scholarly journals SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Yunqing Tang ◽  
Luru Dai ◽  
Xiaoming Zhang ◽  
Junbai Li ◽  
Johnny Hendriks ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Real Time ◽  
Single Molecule ◽  
Super Resolution ◽  
Localization Algorithm

scholarly journals Quantifying accuracy and heterogeneity in single-molecule super-resolution microscopy

2019 ◽  
Author(s):  
Hesam Mazidi ◽  
Tianben Ding ◽  
Arye Nehorai ◽  
Matthew D. Lew
Keyword(s):  
Single Molecule ◽  
Computational Models ◽  
Amyloid Fibrils ◽  
Imaging System ◽  
Simulated Data ◽  
Super Resolution ◽  
Ground Truth ◽  
Localization Algorithm ◽  
Reconstruction Algorithms ◽  
Imaging Data

The resolution and accuracy of single-molecule localization micro-scopes (SMLMs) are routinely benchmarked using simulated data, calibration “rulers,” or comparisons to secondary imaging modalities. However, these methods cannot quantify the nanoscale accuracy of an arbitrary SMLM dataset. Here, we show that by computing localization stability under a well-chosen perturbation with accurate knowledge of the imaging system, we can robustly measure the confidence of individual localizations without ground-truth knowledge of the sample. We demonstrate that our method, termed Wasserstein-induced flux (WIF), measures the accuracy of various reconstruction algorithms directly on experimental 2D and 3D data of microtubules and amyloid fibrils. We further show that WIF confidences can be used to evaluate the mismatch between computational models and imaging data, enhance the accuracy and resolution of recon-structed structures, and discover hidden molecular heterogeneities. As a computational methodology, WIF is broadly applicable to any SMLM dataset, imaging system, and localization algorithm.

Fast Fourier-Domain Localization Algorithm of Single Molecule with Nanometer Resolution for Super-Resolution Fluorescence Imaging

2012 ◽  
Vol 32 (2) ◽  
pp. 0218001
Author(s):  
于斌 Yu Bin ◽  
陈丹妮 Chen Danni ◽  
刘磊 Liu Lei ◽  
屈军乐 Qu Junle ◽  
牛憨笨 Niu Hanben
Keyword(s):  
Fluorescence Imaging ◽  
Single Molecule ◽  
Super Resolution ◽  
Fourier Domain ◽  
Localization Algorithm ◽  
Nanometer Resolution

scholarly journals Super-resolution Localization and Defocused Fluorescence Microscopy on Resonantly Coupled Single-Molecule, Single-Nanorod Hybrids

ACS Nano ◽  
2016 ◽  
Vol 10 (2) ◽  
pp. 2455-2466 ◽  
Author(s):  
Liang Su ◽  
Haifeng Yuan ◽  
Gang Lu ◽  
Susana Rocha ◽  
Michel Orrit ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Single Molecule ◽  
Super Resolution

scholarly journals FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

2020 ◽  
Author(s):  
Matthieu Lagardère ◽  
Ingrid Chamma ◽  
Emmanuel Bouilhol ◽  
Macha Nikolski ◽  
Olivier Thoumine
Keyword(s):  
Real Time ◽  
Protein Dynamics ◽  
Single Molecule ◽  
Imaging Techniques ◽  
Simulated Data ◽  
Super Resolution ◽  
Molecular Complexes ◽  
Live Cell ◽  
Single Molecule Level ◽  
Resolution Imaging

AbstractFluorescence live-cell and super-resolution microscopy methods have considerably advanced our understanding of the dynamics and mesoscale organization of macro-molecular complexes that drive cellular functions. However, different imaging techniques can provide quite disparate information about protein motion and organization, owing to their respective experimental ranges and limitations. To address these limitations, we present here a unified computer program that allows one to model and predict membrane protein dynamics at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments. FluoSim is an interactive real-time simulator of protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. The software, thoroughly validated against experimental data on the canonical neurexin-neuroligin adhesion complex, integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the distribution of thousands of independent molecules in 2D cellular geometries, providing simulated data of protein dynamics and localization directly comparable to actual experiments.

scholarly journals Resolving the internal morphology of core–shell microgels with super-resolution fluorescence microscopy

2020 ◽  
Vol 2 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Pia Otto ◽  
Stephan Bergmann ◽  
Alice Sandmeyer ◽  
Maxim Dirksen ◽  
Oliver Wrede ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Internal Structure ◽  
Single Molecule ◽  
Fluorescent Dyes ◽  
Super Resolution ◽  
Core Shell ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Internal Morphology

We investigate the internal structure of smart core–shell microgels by super-resolution fluorescence microscopy by combining of 3D single molecule localization and structured illumination microscopy using freely diffusing fluorescent dyes.

A literature review of localization methods and emergence of deep learning in single-molecule localization microscopy

2020 ◽  
Author(s):  
Anish Mukherjee
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Super Resolution ◽  
Point Sources ◽  
Learning Approaches ◽  
Localization Algorithm ◽  
Localization Microscopy ◽  
Trade Offs ◽  
Emitter Localization ◽  
Single Molecule Localization Microscopy

The quality of super-resolution images largely depends on the performance of the emitter localization algorithm used to localize point sources. In this article, an overview of the various techniques which are used to localize point sources in single-molecule localization microscopy are discussed and their performances are compared. This overview can help readers to select a localization technique for their application. Also, an overview is presented about the emergence of deep learning methods that are becoming popular in various stages of single-molecule localization microscopy. The state of the art deep learning approaches are compared to the traditional approaches and the trade-offs of selecting an algorithm for localization are discussed.

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