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Method Article
The study aims to develop technology for anesthesia-free heartbeat measurements in moving zebrafish. Our approach combines shortwave-infrared imaging and machine-learning-based tracking of the heart. It is a non-invasive, label-free, and user-friendly technique that suits a wide range of studies on the zebrafish model.
Zebrafish (Danio rerio) is a widely used model organism in physiological, pharmacological, and toxicological research due to its genetic similarity to humans and transparent embryonic stage, which facilitates non-invasive cardiovascular studies. However, current methods for heart rate assessment in zebrafish often rely on anesthesia to immobilize the subject, introducing physiological alterations that compromise data accuracy and reproducibility. This study presents a novel, anesthesia-free technique for measuring heartbeat in freely moving zebrafish larvae, addressing a critical limitation in cardiovascular research. The proposed approach integrates shortwave-infrared imaging with machine-learning-based heart tracking, allowing for precise and continuous cardiac activity monitoring in non-immobilized specimens. A convolutional neural network was trained to detect the heart region, and a photoplethysmographic signal was extracted from image sequences to determine heart rate. Experimental validation demonstrated the method's reliability and consistency across multiple test conditions. A key benefit of the methodology is its ability to preserve the natural physiological state of zebrafish, minimizing stress-induced artifacts. This non-invasive, label-free technique offers significant advantages for studying cardiovascular physiology, drug cardiotoxicity, and environmental toxicology, expanding the potential applications of zebrafish as a model for biomedical research.
Zebrafish (Danio rerio), a small cyprinid fish, has become an essential model organism due to its small size, high reproductive rate, and ease of genetic manipulation1,2,3. The assessment of heart rate in transparent zebrafish embryos is increasingly utilized in physiology, embryology, toxicology, and other fields4,5,6,7,8. On the one hand, this utility is due to the fact that the zebrafish genome includes genes associated with human cardiovascular diseases9, and the Danio rerio heart shares similar structures and signaling pathways with humans10,11. It makes zebrafish an invaluable model for studying heart development and diseases11,12,13. On the other hand, the zebrafish heart rate is sensitive to external influences, making it an excellent model for physiological and toxicological studies by comparing cardiac function in treated and untreated fish7,8,14.
Significant progress has been made in developing non-invasive optical methods for assessing heart rate in transparent fish embryos15,16. These techniques offer the advantage of rapid data collection from large sample sizes. Consequently, fully automated approaches for heart rate assessment in fish embryos have been developed4,5,6,17.
However, certain limitations currently restrict the use of these techniques to the 3-4 dpf period. The first limitation is a loss of transparency due to the pigmentation of the fish body. The second is the increasing movability of the embryos over time. Extending the period of zebrafish's early development during which the optical approaches can be used would enhance their utility, allowing long-term experimental designs to study cardiomyopathy, congenital heart defects, and various impacts on the cardiovascular system, including tracking the dynamics of effects over time. Our group recently addressed the issue of transparency loss by employing imaging in the shortwave infrared range of 900-1700 nm18. This paper focuses on addressing the issue of embryo mobility.
Typically, anesthetics like tricaine methanesulfonate (MS-222) are used to immobilize free-swimming fish embryos and larvae before imaging14,19,20. However, MS-222 significantly reduces heart rate21,22, as do other anesthetics23. It becomes challenging to discern whether observed changes in heart function are due to experimental treatment, the anesthetic, or an interaction between the two. Another way to extend the embryo's low mobility period is by reducing the temperature during early development8. However, this approach is not always compatible with the research goals and only minimally extends the registration period.
In this study, we introduce a novel method to address embryo mobility during heart rate registration. We trained a convolutional neural network to identify the region of interest of the heart in recordings of free-swimming zebrafish embryos. The periodic variation in pixel intensity within this area is utilized to derive the photoplethysmogram (PPG), which is subsequently used to calculate the heart rate. The developed desktop application, AutoHR, utilized both neural network training and image stack processing, ensuring ease of use and protocol reproducibility.
