Correlation analysis framework for localization-based superresolution microscopy

Schnitzbauer, Joerg; Wang, Yina; Zhao, Shijie; Bakalar, Matthew; Nuwal, Tulip; Chen, Baohui; Huang, Bo

doi:10.1073/pnas.1711314115

Cited by 59 publications

(68 citation statements)

References 38 publications

(43 reference statements)

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“…other metrics could be used for the significance test (e.g. pair cross-correlation analysis 7,21 or Ripley's covariate analysis 17 ), especially for testing deviations on length scales beyond the nearest neighbors.…”

Section: Discussionmentioning

confidence: 99%

Verifying molecular clusters by 2-color localization microscopy and significance testing

Arnold

Schneider

Hüsson

et al. 2020

Sci Rep

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While single-molecule localization microscopy (SMLM) offers the invaluable prospect to visualize cellular structures below the diffraction limit of light microscopy, its potential has not yet been fully capitalized due to its inherent susceptibility to blinking artifacts. Particularly, overcounting of single molecule localizations has impeded a reliable and sensitive detection of biomolecular nanoclusters. Here we introduce a 2-Color Localization microscopy And Significance Testing Approach (2-CLASTA), providing a parameter-free statistical framework for the qualitative analysis of two-dimensional SMLM data via significance testing methods. 2-CLASTA yields p-values for the null hypothesis of random biomolecular distributions, independent of the blinking behavior of the chosen fluorescent labels. The method is parameter-free and does not require any additional measurements nor grouping of localizations. We validated the method both by computer simulations as well as experimentally, using protein concatemers as a mimicry of biomolecular clustering. As the new approach is not affected by overcounting artifacts, it is able to detect biomolecular clustering of various shapes at high sensitivity down to a level of dimers. Single Molecule Localization Microscopy (SMLM) has boosted our insights into cellular structures below the diffraction limit of light microscopy 1. Common to all SMLM variants is the stochastic switching of single dye molecules between a bright and a dark state. Conditions are chosen such that only a marginal portion of the molecules is in the bright state, so that single molecule signals are well separated on each frame. The final superresolution image is reconstructed from the localizations of all single molecule signals. Researchers have been particularly intrigued by the possibility to determine the spatial distribution of biomolecules in their natural environment, in most cases the intact cell. For example, models for cellular signaling are crucially affected by the spatial organization of receptor and downstream signaling molecules at the plasma membrane 2,3. Application of SMLM to various plasma membrane proteins revealed the presence of nanoclusters to different degrees 4. More recently concerns were raised that the stochastic activation process of the fluorophores, along with the presence of more than one dye molecule per labeled biomolecule, may lead to multiple observations of the same biomolecule in the superresolution image 5,6. Different attempts were undertaken to approach this problem 5,7-11 , e.g. by merging localization bursts into one localization 12 , by analyzing the number of blinking events per localization cluster 10,11 , or by evaluating the spatial spread of the localization clusters 7. A disadvantage of existing methods is the requirement of user-defined parameters 7,12 or additional experiments to characterize the blinking statistics of the chosen fluorophores 10,11. We recently developed a parameter-free method to identify global protein clustering based on a label titrati...

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Section: Discussionmentioning

confidence: 99%

Verifying molecular clusters by 2-color localization microscopy and significance testing

Arnold

Schneider

Hüsson

et al. 2020

Sci Rep

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“…The technique originated from the electron microscopy (EM) field but recently has been adopted to superresolution optical microscopy data (20)(21)(22). Despite its origin in EM, it has been recognized that the existing EM algorithms may not work well for SMLM data, due to the vastly different noise characteristics (23,24). Heydarian et al (23) recently published an algorithm tailored for SMLM, which performs significantly better than previous software.…”

Section: Application: Particle Fusionmentioning

confidence: 99%

Single-molecule localization microscopy as nonlinear inverse problem

Ji¹,

Elmokadem²

2019

Proc. Natl. Acad. Sci. U.S.A.

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We present a statistical framework to model the spatial distribution of molecules based on a single-molecule localization microscopy (SMLM) dataset. The latter consists of a collection of spatial coordinates and their associated uncertainties. We describe iterative parameter-estimation algorithms based on this framework, as well as a sampling algorithm to numerically evaluate the complete posterior distribution. We demonstrate that the inverse computation can be viewed as a type of image restoration process similar to the classical image deconvolution methods, except that it is performed on SMLM images. We further discuss an application of our statistical framework in the task of particle fusion using SMLM data. We show that the fusion algorithm based on our model outperforms the current state-of-the-art in terms of both accuracy and computational cost.

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“…To compute the closed-form FRC, we must compute three instances of an expression of the same type as (9). Each instance requires the calculation of the Euclidean distance between each point of one set of positions with each point of another set of positions (or itself).…”

Section: Practical Implementationmentioning

confidence: 99%

“…the image binning step is popular [8,9]. We first proceed by introducing the mathematical definition of the FRC (Section 2) and its conventional (discrete) computation, for which we derive an error bound (Section 3).…”

Section: Introductionmentioning

confidence: 99%

Closed-Form Expression Of The Fourier Ring-Correlation For Single-Molecule Localization Microscopy

Pham

Soubies

Sage

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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Single-molecule localization microscopy (SMLM) is a popular microscopic technique that achieves super resolution imaging by localizing individual blinking molecules in thousands of frames. Therefore, the reconstructed high-resolution image is a combination of millions of point sources. This particular computational reconstruction leads to the question of the estimation of the image resolution. Fourier-ring correlation (FRC) is the standard tool for assessing the resolution. It has been proposed for SMLM by computing a discrete correlation in the Fourier domain. In this work, we derive a closed-form expression to compute the continuous FRC. Our implementation provides an exact FRC and an alternative to compute a parameter-free FRC. In addition, it gives insights on the discrepancy of the discrete FRC and yields a rule to select its parameters such as the spatial sampling step or the width of the kernel used as density estimator.

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Correlation analysis framework for localization-based superresolution microscopy

Cited by 59 publications

References 38 publications

Verifying molecular clusters by 2-color localization microscopy and significance testing

Verifying molecular clusters by 2-color localization microscopy and significance testing

Single-molecule localization microscopy as nonlinear inverse problem

Closed-Form Expression Of The Fourier Ring-Correlation For Single-Molecule Localization Microscopy

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