bio. 107 (1), 32-47 (2011).</li></ol>

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.

creating-structurally-realistic-finite-element-geometric-model

Research

JoVE Journal

Bioengineering

The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal  Skin and Melanoma Progression

Most in vitro studies in experimental skin biology have been done in 2-dimensional (2D) monocultures, while accumulating evidence suggests that cells behave differently when they are grown within a 3D extra-cellular matrix and also interact with other cells (1-5). Mouse models have been broadly utilized to study tissue morphogenesis in vivo. However mouse and human skin have significant differences in cellular architecture and physiology, which makes it difficult to extrapolate mouse studies to humans. Since melanocytes in mouse skin are mostly localized in hair follicles, they have distinct biological properties from those of humans, which locate primarily at the basal layer of the epidermis. The recent development of 3D human skin reconstruct models has enabled the field to investigate cell-matrix and cell-cell interactions between different cell types. The reconstructs consist of a "dermis" with fibroblasts embedded in a collagen I matrix, an "epidermis", which is comprised of stratified, differentiated keratinocytes and a functional basement membrane, which separates epidermis from dermis. Collagen provides scaffolding, nutrient delivery, and potential for cell-to-cell interaction. The 3D skin models incorporating melanocytic cells recapitulate natural features of melanocyte homeostasis and melanoma progression in human skin. As in vivo, melanocytes in reconstructed skin are localized at the basement membrane interspersed with basal layer keratinocytes. Melanoma cells exhibit the same characteristics reflecting the original tumor stage (RGP, VGP and metastatic melanoma cells) in vivo. Recently, dermal stem cells have been identified in the human dermis (6). These multi-potent stem cells can migrate to the epidermis and differentiate to melanocytes.

In this report, we describe the three-dimensional skin reconstruct model which mimics human skin in architecture and composition. Melanocyte physiology, melanoma progression and the fate of dermal stem cells have been investigated using the skin reconstruct model. The model is also useful as a preclinical tool for drug assessment.

Most in vitro studies in experimental skin biology have been done in 2-dimensional (2D) monocultures, while accumulating evidence suggests that cells ...

Creating Two-Dimensional Patterned Substrates for Protein and Cell Confinement

Microcontact printing provides a rapid, highly reproducible method for the creation of well-defined patterned substrates.1 While microcontact printing can be employed to directly print a large number of molecules, including proteins,2 DNA,3 and silanes,4 the formation of self-assembled monolayers (SAMs) from long chain alkane thiols on gold provides a simple way to confine proteins and cells to specific patterns containing adhesive and resistant regions. This confinement can be used to control cell morphology and is useful for examining a variety of questions in protein and cell biology. Here, we describe a general method for the creation of well-defined protein patterns for cellular studies.5 This process involves three steps: the production of a patterned master using photolithography, the creation of a PDMS stamp, and microcontact printing of a gold-coated substrate. Once patterned, these cell culture substrates are capable of confining proteins and/or cells (primary cells or cell lines) to the pattern.
The use of self-assembled monolayer chemistry allows for precise control over the patterned protein/cell adhesive regions and non-adhesive regions; this cannot be achieved using direct protein stamping. Hexadecanethiol, the long chain alkane thiol used in the microcontact printing step, produces a hydrophobic surface that readily adsorbs protein from solution. The glycol-terminated thiol, used for backfilling the non-printed regions of the substrate, creates a monolayer that is resistant to protein adsorption and therefore cell growth.6 These thiol monomers produce highly structured monolayers that precisely define regions of the substrate that can support protein adsorption and cell growth. As a result, these substrates are useful for a wide variety of applications from the study of intercellular behavior7 to the creation of microelectronics.8
While other types of monolayer chemistry have been used for cell culture studies, including work from our group using trichlorosilanes to create patterns directly on glass substrates,9 patterned monolayers formed from alkane thiols on gold are straight-forward to prepare. Moreover, the monomers used for monolayer preparation are commercially available, stable, and do not require storage or handling under inert atmosphere. Patterned substrates prepared from alkane thiols can also be recycled and reused several times, maintaining cell confinement.10

Self-assembled monolayers (SAMs) formed from long chain alkane thiols on gold provide well-defined substrates for the formation of protein patterns and cell confinement. Microcontact printing of hexadecanethiol using a polydimethylsiloxane (PDMS) stamp followed by backfilling with a glycol-terminated alkane thiol monomer produces a pattern where protein and cells adsorb only to the stamped hexadecanethiol region.

