WormSNAP: A software for fast, accurate, and unbiased detection of fluorescent puncta in C. elegans

Araven Tiroumalechetty, Elisa B Frankel and Peri T Kurshan

PLOS Computational Biology, 2025, vol. 21, issue 10, 1-19

Abstract: The detection and characterization of fluorescent puncta are critical tasks in image analysis pipelines for fluorescence imaging. Existing methods for quantitative characterization of such puncta often suffer from biases and limitations, compromising the reliability and reproducibility of results. Moreover, the widespread adoption of many available analysis scripts is often hampered by over-optimization for specific samples, requiring extensive coding knowledge to repurpose for other datasets. We present WormSNAP (Worm SyNapse Analysis Program), a license-free, stand-alone, no-code approach to automated unbiased detection and characterization of 2D fluorescent puncta, originally developed to characterize images of the synapses residing in C. elegans nerve cords but suitable for broader 2D fluorescence image analysis. WormSNAP incorporates a local means thresholding algorithm and a user-friendly Graphical User Interface (GUI) for efficient and accurate analysis of large datasets, with user control of thresholding and restriction parameters and visualization options for further refinement. WormSNAP also calculates three types of correlation metrics for 2-channel images, enabling users to select the ideal metric for their dataset. WormSNAP provides robust and accurate fluorescent puncta detection in a variety of conditions, accelerating the image analysis workflow from data acquisition to figure generation.Author summary: Here we describe software designed to increase the ease, speed and reliability of analyzing images of fluorescently tagged proteins in C. elegans, a microscopic worm proven to be a powerful model system for uncovering principles of biology. Many proteins accumulate sub-cellularly into clusters that can be seen as puncta within fluorescent images. Changes in the localization or attributes of these puncta can indicate cellular dysfunction. Our lab studies synapses, or the connections between neurons, often by visualizing tagged synaptic proteins. Existing image analysis tools were difficult to use, often requiring coding skills, with limited flexibility in parameters tailored to specific datasets, or were available only through costly licenses. Others offered only narrow analyses often limited to single-channel images. To address these issues, we developed WormSNAP, a free, stand-alone tool for automated and unbiased puncta detection. With its intuitive interface, WormSNAP enables efficient analysis of large datasets, offering adjustable detection parameters, dataset-wide visualization, multi-channel puncta detection and overlays of detected puncta without requiring any coding knowledge. WormSNAP can be broadly applied to a wide variety of one and two-dimensional fluorescent images and we anticipate it will be a valuable resource across many areas of biology.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013643

DOI: 10.1371/journal.pcbi.1013643

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