Abstract: Parasite virulence genes are usually associated with telomeres. The clustering of the telomeres, together with their particular spatial distribution in the nucleus of human parasites such as Plasmodium falciparum and Trypanosoma brucei, has been suggested to play a role in facilitating ectopic recombination and in the emergence of new antigenic variants. Leishmania parasites, as well as other trypanosomes, have unusual gene expression characteristics, such as polycistronic and constitutive transcription of protein-coding genes. Leishmania subtelomeric regions are even more unique because unlike these regions in other trypanosomes they are devoid of virulence genes. Given these peculiarities of Leishmania, we sought to investigate how telomeres are organized in the nucleus of Leishmania major parasites at both the human and insect stages of their life cycle. We developed a new automated and precise method for identifying telomere position in the three-dimensional space of the nucleus, and we found that the telomeres are organized in clusters present in similar numbers in both the human and insect stages. While the number of clusters remained the same, their distribution differed between the two stages. The telomeric clusters were found more concentrated near the center of the nucleus in the human stage than in the insect stage suggesting reorganization during the parasite's differentiation process between the two hosts. These data provide the first 3D analysis of Leishmania telomere organization. The possible biological implications of these findings are discussed.
Abstract: Cell migration is a field of intense current research, where biologists increasingly rely on methods and expertise from physics and engineering. Signal processing approaches can contribute significantly to this research, most notably to help analyze the exploding quantity of imaging data produced with standard and new microscopy techniques. In this article, we first provide a brief background on the importance of understanding cell movements, then review a selection of current efforts on tracking moving cells, with an emphasis on deformable model approaches (some ideas expressed in this article have been previously discussed in [1] and [2]). We will point out some of the main difficulties posed by cellular imaging, discuss advantages and limitations of different tracking techniques, and suggest a few directions for future advances
Abstract: Cell migrations and deformations play essential roles in biological processes, such as parasite invasion, immune response, embryonic development, and cancer. We describe a fully automatic segmentation and tracking method designed to enable quantitative analyses of cellular shape and motion from dynamic three-dimensional microscopy data. The method uses multiple active surfaces with or without edges, coupled by a penalty for overlaps, and a volume conservation constraint that improves outlining of cell/cell boundaries. Its main advantages are robustness to low signal-to-noise ratios and the ability to handle multiple cells that may touch, divide, enter, or leave the observation volume. We give quantitative validation results based on synthetic images and show two examples of applications to real biological data.
Abstract: Active contour methods have proven to be particularly adapted to multiple cell segmentation and tracking in biology, thanks to their flexibility, robustness to noise and quantitative interpretability. In the context of 4D microscopy, two concurrent techniques have been developped: active surfaces, based on an implicit contour formulation, and recently active meshes, based on an explicit formulation. We evaluate and compare these two approaches in terms of quantitative results and computational cost on simulated and real biological sequences, in order to provide an objective review of their strengths and weaknesses
Abstract: Cellular endocytosis is a mechanism of great interest in biology, for it regulates the communication between the cell and the external medium. With recent advances in fluorescence microscopy, endocytosis has become a popular candidate for image-based high content screening campains. In this context, we have developped an automated framework comprising robust cell segmentation using coupled shape-constrained active contours and efficient endosome extraction using an isotropic undecimated wavelet transform. The resulting method has few parameters and is able to analyze tens of cells per image in the order of seconds. Validation is performed by experimentally confirming previously published results obtained through manual analysis
Abstract: Segmenting and tracking multiple deformable objects is a topic of popular interest in 3D biological imaging. In addition to quantitative measurements, a great importance lies in the visual observation of the results, usually available via time-consuming reconstruction methods, which introduce approximation errors. In this paper, we propose a framework based on deformable mesh models able to segment and track multiple objects simultaneously while providing precise 3D visualization of the model during its evolution. This active mesh framework is evaluated on simulated data, and experimental results on real biological images are shown.
Abstract: Deformable mesh methods have become an alternative of choice to classical deformable models for 3D image understanding. They allow to render the evolving surface directly during the segmentation process in a fast and efficient way, avoiding both the additional timecost and approximation errors induced by 3D reconstruction algorithms after segmentation. Current methods utilize edge-based forces to attract the mesh surface toward the image entities. These forces are inadequate in 3D fluorescence microscopy, where edges are not well defined by gradient. In this paper, we propose a fully automated deformable 3D mesh model that deforms using the reduced Mumford-Shah functional to segment and track objects with fuzzy boundaries. Simultaneous rendering of the mesh evolution allows faster tweaking of the model parameters and offers biologists a more precise insight on the scene and hence better understanding of biological phenomena. We present evaluations on both synthetic and real 3D microscopy data
