An Efficient Fully Automated Method for Gridding Microarray Images

Abstract DNA microarray is a powerful tool and is widely used in genetics to monitor expression levels of thousands of genes in parallel. The gene expression process consists of three stages: gridding, segmentation and quantification. Gridding deals with finding areas in the microarray image which contain one spot using grid lines. This step can be done manually or automatically. In this paper, we propose an efficient and simple automatic gridding method for microarray image analysis. This method was implemented using MATLAB software and found very effective for gridding arrays with low intensity, poor quality spotsand tested by a number of microarray images. Results show that this method gives high accuracy of 76.9% improved to 98.6% when a preprocessing step is considered, rendering the method a promising technique for an efficient and automatic gridding the noisy microarray images

Miniaturized fluorescent RNA dot blot method for rapid quantitation of gene expression

BACKGROUND: RNA dot blot hybridization is a commonly used technique for gene expression assays. However, membrane based RNA dot/slot blot hybridization is time consuming, requires large amounts of RNA, and is less suited for parallel assays of more than one gene at a time. Here, we describe a glass-slide based miniaturized RNA dot blot (RNA array) procedure for rapid and parallel gene expression analysis using fluorescently labeled probes. RESULTS: RNA arrays were prepared by simple manual spotting of RNA onto amino-silane coated microarray glass slides, and used for two-color fluorescent hybridization with specific probes labeled with Cy3 and 18S ribosomal RNA house-keeping gene probe labeled with Cy5 fluorescent dyes. After hybridization, arrays were scanned on a fluorescent microarray scanner and images analyzed using microarray image analysis software. We demonstrate that this method gives comparable results to Northern blot analysis, and enables high throughput quantification of transcripts from nanogram quantities of total RNA in hundreds of samples. CONCLUSION: RNA array on glass slide and detection by fluorescently labeled probes can be used for rapid and parallel gene expression analysis. The method is particularly well suited for gene expression assays that involve quantitation of many transcripts in large numbers of samples

Improved processing of microarray data using image reconstruction techniques

Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy

A digital microarray using interferometric detection of plasmonic nanorod labels

DNA and protein microarrays are a high-throughput technology that allow the simultaneous quantification of tens of thousands of different biomolecular species. The mediocre sensitivity and dynamic range of traditional fluorescence microarrays compared to other techniques have been the technology's Achilles' Heel, and prevented their adoption for many biomedical and clinical diagnostic applications. Previous work to enhance the sensitivity of microarray readout to the single-molecule ('digital') regime have either required signal amplifying chemistry or sacrificed throughput, nixing the platform's primary advantages. Here, we report the development of a digital microarray which extends both the sensitivity and dynamic range of microarrays by about three orders of magnitude. This technique uses functionalized gold nanorods as single-molecule labels and an interferometric scanner which can rapidly enumerate individual nanorods by imaging them with a 10x objective lens. This approach does not require any chemical enhancement such as silver deposition, and scans arrays with a throughput similar to commercial fluorescence devices. By combining single-nanoparticle enumeration and ensemble measurements of spots when the particles are very dense, this system achieves a dynamic range of about one million directly from a single scan

Low-level analysis of microarray data

This thesis consists of an extensive introduction followed by seven papers (A-F) on low-level analysis of microarray data. Focus is on calibration and normalization of observed data. The introduction gives a brief background of the microarray technology and its applications in order for anyone not familiar with the field to read the thesis. Formal definitions of calibration and normalization are given. Paper A illustrates a typical statistical analysis of microarray data with background correction, normalization, and identification of differentially expressed genes (among thousands of candidates). A small analysis on the final results for different number of replicates and different image analysis software is also given. Paper B introduces a novel way for displaying microarray data called the print-order plot, which displays data in the order the corresponding spots were printed to the array. Utilizing these, so called (microtiter-) plate effects are identified. Then, based on a simple variability measure for replicated spots across arrays, different normalization sequences are tested and evidence for the existence of plate effects are claimed. Paper C presents an object-oriented extension with transparent reference variables to the R language. It is provides the necessary foundation in order to implement the microarray analysis package described in Paper F. Paper D is on affine transformations of two-channel microarray data and their effects on the log-ratio log-intensity transform. Affine transformations, that is, the existence of channel biases, can explain commonly observed intensity-dependent effects in the log-ratios. In the light of the affine transformation, several normalization methods are revisited. At the end of the paper, a new robust affine normalization is suggested that relies on iterative reweighted principal component analysis. Paper E suggests a multiscan calibration method where each array is scanned at various sensitivity levels in order to uniquely identify the affine transformation of signals that the scanner and the image-analysis methods introduce. Observed data strongly support this method. In addition, multiscan-calibrated data has an extended dynamical range and higher signal-to-noise levels. This is real-world evidence for the existence of affine transformations of microarray data. Paper F describes the aroma package – An R Object-oriented Microarray Analysis environment – implemented in R and that provides easy access to our and others low-level analysis methods. Paper G provides an calibration method for spotted microarrays with dilution series or spike-ins. The method is based on a heteroscedastic affine stochastic model. The parameter estimates are robust against model misspecification

