Myocardial slices as an in vitro platform to study cardiac disease

In vitro models are the pillars of fundamental research and drug discovery, offering reductionist methods to better understand cellular responses in isolation. Often these methods are oversimplified, which makes their relevance to human biology and clinical translation ambiguous. Living myocardial slices (LMSLMSs) are viable thin (200-400μm) cardiac tissue slices, with preserved native multicellularity, architecture, mechanical and electrophysiological responses. Recent development in their culture, by us and others, paved the way for long-term preservation of adult mammalian heart tissue in vitro, without significant changes in its function and structure. This model has been extensively used in healthy tissue; however, to date, there are no established pathological models to study disease progression in vitro. Here we hypothesised that LMSLMSs can be used as an informative in vitro disease model to study temporal and spatial changes in cardiac function/structure in response to local cardiac damage. Before inducing cardiac damage, we further improved and characterised the cultured LMS model by designing robust tissue holders, optimising the oxygenation of the media, and establishing the best slice thickness (300μ) for oxygen diffusion and tissue stability in culture. We found that the LMSLMSs were adequately oxygenated in the inner layers and responded to mechanical stimuli with an increase in their contraction and hyperpolarisation of the mitochondrial membrane. We then developed a cryoinjury model, by applying a cooled probe on the LMSLMSs. We found that injury created a distinct necrotic area, surrounded by a border zone (BZ). The injury resulted in preserved force but electrical instability, with the presence of spontaneous contractions. Microscopic analysis of the BZ showed the presence of high numbers of spontaneous Ca2+ sparks, which could be affected by inhibiting the activation of Ca2+/calmodulin-dependent protein kinase II (CamKII). The inhibitory effect was more pronounced in endocardial LMSLMSs, showing transmural differences of CamKII under pathological conditions. Structural analysis of the BZ also showed an acute increase of the sarcomere length and loss of t-tubule density upon culture, that could also account for the arrhythmogenicity of the injured LMSLMSs. One application of therapeutic interventions on the model, by using extracellular vesicles (EVs), did not show any functional or molecular improvements. This thesis demonstrates the significance of using diseased LMSLMSs to study the way that local injury affects tissue stability, function, and structure. Further work is required to better understand the link between spontaneous Ca2+ and contraction events, as well as finding successful therapeutic interventions.Open Acces

Three-dimensional cardiac computational modelling: methods, features and applications

