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Novel Optics-Based Approaches for Cardiac Electrophysiology: A Review.

ABSTRACT: Optical techniques for recording and manipulating cellular electrophysiology have advanced rapidly in just a few decades. These developments allow for the analysis of cardiac cellular dynamics at multiple scales while largely overcoming the drawbacks associated with the use of electrodes. The recent advent of optogenetics opens up new possibilities for regional and tissue-level electrophysiological control and hold promise for future novel clinical applications. This article, which emerged from the international NOTICE workshop in 2018, reviews the state-of-the-art optical techniques used for cardiac electrophysiological research and the underlying biophysics. The design and performance of optical reporters and optogenetic actuators are reviewed along with limitations of current probes. The physics of light interaction with cardiac tissue is detailed and associated challenges with the use of optical sensors and actuators are presented. Case studies include the use of fluorescence recovery after photobleaching and super-resolution microscopy to explore the micro-structure of cardiac cells and a review of two photon and light sheet technologies applied to cardiac tissue. The emergence of cardiac optogenetics is reviewed and the current work exploring the potential clinical use of optogenetics is also described. Approaches which combine optogenetic manipulation and optical voltage measurement are discussed, in terms of platforms that allow real-time manipulation of whole heart electrophysiology in open and closed-loop systems to study optimal ways to terminate spiral arrhythmias. The design and operation of optics-based approaches that allow high-throughput cardiac electrophysiological assays is presented. Finally, emerging techniques of photo-acoustic imaging and stress sensors are described along with strategies for future development and establishment of these techniques in mainstream electrophysiological research.

SUBMITTER: Mullenbroich MC

PROVIDER: S-EPMC8637189 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AimsMacrophages (MΦ), known for immunological roles, such as phagocytosis and antigen presentation, have been found to electrotonically couple to cardiomyocytes (CM) of the atrioventricular node via Cx43, affecting cardiac conduction in isolated mouse hearts. Here, we characterize passive and active electrophysiological properties of murine cardiac resident MΦ, and model their potential electrophysiological relevance for CM.Methods and resultsWe combined classic electrophysiological approaches with 3D florescence imaging, RNA-sequencing, pharmacological interventions, and computer simulations. We used Cx3cr1eYFP/+ mice wherein cardiac MΦ are fluorescently labelled. FACS-purified fluorescent MΦ from mouse hearts were studied by whole-cell patch-clamp. MΦ electrophysiological properties include: membrane resistance 2.2±0.1 GΩ (all data mean±SEM), capacitance 18.3±0.1 pF, resting membrane potential -39.6±0.3 mV, and several voltage-activated, outward or inwardly rectifying potassium currents. Using ion channel blockers (barium, TEA, 4-AP, margatoxin, XEN-D0103, and DIDS), flow cytometry, immuno-staining, and RNA-sequencing, we identified Kv1.3, Kv1.5, and Kir2.1 as channels contributing to observed ion currents. MΦ displayed four patterns for outward and two for inward-rectifier potassium currents. Additionally, MΦ showed surface expression of Cx43, a prerequisite for homo- and/or heterotypic electrotonic coupling. Experimental results fed into development of an original computational model to describe cardiac MΦ electrophysiology. Computer simulations to quantitatively assess plausible effects of MΦ on electrotonically coupled CM showed that MΦ can depolarize resting CM, shorten early and prolong late action potential duration, with effects depending on coupling strength and individual MΦ electrophysiological properties, in particular resting membrane potential and presence/absence of Kir2.1.ConclusionOur results provide a first electrophysiological characterization of cardiac resident MΦ, and a computational model to quantitatively explore their relevance in the heterocellular heart. Future work will be focussed at distinguishing electrophysiological effects of MΦ-CM coupling on both cell types during steady-state and in patho-physiological remodelling, when immune cells change their phenotype, proliferate, and/or invade from external sources.

Project description:Human cardiac function is characterized by a complex interplay of mechanical deformation and electrophysiological conduction. Similar to the underlying cardiac anatomy, these interconnected physiological patterns vary considerably across the human population with important implications for the effectiveness of clinical decision-making and the accuracy of computerized heart models. While many previous works have investigated this variability separately for either cardiac anatomy or physiology, this work aims to combine both aspects in a single data-driven approach and capture their intricate interdependencies in a multi-domain setting. To this end, we propose a novel multi-domain Variational Autoencoder (VAE) network to capture combined Electrocardiogram (ECG) and Magnetic Resonance Imaging (MRI)-based 3D anatomy information in a single model. Each VAE branch is specifically designed to address the particular challenges of the respective input domain, enabling efficient encoding, reconstruction, and synthesis of multi-domain cardiac signals. Our method achieves high reconstruction accuracy on a United Kingdom Biobank dataset, with Chamfer Distances between reconstructed and input anatomies below the underlying image resolution and ECG reconstructions outperforming multiple single-domain benchmarks by a considerable margin. The proposed VAE is capable of generating realistic virtual populations of arbitrary size with good alignment in clinical metrics between the synthesized and gold standard anatomies and Maximum Mean Discrepancy (MMD) scores of generated ECGs below those of comparable single-domain approaches. Furthermore, we observe the latent space of our VAE to be highly interpretable with separate components encoding different aspects of anatomical and ECG variability. Finally, we demonstrate that the combined anatomy and ECG representation improves the performance in a cardiac disease classification task by 3.9% in terms of Area Under the Receiver Operating Characteristic (AUROC) curve over the best corresponding single-domain modeling approach.

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Novel Optics-Based Approaches for Cardiac Electrophysiology: A Review.

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