Cellular neural networks, Navier-Stokes equation and microarray image reconstruction

Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time

A digital microarray using interferometric detection of plasmonic nanorod labels

DNA and protein microarrays are a high-throughput technology that allow the simultaneous quantification of tens of thousands of different biomolecular species. The mediocre sensitivity and dynamic range of traditional fluorescence microarrays compared to other techniques have been the technology's Achilles' Heel, and prevented their adoption for many biomedical and clinical diagnostic applications. Previous work to enhance the sensitivity of microarray readout to the single-molecule ('digital') regime have either required signal amplifying chemistry or sacrificed throughput, nixing the platform's primary advantages. Here, we report the development of a digital microarray which extends both the sensitivity and dynamic range of microarrays by about three orders of magnitude. This technique uses functionalized gold nanorods as single-molecule labels and an interferometric scanner which can rapidly enumerate individual nanorods by imaging them with a 10x objective lens. This approach does not require any chemical enhancement such as silver deposition, and scans arrays with a throughput similar to commercial fluorescence devices. By combining single-nanoparticle enumeration and ensemble measurements of spots when the particles are very dense, this system achieves a dynamic range of about one million directly from a single scan.First author draf

Experimental and computational applications of microarray technology for malaria eradication in Africa

Various mutation assisted drug resistance evolved in Plasmodium falciparum strains and insecticide resistance to female Anopheles mosquito account for major biomedical catastrophes standing against all efforts to eradicate malaria in Sub-Saharan Africa. Malaria is endemic in more than 100 countries and by far the most costly disease in terms of human health causing major losses among many African nations including Nigeria. The fight against malaria is failing and DNA microarray analysis need to keep up the pace in order to unravel the evolving parasite’s gene expression profile which is a pointer to monitoring the genes involved in malaria’s infective metabolic pathway. Huge data is generated and biologists have the challenge of extracting useful information from volumes of microarray data. Expression levels for tens of thousands of genes can be simultaneously measured in a single hybridization experiment and are collectively called a “gene expression profile”. Gene expression profiles can also be used in studying various state of malaria development in which expression profiles of different disease states at different time points are collected and compared to each other to establish a classifying scheme for purposes such as diagnosis and treatments with adequate drugs. This paper examines microarray technology and its application as supported by appropriate software tools from experimental set-up to the level of data analysis. An assessment of the level of microarray technology in Africa, its availability and techniques required for malaria eradication and effective healthcare in Nigeria and Africa in general were also underscored

Digital microarrays: single-molecule readout with interferometric detection of plasmonic nanorod labels

DNA and protein microarrays are a high-throughput technology that allow the simultaneous quantification of tens of thousands of different biomolecular species. The mediocre sensitivity and limited dynamic range of traditional fluorescence microarrays compared to other detection techniques have been the technology’s Achilles’ heel and prevented their adoption for many biomedical and clinical diagnostic applications. Previous work to enhance the sensitivity of microarray readout to the single-molecule (“digital”) regime have either required signal amplifying chemistry or sacrificed throughput, nixing the platform’s primary advantages. Here, we report the development of a digital microarray which extends both the sensitivity and dynamic range of microarrays by about 3 orders of magnitude. This technique uses functionalized gold nanorods as single-molecule labels and an interferometric scanner which can rapidly enumerate individual nanorods by imaging them with a 10× objective lens. This approach does not require any chemical signal enhancement such as silver deposition and scans arrays with a throughput similar to commercial fluorescence scanners. By combining single-nanoparticle enumeration and ensemble measurements of spots when the particles are very dense, this system achieves a dynamic range of about 6 orders of magnitude directly from a single scan. As a proof-of-concept digital protein microarray assay, we demonstrated detection of hepatitis B virus surface antigen in buffer with a limit of detection of 3.2 pg/mL. More broadly, the technique’s simplicity and high-throughput nature make digital microarrays a flexible platform technology with a wide range of potential applications in biomedical research and clinical diagnostics.The authors wish to thank Oguzhan Avci and Jacob Trueb for thoughtful comments and suggestions regarding numerical optimization of the optical system. This work was funded in part by a research contract with ASELSAN, Inc. and the Wallace H. Coulter Foundation 2010 Coulter Translational Award. (ASELSAN, Inc.; Wallace H. Coulter Foundation Coulter Translational Award)Accepted manuscrip

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany