Pascal Vallotton

Self-organization of bacterial biofilms is facilitated by extracellular DNA

Twitching motility-mediated biofilm expansion is a complex, multicellular behavior that enables the active colonization of surfaces by many species of bacteria. In this study we have explored the emergence of intricate network patterns of interconnected trails that form in actively expanding biofilms of Pseudomonas aeruginosa. We have used high-resolution, phase-contrast time-lapse microscopy and developed sophisticated computer vision algorithms to track and analyze individual cell movements during expansion of P. aeruginosa biofilms. We have also used atomic force microscopy to examine the topography of the substrate underneath the expanding biofilm. Our analyses reveal that at the leading edge of the biofilm, highly coherent groups of bacteria migrate across the surface of the semisolid media and in doing so create furrows along which following cells preferentially migrate. This leads to the emergence of a network of trails that guide mass transit toward the leading edges of the biofilm. We have also determined that extracellular DNA (eDNA) facilitates efficient traffic flow throughout the furrow network by maintaining coherent cell alignments, thereby avoiding traffic jams and ensuring an efficient supply of cells to the migrating front. Our analyses reveal that eDNA also coordinates the movements of cells in the leading edge vanguard rafts and is required for the assembly of cells into the “bulldozer” aggregates that forge the interconnecting furrows. Our observations have revealed that large-scale self-organization of cells in actively expanding biofilms of P. aeruginosa occurs through construction of an intricate network of furrows that is facilitated by eDNA.

Recovery, Visualization, and Analysis of Actin and Tubulin Polymer Flow in Live Cells: A Fluorescent Speckle Microscopy Study

Fluorescent speckle microscopy (FSM) is becoming the technique of choice for analyzing in vivo the dynamics of polymer assemblies, such as the cytoskeleton. The massive amount of data produced by this method calls for computational approaches to recover the quantities of interest; namely, the polymerization and depolymerization activities and the motions undergone by the cytoskeleton over time. Attempts toward this goal have been hampered by the limited signal-to-noise ratio of typical FSM data, by the constant appearance and disappearance of speckles due to polymer turnover, and by the presence of flow singularities characteristic of many cytoskeletal polymer assemblies. To deal with these problems, we present a particle-based method for tracking fluorescent speckles in time-lapse FSM image series, based on ideas from operational research and graph theory. Our software delivers the displacements of thousands of speckles between consecutive frames, taking into account that speckles may appear and disappear. In this article we exploit this information to recover the speckle flow field. First, the software is tested on synthetic data to validate our methods. We then apply it to mapping filamentous actin retrograde flow at the front edge of migrating newt lung epithelial cells. Our results confirm findings from previously published kymograph analyses and manual tracking of such FSM data and illustrate the power of automated tracking for generating complete and quantitative flow measurements. Third, we analyze microtubule poleward flux in mitotic metaphase spindles assembled in Xenopus egg extracts, bringing new insight into the dynamics of microtubule assemblies in this system.

Computational Analysis of F-Actin Turnover in Cortical Actin Meshworks Using Fluorescent Speckle Microscopy

Fluorescent speckle microscopy (FSM) is a new imaging technique with the potential for simultaneous visualization of translocation and dynamic turnover of polymer structures. However, the use of FSM has been limited by the lack of specialized software for analysis of the positional and photometric fluctuations of hundreds of thousand speckles in an FSM time-lapse series, and for translating this data into biologically relevant information. In this paper we present a first version of a software for automated analysis of FSM movies. We focus on mapping the assembly and disassembly kinetics of a polymer meshwork. As a model system we have employed cortical F-actin meshworks in live newt lung epithelial cells. We lay out the algorithm in detail and present results of our analysis. The high spatial and temporal resolution of our maps reveals a kinetic cycling of F-actin, where phases of polymerization alternate with depolymerization in a spatially coordinated fashion. The cycle rates change when treating cells with a low dose of the drug latrunculin A. This shows the potential of this technique for future quantitative screening of drugs affecting the actin cytoskeleton. Various control experiments demonstrate that the algorithm is robust with respect to intensity variations due to noise and photobleaching and that effects of focus plane drifts can be eliminated by manual refocusing during image acquisition.

Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision

Manual neuron tracing is a very labor‐intensive task. In the drug screening context, the sheer number of images to process means that this approach is unrealistic. Moreover, the lack of reproducibility, objectivity, and auditing capability of manual tracing is limiting even in the context of smaller studies. We have developed fast, sensitive, and reliable algorithms for the purpose of detecting and analyzing neurites in cell cultures, and we have integrated them in software called HCA‐Vision, suitable for the research environment. We validate the software on images of cortical neurons by comparing results obtained using HCA‐Vision with those obtained using an established semi‐automated tracing solution (NeuronJ). The effect of the Sez‐6 deletion was characterized in detail. Sez‐6 null neurons exhibited a significant increase in neurite branching, although the neurite field area was unchanged due to a reduction in mean branch length. HCA‐Vision delivered considerable speed benefits and reliable traces.

