Differential sensitivity to longitudinal and transverse stretch mediates transcriptional responses in mouse neonatal ventricular myocytes.

To identify how cardiomyocyte mechanosensitive signaling pathways are regulated by anisotropic stretch, micropatterned mouse neonatal cardiomyocytes were stretched primarily longitudinally or transversely to the myofiber axis. Four hours of static, longitudinal stretch induced differential expression of 557 genes, compared with 30 induced by transverse stretch, measured using RNA-seq. A logic-based ordinary differential equation model of the cardiac myocyte mechanosignaling network, extended to include the transcriptional regulation and expression of 784 genes, correctly predicted measured expression changes due to anisotropic stretch with 69% accuracy. The model also predicted published transcriptional responses to mechanical load in vitro or in vivo with 63-91% accuracy. The observed differences between transverse and longitudinal stretch responses were not explained by differential activation of specific pathways but rather by an approximately twofold greater sensitivity to longitudinal stretch than transverse stretch. In vitro experiments confirmed model predictions that stretch-induced gene expression is more sensitive to angiotensin II and endothelin-1, via RhoA and MAP kinases, than to the three membrane ion channels upstream of calcium signaling in the network. Quantitative cardiomyocyte gene expression differs substantially with the axis of maximum principal stretch relative to the myofilament axis, but this difference is due primarily to differences in stretch sensitivity rather than to selective activation of mechanosignaling pathways.NEW & NOTEWORTHY Anisotropic stretch applied to micropatterned neonatal mouse ventricular myocytes induced markedly greater acute transcriptional responses when the major axis of stretch was parallel to the myofilament axis than when it was transverse. Analysis with a novel quantitative network model of mechanoregulated cardiomyocyte gene expression suggests that this difference is explained by higher cell sensitivity to longitudinal loading than transverse loading than by the activation of differential signaling pathways.

Impact of artificial intelligence arrhythmia mapping on time to first ablation, procedure duration, and fluoroscopy use.

INTRODUCTION: Artificial intelligence (AI) ECG arrhythmia mapping provides arrhythmia source localization using 12-lead ECG data; whether this information impacts procedural efficiency is unknown. We performed a retrospective, case-control study to evaluate the hypothesis that AI ECG mapping may reduce time to ablation, procedural duration, and fluoroscopy. MATERIALS AND METHODS: Cases in which system output was used were retrospectively enrolled according to IRB-approved protocols at each site. Matched control cases were enrolled in reverse chronological order beginning on the last day for which the technology was unavailable. Controls were matched based upon physician, institution, arrhythmia, and a predetermined complexity rating. Procedural metrics, fluoroscopy data, and clinical outcomes were assessed from time-stamped medical records. RESULTS: The study group consisted of 28 patients (age 65 ± 11 years, 46% female, left atrial dimension 4.1 ± 0.9 cm, LVEF 50 ± 18%) and was similar to 28 controls. The most common arrhythmia types were atrial fibrillation (n = 10), premature ventricular complexes (n = 8), and ventricular tachycardia (n = 6). Use of the system was associated with a 19.0% reduction in time to ablation (133 ± 48 vs. 165 ± 49 min, p = 0.02), a 22.6% reduction in procedure duration (233 ± 51 vs. 301 ± 83 min, p < 0.001), and a 43.7% reduction in fluoroscopy (18.7 ± 13.3 vs. 33.2 ± 18.0 min, p < 0.001) versus controls. At 6 months follow-up, arrhythmia-free survival was 73.5% in the study group and 63.3% in the control group (p = 0.56). CONCLUSION: Use of forward-solution AI ECG mapping is associated with reductions in time to first ablation, procedure duration, and fluoroscopy without an adverse impact on procedure outcomes or complications.

Network model of skeletal muscle cell signalling predicts differential responses to endurance and resistance exercise training.

