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.

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

2020 ◽  
Vol 11 (1) ◽  
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

AbstractThe resolution and accuracy of single-molecule localization microscopes (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 reconstructed structures, and discover hidden molecular heterogeneities. As a computational methodology, WIF is broadly applicable to any SMLM dataset, imaging system, and localization algorithm.

scholarly journals Super-Resolution Image Reconstruction Based on Single-Molecule Localization Algorithm

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 273
Author(s):  
Lixin Liu ◽  
Meijie Qi ◽  
Yujie Liu ◽  
Xinzhu Xue ◽  
Danni Chen ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Single Molecule ◽  
Cell Biology ◽  
Imaging System ◽  
Particle Density ◽  
Super Resolution ◽  
Live Cells ◽  
Biological Macromolecules ◽  
Localization Algorithm ◽  
Fluorescent Molecules

Fluorescence imaging is an important and efficient tool in cell biology and biomedical research. In order to observe the dynamics of biological macromolecules such as DNA, RNA and proteins in live cells, it is extremely necessary to surpass the Abbe diffraction limit in microscopic imaging. Single-molecule localization microscopy (SMLM) is a sort of super-resolution imaging technique that can obtain a large number of images of sparse fluorescent molecules by the use of photoswitchable fluorescent probes and single-molecule localization technology. The center positions of fluorescent molecules in the images are precisely located, and then the entire sample pattern is reconstructed with super resolution. In this paper, we present a single-molecule localization algorithm (SMLA) that is based on blind deconvolution and centroid localization (BDCL) method. Single-molecule localization and image reconstruction of 15,000/9990 frames of original images of tubulins are accomplished. In addition, this fluorophore localization algorithm is used to localize high particle-density images. The results show that our BDCL-SMLA method is a reasonable attempt and useful method for SMLM imaging when the imaging system is unknown.

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 GPU-accelerated real-time reconstruction in Python of three-dimensional datasets from structured illumination microscopy with hexagonal patterns

2021 ◽  
Vol 379 (2199) ◽  
Author(s):  
Hai Gong ◽  
Wenjun Guo ◽  
Mark A. A. Neil
Keyword(s):  
Moving Objects ◽  
Three Dimensional ◽  
Simulated Data ◽  
Super Resolution ◽  
Light Sheet ◽  
Processing Unit ◽  
Structured Illumination ◽  
Reconstruction Algorithms ◽  
Structured Illumination Microscopy ◽  
Axially Moving

We present a structured illumination microscopy system that projects a hexagonal pattern by the interference among three coherent beams, suitable for implementation in a light-sheet geometry. Seven images acquired as the illumination pattern is shifted laterally can be processed to produce a super-resolved image that surpasses the diffraction-limited resolution by a factor of over 2 in an exemplar light-sheet arrangement. Three methods of processing data are discussed depending on whether the raw images are available in groups of seven, individually in a stream or as a larger batch representing a three-dimensional stack. We show that imaging axially moving samples can introduce artefacts, visible as fine structures in the processed images. However, these artefacts are easily removed by a filtering operation carried out as part of the batch processing algorithm for three-dimensional stacks. The reconstruction algorithms implemented in Python include specific optimizations for calculation on a graphics processing unit and we demonstrate its operation on experimental data of static objects and on simulated data of moving objects. We show that the software can process over 239 input raw frames per second at 512 × 512 pixels, generating over 34 super-resolved frames per second at 1024 × 1024 pixels. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)’.

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 QSM Reconstruction Challenge 2.0–Part 1: A Realistic in silico Head Phantom for MRI data simulation and evaluation of susceptibility mapping procedures

2020 ◽  
Author(s):  
José P. Marques ◽  
Jakob Meineke ◽  
Carlos Milovic ◽  
Berkin Bilgic ◽  
Kwok-Shing Chan ◽  
...  
Keyword(s):  
In Silico ◽  
Ground Truth ◽  
Susceptibility Mapping ◽  
Quantitative Mri ◽  
Reconstruction Algorithms ◽  
Imaging Data ◽  
Post Processing ◽  
Simulation Code ◽  
Wide Range ◽  
Echo Time

AbstractPurposeTo create a realistic in-silico head phantom for the second QSM Reconstruction Challenge and for future evaluations of processing algorithms for Quantitative Susceptibility Mapping (QSM).MethodsWe created a whole-head tissue property model by segmenting and post-processing high-resolution, multi-parametric MRI data acquired from a healthy volunteer. We simulated the steady-state magnetization using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based post-processing. We demonstrated some of the phantom’s properties, including the possibility of generating phase data that do not evolve linearly with echo time due to partial volume effects or complex distributions of frequency shifts within the voxel. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the model, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols.ResultsThe brain-part of the phantom features realistic morphology combined with realistic spatial variations in relaxation and susceptibility values. Simulation code allows adjusting the following parameters and effects: repetition time and echo time, voxel size, background fields, and RF phase biases. Additionally, diffusion weighted imaging data of the phantom is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms.ConclusionThe presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative MRI reconstruction algorithms.

scholarly journals Rapid single-wavelength lightsheet localization microscopy for clarified tissue

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Li-An Chu ◽  
Chieh-Han Lu ◽  
Shun-Min Yang ◽  
Yen-Ting Liu ◽  
Kuan-Lin Feng ◽  
...  
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Biological Tissues ◽  
Adult Brain ◽  
Imaging Data ◽  
Transporter Protein ◽  
Index Matching ◽  
Refractive Index Matching ◽  
Nanoscale Imaging ◽  
Resolution Imaging

Abstract Optical super-resolution microscopy allows nanoscale imaging of protein molecules in intact biological tissues. However, it is still challenging to perform large volume super-resolution imaging for entire animal organs. Here we develop a single-wavelength Bessel lightsheet method, optimized for refractive-index matching with clarified specimens to overcome the aberrations encountered in imaging thick tissues. Using spontaneous blinking fluorophores to label proteins of interest, we resolve the morphology of most, if not all, dopaminergic neurons in the whole adult brain (3.64 × 107 µm3) of Drosophila melanogaster at the nanometer scale with high imaging speed (436 µm3 per second) for localization. Quantitative single-molecule localization reveals the subcellular distribution of a monoamine transporter protein in the axons of a single, identified serotonergic Dorsal Paired Medial (DPM) neuron. Large datasets are obtained from imaging one brain per day to provide a robust statistical analysis of these imaging data.

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.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Matthieu Lagardère ◽  
Ingrid Chamma ◽  
Emmanuel Bouilhol ◽  
Macha Nikolski ◽  
Olivier Thoumine
Keyword(s):  
Membrane Protein ◽  
Protein Dynamics ◽  
Single Molecule ◽  
Imaging Techniques ◽  
Simulated Data ◽  
Super Resolution ◽  
Molecular Complexes ◽  
Live Cell ◽  
Resolution Imaging ◽  
Membrane Protein Dynamics

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 issues, we present here a robust computer program, called FluoSim, which is an interactive simulator of membrane protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. FluoSim integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the localization and intensity of thousands of independent molecules in 2D cellular geometries, providing simulated data directly comparable to actual experiments. FluoSim was thoroughly validated against experimental data obtained on the canonical neurexin-neuroligin adhesion complex at cell–cell contacts. This unified software allows one to model and predict membrane protein dynamics and localization at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments.

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