Zebrafish were bred and raised according to established ZFIN protocols24. All procedures were approved by the Bioethics Committee of the Scientific and Technological Centre of Unique Instrumentation of the Russian Academy of Sciences (STC UI RAS), protocol #3/24, dated 08/21/2024, and follow the zebrafish care guidelines of STC UI RAS. Manuals for individual versions are available on request.
1. Preparation of equipment for measurement
2. Image acquisition
3. Training the neural network for labeling
4. Training the neural networks for heart detection
NOTE: This step is performed once for a specific age and imaging condition. NVIDIA GPU is strongly recommended for training as it significantly accelerates the processing.
5. Heart rate quantification
6. Verification of the algorithm outcomes
The heart rate of zebrafish at 12 dpf was determined using the protocol described above (Supplementary Video 1). The videos include a sequence of images of free-swimming zebrafish larvae, a photoplethysmogram derived from these sequences using the proposed protocol, and the corresponding heart rate calculated from the photoplethysmogram.
The labeled data were randomly split into training, testing, and validation sets in a 3:2:1 ratio during training. The loss function was then...
In this study, we present an experimental protocol for measuring the heartbeat of free-swimming zebrafish larvae. We evaluated this approach through several experiments, demonstrating its effectiveness. The key components of the proposed method include both hardware and software solutions. Firstly, we used infrared illumination for imaging, which, as previously demonstrated, avoids issues related to pigmentation and enhances tissue transparency, facilitating accurate heartbeat determination18. Sec...
All authors have disclosed any conflicts of interest.
This study was supported by the Federal State Task Program of STC UI RAS (FFNS-2025-0008). This work was performed using the equipment of the Center for Collective Use of STC UI RAS [http:// https://ckp.ntcup.ru/en/].
Name | Company | Catalog Number | Comments |
Reagents | |||
Low melting agarose | Biozym | 850111 | |
Table salt | Pegasus | N/A | |
Tricaine (Ethyl 3-aminobenzoate methanesulfonate) | Sigma-Aldrich | E10505 | MS-222 |
Equipment | |||
Base with rod | Altami | SM-U1 | |
Collimator lens | JLLSCMGGX | Focal length 30 mm | |
Focusing mechanism | Altami | SM-12 | D=76 mm |
LED | Cree | TR-3535IR-3W | |
Lens | SFK Security | C-Mount, F1.6, 1/3”, | |
Near infrared camera | ToupTek | SWIR1300KMA | |
Pasteur pipette | PE-LD | 149293 | |
Petri Dish 35 x 15 mm | BD Falcon | 351008 | |
Plastic forms | N/A | N/A | Made by 3D printing |
Power supply | Unit-T | UTP3300TFL-II | |
Stage | N/A | N/A | Made by 3D printing |
Stationery knife | ErichKrause | 19145 | |
Test object | Wally Sky | MS-1-EB | |
Software | |||
EfficientDet | N/A | N/A | https://github.com/rwightman/efficientdet-pytorch |
EfficientNet-b0 model | N/A | N/A | https://arxiv.org/abs/1905.11946 |
Google API Client | N/A | Google API Client is a Python client library for Google's discovery-based APIs. https://github.com/googleapis/google-api-python-client | |
Hardware | |||
Multi-scale attention network | N/A | N/A | https://arxiv.org/abs/2209.14145 |
NVIDIA DIGITS | NVIDIA | N/A | NVIDIA DIGITS is a wrapper for Caffe that provides a graphical web interface. https://developer.nvidia.com/digits |
NVIDIA GPU | NVIDIA | N/A | An NVIDIA GPU is needed as some of the software frameworks below will not work otherwise. https://www.nvidia.com |
OpenCV | Intel | N/A | OpenCV is a library for computer vision. https://opencv.org |
Python | Python Software Foundation | N/A | Python is a programming language. https://www.python.org |
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