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Electrical synapses are neuronal gap junction (GJ) channels associated with a macromolecular complex called the electrical synapse density (ESD), which regulates development and dynamically modifies electrical transmission. However, the molecular mechanisms of ESD formation are not well understood. Using the Mauthner cell of zebrafish as a model, we previously found that the intracellular scaffolding protein ZO1b is a member of the ESD, localizing postsynaptically, where it is required for channel localization, electrical communication, neural network function, and behavior (Lasseigne et al., 2021). Here, we show that the complexity of the ESD is further diversified by the genomic structure of the ZO1b gene locus. The ZO1b gene is alternatively initiated at three transcriptional start sites resulting in isoforms with unique N-termini that we call ZO1b-Alpha, -Beta, and -Gamma. We demonstrate that ZO1b-Beta is localized to electrical synapses where it is necessary and sufficient for robust channel localization. Additionally, ZO1b-Gamma is also localized to synapses, yet plays a minor role in channel localization. By contrast, ZO1b-Alpha plays no role at the developmental stage examined. This study expands the notion of molecular complexity at the ESD, revealing that an individual genomic locus can contribute multiple independent isoforms to the macromolecular complex at the synapse with each differentially contributing to structural formation. We propose that ESD molecular complexity arises both from the diversity of unique genes and from distinct isoforms encoded by single genes, and that such proteomic diversity has critical impacts on the structure, function, and plasticity of electrical transmission.

Animal development requires coordinated communication between cells. The Connexin family of proteins is a major contributor to intercellular communication in vertebrates by forming gap junction channels that facilitate the movement of ions, small molecules, and metabolites between cells. Additionally, individual hemichannels can provide a conduit to the extracellular space for paracrine and autocrine signaling. Connexin-mediated communication is well appreciated in epithelial, neural, and vascular development and homeostasis, and most tissues likely use this form of communication. In fact, Connexin disruptions are of major clinical significance contributing to disorders developing from all major germ layers. Despite the fact that Connexins serve as an essential mode of cellular communication, the temporal and cell-type specific expression patterns of connexin genes remain unknown in vertebrates. A major challenge is the large and complex connexin gene family. To overcome this barrier, we probed the expression of all connexins in zebrafish using single-cell RNA-sequencing of entire animals across several stages of organogenesis. Our analysis of expression patterns has revealed that few connexins are broadly expressed, but rather, most are expressed in tissue- or cell-type-specific patterns. Additionally, most tissues possess a unique combinatorial signature of connexin expression with dynamic temporal changes across the organism, tissue, and cell. Our analysis has identified new patterns for well-known connexins and assigned spatial and temporal expression to genes with no-existing information. We provide a field guide relating zebrafish and human connexin genes as a critical step towards understanding how Connexins contribute to cellular communication and development throughout vertebrate organogenesis.

Myopia is the most common developmental disorder of juvenile eyes, and it has become an increasing cause of severe visual impairment. The GJD2 locus has been consistently associated with myopia in multiple independent genome-wide association studies. However, despite the strong genetic evidence, little is known about the functional role of GJD2 in refractive error development. Here, we find that depletion of gjd2a (Cx35.5) or gjd2b (Cx35.1) orthologs in zebrafish, cause changes in the biometry and refractive status of the eye. Our immunohistological and scRNA sequencing studies show that Cx35.5 (gjd2a) is a retinal connexin and its depletion leads to hyperopia and electrophysiological changes in the retina. These findings support a role for Cx35.5 (gjd2a) in the regulation of ocular biometry. Cx35.1 (gjd2b) has previously been identified in the retina, however, we found an additional lenticular role. Lack of Cx35.1 (gjd2b) led to a nuclear cataract that triggered axial elongation. Our results provide functional evidence of a link between gjd2 and refractive error., Quint et al. use zebrafish lines deficient in one of two orthologs of the Gap Junction Delta-2 (GJD2) gene, which is associated with myopia by genome-wide association studies. They link gjd2 with refractive error and report evidence to suggest that gjd2a plays a role in ocular biometry whilst gjd2b, previously found in the retina, possesses an additional lenticular role.

