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Optimized molecule detection in localization microscopy with selected false positive probability

. 2025 Jan 11 ; 16 (1) : 601. [epub] 20250111

Status PubMed-not-MEDLINE Language English Country Great Britain, England Media electronic

Document type Journal Article

Grant support
LQ200402101 Akademie Věd České Republiky (Academy of Sciences of the Czech Republic)
LQ200402101 Akademie Věd České Republiky (Academy of Sciences of the Czech Republic)
LQ200402101 Akademie Věd České Republiky (Academy of Sciences of the Czech Republic)
LM2023066 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
21-17847M Grantová Agentura České Republiky (Grant Agency of the Czech Republic)
21-17847M Grantová Agentura České Republiky (Grant Agency of the Czech Republic)
21-17847M Grantová Agentura České Republiky (Grant Agency of the Czech Republic)

Links

PubMed 39799127
PubMed Central PMC11724879
DOI 10.1038/s41467-025-55952-5
PII: 10.1038/s41467-025-55952-5
Knihovny.cz E-resources

Single-molecule localization microscopy (SMLM) allows imaging beyond the diffraction limit. Detection of molecules is a crucial initial step in SMLM. False positive detections, which are not quantitatively controlled in current methods, are a source of artifacts that affect the entire SMLM analysis pipeline. Furthermore, current methods lack standardization, which hinders reproducibility. Here, we present an optimized molecule detection method which combines probabilistic thresholding with theoretically optimal filtering. The probabilistic thresholding enables control over false positive detections while optimal filtering minimizes false negatives. A theoretically optimal Poisson matched filter is used as a performance benchmark to evaluate existing filtering methods. Overall, our approach allows the detection of molecules in a robust, single-parameter and user-unbiased manner. This will minimize artifacts and enable data reproducibility in SMLM.

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Lelek, M. et al. Single-molecule localization microscopy. PubMed PMC

Fazel, M. & Wester, M. J. Analysis of super-resolution single-molecule localization microscopy data: a tutorial,

Khater, I. M., Nabi, I. R. & Hamarneh, G. A review of super-resolution single-molecule localization microscopy cluster analysis and quantification methods, PubMed PMC

Culley, S. et al. Quantitative mapping and minimization of super-resolution optical imaging artifacts. PubMed DOI PMC

Sage, D. et al. Quantitative evaluation of software packages for single-molecule localization microscopy. PubMed DOI

Sage, D. et al. Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software. PubMed DOI PMC

Ruusuvuori, P. et al. Evaluation of methods for detection of fluorescence-labeled subcellular objects in microscope images. PubMed DOI PMC

Sage, D., Neumann, F. R., Hediger, F., Gasser, S. M. & Unser, M. Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics. PubMed DOI

Starck, J.-L. & Murtagh, F.

Smal, I., Loog, M., Niessen, W. & Meijering, E. Quantitative comparison of spot detection methods in fluorescence microscopy. PubMed

Chenouard, N. et al. Objective comparison of particle tracking methods. PubMed DOI PMC

Gonzales, R. & Woods, R.

Sonka, M., Hlavac, V. & Boyle, R.

Minaee, S. et al. Image segmentation using deep learning: a survey. PubMed

Zou, Z., Chen, K., Shi, Z., Guo, Y. & Ye, J. Object detection in 20 years: a survey,

Kay, S.

Abbott, B. P. et al. Observation of gravitational waves from a binary black hole merger. PubMed DOI

Richards, M. A.

Ofek, E. O. and Zackay, B. Optimal matched filter in the low-number count Poisson noise regime and implications for X-ray source detection.

Reiffen, B. & Sherman, H. An optimum demodulator for Poisson processes: photon source detectors. DOI

Levy, B. C.

Křížek, P., Raška, I. & Hagen, G. M. Minimizing detection errors in single-molecule localization microscopy. PubMed DOI

Ober, R. J., Ward, E. S. & Chao, J.

