Automated high-throughput heart rate measurement in medaka and zebrafish embryos under physiological conditions

Gierten, Jakob; Pylatiuk, Christian; Hammouda, Omar T.; Schock, Christian; Stegmaier, Johannes; Wittbrodt, Joachim; Gehrig, Jochen; Loosli, Felix

doi:10.1101/548594

Cited by 5 publications

(6 citation statements)

References 55 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the heart muscle contracts, the cell membrane becomes more concentrated and obscures more of the transmitted light [21]. A similar approach also has been taken by other researchers [24][25][26]. However, the Matlab programs were too complicated for researchers who did not have a computer programing language background.…”

Section: Dynamic Pixel Changes Methodsmentioning

confidence: 97%

“…Matlab is one of the software commonly used to measure heart rate in zebrafish embryo [18,21,[24][25][26]. Detection and quantification of movement can be performed by algorithms written in Matlab.…”

Section: Dynamic Pixel Changes Methodsmentioning

confidence: 99%

“…Other research manually selected the Region of Interest (ROI) before performing an automatic method of characterizing the cardiac function in zebrafish embryos [23]. On the other hand, for the sake of time efficiency, automatic counting was then developed [18,21,[24][25][26]. The development of automated algorithms for estimating heart rate in zebrafish larvae begins with the automatic determination of a suitable ROI and heart rate calculation [25].…”

Section: Overview Of Embryo Cardiac Rhythm Detectionmentioning

confidence: 99%

See 2 more Smart Citations

An Overview of Methods for Cardiac Rhythm Detection in Zebrafish

et al. 2020

View full text Add to dashboard Cite

The heart is the most important muscular organ of the cardiovascular system, which pumps blood and circulates, supplying oxygen and nutrients to peripheral tissues. Zebrafish have been widely explored in cardiotoxicity research. For example, the zebrafish embryo has been used as a human heart model due to its body transparency, surviving several days without circulation, and facilitating mutant identification to recapitulate human diseases. On the other hand, adult zebrafish can exhibit the amazing regenerative heart muscle capacity, while adult mammalian hearts lack this potential. This review paper offers a brief description of the major methodologies used to detect zebrafish cardiac rhythm at both embryonic and adult stages. The dynamic pixel change method was mostly performed for the embryonic stage. Other techniques, such as kymography, laser confocal microscopy, artificial intelligence, and electrocardiography (ECG) have also been applied to study heartbeat in zebrafish embryos. Nevertheless, ECG is widely used for heartbeat detection in adult zebrafish since ECG waveforms’ similarity between zebrafish and humans is prominent. High-frequency ultrasound imaging (echocardiography) and modern electronic sensor tag also have been proposed. Despite the fact that each method has its benefits and limitations, it is proved that zebrafish have become a promising animal model for human cardiovascular disease, drug pharmaceutical, and toxicological research. Using those tools, we conclude that zebrafish behaviors as an excellent small animal model to perform real-time monitoring for the developmental heart process with transparent body appearance, to conduct the in vivo cardiovascular performance and gene function assays, as well as to perform high-throughput/high content drug screening.

show abstract

Section: Dynamic Pixel Changes Methodsmentioning

confidence: 97%

Section: Dynamic Pixel Changes Methodsmentioning

confidence: 99%

Section: Overview Of Embryo Cardiac Rhythm Detectionmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of Methods for Cardiac Rhythm Detection in Zebrafish

et al. 2020

View full text Add to dashboard Cite

show abstract

“…for the detection of organs like head, trunk and eyes in oriented zebrafish larvae [17,18] (Fig. 1, Additional file 7: Figure S4, Additional file 9: Figure S6 and Additional file 11: Figure S8, Additional file 1: Movie S1), or for the localization of multiple randomly oriented and positioned medaka embryos [19,20] (Fig. 2, Additional file 10: Figure S7 and Additional file 2: Movie S2).…”

Section: Resultsmentioning

confidence: 99%

Multi-template matching: a versatile tool for object-localization in microscopy images

Thomas

Gehrig²

2020

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

Background: The localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching a limit when samples are poorly contrasted with the background, or when only parts of larger structures should be detected. Furthermore, the development of such pipelines requires substantial engineering of analysis workflows and often results in casespecific solutions. Therefore, we propose a new straightforward and generic approach for object-localization by template matching that utilizes multiple template images to improve the detection capacity. Results: We provide a new implementation of template matching that offers higher detection capacity than single template approach, by enabling the detection of multiple template images. To provide an easy-to-use method for the automatic localization of objects of interest in microscopy images, we implemented multi-template matching as a Fiji plugin, a KNIME workflow and a python package. We demonstrate its application for the localization of entire, partial and multiple biological objects in zebrafish and medaka high-content screening datasets. The Fiji plugin can be installed by activating the Multi-Template-Matching and IJ-OpenCV update sites. The KNIME workflow is available on nodepit and KNIME Hub. Source codes and documentations are available on GitHub (https://github. com/multi-template-matching). Conclusion: The novel multi-template matching is a simple yet powerful object-localization algorithm, that requires no data-pre-processing or annotation. Our implementation can be used out-of-the-box by non-expert users for any type of 2D-image. It is compatible with a large variety of applications including, for instance, analysis of large-scale datasets originating from automated microscopy, detection and tracking of objects in time-lapse assays, or as a general image-analysis step in any custom processing pipelines. Using different templates corresponding to distinct object categories, the tool can also be used for classification of the detected regions.

show abstract

“…We successfully tested our pipelines for the detection of organs like head and eyes in oriented zebrafish larvae, or for the localization of multiple randomly oriented and positioned medaka embryos (Gierten et al , 2019) (Fig.1, Suppl. Fig.1, 5-7 and Suppl.…”

Section: Resultsmentioning

confidence: 99%