Exercise-induced muscle adaptations vary based on exercise modality and intensity. We constructed a signalling network model from 87 published studies of human or rodent skeletal muscle cell responses to endurance or resistance exercise in vivo or simulated exercise in vitro. The network comprises 259 signalling interactions between 120 nodes, representing eight membrane receptors and eight canonical signalling pathways regulating 14 transcriptional regulators, 28 target genes and 12 exercise-induced phenotypes. Using this network, we formulated a logic-based ordinary differential equation model predicting time-dependent molecular and phenotypic alterations following acute endurance and resistance exercises. Compared with nine independent studies, the model accurately predicted 18/21 (85%) acute responses to resistance exercise and 12/16 (75%) acute responses to endurance exercise. Detailed sensitivity analysis of differential phenotypic responses to resistance and endurance training showed that, in the model, exercise regulates cell growth and protein synthesis primarily by signalling via mechanistic target of rapamycin, which is activated by Akt and inhibited in endurance exercise by AMP-activated protein kinase. Endurance exercise preferentially activates inflammation via reactive oxygen species and nuclear factor κB signalling. Furthermore, the expected preferential activation of mitochondrial biogenesis by endurance exercise was counterbalanced in the model by protein kinase C in response to resistance training. This model provides a new tool for investigating cross-talk between skeletal muscle signalling pathways activated by endurance and resistance exercise, and the mechanisms of interactions such as the interference effects of endurance training on resistance exercise outcomes.

Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy.

Familial cardiomyopathy is a precursor of heart failure and sudden cardiac death. Over the past several decades, researchers have discovered numerous gene mutations primarily in sarcomeric and cytoskeletal proteins causing two different disease phenotypes: hypertrophic (HCM) and dilated (DCM) cardiomyopathies. However, molecular mechanisms linking genotype to phenotype remain unclear. Here, we employ a systems approach by integrating experimental findings from preclinical studies (e.g., murine data) into a cohesive signaling network to scrutinize genotype to phenotype mechanisms. We developed an HCM/DCM signaling network model utilizing a logic-based differential equations approach and evaluated model performance in predicting experimental data from four contexts (HCM, DCM, pressure overload, and volume overload). The model has an overall prediction accuracy of 83.8%, with higher accuracy in the HCM context (90%) than DCM (75%). Global sensitivity analysis identifies key signaling reactions, with calcium-mediated myofilament force development and calcium-calmodulin kinase signaling ranking the highest. A structural revision analysis indicates potential missing interactions that primarily control calcium regulatory proteins, increasing model prediction accuracy. Combination pharmacotherapy analysis suggests that downregulation of signaling components such as calcium, titin and its associated proteins, growth factor receptors, ERK1/2, and PI3K-AKT could inhibit myocyte growth in HCM. In experiments with patient-specific iPSC-derived cardiomyocytes (MLP-W4R;MYH7-R723C iPSC-CMs), combined inhibition of ERK1/2 and PI3K-AKT rescued the HCM phenotype, as predicted by the model. In DCM, PI3K-AKT-NFAT downregulation combined with upregulation of Ras/ERK1/2 or titin or Gq protein could ameliorate cardiomyocyte morphology. The model results suggest that HCM mutations that increase active force through elevated calcium sensitivity could increase ERK activity and decrease eccentricity through parallel growth factors, Gq-mediated, and titin pathways. Moreover, the model simulated the influence of existing medications on cardiac growth in HCM and DCM contexts. This HCM/DCM signaling model demonstrates utility in investigating genotype to phenotype mechanisms in familial cardiomyopathy.

Histamine-induced biphasic activation of RhoA allows for persistent RhoA signaling.

The small GTPase RhoA is a central signaling enzyme that is involved in various cellular processes such as cytoskeletal dynamics, transcription, and cell cycle progression. Many signal transduction pathways activate RhoA-for instance, Gαq-coupled Histamine 1 Receptor signaling via Gαq-dependent activation of RhoGEFs such as p63. Although multiple upstream regulators of RhoA have been identified, the temporal regulation of RhoA and the coordination of different upstream components in its regulation have not been well characterized. In this study, live-cell measurement of RhoA activation revealed a biphasic increase of RhoA activity upon histamine stimulation. We showed that the first and second phase of RhoA activity are dependent on p63 and Ca2+/PKC, respectively, and further identified phosphorylation of serine 240 on p115 RhoGEF by PKC to be the mechanistic link between PKC and RhoA. Combined approaches of computational modeling and quantitative measurement revealed that the second phase of RhoA activation is insensitive to rapid turning off of the receptor and is required for maintaining RhoA-mediated transcription after the termination of the receptor signaling. Thus, two divergent pathways enable both rapid activation and persistent signaling in receptor-mediated RhoA signaling via intricate temporal regulation.

A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot.