[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01 and TIN2011-28067) and the European Commission (European Regional Development Funds - ERDF - FEDER) and by "eTorso project" (GVA/2013-001404) from the Generalitat Valenciana (Spain). ALP is financially supported by the program "Ayudas para contratos predoctorales para la formacion de doctores" from the Ministerio de Economia y Competitividad of Spain (BES-2013-064089).López Pérez, AD.; Sebastián Aguilar, R.; Ferrero De Loma-Osorio, JM. (2015). Three-dimensional cardiac computational modelling: methods, features and applications. BioMedical Engineering OnLine. 14(35):1-31. https://doi.org/10.1186/s12938-015-0033-5S1311435Koushanpour E, Collings W: Validation and dynamic applications of an ellipsoid model of the left ventricle. J Appl Physiol 1966, 21: 1655–61.Ghista D, Sandler H: An analytic elastic-viscoelastic model for the shape and the forces in the left ventricle. J Biomech 1969, 2: 35–47.Janz RF, Grimm AF: Finite-Element Model for the Mechanical Behavior of the Left Ventricle: prediction of deformation in the potassium-arrested rat heart. Circ Res 1972, 30: 244–52.Van den Broek JHJM, Van den Broek MHLM: Application of an ellipsoidal heart model in studying left ventricular contractions. J Biomech 1980, 13: 493–503.Colli Franzone P, Guerri L, Pennacchio M, Taccardi B: Spread of excitation in 3-D models of the anisotropic cardiac tissue. II. Effects of fiber architecture and ventricular geometry. Math Biosci 1998, 147: 131–71.Kerckhoffs RCP, Bovendeerd PHM, Kotte JCS, Prinzen FW, Smits K, Arts T: Homogeneity of cardiac contraction despite physiological asynchrony of depolarization: a model study. Ann Biomed Eng 2003, 31: 536–47.Sermesant M, Moireau P, Camara O, Sainte-Marie J, Andriantsimiavona R, Cimrman R, et al.: Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties. Med Image Anal 2006, 10: 642–56.Okajima M, Fujino T, Kobayashi T, Yamada K: Computer simulation of the propagation process in excitation of the ventricles. Circ Res 1968, 23: 203–11.Horan LG, Hand RC, Johnson JC, Sridharan MR, Rankin TB, Flowers NC: A theoretical examination of ventricular repolarization and the secondary T wave. Circ Res 1978, 42: 750–7.Miller WT, Geselowitz DB: Simulation studies of the electrocardiogram. I. The normal heart. Circ Res 1978, 43: 301–15.Vetter FJ, McCulloch AD: Three-dimensional analysis of regional cardiac function: a model of rabbit ventricular anatomy. Prog Biophys Mol Biol 1998, 69: 157–83.Nielsen PMF, LeGrice IJ, Smaill BH, Hunter PJ: Mathematical model of geometry and fibrous structure of the heart. Am J Physiol Heart Circ Physiol 1991, 260: H1365–78.Stevens C, Remme E, LeGrice I, Hunter P: Ventricular mechanics in diastole: material parameter sensitivity. J Biomech 2003, 36: 737–48.Aoki M, Okamoto Y, Musha T, Harumi KI: Three-dimensional simulation of the ventricular depolarization and repolarization processes and body surface potentials: normal heart and bundle branch block. IEEE Trans Biomed Eng 1987, 34: 454–62.Thakor NV, Eisenman LN: Three-dimensional computer model of the heart: fibrillation induced by extrastimulation. Comput Biomed Res 1989, 22: 532–45.Freudenberg J, Schiemann T, Tiede U, Höhne KH: Simulation of cardiac excitation patterns in a three-dimensional anatomical heart atlas. Comput Biol Med 2000, 30: 191–205.Trunk P, Mocnik J, Trobec R, Gersak B: 3D heart model for computer simulations in cardiac surgery. Comput Biol Med 2007, 37: 1398–403.Siregar P, Sinteff JP, Julen N, Le Beux P: An interactive 3D anisotropic cellular automata model of the heart. Comput Biomed Res 1998, 31: 323–47.Harrild DM, Henriquez CS: A computer model of normal conduction in the human atria. Circ Res 2000, 87: e25–36.Bodin ON, Kuz’min AV: Synthesis of a realistic model of the surface of the heart. Biomed Eng (NY) 2006, 40: 280–3.Ruiz-Villa CA, Tobón C, Rodríguez JF, Ferrero JM, Hornero F, Saíz J: Influence of atrial dilatation in the generation of re-entries caused by ectopic activity in the left atrium. Comput Cardiol 2009, 36: 457–60.Blanc O, Virag N, Vesin JM, Kappenberger L: A computer model of human atria with reasonable computation load and realistic anatomical properties. IEEE Trans Biomed Eng 2001, 48: 1229–37.Zemlin CW, Herzel H, Ho SY, Panfilov AV: A realistic and efficient model of excitation propagation in the human atria. In Comput Simul Exp Assess Card Electrophysiol. Edited by: Virag N, Kappenberger L, Blanc O. Futura Publishing Company, Inc, Arkmonk, New York; 2001:29–34.Seemann G, Höper C, Sachse FB, Dössel O, Holden AV, Zhang H: Heterogeneous three-dimensional anatomical and electrophysiological model of human atria. Philos Trans R Soc A Math Phys Eng Sci 2006, 364: 1465–81.Zhao J, Butters TD, Zhang H, LeGrice IJ, Sands GB, Smaill BH: Image-based