Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

From segment to somite: segmentation to epithelialization analyzed within quantitative frameworks

One of the most visually striking patterns in the early developing embryo is somite segmentation. Somites form as repeated, periodic structures in pairs along nearly the entire caudal vertebrate axis. The morphological process involves short- and long-range signals that drive cell rearrangements and cell shaping to create discrete, epithelialized segments. Key to developing novel strategies to prevent somite birth defects that involve axial bone and skeletal muscle development is understanding how the molecular choreography is coordinated across multiple spatial scales and in a repeating temporal manner. Mathematical models have emerged as useful tools to integrate spatiotemporal data and simulate model mechanisms to provide unique insights into somite pattern formation. In this short review, we present two quantitative frameworks that address the morphogenesis from segment to somite and discuss recent data of segmentation and epithelialization

Paxillin facilitates timely neurite initiation on soft-substrate environments by interacting with the endocytic machinery.

Neurite initiation is the first step in neuronal development and occurs spontaneously in soft tissue environments. Although the mechanisms regulating the morphology of migratory cells on rigid substrates in cell culture are widely known, how soft environments modulate neurite initiation remains elusive. Using hydrogel cultures, pharmacologic inhibition, and genetic approaches, we reveal that paxillin-linked endocytosis and adhesion are components of a bistable switch controlling neurite initiation in a substrate modulus-dependent manner. On soft substrates, most paxillin binds to endocytic factors and facilitates vesicle invagination, elevating neuritogenic Rac1 activity and expression of genes encoding the endocytic machinery. By contrast, on rigid substrates, cells develop extensive adhesions, increase RhoA activity and sequester paxillin from the endocytic machinery, thereby delaying neurite initiation. Our results highlight paxillin as a core molecule in substrate modulus-controlled morphogenesis and define a mechanism whereby neuronal cells respond to environments exhibiting varying mechanical properties

Mathematical Morphology for Quantification in Biological & Medical Image Analysis

Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology. Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery. Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios. I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown. This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis

Taking aim at moving targets in computational cell migration

Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs

Analysis of geometrical and topological attitude for proteinprotein interaction

Protein-protein interaction takes usually place on an extended area of the external molecules surfaces that are morphologically fitting. Geometric and topological congruence (i.e. concavity and convexity correspondences) is required to support the neighboring interaction of surface patches belonging to the two protein molecules. It is therefore important to adopt representations and data structures that can facilitate the analysis and the implementation of techniques for the evaluation of geometric and topological properties on extended surfaces. These areas of activity are usually roughly “planar” but with local concavity and complexity that must match each other for interacting. To this purpose we are suggesting a solution different from the one of ligand-protein interaction in which are involved a pocket and a small molecule. The solution here suggested is based on the concavity tree representation. Starting from the convex hull of the protein molecule a recursive process leads to a series of concavity and meta-concavity that allows reaching the detail level required. The consequence of the recursive process is obviously a hierarchical data structure (a tree) which at each level supports a complete description of a surface. Each node of the tree contains an array of features that support the geometrical, topological and biochemical properties of the correspondent surface patch

Study of the role of plant nuclear envelope and lamina-like components in nuclear and chromatin organisation using 3D imaging

The linker of nucleoskeleton and cytoskeleton (LINC) complex is an evolutionarily well-conserved protein bridge connecting the cytoplasmic and nuclear compartments across the nuclear membrane. While recent data supports its function in nuclear morphology and meiosis, its implication for chromatin organisation has been less studied in plants. The fi aim of this work was to develop NucleusJ a simple and user-friendly ImageJ plugin dedicated to the characterisation of nuclear morphol- ogy and chromatin organisation in 3D. NucleusJ quantifies 15 parameters including shape and size of nuclei as well as intra-nuclear objects and their position within the nucleus. A step-by-step documentation is available for self-training, together with data sets of nuclei with diff t nuclear organisation. Several improvements are ongoing to release a new version of this plugin. In a second part of this work, 3D imaging methods have been used to investigate nuclear morphology and chromatin organisation in interphase nuclei of the plant model Arabidopsis thaliana in which heterochromatin domains cluster in conspicuous chromatin regions called chromo- centres. Chromocentres form a repressive chromatin environment contributing to the transcriptional silencing of repeated sequences a general mechanism needed for genome stability. Quantitative measurements of 3D position of chromocentres in the nucleus indicate that most chromocentres are situated in close proximity to the periphery of the nucleus but that this distance can be altered according to nuclear volume or in specific mutants affecting the LINC complex. Finally, the LINC com- plex is proposed to contribute at the proper chromatin organisation and positioning since its alteration is associated with the release of transcriptional silencing as well as decompaction of heterochromatic sequences. The last part of this work takes ad- vantage of available genomic sequences and RNA-seq data to explore the evolution of NE proteins in plants and propose a minimal requirement to built the simplest functional NE. Altogether, work achieved in this thesis associate genetics, molecular biology, bioinformatics and imaging to better understand the contribution of the nuclear envelope in nuclear morphology and chromatin organisation and suggests the functional implication of the LINC complex in these processes

3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries

Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.Comment: 39 pages, 14 figures. High resolution figures and supplemental movies available upon reques