Non-invasive red-light optogenetic control of Drosophila cardiac function

Dual color optogenetic tool enables heart arrest, bradycardic, and tachycardic pacing in Drosophila melanogaster

In order to facilitate cardiovascular research to develop non-invasive optical heart pacing methods, we have generated a double-transgenic Drosophila melanogaster (fruit fly) model suitable for optogenetic pacing. We created a fly stock with both excitatory H134R-ChR2 and inhibitory eNpHR2.0 opsin transgenes. Opsins were expressed in the fly heart using the Hand-GAL4 driver. Here we describe Hand > H134R-ChR2; eNpHR2.0 model characterization including bi-directional heart control (activation and inhibition) upon illumination of light with distinct wavelengths. Optical control and real-time visualization of the heart function were achieved non-invasively using an integrated light stimulation and optical coherence microscopy (OCM) system. OCM produced high-speed and high-resolution imaging; simultaneously, the heart function was modulated by blue (470 nm) or red (617 nm) light pulses causing tachycardia, bradycardia and restorable cardiac arrest episodes in the same animal. The irradiance power levels and illumination schedules were optimized to achieve successful non-invasive bi-directional heart pacing in Drosophila larvae and pupae.

Optogenetic modulation of cardiac autonomic nervous system

The following is a narrative review of the fundamentals of optogenetics. It focuses on the advantages and constraints of manipulating the autonomic nervous system by modifying the pathophysiological characteristics that arise in different diseases. Although the use of this technique is currently experimental, we will discuss improvements that have been implemented and identify the necessary measures for potential preclinical translation in the control of the cardiac autonomic nervous system.

Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images

Optical coherence microscopy (OCM) imaging of the Drosophila melanogaster (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology in vivo. OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation. However, image artifacts caused by sample motion or reflections reduce the accuracy of the analysis. To improve the precision and efficiency of image data analysis, we developed an Attention LSTM U-Net model (FlyNet3.0), which incorporates an attention learning mechanism to track the beating fly heart in OCM images. The new model has improved the intersection over union (IOU) compared to FlyNet2.0 + with reflection artifacts from 86% to 89% and with movement from 81% to 89%. We also extended the capabilities of OCM analysis through the introduction of an automated, in vivo heart wall thickness measurement method, which has been validated on a Drosophila model of cardiac hypertrophy. This work will enable the comprehensive, non-invasive characterization of fly heart physiology in a high-throughput manner.

High-precision Drosophila heart segmentation and dynamic cardiac parameter measurement for optogenetics-OCT-based cardiac function research

Drosophila model has been widely used to study cardiac functions, especially combined with optogenetics and optical coherence tomography (OCT) that can continuously acquire mass cross-sectional images of the Drosophila heart in vivo over time. It's urgent to quickly and accurately obtain dynamic Drosophila cardiac parameters such as heartbeat rate for cardiac function quantitative analysis through these mass cross-sectional images of the Drosophila heart. Here we present a deep-learning method that integrates U-Net and generative adversarial network architectures while incorporating residually connected convolutions for high-precision OCT image segmentation of Drosophila heart and dynamic cardiac parameter measurements for optogenetics-OCT-based cardiac function research. We compared our proposed network with the previous approaches and our segmentation results achieved the accuracy of intersection over union and Dice similarity coefficient higher than 98%, which can be used to better quantify dynamic heart parameters and improve the efficiency of Drosophila-model-based cardiac research via the optogenetics-OCT-based platform.

A Drosophila heart optical coherence microscopy dataset for automatic video segmentation

The heart of the fruit fly, Drosophila melanogaster, is a particularly suitable model for cardiac studies. Optical coherence microscopy (OCM) captures in vivo cross-sectional videos of the beating Drosophila heart for cardiac function quantification. To analyze those large-size multi-frame OCM recordings, human labelling has been employed, leading to low efficiency and poor reproducibility. Here, we introduce a robust and accurate automated Drosophila heart segmentation algorithm, called FlyNet 2.0+, which utilizes a long short-term memory (LSTM) convolutional neural network to leverage time series information in the videos, ensuring consistent, high-quality segmentation. We present a dataset of 213 Drosophila heart videos, equivalent to 604,000 cross-sectional images, containing all developmental stages and a wide range of beating patterns, including faster and slower than normal beating, arrhythmic beating, and periods of heart stop to capture these heart dynamics. Each video contains a corresponding ground truth mask. We expect this unique large dataset of the beating Drosophila heart in vivo will enable new deep learning approaches to efficiently characterize heart function to advance cardiac research.

Emerging optogenetics technologies in biomedical applications

Optogenetics is a cutting-edge technology that merges light control and genetics to achieve targeted control of tissue cells. Compared to traditional methods, optogenetics offers several advantages in terms of time and space precision, accuracy, and reduced damage to the research object. Currently, optogenetics is primarily used in pathway research, drug screening, gene expression regulation, and the stimulation of molecule release to treat various diseases. The selection of light-sensitive proteins is the most crucial aspect of optogenetic technology; structural changes occur or downstream channels are activated to achieve signal transmission or factor release, allowing efficient and controllable disease treatment. In this review, we examine the extensive research conducted in the field of biomedicine concerning optogenetics, including the selection of light-sensitive proteins, the study of carriers and delivery devices, and the application of disease treatment. Additionally, we offer critical insights and future implications of optogenetics in the realm of clinical medicine.