BACKGROUND: Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows. METHODS: Cardiovascular magnetic resonance (CMR) images from a cohort of 123 patients with repaired tetralogy of Fallot (rTOF) from two internal sites were used to train and validate each step in the automated pipeline. The complete automated pipeline was tested using CMR images from a cohort of 12 rTOF patients from an internal site and 18 rTOF patients from an external site. Manually and automatically generated shape models from the test set were compared using Euclidean projection distances, global ventricular measurements, and atlas-based shape mode scores. RESULTS: The mean absolute error (MAE) between manually and automatically generated shape models in the test set was similar to the voxel resolution of the original CMR images for end-diastolic models (MAE = 1.9 ± 0.5 mm) and end-systolic models (MAE = 2.1 ± 0.7 mm). Global ventricular measurements computed from automated models were in good agreement with those computed from manual models. The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas. CONCLUSIONS: Using deep learning, accurate three-dimensional, biventricular shape models can be reliably created. This fully automated end-to-end approach dramatically reduces the manual input required to create shape models, thereby enabling the rapid analysis of large-scale datasets and the potential to deploy statistical atlas-based analyses in point-of-care clinical settings. Training data and networks are available from cardiacatlas.org.

Isolation and reconstruction of cardiac mitochondria from SBEM images using a deep learning-based method.

Mitochondrial morphological defects are a common feature of diseased cardiac myocytes. However, quantitative assessment of mitochondrial morphology is limited by the time-consuming manual segmentation of electron micrograph (EM) images. To advance understanding of the relation between morphological defects and dysfunction, an efficient morphological reconstruction method is desired to enable isolation and reconstruction of mitochondria from EM images. We propose a new method for isolating and reconstructing single mitochondria from serial block-face scanning EM (SBEM) images. CDeep3M, a cloud-based deep learning network for EM images, was used to segment mitochondrial interior volumes and boundaries. Post-processing was performed using both the predicted interior volume and exterior boundary to isolate and reconstruct individual mitochondria. Series of SBEM images from two separate cardiac myocytes were processed. The highest F1-score was 95% using 50 training datasets, greater than that for previously reported automated methods and comparable to manual segmentations. Accuracy of separation of individual mitochondria was 80% on a pixel basis. A total of 2315 mitochondria in the two series of SBEM images were evaluated with a mean volume of 0.78 µm3. The volume distribution was very broad and skewed; the most frequent mitochondria were 0.04-0.06 µm3, but mitochondria larger than 2.0 µm3 accounted for more than 10% of the total number. The average short-axis length was 0.47 µm. Primarily longitudinal mitochondria (0-30 degrees) were dominant (54%). This new automated segmentation and separation method can help quantitate mitochondrial morphology and improve understanding of myocyte structure-function relationships.

Cardiomyocyte Expression of ZO-1 Is Essential for Normal Atrioventricular Conduction but Does Not Alter Ventricular Function.

RATIONALE: ZO-1 (Zonula occludens-1), a plasma membrane-associated scaffolding protein regulates signal transduction, transcription, and cellular communication. Global deletion of ZO-1 in the mouse is lethal by embryonic day 11.5. The function of ZO-1 in cardiac myocytes (CM) is largely unknown. OBJECTIVE: To determine the function of CM ZO-1 in the intact heart, given its binding to other CM proteins that have been shown instrumental in normal cardiac conduction and function. METHODS AND RESULTS: We generated ZO-1 CM-specific knockout (KO) mice using α-Myosin Heavy Chain-nuclear Cre (ZO-1cKO) and investigated physiological and electrophysiological function by echocardiography, surface ECG and conscious telemetry, intracardiac electrograms and pacing, and optical mapping studies. ZO-1cKO mice were viable, had normal Mendelian ratios, and had a normal lifespan. Ventricular morphometry and function were not significantly different between the ZO-1cKO versus control (CTL) mice, basally in young or aged mice, or even when hearts were subjected to hemodynamic loading. Atrial mass was increased in ZO-1cKO. Electrophysiological and optical mapping studies indicated high-grade atrioventricular (A-V) block in ZO-1cKO comparing to CTL hearts. While ZO-1-associated proteins such as vinculin, connexin 43, N-cadherin, and α-catenin showed no significant change with the loss of ZO-1, Connexin-45 and Coxsackie-adenovirus (CAR) proteins were reduced in atria of ZO-1cKO. Further, with loss of ZO-1, ZO-2 protein was increased significantly in ventricular CM in a presumed compensatory manner but was still not detected in the AV nodal myocytes. Importantly, the expression of the sodium channel protein NaV1.5 was altered in AV nodal cells of the ZO-1cKO versus CTL. CONCLUSIONS: ZO-1 protein has a unique physiological role in cardiac nodal tissue. This is in alignment with its known interaction with CAR and Cx45, and a new function in regulating the expression of NaV1.5 in AV node. Uniquely, ZO-1 is dispensable for function of the working myocardium.