Microcontact printing provides a rapid, highly reproducible method for the creation of well-defined patterned substrates.1  While microcontact printing ...

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

The aortic valve, located between the left ventricle and the aorta, allows for unidirectional blood flow, preventing backflow into the ventricle. Aortic valve leaflets are composed of interstitial cells suspended within an extracellular matrix (ECM) and are lined with an endothelial cell monolayer. The valve withstands a harsh, dynamic environment and is constantly exposed to shear, flexion, tension, and compression. Research has shown calcific lesions in diseased valves occur in areas of high mechanical stress as a result of endothelial disruption or interstitial matrix damage1-3. Hence, it is not surprising that epidemiological studies have shown high blood pressure to be a leading risk factor in the onset of aortic valve disease4. 
The only treatment option currently available for valve disease is surgical replacement of the diseased valve with a bioprosthetic or mechanical valve5. Improved understanding of valve biology in response to physical stresses would help elucidate the mechanisms of valve pathogenesis. In turn, this could help in the development of non-invasive therapies such as pharmaceutical intervention or prevention. Several bioreactors have been previously developed to study the mechanobiology of native or engineered heart valves6-9. Pulsatile bioreactors have also been developed to study a range of tissues including cartilage10, bone11 and bladder12. The aim of this work was to develop a cyclic pressure system that could be used to elucidate the biological response of aortic valve leaflets to increased pressure loads. 
The system consisted of an acrylic chamber in which to place samples and produce cyclic pressure, viton diaphragm solenoid valves to control the timing of the pressure cycle, and a computer to control electrical devices. The pressure was monitored using a pressure transducer, and the signal was conditioned using a load cell conditioner. A LabVIEW program regulated the pressure using an analog device to pump compressed air into the system at the appropriate rate. The system mimicked the dynamic transvalvular pressure levels associated with the aortic valve; a saw tooth wave produced a gradual increase in pressure, typical of the transvalvular pressure gradient that is present across the valve during diastole, followed by a sharp pressure drop depicting valve opening in systole. The LabVIEW program allowed users to control the magnitude and frequency of cyclic pressure. The system was able to subject tissue samples to physiological and pathological pressure conditions. This device can be used to increase our understanding of how heart valves respond to changes in the local mechanical environment.

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

A cyclic pressure bioreactor capable of subjecting heart valve tissue to physiological and pathological pressure conditions has been designed. A LabVIEW program allows users to control pressure magnitude, amplitude and frequency. This device can be used to study the mechanobiology of heart valve tissue or isolated cells.

The aortic valve, located between the left ventricle and the aorta, allows for unidirectional blood flow, preventing backflow into the ventricle. Aortic ...

Rotating Cell Culture Systems for Human Cell Culture: Human Trophoblast Cells as a Model

The field of human trophoblast research aids in understanding the complex environment established during placentation. Due to the nature of these studies, human in vivo experimentation is impossible. A combination of primary cultures, explant cultures and trophoblast cell lines1 support our understanding of invasion of the uterine wall2 and remodeling of uterine spiral arteries3,4 by extravillous trophoblast cells (EVTs), which is required for successful establishment of pregnancy. Despite the wealth of knowledge gleaned from such models, it is accepted that in vitro cell culture models using EVT-like cell lines display altered cellular properties when compared to their in vivo counterparts5,6. Cells cultured in the rotating cell culture system (RCCS) display morphological, phenotypic, and functional properties of EVT-like cell lines that more closely mimic differentiating in utero EVTs, with increased expression of genes mediating invasion (e.g. matrix metalloproteinases (MMPs)) and trophoblast differentiation7,8,9. The Saint Georges Hospital Placental cell Line-4 (SGHPL-4) (kindly donated by Dr. Guy Whitley and Dr. Judith Cartwright) is an EVT-like cell line that was used for testing in the RCCS.
The design of the RCCS culture vessel is based on the principle that organs and tissues function in a three-dimensional (3-D) environment. Due to the dynamic culture conditions in the vessel, including conditions of physiologically relevant shear, cells grown in three dimensions form aggregates based on natural cellular affinities and differentiate into organotypic tissue-like assemblies10,11,12 . The maintenance of a fluid orbit provides a low-shear, low-turbulence environment similar to conditions found in vivo. Sedimentation of the cultured cells is countered by adjusting the rotation speed of the RCCS to ensure a constant free-fall of cells. Gas exchange occurs through a permeable hydrophobic membrane located on the back of the bioreactor. Like their parental tissue in vivo, RCCS-grown cells are able to respond to chemical and molecular gradients in three dimensions (i.e. at their apical, basal, and lateral surfaces) because they are cultured on the surface of porous microcarrier beads. When grown as two-dimensional monolayers on impermeable surfaces like plastic, cells are deprived of this important communication at their basal surface. Consequently, the spatial constraints imposed by the environment profoundly affect how cells sense and decode signals from the surrounding microenvironment, thus implying an important role for the 3-D milieu13.
We have used the RCCS to engineer biologically meaningful 3-D models of various human epithelial tissues7,14,15,16. Indeed, many previous reports have demonstrated that cells cultured in the RCCS can assume physiologically relevant phenotypes that have not been possible with other models10,17-21. In summary, culture in the RCCS represents an easy, reproducible, high-throughput platform that provides large numbers of differentiated cells that are amenable to a variety of experimental manipulations. 
In the following protocol, using EVTs as an example, we clearly describe the steps required to three-dimensionally culture adherent cells in the RCCS.