Following the Beat: Imaging the Valveless Pumping Function in the Early Embryonic Heart

In vertebrates, the coordinated beat of the early heart tube drives cardiogenesis and supports embryonic growth. How the heart pumps at this valveless stage marks a fascinating problem that is of vital significance for understanding cardiac development and defects. The developing heart achieves its function at the same time as continuous and dramatic morphological changes, which in turn modify its pumping dynamics. The beauty of this muti-time-scale process also highlights its complexity that requires interdisciplinary approaches to study. High-resolution optical imaging, particularly fast, four-dimensional (4D) imaging, plays a critical role in revealing the process of pumping, instructing numerical modeling, and enabling biomechanical analyses. In this review, we aim to connect the investigation of valveless pumping mechanisms with the recent advancements in embryonic cardiodynamic imaging, facilitating interactions between these two areas of study, in hopes of encouraging and motivating innovative work to further understand the early heartbeat.

IR-Based Novel Device for Real-Time Online Acquisition of Fish Heart ECG Signals

We developed an infrared (IR)-based real-time online monitoring device (US Patent No: US 10,571,448 B2) to quantify heart electrocardiogram (ECG) signals to assess the water quality based on physiological changes in fish. The device is compact, allowing us to monitor cardiac function for an extended period (from 7 to 30 days depending on the rechargeable battery capacity) without function injury and disturbance of swimming activity. The electrode samples and the biopotential amplifier and microcontroller process the cardiac-electrical signals. An infrared transceiver transmits denoised electrocardiac signals to complete the signal transmission. The infrared receiver array and biomedical acquisition signal processing system send signals to the computer. The software in the computer processes the data in real time. We quantified ECG indexes (P-wave, Q-wave, R-wave, S-wave, T-wave, PR-interval, QRS-complex, and QT-interval) of carp precisely and incessantly under the different experimental setup (CuSO₄ and deltamethrin). The ECG cue responses were chemical-specific based on CuSO₄ and deltamethrin exposures. This study provides an additional technology for noninvasive water quality surveillance.

The Effects of Repetitive Use and Pathological Remodeling on Channelrhodopsin Function in Cardiomyocytes

Aim: Channelrhodopsins (ChRs) are a large family of light-gated ion channels with distinct properties, which is of great importance in the selection of a ChR variant for a given application. However, data to guide such selection for cardiac optogenetic applications are lacking. Therefore, we investigated the functioning of different ChR variants in normal and pathological hypertrophic cardiomyocytes subjected to various illumination protocols.

Methods and Results: Isolated neonatal rat ventricular cardiomyocytes (NRVMs) were transduced with lentiviral vectors to express one of the following ChR variants: H134R, CatCh, ReaChR, or GtACR1. NRVMs were treated with phenylephrine (PE) to induce pathological hypertrophy (PE group) or left untreated [control (CTL) group]. In these groups, ChR currents displayed unique and significantly different properties for each ChR variant on activation by a single 1-s light pulse (1 mW/mm²: 470, 565, or 617 nm). The concomitant membrane potential (V_m) responses also showed a ChR variant-specific profile, with GtACR1 causing a slight increase in average V_m during illumination (V_plateau: −38 mV) as compared with a V_plateau > −20 mV for the other ChR variants. On repetitive activation at increasing frequencies (10-ms pulses at 1–10 Hz for 30 s), peak currents, which are important for cardiac pacing, decreased with increasing activation frequencies by 17–78% (p < 0.05), while plateau currents, which are critical for arrhythmia termination, decreased by 10–75% (p < 0.05), both in a variant-specific manner. In contrast, the corresponding V_plateau remained largely stable. Importantly, current properties and V_m responses were not statistically different between the PE and CTL groups, irrespective of the variant used (p > 0.05).

Conclusion: Our data show that ChR variants function equally well in cell culture models of healthy and pathologically hypertrophic myocardium but show strong, variant-specific use-dependence. This use-dependent nature of ChR function should be taken into account during the design of cardiac optogenetic studies and the interpretation of the experimental findings thereof.

Controlling the behaviour of Drosophila melanogaster via smartphone optogenetics

Invertebrates such as Drosophila melanogaster have proven to be a valuable model organism for studies of the nervous system. In order to control neuronal activity, optogenetics has evolved as a powerful technique enabling non-invasive stimulation using light. This requires light sources that can deliver patterns of light with high temporal and spatial precision. Currently employed light sources for stimulation of small invertebrates, however, are either limited in spatial resolution or require sophisticated and bulky equipment. In this work, we used smartphone displays for optogenetic control of Drosophila melanogaster. We developed an open-source smartphone app that allows time-dependent display of light patterns and used this to activate and inhibit different neuronal populations in both larvae and adult flies. Characteristic behavioural responses were observed depending on the displayed colour and brightness and in agreement with the activation spectra and light sensitivity of the used channelrhodopsins. By displaying patterns of light, we constrained larval movement and were able to guide larvae on the display. Our method serves as a low-cost high-resolution testbench for optogenetic experiments using small invertebrate species and is particularly appealing to application in neuroscience teaching labs.