Thierry Pécot

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PTEN (Phosphatase and tensin homolog deleted on chromosome 10) expression in stromal fibroblasts suppresses epithelial mammary tumours, but the underlying molecular mechanisms remain unknown. Using proteomic and expression profiling, we show that Pten loss from mammary stromal fibroblasts activates an oncogenic secretome that orchestrates the(More)
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 lineage-specific cre mice we identified two opposing arms of the E2F program, one driven by canonical(More)
Green Fluorescent Protein (GFP)-tagging and time-lapse flu-orescence 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(More)
Tumor fibroblasts are active partners in tumor progression, but the genes and pathways that mediate this collaboration are ill-defined. Previous work demonstrates that Ets2 function in stromal cells significantly contributes to breast tumor progression. Conditional mouse models were used to study the function of Ets2 in both mammary stromal fibroblasts and(More)
This paper describes an original method to detect XFP-tagged proteins in time-lapse microscopy. Non-local measurements able to capture spatial intensity variations are incorporated within a Conditional Random Field (CRF) framework to localize the objects of interest. The minimization of the related energy is performed by a min-cut/max-flow algorithm.(More)
The 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(More)
GFP-tagging and time-lapse fluorescence microscopy can be considered as investigation tools to observe molecular dynamics and interactions in live cells at both the microscopic and nanoscopic scales. Consequently, it is imperative to develop novel image analysis techniques able to quantify dynamics of biological processes observed in such image sequences.(More)
Methods to quantify cellular-level phenotypic differences between genetic groups are a key tool in genomics research. In disease processes such as cancer, phenotypic changes at the cellular level frequently manifest in the modification of cell population profiles. These changes are hard to detect due the ambiguity in identifying distinct cell phenotypes(More)
To 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(More)
Image analysis applied to fluorescence live cell microscopy has become a key tool in molecular biology since it enables to characterize biological processes in space and time at the subcellular level. In fluorescence microscopy imaging, the moving tagged structures of interest, such as vesicles, appear as bright spots over a static or nonstatic background.(More)