Novel use of an irrigated ablation catheter to monitor real-time hemodynamics during ablation.

INTRODUCTION: Hemodynamic decompensation during catheter ablation occurs due to prolonged procedure time and irrigant delivery directly into the cardiac chambers. Real-time hemodynamic monitoring of patients undergoing catheter ablation procedures may identify patients at risk of decompensation; we set out to assess the feasibility of a novel, real-time, intracardiac pressure monitoring system using a standard irrigated ablation catheter. METHODS: We studied 13 consecutive who underwent pressure measurement of the left atrium (LA) and left ventricle (LV) via transeptal access with a Swan Ganz (SG) catheter followed by two commercially available irrigated ablation catheters. Pressure waveform data was extracted to compare LA peak pressure, LV peak systolic pressure, LV end-diastolic pressure, and waveform analysis. RESULTS: Comparison between the SG and ablation catheters (AblA; AblB) demonstrated that LV systolic pressure (0.61-16.8 mmHg; 1.32-18.2 mmHg), and LV end-diastolic pressure (-3.4 to 2.8 mmHg; -3.0 to 3.35 mmHg) were well correlated and had accepted repeatability. Ablation waveforms demonstrated an 89.9 ± 6.4% correlation compared to SG waveforms. CONCLUSION: Pressure measurements derived from an irrigated ablation catheter are accurate and reliable when compared to an SG catheter. Further studies are needed to determine how real-time pressure monitoring can improve outcomes during ablation procedures.

Cholecystokinin-A Signaling Regulates Automaticity of Pacemaker Cardiomyocytes.

Aims: The behavior of pacemaker cardiomyocytes (PCs) in the sinoatrial node (SAN) is modulated by neurohormonal and paracrine factors, many of which signal through G-protein coupled receptors (GPCRs). The aims of the present study are to catalog GPCRs that are differentially expressed in the mammalian SAN and to define the acute physiological consequences of activating the cholecystokinin-A signaling system in isolated PCs. Methods and Results: Using bulk and single cell RNA sequencing datasets, we identify a set of GPCRs that are differentially expressed between SAN and right atrial tissue, including several whose roles in PCs and in the SAN have not been thoroughly characterized. Focusing on one such GPCR, Cholecystokinin-A receptor (CCK A R), we demonstrate expression of Cckar mRNA specifically in mouse PCs, and further demonstrate that subsets of SAN fibroblasts and neurons within the cardiac intrinsic nervous system express cholecystokinin, the ligand for CCK A R. Using mouse models, we find that while baseline SAN function is not dramatically affected by loss of CCK A R, the firing rate of individual PCs is slowed by exposure to sulfated cholecystokinin-8 (sCCK-8), the high affinity ligand for CCK A R. The effect of sCCK-8 on firing rate is mediated by reduction in the rate of spontaneous phase 4 depolarization of PCs and is mitigated by activation of beta-adrenergic signaling. Conclusions: (1) PCs express many GPCRs whose specific roles in SAN function have not been characterized, (2) Activation of the the cholecystokinin-A signaling pathway regulates PC automaticity.

Cholecystokinin-A signaling regulates automaticity of pacemaker cardiomyocytes.

Aims: The behavior of pacemaker cardiomyocytes (PCs) in the sinoatrial node (SAN) is modulated by neurohormonal and paracrine factors, many of which signal through G-protein coupled receptors (GPCRs). The aims of the present study are to catalog GPCRs that are differentially expressed in the mammalian SAN and to define the acute physiological consequences of activating the cholecystokinin-A signaling system in isolated PCs. Methods and results: Using bulk and single cell RNA sequencing datasets, we identify a set of GPCRs that are differentially expressed between SAN and right atrial tissue, including several whose roles in PCs and in the SAN have not been thoroughly characterized. Focusing on one such GPCR, Cholecystokinin-A receptor (CCKAR), we demonstrate expression of Cckar mRNA specifically in mouse PCs, and further demonstrate that subsets of SAN fibroblasts and neurons within the cardiac intrinsic nervous system express cholecystokinin, the ligand for CCKAR. Using mouse models, we find that while baseline SAN function is not dramatically affected by loss of CCKAR, the firing rate of individual PCs is slowed by exposure to sulfated cholecystokinin-8 (sCCK-8), the high affinity ligand for CCKAR. The effect of sCCK-8 on firing rate is mediated by reduction in the rate of spontaneous phase 4 depolarization of PCs and is mitigated by activation of beta-adrenergic signaling. Conclusion: (1) PCs express many GPCRs whose specific roles in SAN function have not been characterized, (2) Activation of the cholecystokinin-A signaling pathway regulates PC automaticity.