Traditional, two dimensional cell culture techniques often result in altered characteristics with respect to differentiation markers, cytokines and growth factors. Three-dimensional cell culture in the rotating cell culture system (RCCS) reestablishes expression of many of these factors as shown here with an extravillous trophoblast cell line.

The field of human trophoblast research aids in  understanding the complex environment established during placentation. Due to the  nature of these ...

Establishment of a Surgically-induced Model in Mice to Investigate the Protective Role of Progranulin in Osteoarthritis

Destabilization of medial meniscus (DMM) model is an important tool for studying the pathophysiological roles of numerous arthritis associated molecules in the pathogenesis of osteoarthritis (OA) in vivo. However, the detailed, especially the visualized protocol for establishing this complicated model in mice, is not available. Herein we took advantage of wildtype and progranulin (PGRN)-/- mice as examples to introduce a protocol for inducing DMM model in mice, and compared the onset of OA following establishment of this surgically induced model. The operations performed on mice were either sham operation, which just opened joint capsule, or DMM operation, which cut the menisco-tibial ligament and caused destabilization of medial meniscus. Osteoarthritis severity was evaluated using histological assay (e.g. Safranin&#160;O staining), expressions of OA-associated genes, degradation of cartilage extracellular matrix molecules, and osteophyte formation. DMM operation successfully induced OA initiation and progression in both wildtype and PGRN-/- mice, and loss of PGNR growth factor led to a more severe OA phenotype in this surgically induced model.

We describe a protocol for the destabilization of the medial meniscus (DMM) model in mice, an effective tool for osteoarthritis (OA) research. In addition, we have demonstrated that deficiency of progranulincan exaggerate OA development and progression by using this model, indicating that progranulin plays a protective role in the pathogenesis of OA.

Destabilization of medial meniscus (DMM) model is an important tool for studying the pathophysiological roles of numerous arthritis associated molecules ...

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.

We describe a design of experiments approach that can be used to determine and model the influence of transgene regulatory elements, plant growth and development parameters, and incubation conditions on the transient expression of monoclonal antibodies and reporter proteins in plants.

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the ...

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

This study offers a combined experimental and finite element (FE) simulation approach for examining the mechanical behavior of soft biomaterials (e.g. brain, liver, tendon, fat, etc.) when exposed to high strain rates. This study utilized a Split-Hopkinson Pressure Bar (SHPB) to generate strain rates of 100-1,500 sec-1. The SHPB employed a striker bar consisting of a viscoelastic material (polycarbonate). A sample of the biomaterial was obtained shortly postmortem and prepared for SHPB testing. The specimen was interposed between the incident and transmitted bars, and the pneumatic components of the SHPB were activated to drive the striker bar toward the incident bar. The resulting impact generated a compressive stress wave (i.e. incident wave) that traveled through the incident bar. When the compressive stress wave reached the end of the incident bar, a portion continued forward through the sample and transmitted bar (i.e. transmitted wave) while another portion reversed through the incident bar as a tensile wave (i.e. reflected wave). These waves were measured using strain gages mounted on the incident and transmitted bars. The true stress-strain behavior of the sample was determined from equations based on wave propagation and dynamic force equilibrium. The experimental stress-strain response was three dimensional in nature because the specimen bulged. As such, the hydrostatic stress (first invariant) was used to generate the stress-strain response. In order to extract the uniaxial (one-dimensional) mechanical response of the tissue, an iterative coupled optimization was performed using experimental results and Finite Element Analysis (FEA), which contained an Internal State Variable (ISV) material model used for the tissue. The ISV material model used in the FE simulations of the experimental setup was iteratively calibrated (i.e. optimized) to the experimental data such that the experiment and FEA strain gage values and first invariant of stresses were in good agreement.

