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

Conference of Advance Research and Innovation (ICARI-2014) 118 ICARI

Abstract With the advent of highly advanced optics and imaging system, currently biological research has reached a stage where scientists can study biological entities and processes at molecular and cellular-level in real time. However, a single experiment consists of hundreds and thousands of parameters to be recorded and a large population of microscopic objects to be tracked. Thus, making manual inspection of such events practically impossible. This calls for an approach to computer-vision based automated tracking and monitoring of cells in biological experiments. This technology promises to revolutionize the research in cellular biology and medical science which includes discovery of diseases by tracking the process in cells, development of therapy and drugs and the study of microscopic biological elements. This article surveys the recent literature in the area of computer vision based automated cell tracking. It discusses the latest trends and successes in the development and introduction of automated cell tracking techniques and systems

Local cellular neighbourhood controls proliferation in cell competition

Cell competition is a quality control mechanism through which tissues eliminate unfit cells. Cell competition can result from short-range biochemical inductions or long-range mechanical cues. However, little is known about how cell-scale interactions give rise to population shifts in tissues, due to the lack of experimental and computational tools to efficiently characterise interactions at the single-cell level. Here, we address these challenges by combining long-term automated microscopy with deep learning image analysis to decipher how single-cell behaviour determines tissue make-up during competition. Using our high-throughput analysis pipeline, we show that competitive interactions between MDCK wild-type cells and cells depleted of the polarity protein scribble are governed by differential sensitivity to local density and the cell-type of each cell's neighbours. We find that local density has a dramatic effect on the rate of division and apoptosis under competitive conditions. Strikingly, our analysis reveals that proliferation of the winner cells is upregulated in neighbourhoods mostly populated by loser cells. These data suggest that tissue-scale population shifts are strongly affected by cellular-scale tissue organisation. We present a quantitative mathematical model that demonstrates the effect of neighbour cell-type dependence of apoptosis and division in determining the fitness of competing cell lines

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy Images

Cataloged from PDF version of article.More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms

Quantitative automated analysis of host-pathogen interactions

This work aims to broaden knowledge about neutrophil biology in their interaction with fungi species that most frequently cause invasive fungal diseases (IFD). The questions addressed include the alteration of neutrophil morphology after interaction with Candida albicans or C. glabrata, revealing factors that modulate the production and composition of neutrophil-derived extracellular vesicles (EVs) obtained in confrontation assay with conidia of Aspergillus fumigatus and analysing EVs activity against this fungus. Alongside fundamental interests, those questions have important applied aspects in the medicine of IFD. In particular, for diagnostic purposes and infection process monitoring. The results of this work include: 1 a novel segmentation and tracking algorithm which is capable of working with low-contrast cell images, producing accurate cell contours and providing data about positions of clusters, which would improve further analysis; 2 a novel workflow algorithm for analysis of neutrophil continuous morphological spectrum without consensus-based manual annotation; 3 quantitative evidence that morphodynamics of isolated neutrophils depends on the infectious agent (C. albicans or C. glabrata) used in whole blood infection assay; 4 quantitative evidence that neutrophil-derived extracellular vesicles, obtained in confrontation assays with conidia of A. fumigatus could inhibit hyphae development and damage hyphae cell wall; 5 quantitative evidence that EVs inhibition activity is strain-specific

Quantitative Multicolor Compositional Imaging Resolves Molecular Domains in Cell-Matrix Adhesions

Background: Cellular processes occur within dynamic and multi-molecular compartments whose characterization requires analysis at high spatio-temporal resolution. Notable examples for such complexes are cell-matrix adhesion sites, consisting of numerous cytoskeletal and signaling proteins. These adhesions are highly variable in their morphology, dynamics, and apparent function, yet their molecular diversity is poorly defined. Methodology/Principal Findings: We present here a compositional imaging approach for the analysis and display of multicomponent compositions. This methodology is based on microscopy-acquired multicolor data, multi-dimensional clustering of pixels according to their composition similarity and display of the cellular distribution of these composition clusters. We apply this approach for resolving the molecular complexes associated with focal-adhesions, and the time-dependent effects of Rho-kinase inhibition. We show here compositional variations between adhesion sites, as well as ordered variations along the axis of individual focal-adhesions. The multicolor clustering approach also reveals distinct sensitivities of different focaladhesion-associated complexes to Rho-kinase inhibition. Conclusions/Significance: Multicolor compositional imaging resolves ‘‘molecular signatures’ ’ characteristic to focaladhesions and related structures, as well as sub-domains within these adhesion sites. This analysis enhances the spatial information with additional ‘‘contents-resolved’ ’ dimensions. We propose that compositional imaging can serve as