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Correction of multiple-blinking artifacts in photoactivated localization microscopy

Abstract

Photoactivated localization microscopy (PALM) produces an array of localization coordinates by means of photoactivatable fluorescent proteins. However, observations are subject to fluorophore multiple blinking and each protein is included in the dataset an unknown number of times at different positions, due to localization error. This causes artificial clustering to be observed in the data. We present a ‘model-based correction’ (MBC) workflow using calibration-free estimation of blinking dynamics and model-based clustering to produce a corrected set of localization coordinates representing the true underlying fluorophore locations with enhanced localization precision, outperforming the state of the art. The corrected data can be reliably tested for spatial randomness or analyzed by other clustering approaches, and descriptors such as the absolute number of fluorophores per cluster are now quantifiable, which we validate with simulated data and experimental data with known ground truth. Using MBC, we confirm that the adapter protein, the linker for activation of T cells, is clustered at the T cell immunological synapse.

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Fig. 1: Illustration of the MBC workflow.
Fig. 2: Comparison of MBC with the state of the art, with molecules on a fixed grid.
Fig. 3: Comparison of MBC with the state of the art, with heavily clustered molecules.
Fig. 4: Testing for spatial randomness.
Fig. 5: Testing on clustered ground-truth datasets.
Fig. 6: Cluster analysis of LAT-mEos3.2 at the T cell immunological synapse.

Data availability

A data simulator to recapitulate simulated data conditions and raw experimental data (point clouds) are available at https://github.com/Louis-Jensen/MBC-for-PALM. Raw experimental data (camera frames) available upon request. Source data are provided with this paper.

Code availability

MBC code is available as Supplementary Material together with installation instructions and example simulated datasets. MBC code is also available at https://github.com/Louis-Jensen/MBC-for-PALM.

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Acknowledgements

L.G.J. was supported by the Center for Stochastic Geometry and Advanced Bioimaging, funded by grant no. 8721 from the Villum Foundation. D.O. acknowledges funding from the Biotechnology and Biological Sciences Research Council (BBSRC) grant no. BB/R007365/1. P.R.D. acknowledges funding from the BBSRC grant no. BB/R007837/1. We also acknowledge the use of the King’s College London Nikon Imaging Center. We thank U. Hahn (Aarhus University) for motivating this project in initial discussions. We acknowledge J. Ries and the European Molecular Biology Laboratory for supplying NPC data.

Author information

Authors and Affiliations

Authors

Contributions

L.G.J. developed the software. L.G.J., T.Y.H., D.M.O. and P.R.-D. conceived the experiments. L.G.J. and T.Y.H. ran the analysis. L.G.J. and T.Y.H. provided simulated data. D.J.W., J.G. and D.S. provided additional simulated and experimental data. L.G.J., T.Y.H., D.M.O. and P.R-D. wrote the manuscript. L.G.J. and P.R.-D. conceived the method.

Corresponding authors

Correspondence to Louis G. Jensen, Patrick Rubin-Delanchy or Dylan M. Owen.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Antony Lee, Aleksandra Radenovic and Jie Xiao for their contribution to the peer review of this work. Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Comparison of MBC with the state-of-the-art for the case of CSR and clustered molecules.

a) Representative ground truth, blinking and corrected data (from one of n = 30 realisations) for CSR molecules. b,c,d) Wasserstein distances (b), image error (c), counting error (d), between corrected data and ground truth. In b), the dashed horizontal line shows the 37 nm benchmark and c,d) the dashed horizontal line shows the optimal value 0. The columns show different blinking conditions (LB: light blinking, HB: heavy blinking) and correction methods (MBC: our method, Boh: Bohrer et al.’s method). Our method has superior performance on all metrics except counting error for the light blinking case. e) Representative ground truth, blinking and corrected data (from one of n = 30 realisations) for clustered molecules. f,g,h) Wasserstein distances (f), image error (g), counting error (h), between corrected data and ground truth. In f), the dashed horizontal line shows the 37 nm benchmark and c,d) the dashed horizontal line shows the optimal value 0. The columns show different blinking conditions (LB: light blinking, HB: heavy blinking) and correction methods (MBC: our method, Boh: Bohrer et al.’s method). Our method has superior performance on all metrics except counting error for the light blinking case. Box plots show median, 25th and 75th percentiles and min, max.

Source data

Extended Data Fig. 2 Comparison of MBC with idealized DTT, with molecules on a fixed grid with light (LB) and heavy (HB) blinking.

a) Representative ground truth, blinking and corrected data (from one of n = 50 realisations) with light blinking. b,c,d) Wasserstein distances (b), image error (c), counting error (d), between corrected data and ground truth. In b), the dashed horizontal line shows the 37 nm benchmark and c,d) the dashed horizontal line shows the optimal value 0. The columns show different correction methods (MBC: our method, iDTT_N: DTT minimising counting error; iDTT_W: DTT minimising Wasserstein distance). Our method is always superior on Wasserstein distance and image error, and comparable in counting error when compared to iDTT_W. e) Representative ground truth, blinking and corrected data (from one of n = 50 realisations) for heavy blinking. f,g,h) Wasserstein distances (f), image error (g), counting error (h), between corrected data and ground truth. In f), the dashed horizontal line shows the 37 nm benchmark and c,d) the dashed horizontal line shows the optimal value 0. The columns show different correction methods (MBC: our method, iDTT_N: DTT minimising counting error; iDTT_W: DTT minimising Wasserstein distance). Our method is always superior on Wasserstein distance and image error, and comparable in counting error when compared to iDTT_W. Box plots show median, 25th and 75th percentiles and min, max.

