Computational Modelling of Optogenetics in Cardiac Cells

2012 ◽  
Author(s):  
Jonathan Wong ◽  
Oscar Abilez ◽  
Ellen Kuhl
Keyword(s):  
Finite Element ◽  
Ion Channel ◽  
Single Cell ◽  
Cardiac Myocytes ◽  
Cardiac Tissue ◽  
Channel Model ◽  
Computational Modelling ◽  
Cardiac Cells ◽  
Cardiac Cell ◽  
Cell Models

Channelrhodopsin-2 (ChR2) is a light-activated ion channel that can allow scientists to electrically activate cells via optical stimulation. Using a combination of existing computational electrophysiological and mechanical cardiac cell models with a novel ChR2 ion channel model, we created a model for ChR2-transduced cardiac myocytes. We implemented this model into a commonly available finite element platform and simulated both the single cell and the tissue electromechanical response. Our simulations show that it is possible to stimulate cardiac tissue optically with ChR2-transduced cells.

scholarly journals A Patient-based Model to Investigate the Effect of Micro-structure Scars on the Optogenetic Defibrillation Success: A Computational Modelling Approach

2020 ◽  
Author(s):  
Elham Zakeri Zafarghandi ◽  
Fariba Bahrami
Keyword(s):  
Single Cell ◽  
Cardiac Tissue ◽  
Cell Model ◽  
Success Probability ◽  
Computational Cost ◽  
Computational Modelling ◽  
Relative Area ◽  
Imaging Data ◽  
Data Set ◽  
Interface Length

AbstractThis study investigates the role of the microstructure of real scars in the success of optogenetic defibrillation. To reduce the computational cost of high-order models (like Ten Tusscher Model, TTM) for a single cell as well as to take advantage of their ability to generate a more realistic output, we developed a low-order model of optogenetic cardiac tissue based on the modified Alieve-Panfilov single-cell model and estimated its parameters using a TTM. Two-dimensional electrophysiological cardiac tissue models were produced including different scar shapes that were extracted from Late Gadolinium-Enhanced (LGE) magnetic resonance imaging data set of 10 patients with non-ischemic dilated cardiomyopathy. The scar shapes were classified based on four criteria: transmurality, relative area, scar entropy, and interface length. Scar with the highest 25% of the relative area showed 25% of successful cases, this ratio is 27%, and 25% for a scar with the most top 25% of entropy, and transmurality, respectively. In comparison, the proportions are 61.54%, 44.44%, and 61.76%, for the lowest 25% of the area, entropy, and transmurality. We also investigated the efficacy of various methods for light-sensitive cells’ distribution within the cardiac tissue with scar. Four types of distributions were defined. Defibrillation within tissues with 0.1 light-sensitive out of all cells was 15 to 25% more successful than their counterparts with 0.05 light-sensitive cells. Lastly, we examined the effect of an earlier stimulation on the success probability of defibrillation. Our results indicated that inducing 0.5 msec earlier resulted in a roughly 15% rise in successful cases.

scholarly journals Reproducible model development in the Cardiac Electrophysiology Web Lab

2018 ◽  
Author(s):  
Aidan C. Daly ◽  
Michael Clerx ◽  
Kylie A. Beattie ◽  
Jonathan Cooper ◽  
David J. Gavaghan ◽  
...  
Keyword(s):  
Experimental Data ◽  
Cardiac Electrophysiology ◽  
Model Building ◽  
Research Effort ◽  
Model Development ◽  
Cardiac Cells ◽  
Cardiac Cell ◽  
Cell Models ◽  
Experimental Protocol ◽  
Wide Range

AbstractThe modelling of the electrophysiology of cardiac cells is one of the most mature areas of systems biology. This extended concentration of research effort brings with it new challenges, foremost among which is that of choosing which of these models is most suitable for addressing a particular scientific question. In a previous paper, we presented our initial work in developing an online resource for the characterisation and comparison of electrophysiological cell models in a wide range of experimental scenarios. In that work, we described how we had developed a novel protocol language that allowed us to separate the details of the mathematical model (the majority of cardiac cell models take the form of ordinary differential equations) from the experimental protocol being simulated. We developed a fully-open online repository (which we termed the Cardiac Electrophysiology Web Lab) which allows users to store and compare the results of applying the same experimental protocol to competing models. In the current paper we describe the most recent and planned extensions of this work, focused on supporting the process of model building from experimental data. We outline the necessary work to develop a machine-readable language to describe the process of inferring parameters from wet lab datasets, and illustrate our approach through a detailed example of fitting a model of the hERG channel using experimental data. We conclude by discussing the future challenges in making further progress in this domain towards our goal of facilitating a fully reproducible approach to the development of cardiac cell models.

