Optimizing Voronoi-based quantifications for reaching interactive analysis of 3D localizations in the million range

Over the last decade, single-molecule localization microscopy (SMLM) has revolutionized cell biology, making it possible to monitor molecular organization and dynamics with spatial resolution of a few nanometers. Despite being a relatively recent field, SMLM has witnessed the development of dozens of analysis methods for problems as diverse as segmentation, clustering, tracking or colocalization. Among those, Voronoi-based methods have achieved a prominent position for 2D analysis as robust and efficient implementations were available for generating 2D Voronoi diagrams. Unfortunately, this was not the case for 3D Voronoi diagrams, and existing methods were therefore extremely time-consuming. In this work, we present a new hybrid CPU-GPU algorithm for the rapid generation of 3D Voronoi diagrams. Voro3D allows creating Voronoi diagrams of datasets composed of millions of localizations in minutes, making any Voronoi-based analysis method such as SR-Tesseler accessible to life scientists wanting to quantify 3D datasets. In addition, we also improve ClusterVisu, a Voronoi-based clustering method using Monte-Carlo simulations, by demonstrating that those costly simulations can be correctly approximated by a customized gamma probability distribution function

SMoLR: visualization and analysis of single-molecule localization microscopy data in R

Background: Single-molecule localization microscopy is a super-resolution microscopy technique that allows for nanoscale determination of the localization and organization of proteins in biological samples. For biological interpretation of the data it is essential to extract quantitative information from the super-resolution data sets. Due to the complexity and size of these data sets flexible and user-friendly software is required. Results: We developed SMoLR (Single Molecule Localization in R): a flexible framework that enables exploration and analysis of single-molecule localization data within the R programming environment. SMoLR is a package aimed at extracting, visualizing and analyzing quantitative information from localization data obtained by singlemolecule microscopy. SMoLR is a platform not only to visualize nanoscale subcellular structures but additionally provides means to obtain statistical information about the distribution and localization of molecules within them. This can be done for individual images or SMoLR can be used to analyze a large set of super-resolution images at once. Additionally, we describe a method using SMoLR for image feature-based particle averaging, resulting in identification of common features among nanoscale structures. Conclusions: Embedded in the extensive R programming environment, SMoLR allows scientists to study the nanoscale organization of biomolecules in cells by extracting and visualizing quantitative information and hence provides insight in a wide-variety of different biological processes at the single-molecule level

Evaporation and clustering of ammonia droplets in a hot environment

Recent developments in the transition to zero-carbon fuels show that ammonia is a valid candidate for combustion. However, liquid ammonia combustion is difficult to stabilize due to a large latent heat of evaporation, which generates a strong cooling effect that adversely affects the flame stabilization and combustion efficiency. In addition, the slow burning rate of ammonia enhances the undesired production of NOx and N2O. To increase the flame speed, ammonia must be blended with a gaseous fuel having a high burning rate. In this context, a deeper understanding of the droplet dynamics is required to optimize the combustor design. To provide reliable physical insights into diluted ammonia sprays blended with gaseous methane, direct numerical simulations are employed. Three numerical experiments were performed with cold, standard, and hot ambient in nonreactive conditions. The droplet radius and velocity distribution, as well as the mass and heat coupling source terms are compared to study the effects on the evaporation. Since the cooling effect is stronger than the heat convection between the droplet and the environment in each case, ammonia droplets do not experience boiling. On the other hand, the entrainment of dry air into the ammonia-methane mixture moves the saturation level beyond 100% and droplets condense. The aforementioned phenomena are found to strongly affect the droplet evolution. Finally, a three-dimensional Voronoi analysis is performed to characterize the dispersive or clustering behavior of droplets by means of the definition of a clustering index

Reconstruction of plant microstructure using distance weighted tessellation algorithm optimized by virtual segmentation

Abstract(#br)The accurate reconstruction model of plant microstructure is important for obtaining the mechanical properties of plant tissues. In this paper, a virtual segmentation technique is proposed to optimize Delaunay triangulation. Based on the optimized Delaunay triangulation, an Optimized Distance Weighted Tessellation (ODWT) algorithm is developed. Two different structures, namely carrot and retting maize vascular bundles, were reconstructed via the ODWT algorithm. The accuracy of ODWT is evaluated statistically by comparing with Centroid-based Voronoi Tessellation (CVT) and Area Weighted Tessellation (AWT). The results show that ODWT has distinct advantages over CVT and AWT. It is worth mentioning that ODWT has better performance than CVT when there exists large diversity in adjacent cell area. It is found that CVT and AWT fail to reconstruct cells with elongated and concave shapes, while ODWT shows excellent feasibility and reliability. Furthermore, ODWT is capable of establishing finite tissue boundary, which CVT and AWT have failed to realize. The purpose of this work is to develop an algorithm with higher accuracy to implement the preprocessing for further numerical study of plants properties. The comparison results of the simulated values of the longitudinal tensile modulus with the experimental value show that ODWT algorithm can improve the prediction accuracy of multi-scale models on mechanical properties

An efficient GUI-based clustering software for simulation and Bayesian cluster analysis of single-molecule localization microscopy data

Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster’s constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays

Quantitative Analysis of Nuclear Lamins Imaged by Super-Resolution Light Microscopy

