SPITFIR(e): a supermaneuverable image restoration algorithm for 2D, 3D and 3D+Time microscopy images

International audienc

BioImageIT: Integration of image data-management with analysis

International audienceOpen science and FAIR principles are major topics in the field of modern microscopy for biology. This is due to both new data acquisition technologies like super-resolution and light sheet microscopy that generate large datasets but also to the new data analysis methodologies such as deep learning that automated data mining with high accuracy. Nevertheless data are still rarely shared and annotated because this implies a lot of manual and tedious work and software packaging. We present BioImageIT an open source framework that integrates automation of image datamanagement with data processing. Scientists then only need to import their data once in BioImageIT, which automatically generates and manages the metadata every time an operation is performed on the data. This accelerates the data mining process with no need anymore to deal with IT integration and manual analysis and annotations. BioImageIT then automatically implements FAIR principles. The interest of bioImageIT is thus twice. Wewill illustrate this through diverse application workflows, including preprocessing of raw data, complex images reconstructions (i.e Lattice Light Sheet or Multi-Angle TIRF micrscopy), deconvolution/denoising (including DL approaches) and analysis (tracking)

A sequential algorithm to detect diffusion switching along intracellular particle trajectories

Recent advances in molecular biology and fluorescence microscopy imaging have made possible the inference of the dynamics of single molecules in living cells. When we observe a long trajectory (more than 100 points), it is possible that the particle switches mode of motion over time. Then, an issue is to estimate the temporal change-points that is the times at which a change of dynamics occurs. We propose a non-parametric procedure based on test statistics [Briane et al., 2018] computed on local windows along the trajectory to detect the change-points. This algorithm controls the number of false change-point detections in the case where the trajectory is fully Brownian. A Monte Carlo study is proposed to demonstrate the performances of the method and also to compare the procedure to two competitive algorithms. At the end, we illustrate the efficacy of the method on real data in 2D and 3D, depicting the motion of mRNA complexes-called mRNP-in neuronal dendrites, Galectin-3 endocytosis and trafficking within the cell. A user-friendly Matlab package containing examples and the code of the simulations used in the paper is available at http://serpico.rennes.inria.fr/doku.php?id=software:cpanalysis: index

EHD2 is a mechanotransducer connecting caveolae dynamics with gene transcription

Caveolae are small invaginated pits that function as dynamic mechanosensors to buffer tension variations at the plasma membrane. Here we show that under mechanical stress, the EHD2 ATPase is rapidly released from caveolae, SUMOylated, and translocated to the nucleus, where it regulates the transcription of several genes including those coding for caveolae constituents. We also found that EHD2 is required to maintain the caveolae reservoir at the plasma membrane during the variations of membrane tension induced by mechanical stress. Metal-replica electron microscopy of breast cancer cells lacking EHD2 revealed a complete absence of caveolae and a lack of gene regulation under mechanical stress. Expressing EHD2 was sufficient to restore both functions in these cells. Our findings therefore define EHD2 as a central player in mechanotransduction connecting the disassembly of the caveolae reservoir with the regulation of gene transcription under mechanical stress

Dense mapping of intracellular diffusion and drift from single-particle tracking data analysis

International audienceIt is of primary interest for biologists to be able to visualize the dynamics of proteins within the cell. In this paper, we propose a new mapping method to robustly estimate dynamics in the entire cell from particle tracks. To obtain satisfyingdiffusion and drift maps, we use a spatiotemporal kernel estimator. Trajectory classification data is used as input andallows to automatically label particle movements into three classes: confined motion (or subdiffusion), Brownian motion, and directed motion (or superdiffusion). We then use this information to calculate diffusion coefficient and drift mapsseparately on each class of motion

4polar-STORM polarized super-resolution imaging of actin filament organization in cells

Advances in single-molecule localization microscopy are providing unprecedented insights into the nanometer-scale organization of protein assemblies in cells and thus a powerful means for interrogating biological function. However, localization imaging alone does not contain information on protein conformation and orientation, which constitute additional key signatures of protein function. Here, we present a new microscopy method which combines for the first time Stochastic Optical Reconstruction Microscopy (STORM) super-resolution imaging with single molecule orientation and wobbling measurements using a four polarization-resolved image splitting scheme. This new method, called 4polar-STORM, allows us to determine both single molecule localization and orientation in 2D and to infer their 3D orientation, and is compatible with high labelling densities and thus ideally placed for the determination of the organization of dense protein assemblies in cells. We demonstrate the potential of this new method by studying the nanometer-scale organization of dense actin filament assemblies driving cell adhesion and motility, and reveal bimodal distributions of actin filament orientations in the lamellipodium, which were previously only observed in electron microscopy studies. 4polar-STORM is fully compatible with 3D localization schemes and amenable to live-cell observations, and thus promises to provide new functional readouts by enabling nanometer-scale studies of orientational dynamics in a cellular context

A sequential algorithm to detect diffusion switching along intracellular particle trajectories

International audienceMotivation: Recent advances in molecular biology and fluorescence microscopy imaging have made possible theinference of the dynamics of single molecules in living cells. Changes of dynam-ics can occur along a trajectory.Then, an issue is to estimate the temporal change-points that is the times at which a change of dynamics occurs. Thenumber of points in the trajectory required to de-tect such changes will depend on both the magnitude and type ofthe motion changes. Here, the number of points per trajectory is of the order of 102, even if in practice dramaticmotion changes can be detected with less points.Results: We propose a non-parametric procedure based on test statistics computed on local windows along thetrajectory to detect the change-points. This algorithm controls the number of false change-point detections in the casewhere the trajectory is fully Brownian. We also develop a strategy for aggregating the detections obtained with differentwindow sizes so that the window size is no longer a parameter to optimize. A Monte Carlo study is proposed todemonstrate the performances of the method and also to compare the procedure to two competitive algorithms. At theend, we illustrate the efficacy of the method on real data in 2D and 3D, depicting the motion of mRNA complexes—calledmRNA-binding proteins—in neuronal dendrites, Galectin-3 endocytosis and trafficking within the cell.Availability and implementation: A user-friendly Matlab package containing examples and the code of thesimulations used in the paper is available at https://team.inria.fr/serpico/software/cpanalysis

4polar-STORM polarized super-resolution imaging of actin filament organization in cells

International audienceSingle-molecule localization microscopy provides insights into the nanometer-scale spatial organization of proteins in cells, however it does not provide information on their conformation and orientation, which are key functional signatures. Detecting single molecules’ orientation in addition totheir localization in cells is still a challenging task, in particular in dense cell samples. Here, we present a polarization splitting scheme which combines Stochastic Optical Reconstruction Microscopy (STORM) with single molecule 2D orientation and wobbling measurements, without requiring a strong deformation of the imaged point spread function. This method called 4polar-STORM allows, thanks to a control of its detection numerical aperture, to determine both single molecules’ localization and orientation in 2D and to infer their 3D orientation. 4polar-STORM is compatible with relatively high densities of diffraction-limited spots in an image, and is thus ideally placed for the investigation of dense protein assemblies in cells. We demonstrate the potential of this method in dense actin filament organizations driving cell adhesion and motility

BioImageIT: Open-source framework for integration of image data management with analysis

International audienc

BioImageIT: Open-source framework for integration of image data-management with analysis

Abstract Open science and FAIR principles have become major topics in the field of bioimaging. This is due to both new data acquisition technologies that generate large datasets, and new analysis approaches that automate data mining with high accuracy. Nevertheless, data are rarely shared and rigorously annotated because it requires a lot of manual and tedious management tasks and software packaging. We present BioImageIT, an open-source framework for integrating data management according to FAIR principles with data processing