"Harshlighting" small blemishes on microarrays

BACKGROUND: Microscopists are familiar with many blemishes that fluorescence images can have due to dust and debris, glass flaws, uneven distribution of fluids or surface coatings, etc. Microarray scans show similar artefacts, which affect the analysis, particularly when one tries to detect subtle changes. However, most blemishes are hard to find by the unaided eye, particularly in high-density oligonucleotide arrays (HDONAs). RESULTS: We present a method that harnesses the statistical power provided by having several HDONAs available, which are obtained under similar conditions except for the experimental factor. This method "harshlights" blemishes and renders them evident. We find empirically that about 25% of our chips are blemished, and we analyze the impact of masking them on screening for differentially expressed genes. CONCLUSION: Experiments attempting to assess subtle expression changes should be carefully screened for blemishes on the chips. The proposed method provides investigators with a novel robust approach to improve the sensitivity of microarray analyses. By utilizing topological information to identify and mask blemishes prior to model based analyses, the method prevents artefacts from confounding the process of background correction, normalization, and summarization

The state of the art in the analysis of two-dimensional gel electrophoresis images

Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field

Microarrays in Lung Cancer Research : From Comparative Analyses to Verified Findings

Keuhkosyöpä on yleisimpiä syöpätauteja. Se jaetaan kahteen päätyyppiin: pienisoluiseen ja ei-pienisoluiseen keuhkosyöpään. Ei-pienisoluinen keuhkosyöpä jaetaan lisäksi alatyyppeihin, joista suurimmat ovat levyepiteeli-, adeno- ja suurisoluinen karsinooma. Keuhkosyövän tärkein riskitekijä on tupakointi, mutta muutkin työ- ja elinympäristön altisteet, kuten asbesti, voivat johtaa syöpään. Väitöstyössä tutkittiin kahdenlaisten keuhkosyöpäryhmien erityispiirteitä. Työssä kartoitettiin, onko löydettävissä muutoksia, jotka erottavat asbestikeuhkosyövät muista syövistä sekä luuytimeen varhaisessa vaiheessa leviävät keuhkosyövät leviämättömistä syövistä. Tutkimusten ensimmäisessä vaiheessa käytettiin mikrosirupohjaisia menetelmiä, jotka mahdollistavat jopa kaikkien geenien tarkastelun yhden kokeen avulla. Vertailevien mikrosirututkimusten avulla on mahdollista paikantaa geenejä tai kromosomialueita, joiden muutokset erottelevat ryhmät toisistaan. Asbestiin liittyvissä tutkimuksissa paikannettiin kuusi kromosomialuetta, joissa geenien kopiolukumäärän sekä ilmenemistason muutokset erottelivat potilaat altistushistorian mukaan. Riippumattomilla laboratoriomenetelmillä tehtyjen jatkoanalyysien avulla pystyttiin varmistamaan, että 19p-alueen häviämä oli yhteydessä asbestialtistukseen. Työssä osoitettiin myös, että 19p-alueen muutoksia voidaan indusoida altistamalla soluja asbestille in vitro. Tutkimuksessa saatiin lisäksi viitteitä asbestispesifisistä muutoksista signaalinvälitysreiteissä, sillä yhdessä toimivien geenien ilmentymisessä havaittiin eroja asbestille altistuneiden ja altistumattomien välillä. Vertailemalla luuytimeen syövän aikaisessa vaiheessa levinneiden ja leviämättömien keuhkoadenokarsinoomien muutosprofiileita toisiinsa, paikannettiin viisi aluetta, joilla geenien kopiolukumäärä- sekä ilmenemistason muutokset erottelivat ryhmät toisistaan. Jatkoanalyyseissä havaittiin, että 4q-alueen häviämää esiintyi adenokarsinoomien lisäksi levyepiteelikarsinoomiin, jotka olivat levinneet luuytimeen. Myös keuhkosyöpien aivometastaaseissa alue oli toistuvasti hävinnyt. Väitöstyön tutkimukset osoittavat, että vertailevien mikrosiruanalyysien avulla saadaan tietoa syöpäryhmien erityispiirteistä. Työssä saadut tulokset osoittavat, että 19p-alueen muutokset ovat tyypillisiä asbestikeuhkosyöville ja 4q-alueen muutokset luuytimeen aikaisessa vaiheessa leviäville keuhkosyöville.Lung cancers (LCs) represent a rather heterogeneous collection of tumors that are characterized by a large number of abnormalities of both chromosome number and structure. The genetic alterations displayed by a given tumor represent a combination of changes that are directly or indirectly caused by inducing factors, such as tobacco carcinogens, and those that rise up secondarily as a consequence of defects in genes that maintain genomic stability. Although a number of genes have been identified to be recurrently aberrated in LCs, numerous genes contributing to lung carcinogenesis are yet to be revealed. In this thesis, opposite categories of lung tumors were investigated using comparative microarray analyses with the aim to identify aberrations that are of significance in distinct types of LCs. The studies involved LCs in two separate themes: those induced by asbestos exposure and those that disseminate into bone marrow (BM) in early stages of tumorigenesis. The category-specific alterations were identified by using a differential region finding method that was developed in this thesis. Distinct chromosome regions were found to be more frequently aberrated in the LCs of asbestos-exposed than non-exposed patients. Aberrations of distinct regions also differentiated lung adenocarcinomas (ACs) from patients with and without evidence of disseminated tumor cells (DTCs) in their BM. In the asbestos-related studies, subsequent analyses verified that aberrations of 19p, caused by losses, were significantly more frequent in tumors of the asbestos-exposed than of the non-exposed patients. We showed also that 19p aberrations can be induced in vitro by means of a crocidolite asbestos treatment. Furthermore, a Gene Ontology (GO) analysis revealed a number of differentially regulated biological processes and molecular functions between the tumor groups with differences in protein ubiquitination and ion transport especially highlighted. In the subsequent dissemination-related studies, further analyses of the 4q region were performed on both primary LCs and brain metastases of LCs. The connection between the loss of 4q and presence of DTCs in BM in ACs could be verified, but additionally, 4q loss was demonstrated to be a common feature of BM-positive tumors across different histological types of LCs. Losses of 4q were also frequently observed in brain metastases of LCs