Source data

Extended Data Fig. 3 Comparison of MBC with DTT for regular and CSR molecules.

a) Representative ground truth for 2500 molecules on a fixed grid, blinking and corrected data (from one of n = 50 realisations). bd) Wasserstein distances (b), image error (c), counting error (d), between corrected data and ground truth. In b), the dashed horizontal line shows the 37 nm benchmark and c,d) the dashed horizontal line shows the optimal value 0. The columns show different blinking conditions (LB: light blinking, HB: heavy blinking) and correction methods. Our method has superior performance on all metrics. e) Representative ground truth for 2500 CSR molecules, blinking and corrected data (from one of n = 30 realisations). f-h) Wasserstein distances (f), image error (g), counting error (h), between corrected data and ground truth. In b), the dashed horizontal line shows the 37 nm benchmark and g,h) the dashed horizontal line shows the optimal value 0. The columns show different blinking conditions (LB: light blinking, HB: heavy blinking) and correction methods. Our method has superior performance on all metrics. Box plots show median, 25th and 75th percentiles and min, max.

Source data

Extended Data Fig. 4 Comparison of MBC with DTT for clustered molecules.

a) Representative ground truth for 10 clusters of 50 molecules and 2000 CSR molecules, blinking and corrected data (from one of n = 30 realisations). b-d) Wasserstein distances (b), image error (c), counting error (d), between corrected data and ground truth. In b), the dashed horizontal line shows the 37 nm benchmark and c,d) the dashed horizontal line shows the optimal value 0. The columns show different blinking conditions (LB: light blinking, HB: heavy blinking) and correction methods. Our method has superior performance on all metrics. e) Representative ground truth for 10 clusters of 200 molecules and 500 CSR molecules, blinking and corrected data (from one of n = 30 realisations). f-h) Wasserstein distances (f), image error (g), counting error (h), between corrected data and ground truth. In b), the dashed horizontal line shows the 37 nm benchmark and g,h) the dashed horizontal line shows the optimal value 0. The columns show different blinking conditions (LB: light blinking, HB: heavy blinking) and correction methods. Our method has superior performance on all metrics. Box plots show median, 25th and 75th percentiles and min, max.

Source data

Extended Data Fig. 5 MBC Performance as a function of camera frame rate and the activating, 405 nm laser power.

a) Example ground-truth and raw localisation maps for the different conditions (n = 30 relisations). b) Example MBC-corrected maps. c) Wasserstein distances. d) Normalised histograms of localisation uncertainty. e) Counting error in estimated number of ground-truth molecules (mean in dashed line). Box plots show median, 25th and 75th percentiles and min, max.

Source data

Extended Data Fig. 6 Comparison of MBC with DTT, with molecules on a fixed grid with blinking dynamics following a three-dark-state model.

a) Representative ground truth, blinking and corrected data (from one of n = 50 realisations). bd) Wasserstein distances (b), image error (c), counting error (d), between corrected data and ground truth. In b), the dashed horizontal line shows the 37 nm benchmark and c,d) the dashed horizontal line shows the optimal value 0. The columns show different correction methods. Our method shows superior performance on all metrics. Box plots show median, 25th and 75th percentiles and min, max.

Source data

Extended Data Fig. 7 Correction of NPC counts, after accounting for effective labeling efficacy.

Histogram of the number of MBC-recovered proteins per NPC (n = 8146), corrected for effective labeling efficacy (red), with mean indicated by the red vertical line, and a kernel density estimate shown as the red curve. For comparison, a histogram of an equally-sized sample of counts under perfect correction is shown in black, with mean indicated by the black vertical line, and kernel density estimate as the black curve.

Source data

Extended Data Fig. 8 Additional statistics from the Bayesian cluster analysis of non-CSR LAT-mEos3.2 regions.

a) Number of detected clusters, b) cluster radii (nm), c) percentage of molecules in clusters, d) number of molecules per ROI and e) relative density of molecules located in clusters as compared to the surrounding region. Box plots show median, 25th and 75th percentiles and min, max. n-numbers = 14 (WT centre), 11 (WT periphery), 8 (YF centre) and 12 (YF periphery).

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Tables 1 and 2.

Reporting Summary

Peer Review File

Supplementary Software 1

Code to run the method presented in the paper.

Supplementary Data 1

HTML folder allowing 3D inspection of point clouds.

Source data

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Jensen, L.G., Hoh, T.Y., Williamson, D.J. et al. Correction of multiple-blinking artifacts in photoactivated localization microscopy. Nat Methods 19, 594–602 (2022). https://doi.org/10.1038/s41592-022-01463-w

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