ATMAD : robust image analysis for Automatic Tissue MicroArray De-arraying

International audienceBackground. Over the last two decades, an innovative technology called Tissue Microarray (TMA),which combines multi-tissue and DNA microarray concepts, has been widely used in the field ofhistology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembledonto a single support – typically a glass slide – according to a design grid (array) layout, in order toallow multiplex analysis by treating numerous samples under identical and standardized conditions.However, during the TMA manufacturing process, the sample positions can be highly distorted fromthe design grid due to the imprecision when assembling tissue samples and the deformation of theembedding waxes. Consequently, these distortions may lead to severe errors of (histological) assayresults when the sample identities are mismatched between the design and its manufactured output.The development of a robust method for de-arraying TMA, which localizes and matches TMAsamples with their design grid, is therefore crucial to overcome the bottleneck of this prominenttechnology.Results. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD)approach dedicated to images acquired with bright field and fluorescence microscopes (or scanners).First, tissue samples are localized in the large image by applying a locally adaptive thresholdingon the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametricshape model is considered for segmenting ellipse-shaped objects at each detected position.Segmented objects that do not meet the size and the roundness criteria are discarded from thelist of tissue samples before being matched with the design grid. Sample matching is performed byestimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimateddeformation, the true tissue samples that were preliminary rejected in the early image processingstep are recognized by running a second segmentation step.Conclusions. We developed a novel de-arraying approach for TMA analysis. By combining waveletbaseddetection, active contour segmentation, and thin-plate spline interpolation, our approach isable to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background andnon-linear deformation of TMA grid. In addition, the deformation estimation produces quantitativeinformation to asset the manufacturing quality of TMAs

A variational method for dejittering large fluorescence line scanner images

International audienceWe propose a variational method dedicated to jitter correction of large fluorescence scanner images. Our method consists in minimizing a global energy functional to estimate a dense displacement field representing the spatially-varying jitter. The computational approach is based on a half-quadratic splitting of the energy functional, which decouples the realignment data term and the dedicated differential-based regularizer. The resulting problem amounts to alternatively solving two convex and nonconvex optimization subproblems with appropriate algorithms. Experimental results on artificial and large real fluorescence images demonstrate that our method is not only capable to handle large displacements but is also efficient in terms of subpixel precision without inducing additional intensity artifacts

Generalized Sparse Variation Regularization for Large Fluorescence Image Deconvolution

In this work, we generalize the sparse variation (SV) combining the total-variation (TV) and the L 1 regularization and introduce a novel family of convex and non-quadratic regularizers for fast deconvolution of large 2D fluorescence images. These regularizers are defined as mixed Lp-L 2 norms (p ≥ 1) which group image intensity and spatial differentials, computed at each pixel of the image. By coupling a regularization term of this family with a quadratic data fidelity term, we propose a fast and efficient deconvolution method by using the primal-dual (proximal) algorithms to minimize the corresponding energy functional. Experiment results on both 2D simulated and real fluorescence scanner images demonstrate the performance of our method in terms of restoration quality as well as computational time

Automatic core segmentation and registration for fast tissue microarray de-arraying

International audienceTissue core de-arraying is one of the most important steps in tissue microarray (TMA) image analysis. However, few solutions and mathematical frameworks are available. This paper presents a robust TMA de-arraying method adapted for digital images from classical optical and new fluorescent devices. The proposed algorithm is composed of three modules: (a) detection, (b) segmentation, and (c) array indexing. The detection of TMA cores is performed by local adaptive thresholding of isotropic wavelet transform coefficients. The segmentation component uses parametric ellipse to delineate the boundaries of potential tissue cores. Array indices of each core are computed by using thin-plate splines to estimate the deformation of the deposited core grid. Our method is appropriate for non-linear deformation and is able to quantify the deformation of TMA grids when compared to existing algorithms

Optical label free biodetection based on the diffraction of light by nanoscale protein gratings

International audienceIn this work, we use a label free biosensing technique based on the diffraction of light by molecular gratings. Molecular gratings are employed as diffractive probe arrays for protein interaction analysis. Indeed, the diffraction efficiency changes in response to analyte binding, due to a shift in line thickness and/or refractive index of the grating. In this perspective, we have developed a scanning instrument, capable of collecting at high speed the diffracted intensity emitted by molecular gratings. Nanogratings composed of anti-GST antibodies lines (500 nm width at a pitch of 1 μm) were immobilized through Microcontact printing on a PLL-g-dextran coated substrate. By monitoring the diffracted intensity of the anti-GST antibodies gratings, we have detected GST molecules at a concentration of 0.1 μM. This biodetection technique appears sensitive to low molecular weight molecules, such as GST (glutathione S-transferase, 26 kDa), which is an interesting result in comparison to other label free detection methods

Controlled deposition and multi-layer architecturing of single biomolecules using automated directed capillary assembly and nano-contact printing processes

International audienceUp to date, a large amount of research studies have been carried out to manipulate and arrange biomo-lecules at the single molecule scale using capillary forces. However, many of these techniques remain in the fundamental research field, their industrial transfer being restricted by poor repeatability and user-dependent processes. We present an automated process for the controlled and large scale deposition of single biomolecules, relying on the use of directed capillary assembly and nano-contact printing processes. The adjustment and control of physical parameters allow for single molecule deposition, and the use of an automate operating arm ensures high reproducibility and freedom in the assembly architectures to create. This methodology was used to assemble DNA molecules and actin filaments (or F-actin), evidencing two distinct assembly mechanisms. In the first one we use capillary forces to trap and elongate preformed 1D structures such as DNA molecules. In the second case, fluid flows created upon evaporation and local pinning of the meniscus favor the F-actin polymerization through continuous supply of monomers. In this way, we introduce a new concept of using capillary assembly as a construction tool to assemble 1D nano-structures. Additionally, by sequentially aligning and printing multiple single molecule assemblies, large-scale multi-layer architectures of single molecules were also obtained. The large-scale capabilities and reliability of our fabrication process render sophisticated single molecule biophysical measurements possible with systematic analysis over a large population for statistical relevance

Additional file 1 of ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying

Isotropic wavelet frame. Direct wavelet decomposition algorithm and reconstruction. Partial derivatives of the ellipse quadratic form. (PDF 255 kb

Cognition, discours, contextes

Ce numéro de Corela a pour objectif de présenter des travaux qui, tout en se réclamant de la linguistique cognitive, ne ressortissent pas strictement ou seulement aux modèles aujourd’hui les plus reconnus, aux domaines d’investigation ou aux terrains d’application les plus visités