The nuclear lamina consists of a dense fibrous meshwork of nuclear lamins, Type V intermediate filaments, and is similar to 14 nm thick according to recent cryo-electron tomography studies. Recent advances in light microscopy have extended the resolution to a scale allowing for the fine structure of the lamina to be imaged in the context of the whole nucleus. We review quantitative approaches to analyze the imaging data of the nuclear lamina as acquired by structured illumination microscopy (SIM) and single molecule localization microscopy (SMLM), as well as the requisite cell preparation techniques. In particular, we discuss the application of steerable filters and graph-based methods to segment the structure of the four mammalian lamin isoforms (A, C, B1, and B2) and extract quantitative information

Single-molecule localization microscopy analysis with ImageJ

ImageJ is a versatile and powerful tool for quantitative image analysis in microscopy. It is open-source software, platform-independent and enables students and researchers to obtain an easy but thorough introduction into image analysis. Especially the image processing package Fiji is a valuable and powerful extension of ImageJ. Several plugins and macros for single-molecule localization microscopy (SMLM) have been developed during the last decade. These novel tools cover the steps from single-molecule localization and image reconstruction to SMLM data postprocessing such as density analysis, image registration or resolution estimation. This article describes how ImageJ/Fiji can be used for image analysis, reviews existing extensions for SMLM, and aims at introducing and motivating novices and advanced SMLM users alike to explore the possibilities of ImageJ/Fiji for automated and quantitative data analysis

Super-resolution mapping of receptor engagement during HIV entry

The plasma membrane (PM) serves as a major interface between the cell and extracellular stimuli. Studies indicate that the spatial organisation and dynamics of receptors correlate with the regulation of cellular responses. However, the nanoscale spatial organisation of specific receptor molecules on the surface of cells is not well understood primarily because these spatial events are beyond the resolving power of available tools. With the development in super-resolution microscopy and quantitative analysis approaches, it optimally poises me to address some of these questions. The human immunodeficiency virus type-1 (HIV-1) entry process is an ideal model for studying the functional correlation of the spatial organisation of receptors. The molecular interactions between HIV envelope glycoprotein (Env) and key receptors, CD4 and co-receptor CCR5/CXCR4, on the PM of target cells have been well characterised. However, the spatial organisation that receptors undergo upon HIV-1 binding remains unclear. In this project, I established a Single Molecule Localisation Microscopy (SMLM) based visualisation and quantitative analysis pipeline to characterise CD4 membrane organisation in CD4+ T cells, the main host cell target for HIV-1 infection. I found that prior to HIV engagement, CD4 and CCR5 molecules are organised in small distinct clusters across the PM. Upon HIV-1 engagement, I observed dynamic congregation and subsequent dispersal of virus-associated CD4 clusters within 10min. I further incorporated statistical modelling to show that this reorganisation is not random. This thesis provides one of the first nanoscale imaging and quantitative pipelines for visualising and quantifying membrane receptors. I showed that this quantitative approach provides a robust methodology for understanding the recruitment of HIV-1 receptors before the formation of a fusion pore. This methodology can be applied to the analyses of the nanoscale organisation of PM receptors to link the spatial organisation to function

GPVI receptor spatial organisation and signalling in platelets

Platelets are crucial players in many (patho)physiological processes including haemostasis, thrombosis and inflammation. The immunoreceptor tyrosine-based activation motif (ITAM)-containing receptor glycoprotein VI (GPVI) is the major platelet signalling receptor for collagen, which is exposed upon vascular injury. Once bound to collagen, multiple GPVI dimers cluster at the platelet surface increasing the avidity for collagen and regulating the signalling. In suspension, GPVI is cleaved by the metalloproteinases ADAM10/17 upon collagen activation. This thesis aimed to elucidate the signalling dynamics underlying GPVI clustering in platelets spread on collagen. Super-resolution microscopy and two-layer cluster analysis revealed that immobilised collagen induces long-term GPVI clustering and signalling in tandem with minimal shedding and low levels of GPVI/ADAM10 colocalisation. Conversely, GPVI is massively shed and the signalling rapidly downregulated in platelet suspensions stimulated with collagen. Syk, but not Src, kinase-dependent signalling is required to maintain clustering of the collagen integrin receptor α2β1, whilst neither is required for GPVI. This thesis proposes that GPVI clustering on immobilised collagen protects GPVI from shedding, thereby enabling longer-lasting Src and Syk-dependent signalling, integrin α2β1 activation and stable adhesion to collagen. Nanobodies targeting human GPVI have potential uses in many different biological applications from anti-platelet approaches to super-resolution imaging

3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations

International audienceMotivation:Single-molecule localization microscopy (SMLM) can play an important role in integrated structural biology approaches to identify, localize and determine the 3D structure of cellular structures. While many tools exist for the 3D analysis and visualization of crystal or cryo-EM structures little exists for 3D SMLM data, which can provide unique insights but are particularly challenging to analyze in three dimensions especially in a dense cellular context.Results:We developed 3DClusterViSu, a method based on 3D Voronoi tessellations that allows local density estimation, segmentation and quantification of 3D SMLM data and visualization of protein clusters within a 3D tool. We show its robust performance on microtubules and histone proteins H2B and CENP-A with distinct spatial distributions. 3DClusterViSu will favor multi-scale and multi-resolution synergies to allow integrating molecular and cellular levels in the analysis of macromolecular complexes.Availability and impementation:3DClusterViSu is available under http://cbi-dev.igbmc.fr/cbi/voronoi3D.Supplementary information:Supplementary figures are available at Bioinformatics online