The current study prescribes a coupled experiment-finite element simulation methodology to obtain the uniaxial dynamic mechanical response of soft biomaterials (brain, liver, tendon, fat, etc.). The multiaxial experimental results that arose because of specimen bulging obtained from Split-Hopkinson Pressure Bar testing were rendered to a uniaxial true stress-strain behavior when simulated through iterative optimization of the finite element analysis of the biomaterial.

This study offers a combined experimental and finite element (FE) simulation approach for examining the mechanical behavior of soft biomaterials (e.g. ...

Concentric Gel System to Study the Biophysical Role of Matrix Microenvironment on 3D Cell Migration

The ability of cells to migrate is crucial in a wide variety of cell functions throughout life&#160;from embryonic development and wound healing&#160;to tumor and cancer metastasis. Despite intense research efforts, the basic biochemical and biophysical principles of cell migration are still not fully understood, especially in the physiologically relevant three-dimensional (3D) microenvironments. Here, we describe an in vitro assay designed to allow quantitative examination of 3D cell migration behaviors. The method exploits the cell&#8217;s mechanosensing ability and propensity to migrate into previously unoccupied extracellular matrix (ECM). We use the invasion of highly invasive breast cancer cells, MDA-MB-231, in collagen gels as a model system. The spread of cell population and the migration dynamics of individual cells over weeks of culture can be monitored using live-cell imaging and analyzed to extract spatiotemporally-resolved data. Furthermore, the method is easily adaptable for diverse extracellular matrices, thus offering a simple yet powerful way to investigate the role of biophysical factors in the microenvironment on cell migration.

The mechanical properties and microstructure of the extracellular matrix strongly affect 3D migration of cells. An in vitro method to study the spatiotemporal cell migration behavior in biophysically variable environments, at both population and individual cell levels, is described.

The ability of cells to migrate is crucial in a wide variety of cell functions throughout life&#160;from embryonic development and wound healing&#160;to ...

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular cartilage impairs its load-bearing function substantially as it experiences tremendous chemical degradation, i.e. proteoglycan loss and collagen fibril disruption. One promising way to investigate chemical damage mechanisms during OA is to expose the cartilage specimens to an external solute and monitor the diffusion of the molecules. The degree of cartilage damage (i.e. concentration and configuration of essential macromolecules) is associated with collisional energy loss of external solutes while moving across articular cartilage creates different diffusion characteristics compared to healthy cartilage. In this study, we introduce a protocol, which consists of several steps and is based on previously developed experimental micro-Computed Tomography (micro-CT) and finite element modeling. The transport of charged and uncharged iodinated molecules is first recorded using micro-CT, which is followed by applying biphasic-solute and multiphasic finite element models to obtain diffusion coefficients and fixed charge densities across cartilage zones.

We propose a protocol to investigate the transport of charged and uncharged molecules across articular cartilage with the aid of recently developed experimental and numerical methods.

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular ...

Human iPSC-Derived Cardiomyocyte Networks on Multiwell Micro-electrode Arrays for Recurrent Action Potential Recordings

Cardiac safety screening is of paramount importance for drug discovery and therapeutics. Therefore, the development of novel high-throughput electrophysiological approaches for hiPSC-derived cardiomyocyte (hiPSC-CM) preparations is much needed for efficient drug testing. Although multielectrode arrays (MEAs) are frequently employed for field potential measurements of excitable cells, a recent publication by Joshi-Mukherjee and colleagues described and validated its application for recurrent action potential (AP) recordings from the same hiPSC-CM preparation over days. The aim here is to provide detailed step-by-step methods for seeding CMs and for measuring AP waveforms via electroporation with high precision and a temporal resolution of 1 &#181;s. This approach addresses the lack of easy-to-use methodology to gain intracellular access for high-throughput AP measurements for reliable electrophysiological investigations. A detailed work flow and methods for plating of hiPSC-CMs on multiwell MEA plates are discussed emphasizing critical steps wherever relevant. In addition, a custom-built MATLAB script for rapid data handling, extraction and analysis is reported for comprehensive investigation of the waveform analysis to quantify subtle differences in morphology for various AP duration parameters implicated in arrhythmia and cardiotoxicity.

This article contains a set of protocols for the development of human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) networks cultured on multiwell MEA plates to reversibly electroporate the cell membrane for action potential measurements. High-throughput recordings are obtained from the same cell sites repeatedly over days.