ArticleBiophysical journal2025
Quality control maps: Real-time quantitative control of single-molecule localization microscopy data.
Article in Biophysical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- BlinkFusion: modular platform quantifying labeling efficiency and photophysics in regular and super-resolution fluorescence microscopy.Frontiers in bioinformatics · 2026Article
- Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy.Nature communications · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-molecule localization microscopy (SMLM) has revolutionized the understanding of cellular organization by reconstructing informative images with quantifiable spatial distributions of molecules far beyond the optical diffraction limit. Much effort has been devoted to optimizing localization accuracy. One such approach is the assessment of SMLM data quality in real time rather than after lengthy postacquisition analysis, which nevertheless represents a computational challenge We overcame this difficulty by implementing an innovative mathematical approach we designed to drastically reduce the computational analysis of particle localization. Our quality control maps (QCM) workflow enables a much higher rate of data processing compared to that limited by the frequency required by current cameras. Accordingly, using an innovative computational approach for the detection step and an estimator based on a Gaussian model of the point spread function, subpixel particle locations and their accuracy can be determined through a straightforward analytical calculation without the need for iterations. As a true parameter-free algorithm, QCM is robust and adaptable to all types of SMLM data, with high speed enabling the real-time calculation of quantitative quality control indicators. Such features are compatible with smart microscopy, the concept of which depends on the adjustment of acquisition parameters in real time according to analytical results. Finally, the offline QCM mode can be used as a tool to evaluate synthetic or previously acquired data, as well as to teach the basic concepts of SMLM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.