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

Multi-functional, self-sensing and automated real-time non-contact liquid dispensing system

Liquid dispensing in the order of pico-liter has become more and more important in biology, electronics and micro-electronic-mechanical-system (MEMS) during the past two decades due to the rapid progress of researches on the deoxyribonucleic acid (DNA) microarray, compact and low-cost direct write technology (DWT), organic semiconductors and nano-particles. The existing approaches, commercialized or experimental, to liquid dispensing in minute amounts have one common shortcoming: open loop control, i.e., they have no direct control on the quality of dispensed liquid. In contrast, the SmartPin has intrinsic self-sensing capability to not only control the process of liquid dispensing, but also the results of the dispensed liquid in real time. The dual purpose fiber optics sensor/plunger is able to detect the status of liquid morphology under dispensing, in real time, by the internal light sensor and control both the amount and the manner of liquid dispensing by its plunger-like movements. This dissertation work has implemented, with the SmartPin technology, a frilly automated DNA microarrayer based on the first generation prototype developed at NJIT\u27s Real Time Control Laboratory. This new DNA microarrayer fulfills all requirements in each step of DNA microarray fabrication, such as thorough cleaning to avoid cross contamination and clogging, aspiration of tiny amount of DNA samples, spotting on multiple slides, and flexible in stream change of DNA samples. Experiment results shows that this DNA microarrayer compares favorably with its commercialized counterpart OmniGrid 100 with SMP3 pins. As a verification of robust implementation and on-the-fly control of spot morphology, high volume of spots (120 K) have been made, from which the corresponding experiment data has been obtained, categorized and normalized as template database. In addition, this dissertation research explores the patterned microline-drawing capability of the SmartPin. Two approaches, spot sequence and liquid-column sweeping, are proposed and implemented. Experiment results show that the SmartPin is promising in the area of patterning of large area organic electronics. Besides the experimental research, computational fluid dynamics (CFD) simulation of the liquid dispensing process has been done by utilizing GAMBIT and FLUENT, which are state-of-the-art computer programs for modeling fluid flow and heat transfer in complex geometries. The CFD simulation results, validated by experimental results, offer a guide to the design of control system for different tasks of liquid dispensation, such as fabrication of protein microarray

