ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging

Ovesný, Martin; Křížek, Pavel; Borkovec, Josef; Švindrych, Zdeněk; Hagen, Guy M.

doi:10.1093/bioinformatics/btu202

Cited by 1,346 publications

(1,341 citation statements)

References 12 publications

Supporting

Mentioning

1,254

Contrasting

Unclassified

Order By: Relevance

“…We begin by assuming a single coordinate for each molecule in the region of interest (ROI), generated by the localization software, in our case ThunderSTORM 24 . The 2D molecular positions are modelled as a set of Gaussian distributed clusters overlaid on a completely spatially random (CSR) background.…”

Section: Resultsmentioning

confidence: 99%

“…Details of the sample preparation method and imaging can be found in the Supplementary Materials and Methods. Multiple blinking of fluorophores and overlapped PSFs were compensated for using ThunderSTORM localisation software 24 and optimal settings estimated using the method of Annibale et al 23 ( Supplementary Fig. 12 and Supplementary Materials and Methods).…”

Section: Analysis Of Protein Clustering In Primary Human T Cellsmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian cluster identification in single-molecule localization microscopy data

et al. 2015

View full text Add to dashboard Cite

Correspondence should be addressed to patrick. rubin-delanchy@bristol.ac.uk and dylan.owen@kcl.ac.uk Single-molecule identification-based super-resolution microscopy techniques such as photo-activated localisation microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) produce pointillist data sets of molecular coordinates. While many algorithms exist for the identification and localisation of molecules from the raw image data, methods for analysing the resulting point patterns for properties such as clustering have remained relatively under-studied. Here, we present the first model-based Bayesian approach to evaluate molecular cluster assignment proposals which, in this article, are generated by analysis based on Ripley's Kfunction. The method is also the first to take full account of the individual localisation precisions calculated for each emitter. The technique is validated using simulated and experimental data from which we characterise the clustering behaviour of CD3ζ, an important subunit of the CD3-T cell receptor complex required for T cell function, in resting and activated primary human T cells.Conventional fluorescence microscopes produce images of the distribution of fluorophores in the sample convolved with the microscope Point Spread Function (PSF). Due to diffraction, this PSF typically has a width of hundreds of nanometres meaning the resulting image has a resolution, as assessed by the Rayleigh criterion, of ~200 nm. Several strategies now exist to circumvent this resolution limit 1 . Some of these, such as Stimulated Emission Depletion (STED) microscopy, rely on narrowing the excitation spot of a confocal microscope by means of a toroidal depletion beam and the process of stimulated emission 2,3 . Despite the increased resolution, these produce conventional fluorescence images, i.e., arrays of pixels with values representing the fluorescence intensity at those locations. Quantification can be performed in the same way as for conventional microscopes.Another strategy is based on Single-Molecule Localisation Microscopy (SMLM) [4][5][6][7] . This relies on the temporal separation of the excitation of fluorophores in the sample whose PSFs would otherwise overlap at the detector.The position of each fluorophore can then be estimated from the centres of the PSFs. Many algorithms are available to extract the x-y coordinates of the molecules [8][9][10] . Each emitter can be localised to a precision between 10 and 30 nm. Common strategies for the temporal separation of molecules involve intra-molecular rearrangements to switch from dark to fluorescent states or the exploitation of non-emitting molecular radicals 11,12 . These strategies are typically pursued using photoactivatable or photoconvertible fluorescent proteins or small molecule probes coupled with a reducing buffer and immunostaining protocols 13 . We refer to all such strategies as SMLM.Unlike non-pointillist microscopy methods, SMLM imaging does not produce a conventional image. Instead, the raw data is a list of the...

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Analysis Of Protein Clustering In Primary Human T Cellsmentioning

confidence: 99%

Bayesian cluster identification in single-molecule localization microscopy data

et al. 2015

View full text Add to dashboard Cite

show abstract

“…We recorded 2,000 images using an EMCCD camera (iXon Ultra 897; Andor) at a frame rate of 50 Hz with field of view of 20 × 20 μm. The super-resolution image was generated using a standard localization algorithm (ThunderSTORM, ImageJ plug-in) (81). To quantify the spatial correlation of the siRNA-NP and endosome, pair correlation functions [g(r)] were calculated using a custom MATLAB code.…”

Section: Flow Cytometry and Immunocytochemistry Analysis Of Tf Knockdmentioning

confidence: 99%

Multiplexed RNAi therapy against brain tumor-initiating cells via lipopolymeric nanoparticle infusion delays glioblastoma progression

Khan

Suvà

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

107

View full text Add to dashboard Cite

Brain tumor-initiating cells (BTICs) have been identified as key contributors to therapy resistance, recurrence, and progression of diffuse gliomas, particularly glioblastoma (GBM). BTICs are elusive therapeutic targets that reside across the blood-brain barrier, underscoring the urgent need to develop novel therapeutic strategies. Additionally, intratumoral heterogeneity and adaptations to therapeutic pressure by BTICs impede the discovery of effective anti-BTIC therapies and limit the efficacy of individual gene targeting. Recent discoveries in the genetic and epigenetic determinants of BTIC tumorigenesis offer novel opportunities for RNAi-mediated targeting of BTICs. Here we show that BTIC growth arrest in vitro and in vivo is accomplished via concurrent siRNA knockdown of four transcription factors (SOX2, OLIG2, SALL2, and POU3F2) that drive the proneural BTIC phenotype delivered by multiplexed siRNA encapsulation in the lipopolymeric nanoparticle 7C1. Importantly, we demonstrate that 7C1 nano-encapsulation of multiplexed RNAi is a viable BTICtargeting strategy when delivered directly in vivo in an established mouse brain tumor. Therapeutic potential was most evident via a convection-enhanced delivery method, which shows significant extension of median survival in two patient-derived BTIC xenograft mouse models of GBM. Our study suggests that there is potential advantage in multiplexed targeting strategies for BTICs and establishes a flexible nonviral gene therapy platform with the capacity to channel multiplexed RNAi schemes to address the challenges posed by tumor heterogeneity.siRNA | lipopolymeric nanoparticle | glioblastoma transcription factor | brain tumor-initiating cells | convection-enhanced delivery G lioblastoma (GBM) is one of the most challenging tumors to treat (1, 2). Despite decades of research and maximal clinical combination therapy encompassing surgical resection, chemotherapy, and radiation, the median life expectancy of patients has not been extended beyond 2 y after diagnosis (2). Increasing evidence suggests that the genetic, epigenetic, and signaling heterogeneity of GBM underlies the ineffectiveness of currently available therapeutics (1, 2). Additionally, therapeutic schemes devised to challenge brain tumor cells are frequently thwarted by insufficient delivery caused by pharmacokinetics, the blood-brain barrier (BBB), and an altered tumor microenvironment in which tumor-derived signaling recruits immunomodulatory cells and induces extracellular matrix remodeling to build safe harbors of tumorigenic niches (3-5). These obstacles call for tailored therapeutic strategies to counter tumor heterogeneity and overcome roadblocks in delivery. RNAi targeting drivers of tumorigenesis shows strong potential to supplement the development of traditional small-molecule pharmaceutics (6). However, delivery remains a key obstacle for efficient RNAi against tumor drivers (5,7,8). RNA-sequencing analysis of patient tissue combined with histology and in situ hybridization (Ivy Glioblastoma Atlas Pro...

show abstract

“…They were photobleached with the 488 nm laser prior to the measurements. Analysis with ThunderSTORM [36] yielded super-resolved PALM images, as shown by a representative example in Figure 4 a. The super-resolved part of the image on the right shows structural details which are obscure in the diffraction-limited image on the left.…”

mentioning

confidence: 98%

Nanoscopic Visualization of Soft Matter Using Fluorescent Diarylethene Photoswitches

et al. 2016

View full text Add to dashboard Cite

The in situ imaging of soft matter is of paramount importance for a detailed understanding of functionality on the nanoscopic scale. Although super-resolution fluorescence microscopy methods with their unprecedented imaging capabilities have revolutionized research in the life sciences, this potential has been far less exploited in materials science. One of the main obstacles for a more universal application of superresolved fluorescence microscopy methods is the limitation of readily available suitable dyes to overcome the diffraction limit. Here, we report a novel diarylethene-based photoswitch with a highly fluorescent closed and a nonfluorescent open form. Its photophysical properties, switching behavior, and high photostability make the dye an ideal candidate for photoactivation localization microscopy (PALM). It is capable of resolving apolar structures with an accuracy far beyond the diffraction limit of optical light in cylindrical micelles formed by amphiphilic block copolymers.The nanoscopic structure of soft-matter materials determines their properties. [1] Therefore, methods to directly visualize structures in the nanometer range are of paramount importance for the ongoing evolution of novel materials with specialized and adaptive properties for sophisticated applications. Scanning probe microscopy techniques give access to the nanometer range and determine surface properties such as topology and softness, [2,3] while modern electron microscopy methods, such as scanning electron microscopy (SEM) [4,5] and transmission electron microscopy (TEM), [6][7][8] can yield structural information even in the subnanometer range when there is sufficient electron density contrast. Despite the success of these methods, they are technically demanding and time-consuming. Furthermore, many softmatter samples possess poor electron contrast, and require non-invasive in situ imaging below the surface as well as the possibility to directly study dynamics. In recent years, superresolved fluorescence microscopy has revolutionized optical imaging, [9][10][11][12][13][14] by utilizing the photophysical or photochemical switching of fluorescent dyes in a sophisticated manner in combination with modern optics. So far, the life sciences have benefited, in particular, from the new possibilities of resolving structures well beyond the diffraction limit of light. Only a few examples of the application of super-resolution microscopy to materials science have been reported, [15][16][17][18] since concepts that require, for example, the addition of (polar) switching buffers often fail for these systems. Therefore, the main bottleneck for more universal applications of super-resolution imaging are switchable dyes with suitable (photo-)physical and chemical properties, such as high photostability, adjustable switching rates, minimum interaction with the environment to be probed, and simple design, with the possibility of multiple and straightforward derivatization for the specific labeling of structures or compartments. [19] ...

show abstract

ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging

Cited by 1,346 publications

References 12 publications

Bayesian cluster identification in single-molecule localization microscopy data

Bayesian cluster identification in single-molecule localization microscopy data

Multiplexed RNAi therapy against brain tumor-initiating cells via lipopolymeric nanoparticle infusion delays glioblastoma progression

Nanoscopic Visualization of Soft Matter Using Fluorescent Diarylethene Photoswitches

Contact Info

Product

Resources

About