Solid supported colorimetric sensing arrays have the advantage of portability and ease of use when deployed in the field, such as crime scenes, disaster zones, or in war zones, but many sensor arrays require complex fabrication methods. Here, we report a practical method for the fabrication of 4 × 4 colorimetric sensor arrays, which are printed on nylon membranes, using a commercially available inkjet printer. In order to test the efficacy of the printed arrays, they were exposed to 43 analytes at concentrations ranging from 0.001 to 3.0 M for a total of 559 samples of inorganic and organic acids or bases including hydrochloric, acetic, phthalic, malonic, picric, and trifluoroacetic acid, ammonium hydroxide, sodium hydroxide, lysine, and water as the control. Colorimetric data from the imaged arrays was analyzed with linear discriminant analysis and k-nearest neighbors to determine the analyte and concentration with ∼88-90% accuracy. Overall, the arrays have impressive analytical power to identify a variety of analytes at different concentrations while being simple to fabricate.

Chemical warfare agents pose significant threats in the 21st century, especially for armed forces. A colorimetric detection array was developed to identify warfare mimics, including mustard gas and nerve agents. In total, 188 sensors were screened to determine the best sensor performance, in order to identify warfare mimics 2-chloro ethyl ethylsulfide, 2-2&prime, thiodiethanol, trifluoroacetic acid, methylphosphonic acid, dimethylphosphite, diethylcyanophosphonate, and diethyl (methylthiomethyl)phosphonate. The highest loadings in the principle component analysis (PCA) plots were used to identify the sensors that were most effective in analyzing the RGB data to classify the warfare mimics. The dataset was reduced to only twelve sensors, and PCA results gave comparable results as the large data did, demonstrating that only twelve sensors are needed to classify the warfare mimics.

There is a significant demand for devices that can rapidly detect chemical–biological–explosive (CBE) threats on-site and allow for immediate responders to mitigate spread, risk, and loss. The key to an effective reconnaissance mission is a unified detection technology that analyzes potential threats in real time. In addition to reviewing the current state of the art in the field, this review illustrates the practicality of colorimetric arrays composed of sensors that change colors in the presence of analytes. This review also describes an outlook toward future technologies, and describes how they could possibly be used in areas such as war zones to detect and identify hazardous substances.

With the increasing availability of digital imaging devices, colorimetric sensor arrays are rapidly becoming a simple, yet effective tool for the identification and quantification of various analytes. Colorimetric arrays utilize colorimetric data from many colorimetric sensors, with the multidimensional nature of the resulting data necessitating the use of chemometric analysis. Herein, an 8 sensor colorimetric array was used to analyze select acid and basic samples (0.5 - 10 M) to determine which chemometric methods are best suited for classification quantification of analytes within clusters. PCA, HCA, and LDA were used to visualize the data set. All three methods showed well-separated clusters for each of the acid or base analytes and moderate separation between analyte concentrations, indicating that the sensor array can be used to identify and quantify samples. Furthermore, PCA could be used to determine which sensors showed the most effective analyte identification. LDA, KNN, and HQI were used for identification of analyte and concentration. HQI and KNN could be used to correctly identify the analytes in all cases, while LDA correctly identified 95 of 96 analytes correctly. Additional studies demonstrated that controlling for solvent and image effects was unnecessary for all chemometric methods utilized in this study.

Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods were compared for the task of classifying 631 solutions (0.1 to 3 M) of acetic acid, malonic acid, lysine, and ammonia using an eight sensor colorimetric array. PCA and LDA (linear discriminant analysis) were effective for visualizing the dataset. For classification, linear discriminant analysis (LDA), (k nearest neighbors) KNN, (soft independent modelling by class analogy) SIMCA, recursive partitioning and regression trees (RPART), and hit quality index (HQI) were very effective with each method classifying compounds with over 90% correct assignments. Support vector machines (SVM) and partial least squares – discriminant analysis (PLS-DA) struggled with ~85 and 39% correct assignments, respectively. Additional mathematical treatments of the data set, such as incrementally increasing the exponents, did not improve the performance of LDA and KNN. The literature precedence indicates that the most common methods for analyzing colorimetric arrays are PCA, LDA, HCA, and KNN. To our knowledge, this is the first report of comparing and contrasting several more diverse chemometric methods to analyze the same colorimetric array data.