Le Cœur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot.

BACKGROUND: Maladaptive remodelling mechanisms occur in patients with repaired tetralogy of Fallot (rToF) resulting in a cycle of metabolic and structural changes. Biventricular shape analysis may indicate mechanisms associated with adverse events independent of pulmonary regurgitant volume index (PRVI). We aimed to determine novel remodelling patterns associated with adverse events in patients with rToF using shape and function analysis. METHODS: Biventricular shape and function were studied in 192 patients with rToF (median time from TOF repair to baseline evaluation 13.5 years). Linear discriminant analysis (LDA) and principal component analysis (PCA) were used to identify shape differences between patients with and without adverse events. Adverse events included death, arrhythmias, and cardiac arrest with median follow-up of 10 years. RESULTS: LDA and PCA showed that shape characteristics pertaining to adverse events included a more circular left ventricle (LV) (decreased eccentricity), dilated (increased sphericity) LV base, increased right ventricular (RV) apical sphericity, and decreased RV basal sphericity. Multivariate LDA showed that the optimal discriminative model included only RV apical ejection fraction and one PCA mode associated with a more circular and dilated LV base (AUC = 0.77). PRVI did not add value, and shape changes associated with increased PRVI were not predictive of adverse outcomes. CONCLUSION: Pathological remodelling patterns in patients with rToF are significantly associated with adverse events, independent of PRVI. Mechanisms related to incident events include LV basal dilation with a reduced RV apical ejection fraction.

Array atomic force microscopy for real-time multiparametric analysis.

Nanoscale multipoint structure-function analysis is essential for deciphering the complexity of multiscale biological and physical systems. Atomic force microscopy (AFM) allows nanoscale structure-function imaging in various operating environments and can be integrated seamlessly with disparate probe-based sensing and manipulation technologies. Conventional AFMs only permit sequential single-point analysis; widespread adoption of array AFMs for simultaneous multipoint study is challenging owing to the intrinsic limitations of existing technological approaches. Here, we describe a prototype dispersive optics-based array AFM capable of simultaneously monitoring multiple probe-sample interactions. A single supercontinuum laser beam is utilized to spatially and spectrally map multiple cantilevers, to isolate and record beam deflection from individual cantilevers using distinct wavelength selection. This design provides a remarkably simplified yet effective solution to overcome the optical cross-talk while maintaining subnanometer sensitivity and compatibility with probe-based sensors. We demonstrate the versatility and robustness of our system on parallel multiparametric imaging at multiscale levels ranging from surface morphology to hydrophobicity and electric potential mapping in both air and liquid, mechanical wave propagation in polymeric films, and the dynamics of living cells. This multiparametric, multiscale approach provides opportunities for studying the emergent properties of atomic-scale mechanical and physicochemical interactions in a wide range of physical and biological networks.

Cardiac myosin activation with 2-deoxy-ATP via increased electrostatic interactions with actin.

The naturally occurring nucleotide 2-deoxy-adenosine 5'-triphosphate (dATP) can be used by cardiac muscle as an alternative energy substrate for myosin chemomechanical activity. We and others have previously shown that dATP increases contractile force in normal hearts and models of depressed systolic function, but the structural basis of these effects has remained unresolved. In this work, we combine multiple techniques to provide structural and functional information at the angstrom-nanometer and millisecond time scales, demonstrating the ability to make both structural measurements and quantitative kinetic estimates of weak actin-myosin interactions that underpin sarcomere dynamics. Exploiting dATP as a molecular probe, we assess how small changes in myosin structure translate to electrostatic-based changes in sarcomere function to augment contractility in cardiac muscle. Through Brownian dynamics simulation and computational structural analysis, we found that deoxy-hydrolysis products [2-deoxy-adenosine 5'-diphosphate (dADP) and inorganic phosphate (Pi)] bound to prepowerstroke myosin induce an allosteric restructuring of the actin-binding surface on myosin to increase the rate of cross-bridge formation. We then show experimentally that this predicted effect translates into increased electrostatic interactions between actin and cardiac myosin in vitro. Finally, using small-angle X-ray diffraction analysis of sarcomere structure, we demonstrate that the proposed increased electrostatic affinity of myosin for actin causes a disruption of the resting conformation of myosin motors, resulting in their repositioning toward the thin filament before activation. The dATP-mediated structural alterations in myosin reported here may provide insight into an improved criterion for the design or selection of small molecules to be developed as therapeutic agents to treat systolic dysfunction.

