Sub-Nanometer Precision using Bayesian Grouping of Localizations

Single-molecule localization microscopy super-resolution methods such as DNA-PAINT and (d)STORM can generate multiple observed localizations over the time course of data acquisition from each dye or binding site that are not a priori assigned to those specific dyes or binding sites. We describe a Bayesian method of grouping and combining localizations from multiple blinking/binding events that can improve localization precision to better than one nanometer. The known statistical distribution of the number of binding/blinking events per dye/docking strand along with the precision of each localization event are used to estimate the true number and location of emitters in closely-spaced clusters.

[1]  R. Heintzmann,et al.  Superresolution by localization of quantum dots using blinking statistics. , 2005, Optics express.

[2]  P. Schwille,et al.  Flat-top TIRF illumination boosts DNA-PAINT imaging and quantification , 2019, Nature Communications.

[3]  S. Hell Far-Field Optical Nanoscopy , 2007, Science.

[4]  Brian Everitt,et al.  Cluster analysis , 1974 .

[5]  Sjoerd Stallinga,et al.  Measuring image resolution in optical nanoscopy , 2013, Nature Methods.

[6]  Keith A. Lidke,et al.  Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo , 2019, Scientific Reports.

[7]  D. Massart,et al.  Looking for natural patterns in data: Part 1. Density-based approach , 2001 .

[8]  Maximilian T. Strauss,et al.  Super-resolution microscopy with DNA-PAINT , 2017, Nature Protocols.

[9]  Florian Schueder,et al.  Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo , 2019, Scientific Reports.

[10]  P. Green Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[11]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[12]  Keith A. Lidke,et al.  Simultaneous multiple-emitter fitting for single molecule super-resolution imaging , 2011, Biomedical optics express.

[13]  Jordan R. Myers,et al.  Ultra-High Resolution 3D Imaging of Whole Cells , 2016, Cell.

[14]  P. Green,et al.  Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .

[15]  J. Lippincott-Schwartz,et al.  Imaging Intracellular Fluorescent Proteins at Nanometer Resolution , 2006, Science.

[16]  Johannes B. Woehrstein,et al.  Quantitative super-resolution imaging with qPAINT , 2016 .

[17]  Matthew R. Lakin,et al.  Sequential super-resolution imaging using DNA strand displacement , 2017, bioRxiv.

[18]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Michael J Rust,et al.  Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) , 2006, Nature Methods.

[20]  S. Ram,et al.  Localization accuracy in single-molecule microscopy. , 2004, Biophysical journal.

[21]  Keith A. Lidke,et al.  Fast, single-molecule localization that achieves theoretically minimum uncertainty , 2010, Nature Methods.

[22]  Sjoerd Stallinga,et al.  Template-free 2D particle fusion in localization microscopy , 2018, Nature Methods.

[23]  Daniel Choquet,et al.  SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data , 2015, Nature Methods.

[24]  M. Heilemann,et al.  Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes. , 2008, Angewandte Chemie.

[25]  David J. Williamson,et al.  Bayesian cluster identification in single-molecule localization microscopy data , 2015, Nature Methods.

[26]  Gerhard J Schütz,et al.  Varying label density allows artifact-free analysis of membrane-protein nanoclusters , 2016, Nature Methods.

[27]  Maximilian T. Strauss,et al.  Direct Visualization of Single Nuclear Pore Complex Proteins Using Genetically‐Encoded Probes for DNA‐PAINT , 2019, bioRxiv.

[28]  Michael D. Mason,et al.  Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. , 2006, Biophysical journal.

[29]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .