In silico Mechanics of Stem Cells Intramyocardially Transplanted with a Biomaterial Injectate for Treatment of Myocardial Infarction.

PURPOSE: Biomaterial and stem cell delivery are promising approaches to treating myocardial infarction. However, the mechanical and biochemical mechanisms underlying the therapeutic benefits require further clarification. This study aimed to assess the deformation of stem cells injected with the biomaterial into the infarcted heart. METHODS: A microstructural finite element model of a mid-wall infarcted myocardial region was developed from ex vivo microcomputed tomography data of a rat heart with left ventricular infarct and intramyocardial biomaterial injectate. Nine cells were numerically seeded in the injectate of the microstructural model. The microstructural and a previously developed biventricular finite element model of the same rat heart were used to quantify the deformation of the cells during a cardiac cycle for a biomaterial elastic modulus (Einj) ranging between 4.1 and 405,900 kPa. RESULTS: The transplanted cells' deformation was largest for Einj = 7.4 kPa, matching that of the cells, and decreased for an increase and decrease in Einj. The cell deformation was more sensitive to Einj changes for softer (Einj ≤ 738 kPa) than stiffer biomaterials. CONCLUSIONS: Combining the microstructural and biventricular finite element models enables quantifying micromechanics of transplanted cells in the heart. The approach offers a broader scope for in silico investigations of biomaterial and cell therapies for myocardial infarction and other cardiac pathologies.

Using machine learning to characterize heart failure across the scales.

Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain [Formula: see text] of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies.

Multiscale characterization of heart failure.

Dilated cardiomyopathy is a progressive irreversible disease associated with contractile dysfunction and heart failure. During dilated cardiomyopathy, elevated diastolic wall strains trigger mechanotransduction pathways that initiate the addition of sarcomeres in series and an overall increase in myocyte length. At the whole organ level, this results in a chronic dilation of the ventricles, an increase in end diastolic and end systolic volumes, and a decrease in ejection fraction. However, how exactly changes in sarcomere number translate into changes in myocyte morphology, and how these cellular changes translate into ventricular dilation remains incompletely understood. Here we combined a chronic animal study, continuum growth modeling, and machine learning to quantify correlations between sarcomere dynamics, myocyte morphology, and ventricular dilation. In an eight-week long volume overload study of six pigs, we found that the average sarcomere number increased by +3.8%/week, from 47 to 62, resulting in a myocyte lengthening of +3.3%/week, from 85 to 108 µm, while the sarcomere length and myocyte width remained unchanged. At the same time, the average end diastolic volume increased by +6.0%/week. Using continuum growth modeling and Bayesian inference, we correlated alterations on the subcellular, cellular, and organ scales and found that the serial sarcomere number explained 88% of myocyte lengthening, which, in turn, explained 54% of cardiac dilation. Our results demonstrate that sarcomere number and myocyte length are closely correlated and constitute the major determinants of dilated heart failure. We anticipate our study to be a starting point for more sophisticated multiscale models of heart failure. Our study suggests that altering sarcomere turnover-and with it myocyte morphology and ventricular dimensions-could be a potential therapeutic target to attenuate or reverse the progression of heart failure. STATEMENT OF SIGNIFICANCE: Heart failure is a significant global health problem that affects more than 25 million people worldwide and increases in prevalence as the population ages. Heart failure has been studied excessively at various scales; yet, there is no compelling concept to connect knowledge from the subcellular, cellular, and organ level across the scales. Here we combined a chronic animal study, continuum growth modeling, and machine learning to quantify correlations between sarcomere dynamics, myocyte morphology, and ventricular dilation. We found that the serial sarcomere number explained 88% of myocyte lengthening, which, in turn, explained 54% of cardiac dilation. Our results show that sarcomere number and myocyte length are closely correlated and constitute the major determinants of dilated heart failure. This suggests that altering the sarcomere turnover-and with it myocyte morphology and ventricular dimensions-could be a potential therapeutic target to attenuate or reverse heart failure.

Effect of intra-myocardial Algisyl-LVR™ injectates on fibre structure in porcine heart failure.

Recent preclinical trials have shown that alginate injections are a promising treatment for ischemic heart disease. Although improvements in heart function and global structure have been reported following alginate implants, the underlying structure is poorly understood. Using high resolution ex vivo MRI and DT-MRI of the hearts of normal control swine (n = 8), swine with induced heart failure (n = 5), and swine with heart failure and alginate injection treatment (n = 6), we visualized and quantified the fibre distribution and implant material geometry. Our findings show that the alginate injectates form solid ellipsoids with a retention rate of 68.7% ±â€¯21.3% (mean ±â€¯SD) and a sphericity index of 0.37 ±â€¯0.03. These ellipsoidal shapes solidified predominantly at the mid-wall position with an inclination of -4.9°â€¯±â€¯31.4° relative to the local circumferential direction. Overall, the change to left ventricular wall thickness and myofiber orientation was minor and was associated with heart failure and not the presence of injectates. These results show that alginate injectates conform to the pre-existing tissue structure, likely expanding along directions of least resistance as mass is added to the injection sites. The alginate displaces the myocardial tissue predominantly in the longitudinal direction, causing minimal disruption to the surrounding myofiber orientations.

Endothelial cells on an aged subendothelial matrix display heterogeneous strain profiles in silico.

Within the artery intima, endothelial cells respond to mechanical cues and changes in subendothelial matrix stiffness. Recently, we found that the aging subendothelial matrix stiffens heterogeneously and that stiffness heterogeneities are present on the scale of one cell length. However, the impacts of these complex mechanical micro-heterogeneities on endothelial cells have not been fully understood. Here, we simulate the effects of matrices that mimic young and aged vessels on single- and multi-cell endothelial cell models and examine the resulting cell basal strain profiles. Although there are limitations to the model which prohibit the prediction of intracellular strain distributions in alive cells, this model does introduce mechanical complexities to the subendothelial matrix material. More heterogeneous basal strain distributions are present in the single- and multi-cell models on the matrix mimicking an aged artery over those exhibited on the young artery. Overall, our data indicate that increased heterogeneous strain profiles in endothelial cells are displayed in silico when there is an increased presence of microscale arterial mechanical heterogeneities in the matrix.