Drosophila Preparation and Longitudinal Imaging of Heart Function In Vivo Using Optical Coherence Microscopy (OCM)

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.

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.

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.

ELAC2/RNaseZ-linked cardiac hypertrophy in Drosophila melanogaster

A severe form of infantile cardiomyopathy (CM) has been linked to mutations in ELAC2, a highly conserved human gene. It encodes Zinc phosphodiesterase ELAC protein 2 (ELAC2), which plays an essential role in the production of mature tRNAs. To establish a causal connection between ELAC2 variants and CM, here we used the Drosophila melanogaster model organism, which carries the ELAC2 homolog RNaseZ. Even though RNaseZ and ELAC2 have diverged in some of their biological functions, our study demonstrates the use of the fly model to study the mechanism of ELAC2-related pathology. We established transgenic lines harboring RNaseZ with CM-linked mutations in the background of endogenous RNaseZ knockout. Importantly, we found that the phenotype of these flies is consistent with the pathological features in human patients. Specifically, expression of CM-linked variants in flies caused heart hypertrophy and led to reduction in cardiac contractility associated with a rare form of CM. This study provides first experimental evidence for the pathogenicity of CM-causing mutations in the ELAC2 protein, and the foundation to improve our understanding and diagnosis of this rare infantile disease.

This article has an associated First Person interview with the first author of the paper.

Summary: A newly established Drosophila model recapitulates key features of human heart pathology linked to mutations in ELAC2, thus providing experimental evidence of the pathogenicity of ELAC2 variants.

Open access tool and microfluidic devices for phenotypic quantification of heart function of intact fruit fly and zebrafish larvae

In this paper, the heartbeat parameters of small model organisms, i.e. Drosophila melanogaster (fruit fly) and Danio rerio (zebrafish), were quantified in-vivo in intact larvae using microfluidics and a novel MATLAB-based software. Among different developmental stages of flies and zebrafish, the larval stage is privileged due to biological maturity, optical accessibility, and the myogenic nature of the heart. Conventional methods for parametric quantification of heart activities are complex and mostly done on dissected, irreversibly immobilized, or anesthetized larvae. Microfluidics has helped with reversible immobilization without the need for anesthesia, but heart monitoring is still done manually due to challenges associated with the movement of floating organs and cardiac interruptions. In our MATLAB software applied to videos recorded in microfluidic-based whole-organism assays, we have used image segmentation to automatically detect the heart and extract the heartbeat signal based on pixel intensity variations of the most contractile region of the heart tube. The smoothness priors approach (SPA) was applied to remove the undesired low-frequency noises caused by environmental light changes or heart movement. Heart rate and arrhythmicity were automatically measured from the detrended heartbeat signal while other parameters including end-diastolic and end-systolic diameters, shortening distance, shortening time, fractional shortening, and shortening velocity were quantified for the first time in intact larvae, using M-mode images under bright field microscopy. The software was able to detect more than 94% of the heartbeats and the cardiac arrests in intact Drosophila larvae. Our user-friendly software enables in-vivo quantification of D. melanogaster and D. rerio larval heart functions in microfluidic devices, with the potential to be applied to other biological models and used for automatic screening of drugs and alleles that affect their heart.

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

Drosophila is a powerful genetic model system for cardiovascular studies. Recently, optogenetic pacing tools have been developed to control Drosophila heart rhythm noninvasively with blue light, which has a limited penetration depth. Here we developed both a red-light sensitive opsin expressing Drosophila system and an integrated red-light stimulation and optical coherence microscopy (OCM) imaging system. We demonstrated noninvasive control of Drosophila cardiac rhythms using a single light source, including simulated tachycardia in ReaChR-expressing flies and bradycardia and cardiac arrest in halorhodopsin (NpHR)-expressing flies at multiple developmental stages. By using red excitation light, we were able to pace flies at higher efficiency and with lower power than with equivalent blue light excitation systems. The recovery dynamics after red-light stimulation of NpHR flies were observed and quantified. The combination of red-light stimulation, OCM imaging, and transgenic Drosophila systems provides a promising and easily manipulated research platform for noninvasive cardiac optogenetic studies.

Men et al. develop an optogenetic pacing tool to control Drosophila heart rhythm noninvasively with red light. Using optical coherence microscopy imaging, they demonstrate effective light-induced tachypacing, bradypacing, and restorable cardiac arrest in transgenic fly models. This study provides a user-friendly research platform for noninvasive cardiac optogenetic studies.

Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images. A speckle-modulating OCT (SM-OCT) was built to generate low speckle images to be used as the ground truth. In total, 210 000 SM-OCT images were used for training and validating the neural network model, which we call SM-GAN. The performance of the SM-GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM-GAN model was compared to traditional OCT denoising methods and other state-of-the-art deep learning based denoise networks. We conclude that the SM-GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions.

FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network

A custom convolutional neural network (CNN) integrated with convolutional long short-term memory (LSTM) achieves accurate 3D (2D + time) segmentation in cross-sectional videos of the Drosophila heart acquired by an optical coherence microscopy (OCM) system. While our previous FlyNet 1.0 model utilized regular CNNs to extract 2D spatial information from individual video frames, convolutional LSTM, FlyNet 2.0, utilizes both spatial and temporal information to improve segmentation performance further. To train and test FlyNet 2.0, we used 100 datasets including 500,000 fly heart OCM images. OCM videos in three developmental stages and two heartbeat situations were segmented achieving an intersection over union (IOU) accuracy of 92%. This increased segmentation accuracy allows morphological and dynamic cardiac parameters to be better quantified.

Optical coherence tomography imaging of cardiac substrates

Cardiovascular disease is the leading cause of morbidity and mortality in the United States. Knowledge of a patient's heart structure will help to plan procedures, potentially identifying arrhythmia substrates, critical structures to avoid, detect transplant rejection, and reduce ambiguity when interpreting electrograms and functional measurements. Similarly, basic research of numerous cardiac diseases would greatly benefit from structural imaging at cellular scale. For both applications imaging on the scale of a myocyte is needed, which is approximately 100 µm × 10 µm. The use of optical coherence tomography (OCT) as a tool for characterizing cardiac tissue structure and function has been growing in the past two decades. We briefly review OCT principles and highlight important considerations when imaging cardiac muscle. In particular, image penetration, tissue birefringence, and light absorption by blood during in vivo imaging are important factors when imaging the heart with OCT. Within the article, we highlight applications of cardiac OCT imaging including imaging heart tissue structure in small animal models, quantification of myofiber organization, monitoring of radiofrequency ablation (RFA) lesion formation, structure-function analysis enabled by functional extensions of OCT and multimodal analysis and characterizing important substrates within the human heart. The review concludes with a summary and future outlook of OCT imaging the heart, which is promising with progress in optical catheter development, functional extensions of OCT, and real time image processing to enable dynamic imaging and real time tracking during therapeutic procedures.

Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks

Convolutional neural networks (CNNs) are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained CNN model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This study demonstrates an efficient heart segmentation method to analyze OCM images of the beating heart in Drosophila.

Conservation of cardiac L-type Ca2+ channels and their regulation in Drosophila: A novel genetically-pliable channelopathic model

Dysregulation of L-type Ca2+ channels (LTCCs) underlies numerous cardiac pathologies. Understanding their modulation with high fidelity relies on investigating LTCCs in their native environment with intact interacting proteins. Such studies benefit from genetic manipulation of endogenous channels in cardiomyocytes, which often proves cumbersome in mammalian models. Drosophila melanogaster, however, offers a potentially efficient alternative as it possesses a relatively simple heart, is genetically pliable, and expresses well-conserved genes. Fluorescence in situ hybridization confirmed an abundance of Ca-α1D and Ca-α1T mRNA in fly myocardium, which encode subunits that specify hetero-oligomeric channels homologous to mammalian LTCCs and T-type Ca2+ channels, respectively. Cardiac-specific knockdown of Ca-α1D via interfering RNA abolished cardiac contraction, suggesting Ca-α1D (i.e. A1D) represents the primary functioning Ca2+ channel in Drosophila hearts. Moreover, we successfully isolated viable single cardiomyocytes and recorded Ca2+ currents via patch clamping, a feat never before accomplished with the fly model. The profile of Ca2+ currents recorded in individual cells when Ca2+ channels were hypomorphic, absent, or under selective LTCC blockage by nifedipine, additionally confirmed the predominance of A1D current across all activation voltages. T-type current, activated at more negative voltages, was also detected. Lastly, A1D channels displayed Ca2+-dependent inactivation, a critical negative feedback mechanism of LTCCs, and the current through them was augmented by forskolin, an activator of the protein kinase A pathway. In sum, the Drosophila heart possesses a conserved compendium of Ca2+ channels, suggesting that the fly may serve as a robust and effective platform for studying cardiac channelopathies.

Drosophila as a model to study obesity and metabolic disease

Excess adipose fat accumulation, or obesity, is a growing problem worldwide in terms of both the rate of incidence and the severity of obesity-associated metabolic disease. Adipose tissue evolved in animals as a specialized dynamic lipid storage depot: adipose cells synthesize fat (a process called lipogenesis) when energy is plentiful and mobilize stored fat (a process called lipolysis) when energy is needed. When a disruption of lipid homeostasis favors increased fat synthesis and storage with little turnover owing to genetic predisposition, overnutrition or sedentary living, complications such as diabetes and cardiovascular disease are more likely to arise. The vinegar fly Drosophila melanogaster (Diptera: Drosophilidae) is used as a model to better understand the mechanisms governing fat metabolism and distribution. Flies offer a wealth of paradigms with which to study the regulation and physiological effects of fat accumulation. Obese flies accumulate triacylglycerols in the fat body, an organ similar to mammalian adipose tissue, which specializes in lipid storage and catabolism. Discoveries in Drosophila have ranged from endocrine hormones that control obesity to subcellular mechanisms that regulate lipogenesis and lipolysis, many of which are evolutionarily conserved. Furthermore, obese flies exhibit pathophysiological complications, including hyperglycemia, reduced longevity and cardiovascular function - similar to those observed in obese humans. Here, we review some of the salient features of the fly that enable researchers to study the contributions of feeding, absorption, distribution and the metabolism of lipids to systemic physiology.