model of atrial anatomy and electrical activation: a computational platform for investigating atrial arrhythmia. IEEE Trans Med Imaging 2013, 32: 18–27.Creswell LL, Wyers SG, Pirolo JS, Perman WH, Vannier MW, Pasque MK: Mathematical modeling of the heart using magnetic resonance imaging. IEEE Trans Med Imaging 1992, 11: 581–9.Lorange M, Gulrajani RM: A computer heart model incorporating anisotropic propagation: I. Model construction and simulation of normal activation. J Electrocardiol 1993, 26: 245–61.Winslow RL, Scollan DF, Holmes A, Yung CK, Zhang J, Jafri MS: Electrophysiological modeling of cardiac ventricular function: from cell to organ. Annu Rev Biomed Eng 2000, 2: 119–55.Virag N, Jacquemet V, Henriquez CS, Zozor S, Blanc O, Vesin JM, et al.: Study of atrial arrhythmias in a computer model based on magnetic resonance images of human atria. Chaos 2002, 12: 754–63.Helm PA, Tseng HJ, Younes L, McVeigh ER, Winslow RL: Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure. Magn Reson Med 2005, 54: 850–9.Arevalo HJ, Helm PA, Trayanova NA: Development of a model of the infarcted canine heart that predicts arrhythmia generation from specific cardiac geometry and scar distribution. Comput Cardiol 2008, 35: 497–500.Plotkowiak M, Rodriguez B, Plank G, Schneider JE, Gavaghan D, Kohl P, et al.: High performance computer simulations of cardiac electrical function based on high resolution MRI datasets. In Int Conf Comput Sci 2008, LNCS 5101. Springer–Verlag, Berlin Heidelberg; 2008:571–80.Heidenreich EA, Ferrero JM, Doblaré M, Rodríguez JF: Adaptive macro finite elements for the numerical solution of monodomain equations in cardiac electrophysiology. Ann Biomed Eng 2010, 38: 2331–45.Gurev V, Lee T, Constantino J, Arevalo H, Trayanova NA: Models of cardiac electromechanics based on individual hearts imaging data: Image-based electromechanical models of the heart. Biomech Model Mechanobiol 2011, 10: 295–306.Deng D, Jiao P, Ye X, Xia L: An image-based model of the whole human heart with detailed anatomical structure and fiber orientation. Comput Math Methods Med 2012, 2012: 16.Aslanidi OV, Nikolaidou T, Zhao J, Smaill BH, Gilbert SH, Holden AV, et al.: Application of micro-computed tomography with iodine staining to cardiac imaging, segmentation, and computational model development. IEEE Trans Med Imaging 2013, 32: 8–17.Haddad R, Clarysse P, Orkisz M, Croisille P, Revel D, Magnin IE: A realistic anthropomorphic numerical model of the beating heart. In Funct Imaging Model Heart 2005, LNCS 3504. Springer–Verlag, Berlin Heidelberg; 2005:384–93.Appleton B, Wei Q, Liu N, Xia L, Crozier S, Liu F, et al.: An electrical heart model incorporating real geometry and motion. In 27th Annu Int Conf Eng Med Biol Soc (IEEE-EMBS 2005). IEEE, Shanghai, China; 2006:345–8.Niederer S, Rhode K, Razavi R, Smith N: The importance of model parameters and boundary conditions in whole organ models of cardiac contraction. In Funct Imaging Model Heart 2009, LNCS 5528. Springer–Verlag, Berlin Heidelberg; 2009:348–56.Yang G, Toumoulin C, Coatrieux JL, Shu H, Luo L, Boulmier D: A 3D static heart model from a MSCT data set. In 27th Annu Int Conf IEEE Eng Med Biol Soc (IEEE-EMBS 2005). IEEE, Shangai, China; 2006:5499–502.Romero D, Sebastian R, Bijnens BH, Zimmerman V, Boyle PM, Vigmond EJ, et al.: Effects of the purkinje system and cardiac geometry on biventricular pacing: a model study. Ann Biomed Eng 2010, 38: 1388–98.Lorenzo-Valdés M, Sanchez-Ortiz GI, Mohiaddin R, Rueckert D: Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In Med Image Comput Comput Assist Interv 2002, LNCS 2488. Springer–Verlag, Berlin Heidelberg; 2002:642–50.Ordas S, Oubel E, Sebastian R, Frangi AF: Computational anatomy atlas of the heart. In 5th Int Symp Image Signal Process Anal (ISPA 2007). IEEE, Istanbul, Turkey; 2007:338–42.Burton RAB, Plank G, Schneider JE, Grau V, Ahammer H, Keeling SL, et al.: Three-dimensional models of individual cardiac histoanatomy: tools and challenges. Ann N Y Acad Sci 2006, 1080: 301–19.Plank G, Burton RAB, Hales P, Bishop M, Mansoori T, Bernabeu MO, et al.: Generation of histo-anatomically representative models of the individual heart: tools and application. Philos Trans R Soc A Math Phys Eng Sci 2009, 367: 2257–92.Bishop MJ, Plank G, Burton RAB, Schneider JE, Gavaghan DJ, Grau V, et al.: Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function. Am J Physiol - Heart Circ Physiol 2010, 298: H699–718.Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, et al.: Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging 2008, 27: 1189–201.Ecabert O, Peters J, Walker MJ, Ivanc T, Lorenz C, von Berg J, et al.: Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med Image Anal 2011, 15: 863–76.Schulte RF, Sands GB, Sachse FB, Dössel O, Pullan AJ: Creation of a human heart model and its customisation using ultrasound images. Biomed Tech Eng 2001, 46: 26–8.Wenk JF, Zhang Z, Cheng G, Malhotra D, Acevedo-Bolton G, Burger M, et al.: First finite element model of the left ventricle with mitral valve: insights into ischemic mitral regurgitation. Ann Thorac Surg 2010, 89: 1546–53.Frangi AF, Rueckert D, Schnabel JA, Niessen WJ: Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans Med Imaging 2002, 21: 1151–66.Hoogendoorn C, Duchateau N, Sánchez-Quintana D, Whitmarsh T, Sukno FM, De Craene M, et al.: A high-resolution atlas and statistical model of the human heart from multislice CT. IEEE Trans Med Imaging 2013, 32: 28–44.Vadakkumpadan F, Rantner LJ, Tice B, Boyle P, Prassl AJ, Vigmond E, et al.: Image-based models of cardiac structure with applications in arrhythmia and defibrillation studies. J Electrocardiol 2009, 42: 157.Perperidis D, Mohiaddin R, Rueckert D: Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification. In Med Image Comput Comput Interv 2005, LNCS 3750. Springer–Verlag, Berlin Heidelberg; 2005:402–10.Lötjönen J, Kivistö S, Koikkalainen J, Smutek D, Lauerma K: Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med Image Anal 2004, 8: 371–86.Lorenz C, von Berg J: A comprehensive shape model of the heart. Med Image Anal 2006, 10: 657–70.Mansoori T, Plank G, Burton R, Schneider J, Khol P, Gavaghan D, et al.: An iterative method for registration of high-resolution cardiac histoanatomical and MRI images. In 4th IEEE Int Symp Biomed Imaging: From Nano to Macro (ISBI 2007). IEEE, Arlington, VA (USA); 2007:572–5.Gibb M, Burton RAB, Bollensdorff C, Afonso C, Mansoori T, Schotten U, et al.: Resolving the three-dimensional histology of the heart. In Comput Methods Syst Biol - Lect Notes Comput Sci 7605. Springer, Berlin Heidelberg; 2012:2–16.Burton RAB, Lee P, Casero R, Garny A, Siedlecka U, Schneider JE, et al.: Three-dimensional histology: tools and application to quantitative assessment of cell-type distribution in rabbit heart. Europace 2014,16(Suppl 4):iv86–95.Niederer SA, Shetty AK, Plank G, Bostock J, Razavi R, Smith NP, et al.: Biophysical modeling to simulate the response to multisite left ventricular stimulation using a quadripolar pacing lead. Pacing Clin Electrophysiol 2012, 35: 204–14.Weese J, Groth A, Nickisch H, Barschdorf H, Weber FM, Velut J, et al.: Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations. Med Biol Eng Comput 2013, 51: 1209–19.Gibb M, Bishop M, Burton R, Kohl P, Grau V, Plank G, et al.: The role of blood vessels in rabbit propagation dynamics and cardiac arrhythmias. In Funct Imaging Model Heart - FIMH 2009, LNCS 5528. Springer, Berlin Heidelberg; 2009:268–76.Prassl AJ, Kickinger F, Ahammer H, Grau V, Schneider JE, Hofer E, et al.: Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems. IEEE Trans Biomed Eng 2009, 56: 1318–30.Dux-Santoy L, Sebastian R, Felix-Rodriguez J, Ferrero JM, Saiz J: Interaction of specialized cardiac conduction system with antiarrhythmic drugs: a simulation study. IEEE Trans Biomed Eng 2011, 58: 3475–8.Lamata P, Niederer S, Nordsletten D, Barber DC, Roy I, Hose DR, et al.: An accurate, fast and robust method to generate patient-specific cubic Hermite meshes. Med Image Anal 2011, 15: 801–13.Pathmanathan P, Cooper J, Fletcher A, Mirams G, Murray P, Osborne J, et al.: A computational study of discrete mechanical tissue models. Phys Biol 2009, 6: 036001.Niederer SA, Kerfoot E, Benson AP, Bernabeu MO, Bernus O, Bradley C, et al.: Verification of cardiac tissue electrophysiology simulators using an N-version benchmark. Philos Trans R Soc A Math Phys Eng Sci 2011, 369: 4331–51.Ten Tusscher KHWJ, Panfilov AV: Cell model for efficient simulation of wave propagation in human ventricular tissue under normal and pathological conditions. Phys Med Biol 2006, 51: 6141–56.LeGrice I, Smaill B, Chai L, Edgar S, Gavin J, Hunter P: Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. Am J Physiol Heart Circ Physiol 1995, 269: H571–82.Anderson RH, Smerup M, Sanchez-Quintana D, Loukas M, Lunkenheimer PP: The three-dimensional arrangement of the myocytes in the ventricular walls. Clin Anat 2009, 22: 64–76.Clerc L: Directional differences of impulse spread in trabecular muscle from mammalian heart. J Physiol 1976, 255: 335–46.Streeter DD Jr, Spotnitz HM, Patel DP, Ross J Jr, Sonnenblick EH: Fiber orientation in the canine left ventricle during diastole and systole. Circ Res 1969, 24: 339–47.Scollan D, Holmes A, Winslow R, Forder J: Histological validation of myocardial microstructure obtained from diffusion tensor magnetic resonance imaging. Am J Physiol Heart Circ Physiol 1998, 275: H2308–18.Hsu EW, Muzikant AL, Matulevicius SA, Penland RC, Henriquez CS: Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation. Am J Physiol