NETWORK TOMOGRAPHY AND MINIMAL PATHS FOR TRAFFIC FLOWESTIMATION IN MOLECULAR IMAGING

International audienceGreen Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells. Original image analysis methods are then required to process challenging 2D or 3D image sequences. To address the tracking problem of several hundreds of objects, we propose an original framework that provides general information about molecule transport, that is about traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the recent advances in Network Tomography commonly used in network communications. In this paper, we address image partition given vesicle stocking areas and multipaths routing for vesicle transport. This approach has been developed for real image sequences and Rab proteins

A signal processing approach for enriched region detection in RNA polymerase II ChIP-seq data

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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

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

Patch-Based Markov Models for Event Detection in Fluorescence Bioimaging

International audienceThe study of protein dynamics is essential for understanding the multi-molecular complexes at subcellular levels. Fluorescent Protein (XFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells, unraveling the live states of the matter. Original image analysis methods are then required to process challenging 2D or 3D image sequences. Recently, tracking methods that estimate the whole trajectories of moving objects have been successfully developed. In this paper, we address rather the detection of meaningful events in spatio-temporal fluorescence image sequences, such as apparent stable "stocking areas" involved in membrane transport. We propose an original patch-based Markov modeling to detect spatial irregularities in fluorescence images with low false alarm rates. This approach has been developed for real image sequences of cells expressing XFP-tagged Rab proteins, known to regulate membrane trafficking

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

NON PARAMETRIC CELL NUCLEI SEGMENTATION BASED ON A TRACKING OVER DEPTH FROM 3D FLUORESCENCE CONFOCAL IMAGES

International audience3D cell nuclei segmentation from fluorescence microscopy images is a key application in many biological studies. We propose a new, fully automated and non parametric method that takes advantage of the resolution anisotropy in fluorescence microscopy. The cell nuclei are first detected in 2D at each image plane and then tracked over depth through a graph based decision to recover their 3D profiles. As the tracking fails to separate very close cell nuclei along depth, we also propose a corrective step based on an intensity projection criterion. Experimental results on real data demonstrate the efficacy of the proposed method

Canonical and Atypical E2Fs Regulate the Mammalian Endocycle

SUMMARY The endocycle is a variant cell cycle consisting of successive DNA synthesis and Gap phases that yield highly polyploid cells. Although essential for metazoan development, relatively little is known about its control or physiologic role in mammals. Using novel lineage-specific cre mice we identified two opposing arms of the E2F program, one driven by canonical transcription activation (E2F1, E2F2 and E2F3) and the other by atypical repression (E2F7 and E2F8), that converge on the regulation of endocycles in vivo. Ablation of canonical activators in the two endocycling tissues of mammals, trophoblast giant cells in the placenta and hepatocytes in the liver, augmented genome ploidy, whereas ablation of atypical repressors diminished ploidy. These two antagonistic arms coordinate the expression of a unique G2/M transcriptional program that is critical for mitosis, karyokinesis and cytokinesis. These results provide in vivo evidence for a direct role of E2F family members in regulating non-traditional cell cycles in mammals

Analysis of multiplexed whole slide images with QuPath and Cytomap

<p>This image was acquired during MIFOBIO 2023 and was used for workshop entitled "Analysis of multiplexed whole slide images with QuPath and Cytomap".</p&gt