An enhanced median filter for removing noise from MR images

In this paper, a novel decision based median (DBM) filter for enhancing MR images has been proposed. The method is based on eliminating impulse noise from MR images. A median-based method to remove impulse noise from digital MR images has been developed. Each pixel is leveled from black to white like gray-level. The method is adjusted in order to decide whether the median operation can be applied on a pixel. The main deficiency in conventional median filter approaches is that all pixels are filtered with no concern about healthy pixels. In this research, to suppress this deficiency, noisy pixels are initially detected, and then the filtering operation is applied on them. The proposed decision method (DM) is simple and leads to fast filtering. The results are more accurate than other conventional filters. Moreover, DM adjusts itself based on the conditions of local detections. In other words, DM operation on detecting a pixel as a noise depends on the previous decision. As a considerable advantage, some unnecessary median operations are eliminated and the number of median operations reduces drastically by using DM. Decision method leads to more acceptable results in scenarios with high noise density. Furthermore, the proposed method reduces the probability of detecting noise-free pixels as noisy pixels and vice versa

An enhanced median filter for removing noise from MR images

In this paper, a novel decision based median (DBM) filter for enhancing MR images has been proposed. The method is based on eliminating impulse noise from MR images. A median-based method to remove impulse noise from digital MR images has been developed. Each pixel is leveled from black to white like gray-level. The method is adjusted in order to decide whether the median operation can be applied on a pixel. The main deficiency in conventional median filter approaches is that all pixels are filtered with no concern about healthy pixels. In this research, to suppress this deficiency, noisy pixels are initially detected, and then the filtering operation is applied on them. The proposed decision method (DM) is simple and leads to fast filtering. The results are more accurate than other conventional filters. Moreover, DM adjusts itself based on the conditions of local detections. In other words, DM operation on detecting a pixel as a noise depends on the previous decision. As a considerable advantage, some unnecessary median operations are eliminated and the number of median operations reduces drastically by using DM. Decision method leads to more acceptable results in scenarios with high noise density. Furthermore, the proposed method reduces the probability of detecting noise-free pixels as noisy pixels and vice versa

Microfluidic methods for single cell analysis in clinically relevant samples

Single cell protein analysis has the potential to comprehensively profile cellular heterogeneity advancing the understanding of cell function, disease progression and drug development. Although it is a possible contributor to the discovery of novel therapy, the protein mapping on the individual cell basis is complex and poses challenges. For example, cancer is a heterogeneous disease which analysis using conventional bulk methods can potentially conceal a dangerous population like Circulating Tumour Cells (CTCs). Analysis of CTCs can only be reliably attained using single cell technologies. The Microfluidic Affinity Capture (MAC) chip is a tool that was developed in our group to study cellular heterogeneity by measuring protein abundance in single cells. Another problem in analysing cancer cells is the difficulty in obtaining samples in a non-invasive manner. This thesis reports on attempts to obtain, concentrate and analyse CTCs from blood using a composite MAC-based device. The ability to analyse single cells has advantages beyond the potential for analysing heterogeneity. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous illness that is characterised by a chronic inflammation. Although the immune response in COPD requires investigation, it is complicated by a lack of biomarkers and methods to non-invasively collect samples. The ability to obtain and effectively analyse precious and scarce biomaterial from lungs, is greatly advantageous for both diagnosis and tracking of COPD. Here, we report on a workflow to achieve this using sputum from negative control volunteers and COPD+ patients. Also, we describe the establishment of a novel MAC chip assay targeting FOXO3 protein which is involved in regulation of processes like tumour suppression, inflammation and senescence. Therefore, it has a biomarker potential to monitor and study diseases like cancer and COPD. We developed the protocol to analyse Forkhead box 3 (FOXO3) protein expression in nasal cells from healthy donor samples that were retrieved with a non-invasive tool (NASAM, nasal synthetic absorptive matrix). Nasal cavity is a front line of exposure to inhaled pernicious substances and, it is speculated to reflect anomalous bioprocesses in lungs preceding respiratory disease development and progression. This study provides the first-time quantification of FOXO3 protein in single cells from nasal samples. This work shows the analytical capacity of the MAC chip to study cellular heterogeneity and quantify important biomarkers in single cells of clinically relevant material.Open Acces