Subcellular Remodeling in Filamin C Deficient Mouse Hearts Impairs Myocyte Tension Development during Progression of Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is a life-threatening form of heart disease that is typically characterized by progressive thinning of the ventricular walls, chamber dilation, and systolic dysfunction. Multiple mutations in the gene encoding filamin C (FLNC), an actin-binding cytoskeletal protein in cardiomyocytes, have been found in patients with DCM. However, the mechanisms that lead to contractile impairment and DCM in patients with FLNC variants are poorly understood. To determine how FLNC regulates systolic force transmission and DCM remodeling, we used an inducible, cardiac-specific FLNC-knockout (icKO) model to produce a rapid onset of DCM in adult mice. Loss of FLNC reduced systolic force development in single cardiomyocytes and isolated papillary muscles but did not affect twitch kinetics or calcium transients. Electron and immunofluorescence microscopy showed significant defects in Z-disk alignment in icKO mice and altered myofilament lattice geometry. Moreover, a loss of FLNC induces a softening myocyte cortex and structural adaptations at the subcellular level that contribute to disrupted longitudinal force production during contraction. Spatially explicit computational models showed that these structural defects could be explained by a loss of inter-myofibril elastic coupling at the Z-disk. Our work identifies FLNC as a key regulator of the multiscale ultrastructure of cardiomyocytes and therefore plays an important role in maintaining systolic mechanotransmission pathways, the dysfunction of which may be key in driving progressive DCM.

Research Priorities for Heart Failure With Preserved Ejection Fraction: National Heart, Lung, and Blood Institute Working Group Summary.

Heart failure with preserved ejection fraction (HFpEF), a major public health problem that is rising in prevalence, is associated with high morbidity and mortality and is considered to be the greatest unmet need in cardiovascular medicine today because of a general lack of effective treatments. To address this challenging syndrome, the National Heart, Lung, and Blood Institute convened a working group made up of experts in HFpEF and novel research methodologies to discuss research gaps and to prioritize research directions over the next decade. Here, we summarize the discussion of the working group, followed by key recommendations for future research priorities. There was uniform recognition that HFpEF is a highly integrated, multiorgan, systemic disorder requiring a multipronged investigative approach in both humans and animal models to improve understanding of mechanisms and treatment of HFpEF. It was recognized that advances in the understanding of basic mechanisms and the roles of inflammation, macrovascular and microvascular dysfunction, fibrosis, and tissue remodeling are needed and ideally would be obtained from (1) improved animal models, including large animal models, which incorporate the effects of aging and associated comorbid conditions; (2) repositories of deeply phenotyped physiological data and human tissue, made accessible to researchers to enhance collaboration and research advances; and (3) novel research methods that take advantage of computational advances and multiscale modeling for the analysis of complex, high-density data across multiple domains. The working group emphasized the need for interactions among basic, translational, clinical, and epidemiological scientists and across organ systems and cell types, leveraging different areas or research focus, and between research centers. A network of collaborative centers to accelerate basic, translational, and clinical research of pathobiological mechanisms and treatment strategies in HFpEF was discussed as an example of a strategy to advance research progress. This resource would facilitate comprehensive, deep phenotyping of a multicenter HFpEF patient cohort with standardized protocols and a robust biorepository. The research priorities outlined in this document are meant to stimulate scientific advances in HFpEF by providing a road map for future collaborative investigations among a diverse group of scientists across multiple domains.

Right-left ventricular shape variations in tetralogy of Fallot: associations with pulmonary regurgitation.