Heart Circ Physiol 1998, 274: H1627–34.Holmes AA, Scollan DF, Winslow RL: Direct histological validation of diffusion tensor MRI in formaldehyde-fixed myocardium. Magn Reson Med 2000, 44: 157–61.Sermesant M, Forest C, Pennec X, Delingette H, Ayache N: Deformable biomechanical models: application to 4D cardiac image analysis. Med Image Anal 2003, 7: 475–88.Peyrat JM, Sermesant M, Pennec X, Delingette H, Xu C, McVeigh ER, et al.: A computational framework for the statistical analysis of cardiac diffusion tensors: application to a small database of canine hearts. IEEE Trans Med Imaging 2007, 26: 1500–14.Toussaint N, Sermesant M, Stoeck CT, Kozerke S, Batchelor PG: In vivo human 3D cardiac fibre architecture: reconstruction using curvilinear interpolation of diffusion tensor images. Med Image Comput Comput Assist Interv 2010,13(Pt 1):418–25.Toussaint N, Stoeck CT, Schaeffter T, Kozerke S, Sermesant M, Batchelor PG: In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing. Med Image Anal 2013, 17: 1243–55.Bishop MJ, Hales P, Plank G, Gavaghan DJ, Scheider J, Grau V: Comparison of rule-based and DTMRI-derived fibre architecture in a whole rat ventricular computational model. In Funct Imaging Model Heart 2009, LNCS 5528. Springer–Verlag, Berlin Heidelberg; 2009:87–96.Bayer JD, Blake RC, Plank G, Trayanova NA: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann Biomed Eng 2012, 40: 2243–54.Dobrzynski H, Anderson RH, Atkinson A, Borbas Z, D’Souza A, Fraser JF, et al.: Structure, function and clinical relevance of the cardiac conduction system, including the atrioventricular ring and outflow tract tissues. Pharmacol Ther 2013, 139: 260–88.Tranum-Jensen J, Wilde AA, Vermeulen JT, Janse MJ: Morphology of electrophysiologically identified junctions between Purkinje fibers and ventricular muscle in rabbit and pig hearts. Circ Res 1991, 69: 429–37.Boyle PM, Deo M, Plank G, Vigmond EJ: Purkinje-mediated effects in the response of quiescent ventricles to defibrillation shocks. Ann Biomed Eng 2010, 38: 456–68.Behradfar E, Nygren A, Vigmond EJ: The role of Purkinje-myocardial coupling during ventricular arrhythmia: a modeling study. PLoS One 2014., 9: Article ID e88000DiFrancesco D, Noble D: A model of cardiac electrical activity incorporating ionic pumps and concentration changes. Philos Trans R Soc B Biol Sci 1985, 307: 353–98.Stewart P, Aslanidi OV, Noble D, Noble PJ, Boyett MR, Zhang H: Mathematical models of the electrical action potential of Purkinje fibre cells. Philos Trans R Soc A Math Phys Eng Sci 2009, 367: 2225–55.Li P, Rudy Y: A model of canine purkinje cell electrophysiology and Ca(2+) cycling: rate dependence, triggered activity, and comparison to ventricular myocytes. Circ Res 2011, 109: 71–9.Chinchapatnam P, Rhode KS, Ginks M, Mansi T, Peyrat JM, Lambiase P, et al.: Estimation of volumetric myocardial apparent conductivity from endocardial electro-anatomical mapping. In 31st Annu Int Conf IEEE Eng Med Biol Soc (EMBC 2009). IEEE, Minneapolis, MN (USA); 2009:2907–10.Durrer D, Van Dam RT, Freud GE, Janse MJ, Meijler FL, Arzbaecher RC: Total excitation of the isolated human heart. Circulation 1970, 41: 899–912.Pollard AE, Barr RC: Computer simulations of activation in an anatomically based model of the human ventricular conduction system. IEEE Trans Biomed Eng 1991, 38: 982–96.Abboud S, Berenfeld O, Sadeh D: Simulation of high-resolution QRS complex using a ventricular model with a fractal conduction system. Effects of ischemia on high-frequency QRS potentials. Circ Res 1991, 68: 1751–60.Sebastian R, Zimmerman V, Romero D, Sanchez-Quintana D, Frangi AF: Characterization and modeling of the peripheral cardiac conduction system. IEEE Trans Med Imaging 2013, 32: 45–55.Bordas R, Gillow K, Lou Q, Efimov IR, Gavaghan D, Kohl P, et al.: Rabbit-specific ventricular model of cardiac electrophysiological function including specialized conduction system. Prog Biophys Mol Biol 2011, 107: 90–100.Stephenson RS, Boyett MR, Hart G, Nikolaidou T, Cai X, Corno AF, et al.: Contrast enhanced micro-computed tomography resolves the 3-dimensional morphology of the cardiac conduction system in mammalian hearts. PLoS One 2012., 7: Article ID e35299Berenfeld O, Jalife J: Purkinje-Muscle reentry as a mechanism of polymorphic ventricular arrhythmias in a 3-dimensional model of the ventricles. Circ Res 1998, 82: 1063–77.Azzouzi A, Coudière Y, Turpault R, Zemzemi N: A mathematical model of the Purkinje-muscle junctions. Math Biosci Eng MBE 2011, 8: 915–30.Dux-Santoy L, Sebastian R, Rodriguez JF, Ferrero JM: Modeling the different sections of the cardiac conduction system to obtain realistic electrocardiograms. In 35th Annu Int Conf IEEE Eng Med Biol Soc (EMBC 2013). IEEE, Osaka, Japan; 2013:6846–9.Cardenes R, Sebastian R, Berruezo A, Camara O: Inverse