scholarly journals Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types

2020 ◽  
Author(s):  
Anirban Nandi ◽  
Tom Chartrand ◽  
Werner Van Geit ◽  
Anatoly Buchin ◽  
Zizhen Yao ◽  
...  
Keyword(s):  
Ion Channel ◽  
Single Cell ◽  
Single Neuron ◽  
Cell Types ◽  
Cell Models ◽  
Cell Type ◽  
Channel Conductance ◽  
Neuron Models ◽  
Inhibitory Cell

AbstractIdentifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined.HighlightsGeneration and evaluation of more than 130 million single-cell models with active conductances along the reconstructed morphology faithfully recapitulate the electrophysiology of 230 in vitro experiments.Optimized ion channel conductances along the cellular morphology (‘all-active’) are characteristic of model complexity and offer enhanced biophysical realism.Ion channel conductance vectors of all-active models classify transcriptomically defined cell-types.Cell type differences in ion channel conductances predicted by the models correlate with experimentally measured single-cell gene expression differences in inhibitory (Pvalb, Sst, Htr3a) and excitatory (Nr5a1, Rbp4) classes.A set of ion channel conductances identified by comparing between cell type model populations explain electrophysiology differences between these types in simulations and brain slice experiments.All-active models recapitulate multimodal properties of excitatory and inhibitory cell types offering a systematic and causal way of linking differences between them.

Confocal imaging of calcium in intact ventricular muscle provides a new view of excitation-contraction coupling

1996 ◽  
Vol 54 ◽  
pp. 738-739
Author(s):  
W.G. Wier
Keyword(s):  
Local Control ◽  
Cardiac Tissue ◽  
Cell Function ◽  
Isolated Heart ◽  
Cardiac Cells ◽  
Confocal Imaging ◽  
Ion Concentration ◽  
Heart Cell ◽  
Free Calcium ◽  
Heart Cells

A fundamentally new understanding of cardiac excitation-contraction (E-C) coupling is being developed from recent experimental work using confocal microscopy of single isolated heart cells. In particular, the transient change in intracellular free calcium ion concentration ([Ca2+]i transient) that activates muscle contraction is now viewed as resulting from the spatial and temporal summation of small (∼ 8 μm3), subcellular, stereotyped ‘local [Ca2+]i-transients' or, as they have been called, ‘calcium sparks'. This new understanding may be called ‘local control of E-C coupling'. The relevance to normal heart cell function of ‘local control, theory and the recent confocal data on spontaneous Ca2+ ‘sparks', and on electrically evoked local [Ca2+]i-transients has been unknown however, because the previous studies were all conducted on slack, internally perfused, single, enzymatically dissociated cardiac cells, at room temperature, usually with Cs+ replacing K+, and often in the presence of Ca2-channel blockers. The present work was undertaken to establish whether or not the concepts derived from these studies are in fact relevant to normal cardiac tissue under physiological conditions, by attempting to record local [Ca2+]i-transients, sparks (and Ca2+ waves) in intact, multi-cellular cardiac tissue.

scholarly journals Cardiac Organoids to Model and Heal Heart Failure and Cardiomyopathies

Biomedicines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 563
Author(s):  
Magali Seguret ◽  
Eva Vermersch ◽  
Charlène Jouve ◽  
Jean-Sébastien Hulot
Keyword(s):  
Tissue Engineering ◽  
Drug Testing ◽  
Cardiac Tissue ◽  
Cardiac Tissue Engineering ◽  
Cardiac Cells ◽  
Cardiac Functions ◽  
Different Types ◽  
Recent Developments ◽  
Human Cardiac Tissue ◽  
Key Aspects