BACKGROUND: Relationships between right ventricular (RV) and left ventricular (LV) shape and function may be useful in determining optimal timing for pulmonary valve replacement in patients with repaired tetralogy of Fallot (rTOF). However, these are multivariate and difficult to quantify. We aimed to quantify variations in biventricular shape associated with pulmonary regurgitant volume (PRV) in rTOF using a biventricular atlas. METHODS: In this cross-sectional retrospective study, a biventricular shape model was customized to cardiovascular magnetic resonance (CMR) images from 88 rTOF patients (median age 16, inter-quartile range 11.8-24.3 years). Morphometric scores quantifying biventricular shape at end-diastole and end-systole were computed using principal component analysis. Multivariate linear regression was used to quantify biventricular shape associations with PRV, corrected for age, sex, height, and weight. Regional associations were confirmed by univariate correlations with distances and angles computed from the models, as well as global systolic strains computed from changes in arc length from end-diastole to end-systole. RESULTS: PRV was significantly associated with 5 biventricular morphometric scores, independent of covariates, and accounted for 12.3% of total shape variation (p < 0.05). Increasing PRV was associated with RV dilation and basal bulging, in conjunction with decreased LV septal-lateral dimension (LV flattening) and systolic septal motion towards the RV (all p < 0.05). Increased global RV radial, longitudinal, circumferential and LV radial systolic strains were significantly associated with increased PRV (all p < 0.05). CONCLUSION: A biventricular atlas of rTOF patients quantified multivariate relationships between left-right ventricular morphometry and wall motion with pulmonary regurgitation. Regional RV dilation, LV reduction, LV septal-lateral flattening and increased RV strain were all associated with increased pulmonary regurgitant volume. Morphometric scores provide simple metrics linking mechanisms for structural and functional alteration with important clinical indices.

Atlas-based methods for efficient characterization of patient-specific ventricular activation patterns.

AIMS: Ventricular activation patterns can aid clinical decision-making directly by providing spatial information on cardiac electrical activation or indirectly through derived clinical indices. The aim of this work was to derive an atlas of the major modes of variation of ventricular activation from model-predicted 3D bi-ventricular activation time distributions and to relate these modes to corresponding vectorcardiograms (VCGs). We investigated how the resulting dimensionality reduction can improve and accelerate the estimation of activation patterns from surface electrogram measurements. METHODS AND RESULTS: Atlases of activation time (AT) and VCGs were derived using principal component analysis on a dataset of simulated electrophysiology simulations computed on eight patient-specific bi-ventricular geometries. The atlases provided significant dimensionality reduction, and the modes of variation in the two atlases described similar features. Utility of the atlases was assessed by resolving clinical waveforms against them and the VCG atlas was able to accurately reconstruct the patient VCGs with fewer than 10 modes. A sensitivity analysis between the two atlases was performed by calculating a compact Jacobian. Finally, VCGs generated by varying AT atlas modes were compared with clinical VCGs to estimate patient-specific activation maps, and the resulting errors between the clinical and atlas-based VCGs were less than those from more computationally expensive method. CONCLUSION: Atlases of activation and VCGs represent a new method of identifying and relating the features of these high-dimensional signals that capture the major sources of variation between patients and may aid in identifying novel clinical indices of arrhythmia risk or therapeutic outcome.

Methods and sensors for functional genomic studies of cell-cycle transitions in single cells.

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1-5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

Improved compressed sensing and super-resolution of cardiac diffusion MRI with structure-guided total variation.

PURPOSE: Structure-guided total variation is a recently introduced prior that allows reconstruction of images using knowledge of the location and orientation of edges in a reference image. In this work, we demonstrate the advantages of a variant of structure-guided total variation known as directional total variation (DTV), over traditional total variation (TV), in the context of compressed-sensing reconstruction and super-resolution. METHODS: We compared TV and DTV in retrospectively undersampled ex vivo diffusion tensor imaging and diffusion spectrum imaging data from healthy, sham, and hypertrophic rat hearts. RESULTS: In compressed sensing at an undersampling factor of 8, the RMS error of mean diffusivity and fractional anisotropy relative to the fully sampled ground truth were 44% and 20% lower in DTV compared with TV. In super-resolution, these values were 29% and 14%, respectively. Similarly, we observed improvements in helix angle, transverse angle, sheetlet elevation, and sheetlet azimuth. The RMS error of the diffusion kurtosis in the undersampled data relative to the ground truth was uniformly lower (22% on average) with DTV compared to TV. CONCLUSION: Acquiring one fully sampled non-diffusion-weighted image and 10 diffusion-weighted images at 8× undersampling would result in an 80% net reduction in data needed. We demonstrate efficacy of the DTV algorithm over TV in reducing data sampling requirements, which can be translated into higher apparent resolution and potentially shorter scan times. This method would be equally applicable in diffusion MRI applications outside the heart.