Virtual cardiac monolayers for electrical wave propagation

The complex structure of cardiac tissue is considered to be one of the main determinants of an arrhythmogenic substrate. This study is aimed at developing the first mathematical model to describe the formation of cardiac tissue, using a joint in silico-in vitro approach. First, we performed experiments under various conditions to carefully characterise the morphology of cardiac tissue in a culture of neonatal rat ventricular cells. We considered two cell types, namely, cardiomyocytes and fibroblasts. Next, we proposed a mathematical model, based on the Glazier-Graner-Hogeweg model, which is widely used in tissue growth studies. The resultant tissue morphology was coupled to the detailed electrophysiological Korhonen-Majumder model for neonatal rat ventricular cardiomyocytes, in order to study wave propagation. The simulated waves had the same anisotropy ratio and wavefront complexity as those in the experiment. Thus, we conclude that our approach allows us to reproduce the morphological and physiological properties of cardiac tissue

Computational modeling of nanodrug-induced effects on cardiac electromechanics

This work generally aims to promote the use of computational models for predicting side effects of nanodrugs under development, as a means to speed up the cycle of drug development, with potential savings on testing, and reduction in the need for animal or human testing. The specific objective of this thesis has been to accurately model a single ventricular contraction-relaxation cycle, and monitor the effects induced by nanodrugs on the electro-mechano-physiology of the left and right ventricles. Nanodrug interaction with ion channels located on cardiac cell membranes, such as those for sodium, potassium and calcium, can distort an electrical wave propagating through the tissue and can affect cardiac macroscale functions. In this study, a material model after Holzapfel and Ogden was developed to account for the anisotropic hyperelastic behavior of cardiac tissue, which was implemented on the open source software library Chaste. A coupled drug-electro-mechano-physiological system was then set up, also on Chaste, where a nanodrug effect was introduced into the cellular structure (nanoscale) as an ion channel inhibitor, and its influence then solved for, with respect to resulting electro-mechanical ventricular behaviors. Using quantifiable biomarkers, these effects were compared to the literature and clinical data. In this work we identified the following main results. Nanodrugs causing sodium channel blockage were found to produce the anticipated delays in electro-mechanics. Our study further predicted additional effects on LV twisting and LV & RV strain. On the other hand, nanodrugs causing potassium and calcium channel blockage revealed that cardiac mechanics is less responsive to mild alterations in electrophysiology, than electrophysiology is to ionic changes. Nonetheless, it is important to quantify these changes, as even a very small deviation from normal could accumulate over multiple cardiac cycles, and lead to adverse consequences on cardiac health in the long term

Computational biomechanics of acute myocardial infarction and its treatment

The intramyocardial injection of biomaterials is an emerging therapy for myocardial infarction. Computational methods can help to study the mechanical effect s of biomaterial injectates on the infarcted heart s and can contribute to advance and optimise the concept of this therapy. The distribution of polyethylene glycol hydrogel injectate delivered immediately after the infarct induction was studied using rat infarct model. A micro-structural three-dimensional geometrical model of the entire injectate was reconstructed from histological micro graphs. The model provides a realistic representation of biomaterial injectates in computational models at macroscopic and microscopic level. Biaxial and compression mechanical testing was conducted for healing rat myocardial infarcted tissue at immediate (0 day), 7, 14 and 28 days after infarction onset. Infarcts were found to be mechanically anisotropic with the tissue being stiffer in circumferential direction than in longitudinal direction. The 0, 7, 14 and 28 days infarcts showed 443, 670, 857 and 1218 kPa circumferential tensile moduli. The 28 day infarct group showed a significantly higher compressive modulus compared to the other infarct groups (p= 0.0055, 0.028, and 0.018 for 0, 7 and 14 days groups). The biaxial mechanical data were utilized to establish material constitutive models of rat healing infarcts. Finite element model s and genetic algorithms were employed to identify the parameters of Fung orthotropic hyperelastic strain energy function for the healing infarcts. The provided infarct mechanical data and the identified constitutive parameters offer a platform for investigations of mechanical aspects of myocardial infarction and therapies in the rat, an experimental model extensively used in the development of infarct therapies. Micro-structurally detailed finite element model of a hydrogel injectate in an infarct was developed to provide an insight into the micromechanics of a hydrogel injectate and infarct during the diastolic filling. The injectate caused the end-diastolic fibre stresses in the infarct zone to decrease from 22.1 to 7.7 kPa in the 7 day infarct and from 35.7 to 9.7 kPa in the 28 day infarct. This stress reduction effect declined as the stiffness of the biomaterial increased. It is suggested that the gel works as a force attenuating system through micromechanical mechanisms reducing the force acting on tissue layers during the passive diastolic dilation of the left ventricle and thus reducing the stress induced in these tissue layers

A computational study of post-infarct mechanical effects of injected biomaterial into ischaemic myocardium

Includes abstract.Includes bibliographical references.Cardiovascular diseases account for one third of all deaths worldwide, more than 33% of which are related to ischaemic heart disease, involving a myocardial infarction (MI). Emerging MI therapies involving biomaterial injections have shown some benefits; the underlying mechanisms of which remain unclear. Computational models offer considerable potential to study the biomechanics of a myocardial infarction and novel therapies. Geometrical data of a healthy human left ventricle (LV) obtained from magnetic resonance images (MRI) was used to create a finite element (FE) mesh of the LV at the end-systolic time point using Continuity® 6.3 (University of California in San Diego, US). A mesh of 96 hexahedral elements with high order basis functions was employed to adequately describe the geometry of the LV. Simulations of diastolic filling and systolic contraction were performed using a transversely isotropic exponential strain energy function and a model for active stress based on contraction at the cellular level. An anterior apical, transmural MI was modelled in the LV encompassing 16% of the LV wall volume. The infarct was modelled at acute and fibrotic stages of post-infarct LV remodelling by altering the constitutive and active stress models to best describe passive and active behaviour of the ischaemic myocardium at each time point. The geometry of the LV with the fibrotic infarct was adjusted to represent the wall thinning that occurs during LV post-MI remodelling. Hydrogel injection was modelled as layers with material properties differing from those of the surrounding myocardium while accounting for thickening of the LV wall at the injection site. The study design comprised a healthy case and two infarcted cases with and without hydrogel injection. The end-diastolic volume (EDV) increased in the acute infarct model compared to the healthy case due to the reduced stiffness in the infarct wall. An EDV increase was not observed in the fibrotic infarct model compared to the healthy case. This was partially attributed to the increase in infarct stiffness and partially due to the fact that remodelling-related dilation of the LV was not implemented in the model. Inclusion of hydrogel lowered EDV in both the acute and fibrotic models. The predicted ejection fraction (EF) decreased from 41.2% for the healthy case to 28.5% and 33.0% for the acute and fibrotic infarct models, respectively. Inclusion of hydrogel layers caused an improvement in EF in the acute model only

Investigating left ventricular infarct extension after myocardial infarction using cardiac imaging and patient-specific modelling