Real-Time Optical Mapping of Contracting Cardiac Tissues With GPU-Accelerated Numerical Motion Tracking.

Optical mapping of action potentials or calcium transients in contracting cardiac tissues are challenging because of the severe sensitivity of the measurements to motion. The measurements rely on the accurate numerical tracking and analysis of fluorescence changes emitted by the tissue as it moves, and inaccurate or no tracking can produce motion artifacts and lead to imprecise measurements that can prohibit the analysis of the data. Recently, it was demonstrated that numerical motion-tracking and -stabilization can effectively inhibit motion artifacts, allowing highly detailed simultaneous measurements of electrophysiological phenomena and tissue mechanics. However, the field of electromechanical optical mapping is still young and under development. To date, the technique is only used by a few laboratories, the processing of the video data is time-consuming and performed offline post-acquisition as it is associated with a considerable demand for computing power. In addition, a systematic review of numerical motion tracking algorithms applicable to optical mapping data is lacking. To address these issues, we evaluated 5 open-source numerical motion-tracking algorithms implemented on a graphics processing unit (GPU) and compared their performance when tracking and compensating motion and measuring optical traces in voltage- or calcium-sensitive optical mapping videos of contracting cardiac tissues. Using GPU-accelerated numerical motion tracking, the processing times necessary to analyze optical mapping videos become substantially reduced. We demonstrate that it is possible to track and stabilize motion and create motion-compensated optical maps in real-time with low-resolution (128 x 128 pixels) and high resolution (800 x 800 pixels) optical mapping videos acquired at 500 and 40 fps, respectively. We evaluated the tracking accuracies and motion-stabilization capabilities of the GPU-based algorithms on synthetic optical mapping videos, determined their sensitivity to fluorescence signals and noise, and demonstrate the efficacy of the Farnebäck algorithm with recordings of contracting human cardiac cell cultures and beating hearts from 3 different species (mouse, rabbit, pig) imaged with 4 different high-speed cameras. GPU-accelerated processing provides a substantial increase in processing speed, which could open the path for more widespread use of numerical motion tracking and stabilization algorithms during routine optical mapping studies.

Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks.

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.

Genomic analysis of atypical fibroxanthoma.

Atypical fibroxanthoma (AFX), is a rare type of skin cancer affecting older individuals with sun damaged skin. Since there is limited genomic information about AFX, our study seeks to improve the understanding of AFX through whole-exome and RNA sequencing of 8 matched tumor-normal samples. AFX is a highly mutated malignancy with recurrent mutations in a number of genes, including COL11A1, ERBB4, CSMD3, and FAT1. The majority of mutations identified were UV signature (C>T in dipyrimidines). We observed deletion of chromosomal segments on chr9p and chr13q, including tumor suppressor genes such as KANK1 and CDKN2A, but no gene fusions were found. Gene expression profiling revealed several biological pathways that are upregulated in AFX, including tumor associated macrophage response, GPCR signaling, and epithelial to mesenchymal transition (EMT). To further investigate the presence of EMT in AFX, we conducted a gene expression meta-analysis that incorporated RNA-seq data from dermal fibroblasts and keratinocytes. Ours is the first study to employ high throughput sequencing for molecular profiling of AFX. These data provide valuable insights to inform models of carcinogenesis and additional research towards tumor-directed therapy.

Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems.

Adeno-associated viruses (AAVs) are commonly used for in vivo gene transfer. Nevertheless, AAVs that provide efficient transduction across specific organs or cell populations are needed. Here, we describe AAV-PHP.eB and AAV-PHP.S, capsids that efficiently transduce the central and peripheral nervous systems, respectively. In the adult mouse, intravenous administration of 1 × 1011 vector genomes (vg) of AAV-PHP.eB transduced 69% of cortical and 55% of striatal neurons, while 1 × 1012 vg of AAV-PHP.S transduced 82% of dorsal root ganglion neurons, as well as cardiac and enteric neurons. The efficiency of these vectors facilitates robust cotransduction and stochastic, multicolor labeling for individual cell morphology studies. To support such efforts, we provide methods for labeling a tunable fraction of cells without compromising color diversity. Furthermore, when used with cell-type-specific promoters and enhancers, these AAVs enable efficient and targetable genetic modification of cells throughout the nervous system of transgenic and non-transgenic animals.