scholarly journals A Bayesian nonparametric approach to super-resolution single-molecule localization

2020 ◽  
Author(s):  
Mariano I. Gabitto ◽  
Herve Marie-Nelly ◽  
Ari Pakman ◽  
Andras Pataki ◽  
Xavier Darzacq ◽  
...  
Keyword(s):  
Single Molecule ◽  
Dirichlet Process ◽  
Super Resolution ◽  
Ground Truth ◽  
Field Of View ◽  
Data Sets ◽  
Bayesian Nonparametric ◽  
Individual Molecule ◽  
Ground Truth Data ◽  
Super Resolution Microscopy

We consider the problem of single-molecule identification in super-resolution microscopy. Super-resolution microscopy overcomes the diffraction limit by localizing individual fluorescing molecules in a field of view. This is particularly difficult since each individual molecule appears and disappears randomly across time and because the total number of molecules in the field of view is unknown. Additionally, data sets acquired with super-resolution microscopes can contain a large number of spurious fluorescent fluctuations caused by background noise.To address these problems, we present a Bayesian nonparametric framework capable of identifying individual emitting molecules in super-resolved time series. We tackle the localization problem in the case in which each individual molecule is already localized in space. First, we collapse observations in time and develop a fast algorithm that builds upon the Dirichlet process. Next, we augment the model to account for the temporal aspect of fluorophore photo-physics. Finally, we assess the performance of our methods with ground-truth data sets having known biological structure.

scholarly journals Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors

2018 ◽  
Author(s):  
Tomáš Lukeš ◽  
Jakub Pospíšil ◽  
Karel Fliegel ◽  
Theo Lasser ◽  
Guy M. Hagen
Keyword(s):  
Data Analysis ◽  
Growth Factor ◽  
Single Molecule ◽  
Super Resolution ◽  
Growth Factor Receptors ◽  
Data Sets ◽  
Localization Microscopy ◽  
Super Resolution Microscopy ◽  
Resolution Single ◽  
Single Molecule Localization Microscopy

BackgroundSuper-resolution single molecule localization microscopy (SMLM) is a method for achieving resolution beyond the classical limit in optical microscopes (approx. 200 nm laterally). Yellow fluorescent protein (YFP) has been used for super-resolution single molecule localization microscopy, but less frequently than other fluorescent probes. Working with YFP in SMLM is a challenge because a lower number of photons are emitted per molecule compared to organic dyes which are more commonly used. Publically available experimental data can facilitate development of new data analysis algorithms.FindingsFour complete, freely available single molecule super-resolution microscopy datasets on YFP-tagged growth factor receptors expressed in a human cell line are presented including both raw and analyzed data. We report methods for sample preparation, for data acquisition, and for data analysis, as well as examples of the acquired images. We also analyzed the SMLM data sets using a different method: super-resolution optical fluctuation imaging (SOFI). The two modes of analysis offer complementary information about the sample. A fifth single molecule super-resolution microscopy dataset acquired with the dye Alexa 532 is included for comparison purposes.ConclusionThis dataset has potential for extensive reuse. Complete raw data from SMLM experiments has typically not been published. The YFP data exhibits low signal to noise ratios, making data analysis a challenge. These data sets will be useful to investigators developing their own algorithms for SMLM, SOFI, and related methods. The data will also be useful for researchers investigating growth factor receptors such as ErbB3.

scholarly journals DeepLoco: Fast 3D Localization Microscopy Using Neural Networks

2018 ◽  
Author(s):  
Nicholas Boyd ◽  
Eric Jonas ◽  
Hazen Babcock ◽  
Benjamin Recht
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Single Molecule ◽  
Super Resolution ◽  
Ground Truth ◽  
Optical Systems ◽  
The Neural Network ◽  
Time Calibration ◽  
Future Work ◽  
Super Resolution Microscopy

AbstractSingle-molecule localization super-resolution microscopy (SMLM) techniques like STORM and PALM have transformed cellular microscopy by substantially increasing spatial resolution. In this paper we introduce a new algorithm for a critical part of the SMLM process: estimating the number and locations of the fluorophores in a single frame. Our algorithm can analyze a 20000-frame experimental 3D SMLM dataset in about one second — substantially faster than real-time and existing algorithms. Our approach is straightforward but very different from existing algorithms: we train a neural network to minimize the Bayes’ risk under a generative model for single SMLM frames. The neural network maps a frame directly to a collection of fluorophore locations, which we compare to the ground truth using a novel loss function. While training the neural network takes several hours, it only has to be done once for a given experimental setup. After training, localizing fluorophores in new images is extremely fast — orders of magnitude faster than existing algorithms. Faster recovery opens the door to real-time calibration and accelerated acquisition, and future work could tackle more complicated optical systems and more realistic simulators.

scholarly journals Simultaneous spatiotemporal super-resolution and multi-parametric fluorescence microscopy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jagadish Sankaran ◽  
Harikrushnan Balasubramanian ◽  
Wai Hoh Tang ◽  
Xue Wen Ng ◽  
Adrian Röllin ◽  
...  
Keyword(s):  
Single Molecule ◽  
Temporal Dynamics ◽  
Growth Factor Receptor ◽  
Super Resolution ◽  
Actin Binding ◽  
Biological Knowledge ◽  
Data Set ◽  
Epidermal Growth ◽  
Molecular Brightness ◽  
Super Resolution Microscopy