Acute myocardial infarction (MI) is one of the leading causes of death worldwide that commonly affects the left ventricle (LV). Following MI, the LV mechanical loading is altered and may undergo a maladaptive compensatory mechanism that progressively leads to adverse LV remodelling and then heart failure. One of the remodelling processes is the infarct extension which involves necrosis of healthy myocardium in the border zone (BZ), progressively enlarging the infarct zone (IZ) and recruiting the remote zone (RZ) into the BZ. The mechanisms underlying infarct extension remain unclear, but myocyte stretching has been suggested as the most likely cause. A recent personalized LV modelling work found that infarct extension was correlated to inadequate diastolic fibre stretch and higher infarct stiffness. However, other possible factors of infarct extension may not have been elucidated in this work due to the limited number of myocardial locations analysed at the subendocardium only. Using human patient-specific left- ventricular (LV) models established from cardiac magnetic resonance imaging (MRI) of 6 MI patients, the correlation between infarct extension and regional mechanics impairment was studied. Prior to the modelling, a 2D-4D registration-cum-segmentation framework for the delineation of LV in late gadolinium enhanced (LGE) MRI was first developed, which is a pre-requisite for infarct scar quantification and localization in patient-specific 3D LV models. This framework automatically corrects for motion artifacts in multimodal MRI scans, resolving the issue of inaccurate infarct mapping and geometry reconstruction which is typically done manually in most patient-specific modelling work. The registration framework was evaluated against cardiac MRI data from 27 MI patients and showed high accuracy and robustness in delineating LV in LGE MRI of various quality and different myocardial features. This framework allows the integration of LV data from both LGE and cine scans and to facilitate the reconstruction of accurate 3D LV and infarct geometries for subsequent computational study. In the patient-specific LV mechanical modelling, the LV mechanics were formulated using a quasi-static and nearly incompressible hyperelastic material law with transversely isotropic behaviour. The patient-specific models were incorporated with realistic fibre orientation and excitable contracting myocardium. Optimisation of passive and active material parameters were done by minimizing the myocardial wall distance between the reference and end-diastole/end-systole LV geometries. Full cardiac cycle of the LV models was then simulated and stress/strain data were extracted to determine the correlation between regional mechanics abnormality and infarct extension. The fibre stress-strain loops (FSSLs) were analysed and its abnormality was characterized using the directional regional external work (DREW) index, which measures FSSL area and loop direction. Sensitivity studies were also performed to investigate the effect of infarct stiffness on regional myocardial mechanics and potential for infarct extension. It was found that infarct extension was correlated to severely abnormal FSSL in the form of counter-clockwise loop, as indicated by negative DREW values. In regions demonstrating negative DREW values, substantial isovolumic relaxation (IVR) fibre stretching was observed. Further analysis revealed that the occurrence of severely abnormal FSSL near the RZ-BZ boundary was due to a large amount of surrounding infarcted tissue that worsen with excessively stiff IZ

The development of a platform to manipulate cardiomyocyte structure and function

Cardiac tissue engineering to replace damaged areas of the postmitotic heart is still presented with significant challenges, due to the complex and dynamic interplay of electrical, mechanical and biochemical signals involved in the myocardium. The advancement of regenerative approaches is focussed on understanding the underlying regulatory mechanisms involved throughout cardiac development. However, current knowledge of how biophysical cues in the stem cell niche can modulate cell behaviour is limited. Firstly, polyacrylamide-co-acrylic acid was used as an in vitro stiffness-tuneable platform to test the effect of substrate mechanics on human induced pluripotent stem cell (hiPSC) differentiation into cardiomyocytes (CM). The results showed that the optimum differentiation efficiency level peaked at the embryonic-like stiffness of 560 Pa, with increased upregulation of cardiac genes. Functionally, hiPSC-CMs showed a biphasic relationship with a faster calcium transient and higher force generation at cardiac physiological stiffness. Next, shape was incorporated into the experimental design via CardioArray, a custom-built platform which mimics both the stiffness and shape of an adult human CM. This system can accommodate individual hiPSC-CMs to adopt the 3D geometry of an adult CM, while at the same time providing the relevant stiffness cues from the underlying substrate. The results highlighted the specific contribution of stiffness and 3D shape to α-sarcomeric structure, cell membrane stiffness, single cell gene expression and intracellular calcium cycling. Finally, the electrical microenvironment was investigated as a third infleuncing factor on hiPSC-CM development. A hybrid conductive polyaniline-Scl2 scaffold was fabricated, showing long term electronic stability and no cell toxicity when interfaced with electrosensitive hiPSC-CMs. This could provide electromechanical stability in model studies. Improvement of conduction velocity was observed in an in vitro myocardial slice model. As a whole, this thesis demonstrates the differential effects of substrate mechanics on hiPSC cardiac differentiation, providing a novel crucial understanding of how biophysical cues modulate the stem cell niche during differentiation and in vitro culture.Open Acces