TransiScope: An Interactive Open-Source Platform for Automated Detection and Analysis of Transient Events in Time-Lapse Microscopy

Abstract The quantitative analysis of dynamic cellular events from time-lapse microscopy is critical for understanding biological processes but is often hindered by low signal-to-signal ratios and subjective manual parameter 1,2.To address these limitations, TransiScope was developed as an open-source tool built on Python and the napari viewer, offering a seamless workflow within a single graphical user interface2–4. Its key innovation is a data driven, interactive approach to parameter optimization, where the software analyzes user defined regions of interest (ROIs) to propose optimal settings for algorithms like the Difference of Gaussians (DoG) filter5,6, ensuring consistency by averaging signals from multiple ROIs. The platforms performance, validated using open-resource .avi files from published studies, demonstrates high specificity and a low false-positive rate, accurately quantifying events in signal-positive regions while correctly identifying zero events in background areas7–11. By replacing manual trial-and-error with a guided workflow, TransiScope enhances the objectivity, speed, and reproducibility of transient event analysis, providing an accessible solution for robust quantitative imaging12. Competing Interest Statement The authors have declared no competing interest. Footnotes This revised version of the manuscript incorporates significant updates that enhance both the technical foundation and clarity of our presented work. The revisions address two primary aspects: the upgrade of our underlying software framework and the rebranding of our analytical tool to better reflect its specialized functionality. First, we have updated our software implementation to incorporate the latest version of Python napari, a multi-dimensional image viewer that serves as the backbone of our visualization and analysis platform. The napari framework has undergone substantial improvements since our initial manuscript submission, including enhanced performance, expanded plugin architecture, and improved support for large-scale bioimage data. By upgrading to the current napari version, our tool benefits from these advancements, including more robust image handling capabilities, smoother user interactions, and better integration with the broader scientific Python ecosystem. This upgrade ensures that our software remains compatible with modern computational environments and provides users with access to the latest features and bug fixes available in the napari community. Second, and perhaps most notably, we have renamed our tool from "BioImageSuiteLite" to "TransiScope." This rebranding decision was made after careful consideration of several factors. The original name, while descriptive of the tool's lightweight nature and biological imaging focus, created potential confusion with other established software packages in the bioimage analysis field. The new name, TransiScope, more accurately captures the tool's primary purpose: analyzing transient calcium imaging data and facilitating the exploration (scope) of transient biological signals. This nomenclature better reflects the tool's specialized functionality in detecting, measuring, and characterizing calcium transients in fluorescence microscopy data. Throughout the manuscript, all references to BioImageSuiteLite have been systematically replaced with TransiScope to maintain consistency. This includes updates to the title, abstract, methods section, figures, supplementary materials, and code repositories. We have ensured that the GitHub repository, documentation, installation instructions, and all associated resources now reflect this new identity. The rebranding also extends to our package naming conventions, ensuring that users can easily locate and cite the tool without ambiguity. These revisions do not alter the fundamental methodologies, experimental results, or scientific conclusions presented in our work. Rather, they strengthen the manuscript by aligning it with current software standards and providing clearer, more distinctive identification of our contribution to the bioimage analysis community. We believe these updates enhance the accessibility and long-term usability of our tool for researchers working with calcium imaging data. List of abbreviations - AP-1 - Protein 1 - AP-3 - Adaptor Protein 3 - CSV - Comma-Separated Values - DoG - Difference of Gaussians - F₀ - Baseline fluorescence - GUI - Graphical User Interface - ROI - Region of Interest - SD - Standard Deviation - SE - Standard Error - SgII - Secretogranin - TIFF - Tagged Image File Format