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Optical control of excitation waves in cardiac tissue.

ABSTRACT: In nature, macroscopic excitation waves1,2 are found in a diverse range of settings including chemical reactions, metal rust, yeast, amoeba and the heart and brain. In the case of living biological tissue, the spatiotemporal patterns formed by these excitation waves are different in healthy and diseased states2,3. Current electrical and pharmacological methods for wave modulation lack the spatiotemporal precision needed to control these patterns. Optical methods have the potential to overcome these limitations, but to date have only been demonstrated in simple systems, such as the Belousov-Zhabotinsky chemical reaction4. Here, we combine dye-free optical imaging with optogenetic actuation to achieve dynamic control of cardiac excitation waves. Illumination with patterned light is demonstrated to optically control the direction, speed and spiral chirality of such waves in cardiac tissue. This all-optical approach offers a new experimental platform for the study and control of pattern formation in complex biological excitable systems.

SUBMITTER: Burton RA

PROVIDER: S-EPMC4821438 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Optical control of excitation waves in cardiac tissue.

Burton Rebecca A B RA Klimas Aleksandra A Ambrosi Christina M CM Tomek Jakub J Corbett Alex A Entcheva Emilia E Bub Gil G

Nature photonics 20151019 12

In nature, macroscopic excitation waves<sup>1,2</sup> are found in a diverse range of settings including chemical reactions, metal rust, yeast, amoeba and the heart and brain. In the case of living biological tissue, the spatiotemporal patterns formed by these excitation waves are different in healthy and diseased states<sup>2,3</sup>. Current electrical and pharmacological methods for wave modulation lack the spatiotemporal precision needed to control these patterns. Optical methods have the po ...[more]

PMID: 27057206

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Project description:The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.

Project description:BackgroundCardiac optical mapping is an imaging technique that measures fluorescent signals across a cardiac preparation. Dual optical imaging of voltage-sensitive and calcium-sensitive probes allows for simultaneous recordings of cardiac action potentials and intracellular calcium transients with high spatiotemporal resolution. The analysis of these complex optical datasets is both time intensive and technically challenging; as such, we have developed a software package for semi-automated image processing and analysis. Herein, we report an updated version of our software package (KairoSight-3.0) with features to enhance the characterization of cardiac parameters using optical signals.MethodsTo test software validity and applicability, we used Langendorff-perfused heart preparations to record transmembrane voltage and intracellular calcium signals from the epicardial surface. Isolated hearts from guinea pigs and rats were loaded with a potentiometric dye (RH237) and/or calcium indicator dye (Rhod-2AM) and fluorescent signals were acquired. We used Python 3.8.5 programming language to develop the KairoSight-3.0 software. Cardiac maps were validated with a user-specified manual mapping approach.ResultsManual maps of action potential duration (30 or 80 % repolarization), calcium transient duration (30 or 80 % reuptake), action potential and calcium transient alternans were constituted to validate the accuracy of software-generated maps. Manual and software maps had high accuracy, with >97 % of manual and software values falling within 10 ms of each other and >75 % within 5 ms for action potential duration and calcium transient duration measurements (n = 1000-2000 pixels). Further, our software package includes additional measurement tools to analyze signal-to-noise ratio, conduction velocity, action potential and calcium transient alternans, and action potential-calcium transient coupling time to produce physiologically meaningful optical maps.ConclusionsKairoSight-3.0 has enhanced capabilities to perform measurements of cardiac electrophysiology, calcium handling, alternans, and the excitation-contraction coupling with satisfactory accuracy.

Dataset Information

Optical control of excitation waves in cardiac tissue.

Publications

Optical control of excitation waves in cardiac tissue.

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