A Common Image Representation And A Patch-Based Search For Correlative Light-Electron-Microscopy (Clem) Registration

International audienceCorrelative light-electron microscopy (CLEM) enables to relate dynamics (or functions) with structure for a better understanding of cell mechanisms. However, the LM and EM images are of very different size, spatial resolution, field of view, and appearance. Registration of LM and EM modalities is then a timely, important but difficult open problem, which still requires some manual assistance. We have designed an original automated CLEM retracing-and-registration method involving a common representation with an adaptive associated scale (or blurring), the determination of the EM patch geometry, and the specification of appropriate descriptors and similarity criterion for the EM patch search. Its efficiency is demonstrated on real CLEM images

Cell Detection by Functional Inverse Diffusion and Non-negative Group Sparsity−-Part I: Modeling and Inverse Problems

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs' Green function to derive a novel parametrization of the acquired images. This parametrization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a non-negative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.Comment: published, 15 page

Cell Detection by Functional Inverse Diffusion and Non-negative Group Sparsity−-Part II: Proximal Optimization and Performance Evaluation

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this second part, we focus on our algorithmic contributions. We provide an algorithm for functional inverse diffusion that solves the variational problem we posed in Part I. As part of the derivation of this algorithm, we present the proximal operator for the non-negative group-sparsity regularizer, which is a novel result that is of interest in itself, also in comparison to previous results on the proximal operator of a sum of functions. We then present a discretized approximated implementation of our algorithm and evaluate it both in terms of operational cell-detection metrics and in terms of distributional optimal-transport metrics.Comment: published, 16 page

A Fast Automatic Colocalization Method for 3D Live Cell and Super-Resolution Microscopy

Colocalizing two fluorescent-labeled proteins remains an open issue in diffraction-limited micro-scopy and raises new challenges with the emergence of super-resolution imaging, single molecule tagging (PALM, dSTORM...) and high content screening. Two distinct colocalization approaches are usually considered to address this problem : the intensity-based methods are very popular but are known to be sensitive to high intensity backgrounds and provide errors if the signal-to-noise ratio (SNR) is low ; the object-based methods analyze the spatial distribution of the two sets of detected spots by using point process statistics but unfortunately get rid of valuable information by reducing objects to points. We propose a unique method (GcoPS : Geo-coPositioning System) that reconciles intensity-based and object-based methods for various applications in both conventional diffraction-limited and super-resolution microscopy. Unlike previous methods, GcoPS is very fast, robust-to-noise and versatile since it efficiently handles 2D and 3D images, variable signal-to-noise ratios (SNR) and any kind of cell shapes and sizes. The experimental results demonstrate that GcoPS unequivocally outperforms the best competitive methods in adverse situations (noise, chromatic aberrations, ...). The method is able to automatically evaluate the colocalization between large regions and small dots and to detect significant negative colocalization. Since the one-parameter (p-value) GcoPS procedure is very fast in 2D and 3D, it should greatly facilitate objective analysis in large-scale high-content screening experiments

A quantitative approach for analyzing the spatio-temporal distribution of 3D intracellular events in fluorescence microscopy

International audienceAnalysis of the spatial distribution of endomembrane trafficking is fundamental to understand the mechanisms controlling cellular dynamics, cell homeostasy, and cell interaction with its external environment in normal and pathological situations. We present a semi-parametric framework to quantitatively analyze and visualize the spatio-temporal distribution of intracellular events from different conditions. From the spatial coordinates of intracellular features such as segmented subcellular structures or vesicle trajectories, QuantEv automatically estimates weighted densities that are easy to interpret and performs a comprehensive statistical analysis from distribution distances. We apply this approach to study the spatio-temporal distribution of moving Rab6 fluorescently labeled membranes with respect to their direction of movement in crossbow-and disk-shaped cells. We also investigate the position of the generating hub of Rab11-positive membranes and the effect of actin disruption on Rab11 trafficking in coordination with cell shape

Fluoresoivien partikkeleiden havaitseminen kolibakteereissa

Escherichia coli are one of the most commonly used bacteria to study important biolog-ical processes such as transcription and translation. This is due to its simple structure and gene expression system, as well as the easiness to maintain live cultures in a laboratory environment. Due to recent developments in fluorescence microscopy and fluorescence labeling, it is now possible to study such biological processes in live cells at single cell and single molecule level. When analyzing such biological processes, the detection of fluorescent objects and subcellular particles is usually one of the first tasks providing important information for subsequent data analysis. Although many algorithms have been proposed for the task, it still remains a challenge due to the limitations of image acquisition when imaging live cells. For example, the intensity of the illumination light and the exposure time is usually minimized to prevent damage to the cells, resulting in images with low signal-to-noise ratio. Due to this and the large amount of data typically used for these studies, automated, high quality parti-cle detection algorithms are needed. In this thesis, we present a novel method for detecting fluorescently labeled subcellular particles in Escherichia coli. The proposed method is tested in both synthetic and em-pirical images and is compared to previous, commonly used methods using standard performance evaluation metrics. The results indicate that the proposed algorithm has a good performance with all image types tested and that it outperforms the previous methods. It is also able to achieve good results with other types of cells than E. coli. Moreover, it allows a robust detection of particles from low signal-to-noise ratio images with good accuracy, thus providing accurate and unbiased results for subsequent analy-sis

Adaptive spot detection with optimal scale selection in fluorescence microscopy images

International audienceAccurately detecting subcellular particles in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking, or classification. Our primary goal is to segment vesicles likely to share nearly the same size in fluorescence microscopy images. Our method termed adaptive thresholding of Laplacian of Gaussian (LoG) images with autoselected scale (ATLAS) automatically selects the optimal scale corresponding to the most frequent spot size in the image. Four criteria are proposed and compared to determine the optimal scale in a scale-space framework. Then, the segmentation stage amounts to thresholding the LoG of the intensity image. In contrast to other methods, the threshold is locally adapted given a probability of false alarm (PFA) specified by the user for the whole set of images to be processed. The local threshold is automatically derived from the PFA value and local image statistics estimated in a window whose size is not a critical parameter. We also propose a new data set for benchmarking, consisting of six collections of one hundred images each, which exploits backgrounds extracted from real microscopy images. We have carried out an extensive comparative evaluation on several data sets with ground-truth, which demonstrates that ATLAS outperforms existing methods. ATLAS does not need any fine parameter tuning and requires very low computation time. Convincing results are also reported on real total internal reflection fluorescence microscopy images