Swets, J. A., Dawes, R. M. & Monahan, J. Better decisions through science. PubMed DOI

Sergé, A., Bertaux, N., Rigneault, H. & Marguet, D. Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes. PubMed DOI

Köthe, U., Herrmannsdörfer, F., Kats, I. & Hamprecht, F. A. SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy. PubMed DOI

Smith, C. S., Stallinga, S., Lidke, K. A., Rieger, B. & Grunwald, D. Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking. PubMed DOI PMC

Hoogendoorn, E. et al. The fidelity of stochastic single-molecule super-resolution reconstructions critically depends upon robust background estimation. PubMed DOI PMC

Cheng, C.-Y. & Hsieh, C.-L. Background estimation and correction for high-precision localization microscopy. DOI

Möckl, L., Roy, A. R., Petrov, P. N. & Moerner, W. Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet. PubMed DOI PMC

Abrahamsson, S. et al. Fast multicolor 3D imaging using aberration-corrected multifocus microscopy. PubMed DOI PMC

Juette, M. F. et al. Three-dimensional sub-100 nm resolution fluorescence microscopy of thick samples. PubMed DOI

von Diezmann, L., Shechtman, Y. & Moerner, W. Three-dimensional localization of single molecules for super-resolution imaging and single-particle tracking. PubMed DOI PMC

Hajj, B., El Beheiry, M., Izeddin, I., Darzacq, X. & Dahan, M. Accessing the third dimension in localization-based super-resolution microscopy. PubMed DOI

Fawcett, T. An introduction to ROC analysis.

Maier-Hein, L. et al. Metrics reloaded: recommendations for image analysis validation. PubMed DOI PMC

Szeliski, R.

Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. PubMed DOI

Kirillov, A. et al. Segment anything. In

Ouyang, W., Aristov, A., Lelek, M., Hao, X. & Zimmer, C. Deep learning massively accelerates super-resolution localization microscopy. PubMed DOI

Nehme, E., Weiss, L. E., Michaeli, T. & Shechtman, Y. Deep-STORM: super-resolution single-molecule microscopy by deep learning. DOI

Young, I. T., Gerbrands, J. J. & Van Vliet, L. J.

Van Vliet, L. J., Boddeke, F. R., Sudar, D. & Young, I. T. Image detectors for digital image microscopy,

Neubeck, A. & Van Gool, L. Efficient non-maximum suppression. In

Zhang, B., Zerubia, J. & Olivo-Marin, J.-C. Gaussian approximations of fluorescence microscope point-spread function models. PubMed DOI

Boyd, S. & Vandenberghe, L.

Kay, S.

Huang, T., Yang, G. & Tang, G. A fast two-dimensional median filtering algorithm. DOI

Perreault, S. & Hébert, P. Median filtering in constant time. PubMed DOI

Rousseeuw, P. J. & Croux, C. Alternatives to the median absolute deviation. DOI

Donoho, D. L. & Johnstone, I. M. Ideal spatial adaptation by wavelet shrinkage. DOI

Izeddin, I. et al. Wavelet analysis for single molecule localization microscopy. PubMed DOI

Huang, F., Schwartz, S. L., Byars, J. M. & Lidke, K. A. Simultaneous multiple-emitter fitting for single molecule super-resolution imaging, PubMed PMC

Huang, F. et al. Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms. PubMed DOI PMC

Ries, J. SMAP: a modular super-resolution microscopy analysis platform for SMLM data. PubMed DOI

Henriques, R. et al. QuickPALM: 3D real-time photoactivation nanoscopy image processing in ImageJ. PubMed DOI

Martens, K. J., Turkowyd, B. & Endesfelder, U. Raw data to results: a hands-on introduction and overview of computational analysis for single-molecule localization microscopy. PubMed DOI PMC

Tinevez, J.-Y. et al. TrackMate: an open and extensible platform for single-particle tracking. PubMed DOI

Holden, S. J., Uphoff, S. & Kapanidis, A. N. DAOSTORM: an algorithm for high-density super-resolution microscopy. PubMed DOI

Schnitzbauer, J., Strauss, M. T., Schlichthaerle, T., Schueder, F. & Jungmann, R. Super-resolution microscopy with DNA-PAINT. PubMed DOI

Ovesný, M., Křížek, P., Borkovec, J., Švindrych, Z. & Hagen, G. M. ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. PubMed DOI PMC

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