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WIREs Syst Biol Med
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Bioimage informatics for understanding spatiotemporal dynamics of cellular processes

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Abstract The inner environment of the cell is highly dynamic and heterogeneous yet exquisitely organized. Successful completion of cellular processes within this environment depends on the right molecules or molecular complexes to function at the right place at the right time. Understanding spatiotemporal behaviors of cellular processes is therefore essential to understanding their molecular mechanisms at the systems level. These behaviors are usually visualized and recorded using imaging techniques. However, to infer from them systems‐level molecular mechanisms, computational analysis and understanding of recorded image data is crucial, not only for acquiring quantitative behavior measurements but also for comprehending complex interactions among the molecules or molecular complexes involved. The technology of computational analysis and understanding of biological images is often referred to simply as bioimage informatics. This article introduces fundamentals of bioimage informatics for understanding spatiotemporal dynamics of cellular processes and reviews recent advances on this topic. Basic bioimage informatics concepts and techniques for characterizing spatiotemporal cell dynamics are introduced first. Studies on specific cellular processes such as cell migration and signal transduction are then used as examples to analyze and summarize recent advances, with the focus on transforming quantitative measurements of spatiotemporal cellular behaviors into knowledge of underlying molecular mechanisms. Despite the advances made, substantial technological challenges remain, especially in representation of spatiotemporal cellular behaviors and inference of systems‐level molecular mechanisms. These challenges are briefly discussed. Overall, understanding spatiotemporal cell dynamics will provide critical insights into how specific cellular processes as well as the entire inner cellular environment are dynamically organized and regulated. WIREs Syst Biol Med 2013, 5:367–380. doi: 10.1002/wsbm.1214 This article is categorized under: Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Imaging

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Spatiotemporal dynamics patterns of different cellular processes. (a) Upper row: phase‐contrast images of a T cell (left) activated by contact with an antigen‐present cell (right). Lower row: activities of fluorescently labeled LAT (linker of activated T cells), one of the many signaling molecules involved in the activation of the T cell.10 (Reprinted with permission from Ref 10. Copyright 2009 AAAS). (b) Activities of Rho GTPase Rac1, Cdc42, and RhoA visualized by fluorescence biosensors in protruding mouse embryonic fibroblasts. Activity levels are encoded in colors.11 Scale bars: 20 µm. (Reprinted with permission from Ref 11. Copyright 2009 Nature Publishing Group). (c) Reorganization of mitotic motor Eg5 (green) relative to the spindle microtubule network (red) at different stages of mitosis of LLC‐PK1 cells.12 Scale bar: 10 µm. (Reprinted with permission from Ref 12. Copyright 2012 ASCB)

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Systems‐level representation of spatiotemporal dynamics of cell protrusion and signal transduction. (a) Spatiotemporal dynamics of cell protrusion is represented using a morphodynamic map. Upper panel: to generate the map, a series of sampling windows are defined along the cell edge. Lower panel: protrusion or retraction of cell edge within each sampling windows was tracked using bioimage informatics techniques. Protrusion/retraction activities over the entire edge is quantitatively represented in color‐coding in a morphodynamic map. The vertical axis of the map corresponds to the series of sampling window. The horizontal axis of the map corresponds to time. Each column of the map represents in color‐coding protrusion or retraction rates within the entire series of sampling windows at a given time, whereas each row represents protrusion or retraction rates within a specific sampling window along the cell edge over the entire duration analyzed. (Reprinted with permission from Ref 69. Copyright 2006 Cell Press). (b) Computer simulated spatial distribution of cAMP concentrations in neurites of actual geometry at a given time point. (Reprinted with permission from Ref 68. Copyright 2008 Cell Press)

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Characterizing spatiotemporal dynamics of the mitotic spindle using single particle tracking. (a) Left panel: an X‐rhodamine tubulin speckle image of a Xenopus egg extract spindle. The dotted rectangular region is magnified in the right upper panel. Right lower panel: tubulin speckles detected by software are marked in red and overlaid onto the original image. Scale bar: 10 µm. (b) Left panel: speckle trajectories recovered using single particle tracking. Right panel: trajectories within the dotted rectangular region. Warmer colors indicate faster speckle velocities. (c) Global distribution of speckle movement velocities with the spindle. (a–c: Reprinted with permission from Ref 53. Copyright 2008 Rockefeller University Press). (d) Kinesin‐5 distribution in early anaphase spindles of LLC‐PK1 cells visualized by fluorescence microscopy. Left panel: kinesin‐5 visualized using wide field imaging, which produces a low‐contrast continuous region image. Middle panel: kinesin‐5 visualized using TIRF, which produces a single particle image. Right panel: overlay. TIRF imaging provides significantly improved image contrast. Scale bar: 10 µm. (e) Identified regions of midzone microtubules and astral microtubules. (f) Velocities of kinesin5 along midzone and astral microtubules are determined using linear regression of kinesin‐5 particle mean displacement (MD), calculated from individual kinesin‐5 trajectories. (d–f: Reprinted with permission from Ref 12. Copyright 2012 ASCB)

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Fluorescent speckle microscopy: an example of single particle images versus continuous region images. Fluorescent speckle microscopy is an imaging technique that is often used for high‐resolution visualization of dynamics of the cytoskeleton.31 It provides a good example for illustrating the differences between single particle images and continuous region images. Here, the basic principle of FSM is explained based on its application in microtubule imaging. (a) To visualize individual microtubules in vitro, fluorescently labeled tubulin subunits (solid black) are added to the solution. These subunits are randomly incorporated into individual microtubules undergoing polymerization. (b) When the fraction of labeled tubulin (indicated to the right of each image) relative to the total tubulin pool is low (e.g., 1.25–2.5%), individual labeled tubulin subunits appear as separated particles after being incorporated into the microtubule. This produces a single particle image with speckle‐like appearance (b, upper two images), hence the name of fluorescence speckle microscopy.30 When the fraction of labeled tubulin is increased, individuals labeled tubulins become spatially closer to each other after being incorporated. Eventually, when the fraction of labeled tubulin is very high (e.g., 10–50%), individual labeled tubulin subunits can no longer be resolved readily following incorporation. This produces a continuous region image (b, lower three images). (Reprinted with permission from Ref 31 Copyright 2006 Annual Reviews). (c) and (d) Following the same principle for single microtubules, when multiple microtubules are adjacent to each other, even lower fractions of labeled tubulin [indicated at the bottom of each image in (d)] are required to produce single particle images. This is because labeled tubulin subunits from different microtubules may fall within a distance smaller than the Rayleigh limit of image resolution and become unresolvable, as illustrated in (c). This observation is confirmed experimentally in microtubule networks of Xenopus egg extract spindles, an in vitro model for studying cell division, as shown in (d). Compared to the continuous region image (d: left panel), much lower fractions of labeled tubulin are required to produce single particle images (d: middle and right panels). Imaging low fractions of labeled tubulin is more challenging because substantially longer exposure is required. Scale bars: 10 µm.

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General procedure of using bioimage informatics techniques to study spatiotemporal cell dynamics.

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