Statistical parametrization of cell cytoskeleton reveals lung cancer cytoskeletal phenotype with partial EMT signature.

Epithelial-mesenchymal Transition (EMT) is a multi-step process that involves cytoskeletal rearrangement. Here, developing and using an image quantification tool, Statistical Parametrization of Cell Cytoskeleton (SPOCC), we have identified an intermediate EMT state with a specific cytoskeletal signature. We have been able to partition EMT into two steps: (1) initial formation of transverse arcs and dorsal stress fibers and (2) their subsequent conversion to ventral stress fibers with a concurrent alignment of fibers. Using the Orientational Order Parameter (OOP) as a figure of merit, we have been able to track EMT progression in live cells as well as characterize and quantify their cytoskeletal response to drugs. SPOCC has improved throughput and is non-destructive, making it a viable candidate for studying a broad range of biological processes. Further, owing to the increased stiffness (and by inference invasiveness) of the intermediate EMT phenotype compared to mesenchymal cells, our work can be instrumental in aiding the search for future treatment strategies that combat metastasis by specifically targeting the fiber alignment process.

Single yeast cell nanomotions correlate with cellular activity.

Living single yeast cells show a specific cellular motion at the nanometer scale with a magnitude that is proportional to the cellular activity of the cell. We characterized this cellular nanomotion pattern of nonattached single yeast cells using classical optical microscopy. The distribution of the cellular displacements over a short time period is distinct from random motion. The range and shape of such nanomotion displacement distributions change substantially according to the metabolic state of the cell. The analysis of the nanomotion frequency pattern demonstrated that single living yeast cells oscillate at relatively low frequencies of around 2 hertz. The simplicity of the technique should open the way to numerous applications among which antifungal susceptibility tests seem the most straightforward.

SVRG-MKL: A Fast and Scalable Multiple Kernel Learning Solution for Features Combination in Multi-Class Classification Problems.

In this paper, we present a novel strategy to combine a set of compact descriptors to leverage an associated recognition task. We formulate the problem from a multiple kernel learning (MKL) perspective and solve it following a stochastic variance reduced gradient (SVRG) approach to address its scalability, currently an open issue. MKL models are ideal candidates to jointly learn the optimal combination of features along with its associated predictor. However, they are unable to scale beyond a dozen thousand of samples due to high computational and memory requirements, which severely limits their applicability. We propose SVRG-MKL, an MKL solution with inherent scalability properties that can optimally combine multiple descriptors involving millions of samples. Our solution takes place directly in the primal to avoid Gram matrices computation and memory allocation, whereas the optimization is performed with a proposed algorithm of linear complexity and hence computationally efficient. Our proposition builds upon recent progress in SVRG with the distinction that each kernel is treated differently during optimization, which results in a faster convergence than applying off-the-shelf SVRG into MKL. Extensive experimental validation conducted on several benchmarking data sets confirms a higher accuracy and a significant speedup of our solution. Our technique can be extended to other MKL problems, including visual search and transfer learning, as well as other formulations, such as group-sensitive (GMKL) and localized MKL (LMKL) in convex settings.

A Robust Actin Filaments Image Analysis Framework.

The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a 'cartoon' part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the 'cartoon' image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in two different conditions: static (control) and fluid shear stress. The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods.

Lectin-Glycan Interaction Network-Based Identification of Host Receptors of Microbial Pathogenic Adhesins.

UNLABELLED: The first step in the infection of humans by microbial pathogens is their adherence to host tissue cells, which is frequently based on the binding of carbohydrate-binding proteins (lectin-like adhesins) to human cell receptors that expose glycans. In only a few cases have the human receptors of pathogenic adhesins been described. A novel strategy-based on the construction of a lectin-glycan interaction (LGI) network-to identify the potential human binding receptors for pathogenic adhesins with lectin activity was developed. The new approach is based on linking glycan array screening results of these adhesins to a human glycoprotein database via the construction of an LGI network. This strategy was used to detect human receptors for virulent Escherichia coli (FimH adhesin), and the fungal pathogens Candida albicans (Als1p and Als3p adhesins) and C. glabrata (Epa1, Epa6, and Epa7 adhesins), which cause candidiasis. This LGI network strategy allows the profiling of potential adhesin binding receptors in the host with prioritization, based on experimental binding data, of the most relevant interactions. New potential targets for the selected adhesins were predicted and experimentally confirmed. This methodology was also used to predict lectin interactions with envelope glycoproteins of human-pathogenic viruses. It was shown that this strategy was successful in revealing that the FimH adhesin has anti-HIV activity. IMPORTANCE: Microbial pathogens may express a wide range of carbohydrate-specific adhesion proteins that mediate adherence to host tissues. Pathogen attachment to host cells is achieved through the binding of these lectin-like adhesins to glycans on human glycoproteins. In only a few cases have the human receptors of pathogenic adhesins been described. We developed a new strategy to predict these interacting receptors. Therefore, we developed a novel LGI network that would allow the mapping of potential adhesin binding receptors in the host with prioritization, based on the experimental binding data, of the most relevant interactions. New potential targets for the selected adhesins (bacterial uroepithelial FimH from E. coli and fungal Epa and Als adhesins from C. glabrata and C. albicans) were predicted and experimentally confirmed. This methodology was also used to predict lectin interactions with human-pathogenic viruses and to discover whether FimH adhesin has anti-HIV activity.