TestSTORM: Versatile simulator software for multimodal super-resolution localization fluorescence microscopy

Novák, Tibor; Gajdos, Tamás; Sinkó, József; Szabó, Gábor; Erdélyi, Miklós

doi:10.1038/s41598-017-01122-7

Cited by 35 publications

(31 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The simulators facilitate the generation of data for use in cluster analysis applied to various biological structures. Popular SMLM simulators software includes SuReSim, 137 TestSTORM, 138 and SMeagol. 139 Synthetic data generation offers controlled creation of clusters and background with known density distributions (e.g., Gaussian, uniform) and cluster shapes (e.g., circular, tubular).…”

Section: Main Textmentioning

confidence: 99%

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

2020

View full text Add to dashboard Cite

Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10–20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.

show abstract

Section: Main Textmentioning

confidence: 99%

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

2020

View full text Add to dashboard Cite

show abstract

“…For single-molecule localization microscopy (SMLM), the limited number of useful emitters and limited SNR poses a challenge. To quantitatively assess the applicability of our method to multicolor SMLM, we generated movies of simulated blinking emitters using the Test-STORM toolbox [48] with a known ground truth. In simulations, two fluorophores were simulated: Alexa 647 and Alexa 565 ( Figs.…”

Section: Single-molecule Localization Microscopymentioning

confidence: 99%

Multicolor localization microscopy and point-spread-function engineering by deep learning

Park

Shoman

et al. 2019

Opt. Express

View full text Add to dashboard Cite

Deep learning has become an extremely effective tool for image classification and image restoration problems. Here, we apply deep learning to microscopy and demonstrate how neural networks can exploit the chromatic dependence of the point-spread function to classify the colors of single emitters imaged on a grayscale camera. While existing localization microscopy methods for spectral classification require additional optical elements in the emission path, e.g., spectral filters, prisms, or phase masks, our neural net correctly identifies static and mobile emitters with high efficiency using a standard, unmodified singlechannel configuration. Furthermore, we show how deep learning can be used to design new phase-modulating elements that, when implemented into the imaging path, result in further improved color differentiation between species, including simultaneously differentiating four species in a single image.

show abstract

“…Therefore, the applicability of the used 2D data analysis requires a validation process. A test sample simulator software (TestSTORM 63,64 ) was used to generate the ground truth model and comparative 2D and 3D evaluations were performed on the reconstructed super-resolved images. Images were evaluated from the same aspect (number of clusters, mean number of cluster elements etc.)…”

Section: Supplementary Informationsmentioning

confidence: 99%

Quantification of DNA damage induced γH2AX focus formation via super-resolution dSTORM localization microscopy

Varga

Majoros

Újfaludi

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

SUMMARYIn eukaryotic cells, each process, in which DNA is involved, should take place in the context of chromatin structure. DNA double-strand breaks (DSBs) are one of the most deleterious damages often leading to chromosomal rearrangement. In response to environmental stresses, cells have developed repair mechanisms to eliminate the DSBs. Upon DSB induction, several factors play roles in chromatin relaxation by catalysing the appropriate histone posttranslational modification (PTM) steps, therefore promoting the access of the repair factors to the DSBs. Among these PTMs, the phosphorylation of the histone variant H2AX at its Ser139 residue (also known as γH2AX) could be observed at the break sites. The structure of γH2AX focus has to be organized during the repair as it contributes to accessibility of specific repair proteins to the damaged site. Our aim was to develop a quantitative approach to analyse the morphology of individual repair foci by super-resolution dSTORM microscopy to gain insight into genome organization in DNA repair. We have established a specific dSTORM measurement process by developing a new analytical algorithm for gaining quantitative information about chromatin morphology and repair foci topology at individual γH2AX enriched repair focus. By this method we quantified unique repair foci to show the average distribution of γH2AX clusters. By monitoring γH2AX signal, we could reach 20 nm spatial resolution and resolve a single DNA damage spot, which allow us to identify different chromatin sub-clusters around the break site. Additionally, based on our new analysis method, we were able to show the number of nucleosomes in each sub-cluster that could allow us to define the possible chromatin structure and the nucleosome density around the break sites. This method is the first demonstration of a single-cell based quantitative measurement of a discrete repair focus, which could provide new opportunities to categorize spatial organization of dot patterns by parametric determination of topological similarity.

show abstract

TestSTORM: Versatile simulator software for multimodal super-resolution localization fluorescence microscopy

Cited by 35 publications

References 46 publications

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

Multicolor localization microscopy and point-spread-function engineering by deep learning

Quantification of DNA damage induced γH2AX focus formation via super-resolution dSTORM localization microscopy

Contact Info

Product

Resources

About