A Patch-Based Method for Repetitive and Transient Event Detection in Fluorescence Imaging

Automatic detection and characterization of molecular behavior in large data sets obtained by fast imaging in advanced light microscopy become key issues to decipher the dynamic architectures and their coordination in the living cell. Automatic quantification of the number of sudden and transient events observed in fluorescence microscopy is discussed in this paper. We propose a calibrated method based on the comparison of image patches expected to distinguish sudden appearing/vanishing fluorescent spots from other motion behaviors such as lateral movements. We analyze the performances of two statistical control procedures and compare the proposed approach to a frame difference approach using the same controls on a benchmark of synthetic image sequences. We have then selected a molecular model related to membrane trafficking and considered real image sequences obtained in cells stably expressing an endocytic-recycling trans-membrane protein, the Langerin-YFP, for validation. With this model, we targeted the efficient detection of fast and transient local fluorescence concentration arising in image sequences from a data base provided by two different microscopy modalities, wide field (WF) video microscopy using maximum intensity projection along the axial direction and total internal reflection fluorescence microscopy. Finally, the proposed detection method is briefly used to statistically explore the effect of several perturbations on the rate of transient events detected on the pilot biological model

An extended model of vesicle fusion at the plasma membrane to estimate protein lateral diffusion from TIRF microscopy images.

BACKGROUND: Characterizing membrane dynamics is a key issue to understand cell exchanges with the extra-cellular medium. Total internal reflection fluorescence microscopy (TIRFM) is well suited to focus on the late steps of exocytosis at the plasma membrane. However, it is still a challenging task to quantify (lateral) diffusion and estimate local dynamics of proteins. RESULTS: A new model was introduced to represent the behavior of cargo transmembrane proteins during the vesicle fusion to the plasma membrane at the end of the exocytosis process. Two biophysical parameters, the diffusion coefficient and the release rate parameter, are automatically estimated from TIRFM image sequences, to account for both the lateral diffusion of molecules at the membrane and the continuous release of the proteins from the vesicle to the plasma membrane. Quantitative evaluation on 300 realistic computer-generated image sequences demonstrated the efficiency and accuracy of the method. The application of our method on 16 real TIRFM image sequences additionally revealed differences in the dynamic behavior of Transferrin Receptor (TfR) and Langerin proteins. CONCLUSION: An automated method has been designed to simultaneously estimate the diffusion coefficient and the release rate for each individual vesicle fusion event at the plasma membrane in TIRFM image sequences. It can be exploited for further deciphering cell membrane dynamics

Non-parametric regression for patch-based fluorescence microscopy image sequence denoising

We present a non-parametric regression method for denoising 3D image sequences acquired in fluorescence microscopy. The proposed method exploits 3D+time information to improve the signal-to-noise ratio of images corrupted by mixed Poisson-Gaussian noise. A variance stabilization transform is first applied to the image-data to introduce independence between the mean and variance. This pre-processing requires the knowledge of parameters related to the acquisition system, also estimated in our approach. In a second step, we propose an original statistical patch-based framework for noise reduction and preservation of space-time discontinuities. In our study, discontinuities are related to small moving spots with high velocity observed in fluorescence video-microscopy. The idea is to minimize an objective nonlocal energy functional involving spatio-temporal image patches. The minimizer has a simple form and is defined as the weighted average of input data taken in spatially-varying neighborhoods. The size of each neighborhood is optimized to improve the performance of the pointwise estimator. The performance of the algorithm which requires no motion estimation, is then demonstrated on both synthetic and real image sequences using qualitative and quantitative criteria

Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs

SIAM Journal Multiscale Modeling & SimulationTo develop better image change detection algorithms, new models able to capture spatio-temporal regularities and geometries present in an image pair are needed. In this paper, we propose a multiscale formulation for modeling semi-local inter-image interactions and detecting local or regional changes in an image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections obtained from the neighboring pixels, for different patch sizes. We study the statistical properties of the non-parametric detection approach that guarantees small probabilities of false alarms. Experimental results on several applications demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting occlusions and meaningful changes for a variety of illumination conditions and signal-to-noise ratios. The number of control parameters of the algorithm is small and the adjustment is intuitive in most cases

Estimation of the flow of particles within a partition of the image domain in fluorescence video-microscopy