Identification of a distal GLUT4 trafficking event controlled by actin polymerization.

The insulin-stimulated trafficking of GLUT4 to the plasma membrane in muscle and fat tissue constitutes a central process in blood glucose homeostasis. The tethering, docking, and fusion of GLUT4 vesicles with the plasma membrane (PM) represent the most distal steps in this pathway and have been recently shown to be key targets of insulin action. However, it remains unclear how insulin influences these processes to promote the insertion of the glucose transporter into the PM. In this study we have identified a previously uncharacterized role for cortical actin in the distal trafficking of GLUT4. Using high-frequency total internal reflection fluorescence microscopy (TIRFM) imaging, we show that insulin increases actin polymerization near the PM and that disruption of this process inhibited GLUT4 exocytosis. Using TIRFM in combination with probes that could distinguish between vesicle transport and fusion, we found that defective actin remodeling was accompanied by normal insulin-regulated accumulation of GLUT4 vesicles close to the PM, but the final exocytotic fusion step was impaired. These data clearly resolve multiple steps of the final stages of GLUT4 trafficking, demonstrating a crucial role for actin in the final stage of this process.

Fast linear feature detection using multiple directional non-maximum suppression

Linear feature detection is a very important issue in the areas of image analysis, computer vision, and pattern recognition. It has found applications in many diverse areas such as neurite outgrowth detection, compartment assay analysis, retinal vessel extraction, skin hair removal for malonoma detection, plant root analysis, and roads detection. We have developed a new algorithm for linear feature detection using multiple directional non-maximum suppression. The algorithm is very fast compared with methods in the literature. We also show a large number of application examples using our linear feature detection algorithm, and very good results have been obtained

Shifting views on the leading role of the lamellipodium in cell migration: speckle tracking revisited.

Migrating cells advance by first protruding a lamellipodium, a thin sheet of cytoplasm that consists mostly of filamentous actin (F-actin). The region immediately behind the lamellipodium is also relatively flat and has been termed the lamella ([Heath and Holifield, 1991][1]). Adhesion to the matrix

Exocytotic vesicle behaviour assessed by total internal reflection fluorescence microscopy.

The regulated trafficking or exocytosis of cargo‐containing vesicles to the cell surface is fundamental to all cells. By coupling the technology of fluorescently tagged fusion proteins with total internal reflection fluorescence microscopy (TIRFM), it is possible to achieve the high spatio‐temporal resolution required to study the dynamics of sub‐plasma membrane vesicle trafficking and exocytosis. TIRFM has been used in a number of cell types to visualize and dissect the various steps of exocytosis revealing how molecules identified via genetic and/or biochemical approaches are involved in the regulation of this process. Here, we summarize the contribution of TIRFM to our understanding of the mechanism of exocytosis and discuss the novel methods of analysis that are required to exploit the large volumes of data that can be produced using this technique.

Tri-track: free software for large-scale particle tracking.

The ability to correctly track objects in time-lapse sequences is important in many applications of microscopy. Individual object motions typically display a level of dynamic regularity reflecting the existence of an underlying physics or biology. Best results are obtained when this local information is exploited. Additionally, if the particle number is known to be approximately constant, a large number of tracking scenarios may be rejected on the basis that they are not compatible with a known maximum particle velocity. This represents information of a global nature, which should ideally be exploited too. Some time ago, we devised an efficient algorithm that exploited both types of information. The tracking task was reduced to a max-flow min-cost problem instance through a novel graph structure that comprised vertices representing objects from three consecutive image frames. The algorithm is explained here for the first time. A user-friendly implementation is provided, and the specific relaxation mechanism responsible for the methods effectiveness is uncovered. The software is particularly competitive for complex dynamics such as dense antiparallel flows, or in situations where object displacements are considerable. As an application, we characterize a remarkable vortex structure formed by bacteria engaged in interstitial motility.

Fast Linear Feature Detection Using Multiple Directional Non-Maximum Suppression

Linear feature detection is a very important issue in the areas of image analysis, computer vision, and pattern recognition. It has found applications in many diverse areas such as neurite outgrowth detection, compartment assay analysis, retinal vessel extraction, skin hair removal for malonoma detection, plant root analysis, and roads detection. We have developed a new algorithm for linear feature detection using multiple directional non-maximum suppression. The algorithm is very fast compared with methods in the literature. We also show a large number of application examples using our linear feature detection algorithm, and very good results have been obtained.