BlinkFusion: modular platform quantifying labeling efficiency and photophysics in regular and super-resolution...

Quantitative analysis in fluorescence microscopy presents challenges: most open-source Single Molecule Localization Microscopy toolkits emphasize visualization over downstream metrics, and practitioners must iteratively juggle sample preparation variables (e.g., labeling density) with acquisition parameters (e.g., photoswitching conditions) once imaging is underway, obscuring cause effect and slowing optimization. We address this gap with BlinkFusion, a modular, open-source Python platform that unifies filament labeling efficiency and STORM photophysics in a single, reproducible workflow. The system ingests image stacks, extracts metadata, and runs two complementary pipelines: (i) a confocal/filament branch that applies ridge guided ROI selection and Stretching Open Active Contours (SOACs) to quantify degree of labeling (DOL) and morphometrics, providing pre-STORM feedback on staining quality; and (ii) a STORM branch that merges localizations into molecules and computes duty cycle, survival fraction, photon yields, and switching cycles within a quasi-equilibrium window for fair cross dataset comparison. An interactive dashboard enables side by side dataset review, rapid parameter sweeps, and immediate reprocessing. On nanobody labeled tubulin, the filament pipeline automatically captures expected trends in continuity, contrast, and intensity across preparation and illumination settings; on Cy5 benchmarks, the STORM pipeline reproduces literature photophysics within 20% under matched conditions, while reducing peak CPU/heap demand and manual effort. A streamlined DOL workflow cuts processing time versus prior manual practice. BlinkFusion therefore links structural and photophysical readouts to deliver immediate, quantitative feedback and a practical path towards real time experimental optimization.