Cardiac tissue engineering aims at creating contractile structures that can optimally reproduce the features of human cardiac tissue. These constructs are becoming valuable tools to model some of the cardiac functions, to set preclinical platforms for drug testing, or to alternatively be used as therapies for cardiac repair approaches. Most of the recent developments in cardiac tissue engineering have been made possible by important advances regarding the efficient generation of cardiac cells from pluripotent stem cells and the use of novel biomaterials and microfabrication methods. Different combinations of cells, biomaterials, scaffolds, and geometries are however possible, which results in different types of structures with gradual complexities and abilities to mimic the native cardiac tissue. Here, we intend to cover key aspects of tissue engineering applied to cardiology and the consequent development of cardiac organoids. This review presents various facets of the construction of human cardiac 3D constructs, from the choice of the components to their patterning, the final geometry of generated tissues, and the subsequent readouts and applications to model and treat cardiac diseases.

scholarly journals EPEN-21. IMPAIRED NEURONAL-GLIAL FATE SPECIFICATION IN PEDIATRIC EPENDYMOMA REVEALED BY SINGLE-CELL RNA-SEQ

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii311-iii312
Author(s):  
Bernhard Englinger ◽  
Johannes Gojo ◽  
Li Jiang ◽  
Jens M Hübner ◽  
McKenzie L Shaw ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Regulatory Networks ◽  
Target Genes ◽  
Target Identification ◽  
Rna Seq ◽  
Cell Models ◽  
Pediatric Ependymoma ◽  
Glial Fate ◽  
Anatomic Locations

Abstract Ependymoma represents a heterogeneous disease affecting the entire neuraxis. Extensive molecular profiling efforts have identified molecular ependymoma subgroups based on DNA methylation. However, the intratumoral heterogeneity and developmental origins of these groups are only partially understood, and effective treatments are still lacking for about 50% of patients with high-risk tumors. We interrogated the cellular architecture of ependymoma using single cell/nucleus RNA-sequencing to analyze 24 tumor specimens across major molecular subgroups and anatomic locations. We additionally analyzed ten patient-derived ependymoma cell models and two patient-derived xenografts (PDXs). Interestingly, we identified an analogous cellular hierarchy across all ependymoma groups, originating from undifferentiated neural stem cell-like populations towards different degrees of impaired differentiation states comprising neuronal precursor-like, astro-glial-like, and ependymal-like tumor cells. While prognostically favorable ependymoma groups predominantly harbored differentiated cell populations, aggressive groups were enriched for undifferentiated subpopulations. Projection of transcriptomic signatures onto an independent bulk RNA-seq cohort stratified patient survival even within known molecular groups, thus refining the prognostic power of DNA methylation-based profiling. Furthermore, we identified novel potentially druggable targets including IGF- and FGF-signaling within poorly prognostic transcriptional programs. Ependymoma-derived cell models/PDXs widely recapitulated the transcriptional programs identified within fresh tumors and are leveraged to validate identified target genes in functional follow-up analyses. Taken together, our analyses reveal a developmental hierarchy and transcriptomic context underlying the biologically and clinically distinct behavior of ependymoma groups. The newly characterized cellular states and underlying regulatory networks could serve as basis for future therapeutic target identification and reveal biomarkers for clinical trials.

scholarly journals Recapitulating Cardiac Structure and Function In Vitro from Simple to Complex Engineering

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 386
Author(s):  
Ana Santos ◽  
Yongjun Jang ◽  
Inwoo Son ◽  
Jongseong Kim ◽  
Yongdoo Park
Keyword(s):  
Tissue Engineering ◽  
Extracellular Matrix ◽  
Computational Modeling ◽  
Cardiac Tissue ◽  
Cardiac Development ◽  
Heart Tissue ◽  
Cardiac Tissue Engineering ◽  
Cardiac Cells

Cardiac tissue engineering aims to generate in vivo-like functional tissue for the study of cardiac development, homeostasis, and regeneration. Since the heart is composed of various types of cells and extracellular matrix with a specific microenvironment, the fabrication of cardiac tissue in vitro requires integrating technologies of cardiac cells, biomaterials, fabrication, and computational modeling to model the complexity of heart tissue. Here, we review the recent progress of engineering techniques from simple to complex for fabricating matured cardiac tissue in vitro. Advancements in cardiomyocytes, extracellular matrix, geometry, and computational modeling will be discussed based on a technology perspective and their use for preparation of functional cardiac tissue. Since the heart is a very complex system at multiscale levels, an understanding of each technique and their interactions would be highly beneficial to the development of a fully functional heart in cardiac tissue engineering.

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