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Conducting Multiple Imaging Modes with One Fluorescence Microscope
Published on: October 28, 2018
BlinkFusion: modular platform quantifying labeling efficiency and photophysics in regular and super-resolution
Alejandro Salgado1, Nada Naguib2, Ulrich B Wiesner3,4
1Department of Electrical and Electronics Engineering, Universidad de Los Andes, Bogotá, Colombia.
Frontiers in Bioinformatics
|June 15, 2026
Summary
BlinkFusion is an open-source platform that unifies filament labeling and STORM microscopy analysis. It provides quantitative feedback for optimizing single-molecule localization microscopy experiments.
Area of Science:
- Biophysics
- Microscopy
- Computational Biology
Background:
- Quantitative analysis in fluorescence microscopy, particularly Single Molecule Localization Microscopy (SMLM), faces challenges with existing toolkits prioritizing visualization over quantitative metrics.
- Optimizing SMLM experiments requires balancing sample preparation (e.g., labeling density) and acquisition parameters (e.g., photoswitching), which is often a slow, iterative process.
- A need exists for integrated workflows that provide immediate, quantitative feedback to streamline SMLM optimization.
Purpose of the Study:
- To introduce BlinkFusion, a modular, open-source Python platform designed to unify filament labeling efficiency and SMLM photophysics analysis.
- To provide a reproducible workflow that enables quantitative assessment of both sample preparation and imaging parameters.
- To facilitate real-time experimental optimization through immediate, quantitative feedback.
Main Methods:
- BlinkFusion ingests image stacks and extracts metadata, processing data through two complementary pipelines: a confocal/filament branch and an SMLM branch.
- The confocal/filament branch uses ridge-guided ROI selection and Stretching Open Active Contours (SOACs) to quantify labeling degree (DOL) and morphometrics.
- The SMLM branch merges localizations, computing photophysical parameters like duty cycle, survival fraction, photon yields, and switching cycles within a quasi-equilibrium window.
Main Results:
- The filament pipeline accurately quantifies trends in tubulin continuity, contrast, and intensity across varying preparation and illumination settings.
- The SMLM pipeline reproduces established Cy5 photophysics benchmarks within 20% under matched conditions, while reducing computational demands and manual effort.
- A streamlined DOL workflow significantly reduces processing time compared to previous manual methods.
Conclusions:
- BlinkFusion effectively links structural and photophysical readouts in SMLM, offering immediate quantitative feedback.
- The platform provides a practical solution for optimizing SMLM experiments in real-time.
- BlinkFusion enhances reproducibility and efficiency in quantitative SMLM analysis.
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