Predicting the effects of dATP on cardiac contraction using multiscale modeling of the sarcomere.

2'-deoxy-ATP (dATP) is a naturally occurring small molecule that has shown promise as a therapeutic because it significantly increases cardiac myocyte force development even at low dATP/ATP ratios. To investigate mechanisms by which dATP alters myosin crossbridge dynamics, we used Brownian dynamics simulations to calculate association rates between actin and ADP- or dADP-bound myosin. These rates were then directly incorporated in a mechanistic Monte Carlo Markov Chain model of cooperative sarcomere contraction. A unique combination of increased powerstroke and detachment rates was required to match experimental steady-state and kinetic data for dATP force production in rat cardiac myocytes when the myosin attachment rate in the model was constrained by the results of a Brownian dynamics simulation. Nearest-neighbor cooperativity was seen to contribute to, but not fully explain, the steep relationship between dATP/ATP ratio and steady-state force-development observed at lower dATP concentrations. Dynamic twitch simulations performed using measured calcium transients as inputs showed that the effects of dATP on the crossbridge alone were not sufficient to explain experimentally observed enhancement of relaxation kinetics by dATP treatment. Hence, dATP may also affect calcium handling even at low concentrations. By enabling the effects of dATP on sarcomere mechanics to be predicted, this multi-scale modeling framework may elucidate the molecular mechanisms by which dATP can have therapeutic effects on cardiac contractile dysfunction.

Properties of cardiac conduction in a cell-based computational model.

The conduction of electrical signals through cardiac tissue is essential for maintaining the function of the heart, and conduction abnormalities are known to potentially lead to life-threatening arrhythmias. The properties of cardiac conduction have therefore been the topic of intense study for decades, but a number of questions related to the mechanisms of conduction still remain unresolved. In this paper, we demonstrate how the so-called EMI model may be used to study some of these open questions. In the EMI model, the extracellular space, the cell membrane, the intracellular space and the cell connections are all represented as separate parts of the computational domain, and the model therefore allows for study of local properties that are hard to represent in the classical homogenized bidomain or monodomain models commonly used to study cardiac conduction. We conclude that a non-uniform sodium channel distribution increases the conduction velocity and decreases the time delays over gap junctions of reduced coupling in the EMI model simulations. We also present a theoretical optimal cell length with respect to conduction velocity and consider the possibility of ephaptic coupling (i.e. cell-to-cell coupling through the extracellular potential) acting as an alternative or supporting mechanism to gap junction coupling. We conclude that for a non-uniform distribution of sodium channels and a sufficiently small intercellular distance, ephaptic coupling can influence the dynamics of the sodium channels and potentially provide cell-to-cell coupling when the gap junction connection is absent.

A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows.

Multi-scale computational modeling is a major branch of computational biology as evidenced by the US federal interagency Multi-Scale Modeling Consortium and major international projects. It invariably involves specific and detailed sequences of data analysis and simulation, often with multiple tools and datasets, and the community recognizes improved modularity, reuse, reproducibility, portability and scalability as critical unmet needs in this area. Scientific workflows are a well-recognized strategy for addressing these needs in scientific computing. While there are good examples if the use of scientific workflows in bioinformatics, medical informatics, biomedical imaging and data analysis, there are fewer examples in multi-scale computational modeling in general and cardiac electrophysiology in particular. Cardiac electrophysiology simulation is a mature area of multi-scale computational biology that serves as an excellent use case for developing and testing new scientific workflows. In this article, we develop, describe and test a computational workflow that serves as a proof of concept of a platform for the robust integration and implementation of a reusable and reproducible multi-scale cardiac cell and tissue model that is expandable, modular and portable. The workflow described leverages Python and Kepler-Python actor for plotting and pre/post-processing. During all stages of the workflow design, we rely on freely available open-source tools, to make our workflow freely usable by scientists.