AbstractSuper-resolution microscopy and single molecule fluorescence spectroscopy require mutually exclusive experimental strategies optimizing either temporal or spatial resolution. To achieve both, we implement a GPU-supported, camera-based measurement strategy that highly resolves spatial structures (~100 nm), temporal dynamics (~2 ms), and molecular brightness from the exact same data set. Simultaneous super-resolution of spatial and temporal details leads to an improved precision in estimating the diffusion coefficient of the actin binding polypeptide Lifeact and corrects structural artefacts. Multi-parametric analysis of epidermal growth factor receptor (EGFR) and Lifeact suggests that the domain partitioning of EGFR is primarily determined by EGFR-membrane interactions, possibly sub-resolution clustering and inter-EGFR interactions but is largely independent of EGFR-actin interactions. These results demonstrate that pixel-wise cross-correlation of parameters obtained from different techniques on the same data set enables robust physicochemical parameter estimation and provides biological knowledge that cannot be obtained from sequential measurements.

scholarly journals Comparing Super-Resolution Microscopy Techniques to Analyze Chromosomes

2021 ◽  
Vol 22 (4) ◽  
pp. 1903
Author(s):  
Ivona Kubalová ◽  
Alžběta Němečková ◽  
Klaus Weisshart ◽  
Eva Hřibová ◽  
Veit Schubert
Keyword(s):  
Single Molecule ◽  
Imaging Techniques ◽  
Chromatin Condensation ◽  
Super Resolution ◽  
Stimulated Emission ◽  
Wide Field ◽  
Structured Illumination Microscopy ◽  
Metaphase Chromosomes ◽  
Localization Microscopy ◽  
Super Resolution Microscopy

The importance of fluorescence light microscopy for understanding cellular and sub-cellular structures and functions is undeniable. However, the resolution is limited by light diffraction (~200–250 nm laterally, ~500–700 nm axially). Meanwhile, super-resolution microscopy, such as structured illumination microscopy (SIM), is being applied more and more to overcome this restriction. Instead, super-resolution by stimulated emission depletion (STED) microscopy achieving a resolution of ~50 nm laterally and ~130 nm axially has not yet frequently been applied in plant cell research due to the required specific sample preparation and stable dye staining. Single-molecule localization microscopy (SMLM) including photoactivated localization microscopy (PALM) has not yet been widely used, although this nanoscopic technique allows even the detection of single molecules. In this study, we compared protein imaging within metaphase chromosomes of barley via conventional wide-field and confocal microscopy, and the sub-diffraction methods SIM, STED, and SMLM. The chromosomes were labeled by DAPI (4′,6-diamidino-2-phenylindol), a DNA-specific dye, and with antibodies against topoisomerase IIα (Topo II), a protein important for correct chromatin condensation. Compared to the diffraction-limited methods, the combination of the three different super-resolution imaging techniques delivered tremendous additional insights into the plant chromosome architecture through the achieved increased resolution.

scholarly journals Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 212
Author(s):  
Youssef Skandarani ◽  
Pierre-Marc Jodoin ◽  
Alain Lalande
Keyword(s):  
Deep Learning ◽  
Cardiac Mri ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Ground Truth ◽  
Cine Mri ◽  
Data Sets ◽  
Mri Segmentation ◽  
Segmentation Evaluation ◽  
Ground Truth Data

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.

scholarly journals Graphene Energy Transfer for Single‐Molecule Biophysics, Biosensing, and Super‐Resolution Microscopy

2021 ◽  
pp. 2101099
Author(s):  
Izabela Kamińska ◽  
Johann Bohlen ◽  
Renukka Yaadav ◽  
Patrick Schüler ◽  
Mario Raab ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Single Molecule ◽  
Super Resolution ◽  
Single Molecule Biophysics ◽  
Super Resolution Microscopy

Sodium sulfite as a switching agent for single molecule based super-resolution optical microscopy

2021 ◽  
Author(s):  
Anders K Engdahl ◽  
Oleg Grauberger ◽  
Mark Schüttpelz ◽  
Thomas Huser
Keyword(s):  
Single Molecule ◽  
Fluorescent Dyes ◽  
Sodium Sulfite ◽  
Super Resolution ◽  
Oxygen Scavenger ◽  
Dark State ◽  
Alexa Fluor ◽  
High Illumination ◽  
Human Osteosarcoma Cells ◽  
Super Resolution Microscopy

Photoinduced off-switching of organic fluorophores is routinely used in super-resolution microscopy to separate and localize single fluorescent molecules, but the method typically relies on the use of complex imaging buffers. The most common buffers use primary thiols to reversibly reduce excited fluorophores to a non-fluorescent dark state, but these thiols have a limited shelf life and additionally require high illumination intensities in order to efficiently switch the emission of fluorophores. Recently a high-index, thiol-containing imaging buffer emerged which used sodium sulfite as an oxygen scavenger, but the switching properties of sulfite was not reported on. Here, we show that sodium sulfite in common buffer solutions reacts with fluorescent dyes, such as Alexa Fluor 647 and Alexa Fluor 488 under low to medium intensity illumination to form a semi-stable dark state. The duration of this dark state can be tuned by adding glycerol to the buffer. This simplifies the realization of different super-resolution microscopy modalities such as direct Stochastic Reconstruction Microscopy (dSTORM) and Super-resolution Optical Fluctuation Microscopy (SOFI). We characterize sulfite as a switching agent and compare it to the two most common switching agents by imaging cytoskeleton structures such as microtubules and the actin cytoskeleton in human osteosarcoma cells.

scholarly journals Graphene Energy Transfer for Single‐Molecule Biophysics, Biosensing, and Super‐Resolution Microscopy

2021 ◽  
Vol 33 (42) ◽  
pp. 2105719
Author(s):  
Izabela Kamińska ◽  
Johann Bohlen ◽  
Renukka Yaadav ◽  
Patrick Schüler ◽  
Mario Raab ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Single Molecule ◽  
Super Resolution ◽  
Single Molecule Biophysics ◽  
Super Resolution Microscopy
Sign in / Sign up

Export Citation Format

Share Document