Wavelet-based noise reduction of cDNA microarray images

The advent of microarray imaging technology has lead to enormous progress in the life sciences by allowing scientists to analyze the expression of thousands of genes at a time. For complementary DNA (cDNA) microarray experiments, the raw data are a pair of red and green channel images corresponding to the treatment and control samples. These images are contaminated by a high level of noise due to the numerous noise sources affecting the image formation. A major challenge of microarray image analysis is the extraction of accurate gene expression measurements from the noisy microarray images. A crucial step in this process is denoising, which consists of reducing the noise in the observed microarray images while preserving the signal information as much as possible. This thesis deals with the problem of developing novel methods for reducing noise in cDNA microarray images for accurate estimation of the gene expression levels. Denoising methods based on the wavelet transform have shown significant success when applied to natural images. However, these methods are not very efficient for reducing noise in cDNA microarray images. An important reason for this is that existing methods are only capable of processing the red and green channel images separately. In doing so. they ignore the signal correlation as well as the noise correlation that exists between the wavelet coefficients of the two channels. The primary objective of this research is to design efficient wavelet-based noise reduction algorithms for cDNA microarray images that take into account these inter-channel dependencies by 'jointly' estimating the noise-free coefficients in both the channels. Denoising algorithms are developed using two types of wavelet transforms, namely, the frequently-used discrete wavelet transform (DWT) and the complex wavelet transform (CWT). The main advantage of using the DWT for denoising is that this transform is computationally very efficient. In order to obtain a better denoising performance for microarray images, however, the CWT is preferred to DWT because the former has good directional selectivity properties that are necessary for better representation of the circular edges of spots. The linear minimum mean squared error and maximum a posteriori estimation techniques are used to develop bivariate estimators for the noise-free coefficients of the two images. These estimators are derived by utilizing appropriate joint probability density functions for the image coefficients as well as the noise coefficients of the two channels. Extensive experimentations are carried out on a large set of cDNA microarray images to evaluate the performance of the proposed denoising methods as compared to the existing ones. Comparisons are made using standard metrics such as the peak signal-to-noise ratio (PSNR) for measuring the amount of noise removed from the pixels of the images, and the mean absolute error for measuring the accuracy of the estimated log-intensity ratios obtained from the denoised version of the images. Results indicate that the proposed denoising methods that are developed specifically for the microarray images do, indeed, lead to more accurate estimation of gene expression levels. Thus, it is expected that the proposed methods will play a significant role in improving the reliability of the results obtained from practical microarray experiments

