Chapter 17: Bioimage Informatics for Systems Pharmacology

University of Maryland, Baltimore County, United States of America
PLoS Computational Biology (Impact Factor: 4.62). 04/2013; 9(4):e1003043. DOI: 10.1371/journal.pcbi.1003043
Source: PubMed


Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.

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    • "However, these tasks face several challenges including the generally poor image quality (low-SIR), the varying cell populations due to cells entering or leaving the field of view, and the possibility of irregular interaction among cells. Over the past decade, a number of cell tracking algorithms have been proposed (see [3] for a review). These algorithms concentrate on a variety of cell types and are based on different tracking methods. "
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    ABSTRACT: This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.
    Computational and Mathematical Methods in Medicine 05/2015; 2015:1-18. DOI:10.1155/2015/695054 · 0.77 Impact Factor
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    • "In the last years the great advances in biological tissue labeling and automated microscopic imaging systems have permitted to acquire images of biological structures at macro-, meso-and microscale [1], [2], [3], [4], [5], [6], [7], [8], [9]. These imaging systems can produce terabytes-sized volumes of multi-dimensional images that, according to the acquisition scale, are rich in information at super-cellular, cellular, subcellular and molecular levels [4], [5], [10], [11], [12]. Such advances not only have modified how biologists and neuroscientists visualize and study their corresponding functions, but also they have been asking for extracting objective information from the images. "
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    ABSTRACT: Bioimage Informatics is a rapidly growing research field that is giving fundamental contributions to research in biology and biomedicine aiming at facilitating the extraction of quantitative information from images. Great advances in biological tissue labeling and microscopic imaging are radically changing how biologists visualize and study the molecular and cellular structures. These devices nowadays produce terabyte-sized multi-dimensional images: how to automatically and efficiently extract objective knowledge from such images has become a major challenge. In this manuscript we analyze the state-of-the-art of Bioimage Informatics, with a special focus on neuroscience. We show that there are increasing efforts to deliver methods and software tools providing functionalities for visualization, representation, management and analysis of 3D multichannel images. Nevertheless, most of them have been applied on datasets with size of MVoxel or few GVoxel, where the variations in contrast, illumination, as well as object shape and dimensions are limited. The huge dimensions of new 3D image stacks therefore ask for fully automated processing methods, whose parameters should be dynamically adapted to different regions in the volume. In this respect, this manuscript deepens in a recent contribution that digitally charts the Purkinje cells of whole mouse cerebellum, corresponding to an image dataset of 120 GVoxels.
    2014 International Conference on Digital Technologies (DT); 07/2014
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    • "However, the manual or semi-manual analysis of the large data sets acquired from these studies is very labor-intensive and there is a urgent need for automated methods which can rapidly and objectively process and analyze the image data. [24] [27] The efficient analysis and processing of fluorescence images presents several additional challenges with respect to standard image processing problems. Fluorescence images are formed by detecting small amount of lights emitted by the fluorophores, a process which is affected by the randomness of photon emission, the spatial uncertainty inherent with the photon detection and photochemical reactions such as photobleaching, where a fluorophore loses its ability to fluoresce over time [28]. "
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    ABSTRACT: Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data.
    Mathematical Modelling of Natural Phenomena 01/2014; 9(5). DOI:10.1051/mmnp/20149512 · 0.81 Impact Factor
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