International audienceAutomatic analysis of the dynamic content in fluorescence video-microscopy is crucial for understanding molecular mechanisms involved in cell functions. In this paper, we propose an original approach for analyzing particle trafficking in these sequences. Instead of individually tracking every particle, we estimate the particle flows between predefined regions. This approach allows us to process image sequences with a high number of particles and a low frame rate. We investigate several ways to estimate the particle flow at the cellular level and evaluate their performance in synthetic and real image sequences

Sélection d'échelle automatique précise et seuillage localement adapté pour la segmentation de vésicules en microscopie TIRF

National audienceEn imagerie cellulaire et microscopie de fluorescence, la première phase de l'analyse consiste généralement à détecter une grande quantité d'éléments (molécules, vésicules). De nombreuses méthodes de détection ont été développées dans ce contexte, mais elles réclament souvent un paramétrage assez fin et ne peuvent pas toujours s'adapter à l'échelle des éléments à détecter. Nous proposons une méthode incluant une sélection d'échelle automatique précise et un seuillage localement adapté. Des expérimentations quantitatives sur images simulées montrent les avantages et les meilleures performances de notre méthode. Des résultats sur séquences réelles confirment son intérêt

QuantEv: quantifying the spatial distribution of intracellular events

Analysis 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. The development of automated methods to visualize and quantify the spatial distribution of intracellular events is essential to process the ever-increasing amount of data generated with modern light mi-croscopy. We present a generic and non-parametric framework to quantitatively analyze and visualize the spatio-temporal distribution of intracellular events from different conditions in fluorescence microscopy. From the spatial coordinates of intracellular features such as segmented subcellular structures or dynamic processes like vesicle trajectories, QuantEv automatically estimates weighted densities for each dimension of the 3D cylindrical coordinate system 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 cells constrained in crossbow-and disk-shaped fibronectin patterns. 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. An Icy plugin and a tutorial are available athttp://icy.bioimageanalysis.org/plugin/QuantEv

Modélisation et estimation de la diffusion de protéines à l’exocytose dans des séquences d’images de microscopie TIRF

National audienceDans le contexte de l’analyse de la dynamique membra-naire, nous proposons un nouveau modèle pour représen-ter le comportement de protéines au cours de la fusion devésicules dans la membrane plasmique à la fin du proces-sus d’exocytose. Le modèle introduit incorpore la phase delibération continue des protéines de la vésicule, caracté-risée par le taux de libération. Les protéines diffusent en-suite dans la membrane, ce qui ajoute un second paramètrebiophysique, le coefficient de diffusion. Une méthode origi-nale est élaborée pour estimer ces deux paramètres à par-tir d’une séquence d’images. Une évaluation quantitativeen démontre l’efficacité, et les résultats sur des séquencesréelles de microscopie TIRF permettent de mettre en évi-dence des différences de comportement des protéines Lan-gerin et TfR

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Éditoral / Editoria

Exposure to Farm Animals and Risk of Lung Cancer in the AGRICAN Cohort.

Epidemiologic studies have found lower risks of lung cancer in farmers. However, little is known about the types of agricultural activities concerned. In the Agriculture and Cancer cohort, we assessed the relationship between animal farming and lung cancer by investigating the types of animals, tasks, and timing of exposure. Analyses included 170,834 participants from the Agriculture and Cancer (AGRICAN) cohort in France. Incident lung cancers were identified through linkage with cancer registries from enrollment (2005-2007) to 2011. A Cox model, adjusting for pack-years of cigarette smoking, was used to calculate hazard ratios and 95% confidence intervals. Lung cancer risk was inversely related to duration of exposure to cattle (≥40 years: hazard ratio = 0.60, 95% confidence interval: 0.41, 0.89; P for trend < 0.01) and to horse farming (≥20 years: hazard ratio = 0.64, 95% confidence interval: 0.35, 1.17; P for trend = 0.09), especially for adenocarcinomas, but not with poultry or pig farming. More pronounced decreased risks were reported among individuals who had cared for animals, undertaken milking, and who had been exposed to cattle in infancy. Our study provides strong evidence of an inverse association between lung cancer and cattle and horse farming. Further research is warranted to identify the etiologic protective agents and biological mechanisms