Transcriptional Profiling of Organ‐Specific Autoimmunity in Human

Our understanding of the pathogenesis of organ‐specific autoinflammation has been restricted by limited access to the target organs. Peripheral blood, however, as a preferred transportation route for immune cells, provides a window to assess the entire immune system throughout the body. Transcriptional profiling with RNA stabilizing blood collection tubes reflects in vivo expression profiles at the time the blood is drawn, allowing detection of the disease activity in different samples or within the same sample over time. The main objective of this Ph.D. study was to apply gene‐expression microarrays in the characterization of peripheral blood transcriptional profiles in patients with autoimmune diseases. To achieve this goal a custom cDNA microarray targeted for gene‐expression profiling of human immune system was designed and produced. Sample collection and preparation was then optimized to allow gene‐expression profiling from whole‐blood samples. To overcome challenges resulting from minute amounts of sample material, RNA amplification was successfully applied to study pregnancy related immunosuppression in patients with multiple sclerosis (MS). Furthermore, similar sample preparation was applied to characterize longitudinal genome‐wide expression profiles in children with type 1 diabetes (T1D) associated autoantibodies and eventually clinical T1D. Blood transcriptome analyses, using both the ImmunoChip cDNA microarray with targeted probe selection and genome‐wide Affymetrix U133 Plus 2.0 oligonucleotide array, enabled monitoring of autoimmune activity. Novel disease related genes and general autoimmune signatures were identified. Notably, down‐regulation of the HLA class Ib molecules in peripheral blood was associated with disease activity in both MS and T1D. Taken together, these studies demonstrate the potential of peripheral blood transcriptional profiling in biomedical research and diagnostics. Imbalances in peripheral blood transcriptional activity may reveal dynamic changes that are relevant for the disease but might be completely missed in conventional cross‐sectional studies.Geenien ilmentyminen ihmisen kudos‐spesifisissä autoimmuunisairauksissa Kohdekudosten hankala saatavuus on rajoittanut kudos‐spesifisten autoimmuunisairauksien tutkimusta. Immuunijärjestelmää voidaan kuitenkin tarkastella myös potilaan verestä, joka toimii immuunijärjestelmän solujen tärkeimpänä kuljetusreittinä. Käyttämällä erityisesti RNA‐molekyylien säilyttämiseksi tarkoitettuja näytteenottoputkia, voidaan tarkastella geenien ilmentymistä elimistössä näytteenottohetkellä ja siten seurata immuunijärjestelmän aktiivisuutta. Tämän väitöskirjatyön tavoitteena oli tarkastella DNA‐mikrosirujen avulla geenien ilmentymistä potilaiden veressä immuunijärjestelmän aktiivisuuden muuttuessa. Tätä tarkoitusta varten suunniteltiin ja valmistettiin keskeiset immuunijärjestelmän geenit sisältävä cDNA‐mikrosiru, jota käytettiin raskauden aikaansaaman immuunivasteen heikkenemisen tarkasteluun MS‐potilailla. Tutkimusta varten optimoitiin verinäytteiden keruu‐ ja RNA‐eristysmenetelmät, ja koska verinäytteiden RNA‐määrät olivat pieniä, eristetty RNA monistettiin ennen analysointia DNAmikrosiruilla. Samaa näytteenkäsittelymenetelmää käytettiin myös kerättäessä näytesarjoja lapsista, joilla oli jo havaittu tyypin 1 diabetekseen yhdistettyjä autovasta‐aineita. Näytesarjat lapsista, jotka myöhemmin sairastuivat tyypin 1 diabetekseen, analysoitiin kaupallisella koko genomin kattavalla sirulla. Tutkimuksissa löydettiin aikaisemmin autoimmuunijärjestelmään yhdistettyjen geenien lisäksi uusia löydöksiä sekä itse suunniteltua ja valmistettua ImmunoChip cDNA‐mikrosirua että koko genomin kattavaa Affymetrix U133 Plus 2.0 oligonukleotidisirua käytettäessä. Erityisen merkillepantavaa oli luokan 1b HLA geenien hiljeneminen sekä MS‐taudin että tyypin 1 diabeteksen aktiivisuuden lisääntyessä. Väitöskirjatyön tutkimukset osoittivat, että immuunijärjestelmän aktiivisuutta voidaan seurata potilaiden verinäytteissä ilmenevien geenien kautta, ja veren soluissa ilmenevien geenien tarkastelua voidaan hyödyntää biolääketieteen tutkimuksessa ja diagnostiikassa. Lisäksi, geenien ilmentymisen seuraaminen saman potilaan peräkkäisissä näytteissä voi paljastaa toiminnallisia muutoksia, jotka perinteisessä poikkileikkaustutkimuksessa saattaisivat jäädä kokonaan huomioimattaSiirretty Doriast