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Microarrays, Volume 3, Issue 3 (September 2014), Pages 159-211

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Research

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Open AccessArticle Application of Tissue Microarray Technology to Stem Cell Research
Microarrays 2014, 3(3), 159-167; doi:10.3390/microarrays3030159
Received: 2 April 2014 / Revised: 13 May 2014 / Accepted: 16 May 2014 / Published: 26 June 2014
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Abstract
There is virtually an unlimited number of possible Tissue Microarray (TMA) applications in basic and clinical research and ultimately in diagnostics. However, to assess the functional importance of novel markers, researchers very often turn to cell line model systems. The appropriate choice of
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There is virtually an unlimited number of possible Tissue Microarray (TMA) applications in basic and clinical research and ultimately in diagnostics. However, to assess the functional importance of novel markers, researchers very often turn to cell line model systems. The appropriate choice of a cell line is often a difficult task, but the use of cell microarray (CMA) technology enables a quick screening of several markers in cells of different origins, mimicking a genomic-scale analysis. In order to improve the morphological evaluations of the CMA slides we harvested the cells by conventional trypsinization, mechanical scraping and cells grown on coverslips. We show that mechanical scraping is a good evaluation method since keeps the real morphology very similar to those grown on coverslips. Immunofluorescence images are of higher quality, facilitating the reading of the biomarker cellular and subcellular localization. Here, we describe CMA technology in stem cell research. Full article
Open AccessArticle A New Modified Histogram Matching Normalization for Time Series Microarray Analysis
Microarrays 2014, 3(3), 203-211; doi:10.3390/microarrays3030203
Received: 24 March 2014 / Revised: 19 June 2014 / Accepted: 25 June 2014 / Published: 1 July 2014
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Abstract
Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if
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Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data. Full article
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Review

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Open AccessReview Molecular Diagnostic Applications in Colorectal Cancer
Microarrays 2014, 3(3), 168-179; doi:10.3390/microarrays3030168
Received: 28 March 2014 / Revised: 4 June 2014 / Accepted: 16 June 2014 / Published: 26 June 2014
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Abstract
Colorectal cancer, a clinically diverse disease, is a leading cause of cancer-related death worldwide. Application of novel molecular diagnostic tests, which are summarized in this article, may lead to an improved survival of colorectal cancer patients.  Distinction of these applications is based on
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Colorectal cancer, a clinically diverse disease, is a leading cause of cancer-related death worldwide. Application of novel molecular diagnostic tests, which are summarized in this article, may lead to an improved survival of colorectal cancer patients.  Distinction of these applications is based on the different molecular principles found in colorectal cancer (CRC). Strategies for molecular analysis of single genes (as KRAS or TP53) as well as microarray based techniques are discussed. Moreover, in addition to the fecal occult blood testing (FOBT) and colonoscopy some novel assays offer approaches for early detection of colorectal cancer like the multitarget stool DNA test or the blood-based Septin 9 DNA methylation test. Liquid biopsy analysis may also exhibit great diagnostic potential in CRC for monitoring developing resistance to treatment. These new diagnostic tools and the definition of molecular biomarkers in CRC will improve early detection and targeted therapy of colorectal cancer. Full article
(This article belongs to the Special Issue Clinical Applications of Microarrays)
Open AccessReview Protein Microarrays with Novel Microfluidic Methods: Current Advances
Microarrays 2014, 3(3), 180-202; doi:10.3390/microarrays3030180
Received: 30 April 2014 / Revised: 10 June 2014 / Accepted: 16 June 2014 / Published: 1 July 2014
Cited by 2 | PDF Full-text (10133 KB) | HTML Full-text | XML Full-text
Abstract
Microfluidic-based micromosaic technology has allowed the pattering of recognition elements in restricted micrometer scale areas with high precision. This controlled patterning enabled the development of highly multiplexed arrays multiple analyte detection. This arraying technology was first introduced in the beginning of 2001 and
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Microfluidic-based micromosaic technology has allowed the pattering of recognition elements in restricted micrometer scale areas with high precision. This controlled patterning enabled the development of highly multiplexed arrays multiple analyte detection. This arraying technology was first introduced in the beginning of 2001 and holds tremendous potential to revolutionize microarray development and analyte detection. Later, several microfluidic methods were developed for microarray application. In this review we discuss these novel methods and approaches which leverage the property of microfluidic technologies to significantly improve various physical aspects of microarray technology, such as enhanced imprinting homogeneity, stability of the immobilized biomolecules, decreasing assay times, and reduction of the costs and of the bulky instrumentation. Full article
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