Development of Chip-Based Electrochemically- and Light-Directed Peptide Microarray Synthesis

abstract: ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.Dissertation/ThesisPh.D. Chemistry 201

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

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

Native American ancestry significantly contributes to neuromyelitis optica susceptibility in the admixed Mexican population

Neuromyelitis Optica (NMO) is an autoimmune disease with a higher prevalence in non-European populations. Because the Mexican population resulted from the admixture between mainly Native American and European populations, we used genome-wide microarray, HLA high-resolution typing and AQP4 gene sequencing data to analyze genetic ancestry and to seek genetic variants conferring NMO susceptibility in admixed Mexican patients. A total of 164 Mexican NMO patients and 1,208 controls were included. On average, NMO patients had a higher proportion of Native American ancestry than controls (68.1% vs 58.6%; p = 5 × 10–6). GWAS identified a HLA region associated with NMO, led by rs9272219 (OR = 2.48, P = 8 × 10–10). Class II HLA alleles HLA-DQB1*03:01, -DRB1*08:02, -DRB1*16:02, -DRB1*14:06 and -DQB1*04:02 showed the most significant associations with NMO risk. Local ancestry estimates suggest that all the NMO-associated alleles within the HLA region are of Native American origin. No novel or missense variants in the AQP4 gene were found in Mexican patients with NMO or multiple sclerosis. To our knowledge, this is the first study supporting the notion that Native American ancestry significantly contributes to NMO susceptibility in an admixed population, and is consistent with differences in NMO epidemiology in Mexico and Latin America.Fil: Romero Hidalgo, Sandra. Instituto Nacional de Medicina Genómica; MéxicoFil: Flores Rivera, José. Instituto Nacional de Neurología y Neurocirugía; MéxicoFil: Rivas Alonso, Verónica. Instituto Nacional de Neurología y Neurocirugía; MéxicoFil: Barquera, Rodrigo. Max Planck Institute For The Science Of Human History; Alemania. Instituto Nacional de Antropología e Historia; MéxicoFil: Villarreal Molina, María Teresa. Instituto Nacional de Medicina Genómica; MéxicoFil: Antuna Puente, Bárbara. Instituto Nacional de Medicina Genómica; MéxicoFil: Macias Kauffer, Luis Rodrigo. Universidad Nacional Autónoma de México; MéxicoFil: Villalobos Comparán, Marisela. Instituto Nacional de Medicina Genómica; MéxicoFil: Ortiz Maldonado, Jair. Instituto Nacional de Neurología y Neurocirugía; MéxicoFil: Yu, Neng. American Red Cross; Estados UnidosFil: Lebedeva, Tatiana V.. American Red Cross; Estados UnidosFil: Alosco, Sharon M.. American Red Cross; Estados UnidosFil: García Rodríguez, Juan Daniel. Instituto Nacional de Medicina Genómica; MéxicoFil: González Torres, Carolina. Instituto Nacional de Medicina Genómica; MéxicoFil: Rosas Madrigal, Sandra. Instituto Nacional de Medicina Genómica; MéxicoFil: Ordoñez, Graciela. Neuroimmunología, Instituto Nacional de Neurología y Neurocirugía; MéxicoFil: Guerrero Camacho, Jorge Luis. Instituto Nacional de Neurología y Neurocirugía; MéxicoFil: Treviño Frenk, Irene. American British Cowdray Medical Center; México. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Escamilla Tilch, Monica. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: García Lechuga, Maricela. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Tovar Méndez, Víctor Hugo. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Pacheco Ubaldo, Hanna. Instituto Nacional de Antropología E Historia. Escuela Nacional de Antropología E Historia; MéxicoFil: Acuña Alonzo, Victor. Instituto Nacional de Antropología E Historia. Escuela Nacional de Antropología E Historia; MéxicoFil: Bortolini, María Cátira. Universidade Federal do Rio Grande do Sul; BrasilFil: Gallo, Carla. Universidad Peruana Cayetano Heredia; PerúFil: Bedoya Berrío, Gabriel. Universidad de Antioquia; ColombiaFil: Rothhammer, Francisco. Universidad de Tarapacá; ChileFil: Gonzalez-Jose, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; ArgentinaFil: Ruiz Linares, Andrés. Colegio Universitario de Londres; Reino UnidoFil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; MéxicoFil: Yunis, Edmond. Dana Farber Cancer Institute; Estados UnidosFil: Granados, Julio. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Corona, Teresa. Instituto Nacional de Neurología y Neurocirugía; Méxic

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri

Local alignment of two-base encoded DNA sequence

<p>Abstract</p> <p>Background</p> <p>DNA sequence comparison is based on optimal local alignment of two sequences using a similarity score. However, some new DNA sequencing technologies do not directly measure the base sequence, but rather an encoded form, such as the two-base encoding considered here. In order to compare such data to a reference sequence, the data must be decoded into sequence. The decoding is deterministic, but the possibility of measurement errors requires searching among all possible error modes and resulting alignments to achieve an optimal balance of fewer errors versus greater sequence similarity.</p> <p>Results</p> <p>We present an extension of the standard dynamic programming method for local alignment, which simultaneously decodes the data and performs the alignment, maximizing a similarity score based on a weighted combination of errors and edits, and allowing an affine gap penalty. We also present simulations that demonstrate the performance characteristics of our two base encoded alignment method and contrast those with standard DNA sequence alignment under the same conditions.</p> <p>Conclusion</p> <p>The new local alignment algorithm for two-base encoded data has substantial power to properly detect and correct measurement errors while identifying underlying sequence variants, and facilitating genome re-sequencing efforts based on this form of sequence data.</p

Automatic Spot Addressing in cDNA Microarray Images

Complementary DNA (cDNA) microarrays are a powerful high throughput technology developed in the last decade allowing researchers to analyze the behaviour and interaction of thousands of genes simultaneously. The large amount of information provided by microarray images requires automatic techniques to develop accurate and ef cient processing. Each spot in the microarray contains the hybridization level of a single gene. One of the most important features of these images are the regularity and pseudo-periodicity implicit in the spot arrangement. In this paper, an automatic approach based on texture analysis techniques is proposed to localize spots in microarray images. The method estimates the displacement vectors which characterize the texture (i.e. the spot arrangement). This is achieved by means of applying the generalized Hough transform on the 2D autocorrelation function previously segmented via morphological operations. The obtained displacement vectors are used to generate a grid template which is matched to the original image. The root mean square error between the estimated locations and the ones computed via a semiautomatic tool is computed to evaluate the accuracy of the process. The method yields promising results with low errors.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Supervised Classification: Quite a Brief Overview

The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or inputs---to the so-called class label---or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner

Automatic Spot Adressing in cDNA Microarray Images

Complementary DNA (cDNA) microarrays are a powerful high throughput technology developed in the last decade allowing researchers to analyze the behaviour and interaction of thousands of genes simultaneously. The large amount of information provided by microarray images requires automatic techniques to develop accurate and efficient processing. Each spot in the microarray contains the hybridization level of a single gene. One of the most important features of these images are the regularity and pseudo-periodicity implicit in the spot arrangement. In this paper, an automatic approach based on texture analysis characterization techniques is proposed to localize spots in microarray images. The method estimates the displacement vectors which characterize the texture (i.e. the spot arrangement). This is achieved by means of applying the generalized Hough transform on the 2D autocorrelation function previously segmented via morphological operations. The obtained displacement vectors are used to generate a grid template which overlaps the original image. The Root-Mean-Square-Error (RMSE) between the estimated locations and the ones computed via a semiautomatic tool is calculated to evaluate the accuracy of the process. The method yields promising results.Facultad de Informátic