Background

In vertebrates, the muscles of the face and neck are innervated by cranial motor neurons [1]. Communication between the brain and muscle is mediated through a specialized synapse known as the neuromuscular junction (NMJ). NMJs typically comprise three components: 1) the axonal end of the motor neuron, forming the presynaptic terminal; 2) the muscle endplate, forming the postsynaptic terminal; and 3) a synaptic cleft that lies between these two terminals. Neuromuscular communication occurs when acetylcholine, released from the presynaptic terminal into the synaptic cleft, binds acetylcholine receptors clustered in the postsynaptic terminal [2]. Alteration in NMJ morphology or distribution can lead to reduced synaptic transmission and result in neuromuscular disease. Zebrafish has emerged as an excellent model to study neuromuscular pathologies [3]. However, characterization and quantification of NMJs in the cranial muscles of zebrafish can be particularly challenging due to the anatomical complexity of the head.

Previous studies involving the quantification of NMJs in the cranial muscles were primarily carried out manually. Manual counting is arduous and can lead to individual bias and subjectivity. Some recent studies have carried out automated quantification using image analysis tools. However, most of these studies were carried out in somites of the zebrafish trunk region [4,5]. Unlike the head, the muscles of the trunk have a simpler, reiterated anatomy. Another study employed Imaris software for surface rendering with automatic thresholding to characterize the NMJs in the cranial musculature [6]. This method is useful for studying the number, shape, and architecture of NMJs. However, this also requires buying licensed software, making it inaccessible to some labs. Also, most of these studies use auto-thresholding, which can change the cutoff value for thresholding in individual images, unintentionally introducing errors [7].

Here, we developed a protocol for NMJ visualization and quantification in the cranial muscles using FIJI/ImageJ software, which is free of cost [8]. We immunolabel the presynaptic and postsynaptic terminals using SV2 antibody and alpha-bungarotoxin, respectively. We then capture high-resolution images of NMJs using confocal microscopy. Using the Area Selection tool, we crop out a region of specific dimensions containing the muscles of interest. We then use the Rolling Ball Background Subtraction plugin, Global Thresholding, and Watershed plugin to segment the images. Instead of auto-thresholding, we set the same cutoff value for thresholding each image, ensuring uniformity across all images. The number of particles in the segmented images is then counted using the Analyze Particles tool. We have used this protocol to investigate the effect of alcohol on NMJ count in a specific set of cranial muscles. However, the method can be applied to other cranial and trunk muscles of zebrafish. The method can also be used to quantify NMJs in other organisms like chickens and mice. Additionally, we used this method with 20× magnification for NMJ counting, but the method could readily be adapted for analyzing images of other magnifications. This method is broadly applicable to quantify other fluorescently labeled particles such as apoptotic cells, proliferating cells, or cells in general by counting DAPI-stained nuclei.