Cytosoftware-RPQ: A Kinetics-Aware Computational Workflow for Red Particle Quantification v1

The quantitative analysis of fluorescent particles in time-series microscopy data presents a significant scientific challenge, requiring robust methods to distinguish genuine signals from background noise and artifacts across thousands of frames. To address this, the Red Particle Quantification (RPQ) system has been developed as a specialized, Jupyter-based Python workflow. Its primary function is to detect and count candidate red fluorescent particles within standard MP4 video files, providing researchers with a powerful tool for kinetic analysis. The core of the RPQ system is built on a layered architecture that ensures maintainability and clarity. It features a robust, multi-stage image processing pipeline centered on a sophisticated Laplacian-of-Gaussian (LoG) algorithm for blob detection. This processing core is coupled with an interactive parameterization workflow, which empowers users to visually tune detection settings for their specific imaging conditions before committing to a full analysis run. The purpose of this white paper is to provide a detailed technical overview of RPQ's architecture, methodology, and capabilities for an audience of scientists and software engineers seeking to understand and leverage its functionality.