Next Issue
Volume 4, December
Previous Issue
Volume 4, June

Table of Contents

Microarrays, Volume 4, Issue 3 (September 2015) , Pages 311-431

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Order results
Result details
Select all
Export citation of selected articles as:
Open AccessReview
Aptamer-Based Screens of Human Body Fluids for Biomarkers
Microarrays 2015, 4(3), 424-431; https://doi.org/10.3390/microarrays4030424 - 22 Sep 2015
Cited by 4 | Viewed by 1885
Abstract
In recent years, aptamers have come to replace antibodies in high throughput multiplexed experiments. The aptamer-based biomarker screening technology, which kicked off in 2010, is capable of interrogating thousands of proteins in a very small sample volume. With this new technology, researchers hope [...] Read more.
In recent years, aptamers have come to replace antibodies in high throughput multiplexed experiments. The aptamer-based biomarker screening technology, which kicked off in 2010, is capable of interrogating thousands of proteins in a very small sample volume. With this new technology, researchers hope to find clinically appropriate biomarkers for a myriad of illnesses by screening human body fluids. In this work, we have reviewed a total of eight studies utilizing aptamer-based biomarker screens of human body fluids, and have highlighted novel protein biomarkers discovered. Full article
(This article belongs to the Special Issue High-Throughput Microarray for Protein Biomarker Discovery)
Open AccessReview
The Role of Constitutional Copy Number Variants in Breast Cancer
Microarrays 2015, 4(3), 407-423; https://doi.org/10.3390/microarrays4030407 - 08 Sep 2015
Cited by 3 | Viewed by 1944
Abstract
Constitutional copy number variants (CNVs) include inherited and de novo deviations from a diploid state at a defined genomic region. These variants contribute significantly to genetic variation and disease in humans, including breast cancer susceptibility. Identification of genetic risk factors for breast cancer [...] Read more.
Constitutional copy number variants (CNVs) include inherited and de novo deviations from a diploid state at a defined genomic region. These variants contribute significantly to genetic variation and disease in humans, including breast cancer susceptibility. Identification of genetic risk factors for breast cancer in recent years has been dominated by the use of genome-wide technologies, such as single nucleotide polymorphism (SNP)-arrays, with a significant focus on single nucleotide variants. To date, these large datasets have been underutilised for generating genome-wide CNV profiles despite offering a massive resource for assessing the contribution of these structural variants to breast cancer risk. Technical challenges remain in determining the location and distribution of CNVs across the human genome due to the accuracy of computational prediction algorithms and resolution of the array data. Moreover, better methods are required for interpreting the functional effect of newly discovered CNVs. In this review, we explore current and future application of SNP array technology to assess rare and common CNVs in association with breast cancer risk in humans. Full article
(This article belongs to the Special Issue SNP Array)
Open AccessReview
Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery
Microarrays 2015, 4(3), 389-406; https://doi.org/10.3390/microarrays4030389 - 21 Aug 2015
Cited by 19 | Viewed by 3641
Abstract
The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature [...] Read more.
The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers. Full article
(This article belongs to the Special Issue High-Throughput Microarray for Protein Biomarker Discovery)
Show Figures

Figure 1

Open AccessCommunication
SPOTing Acetyl-Lysine Dependent Interactions
Microarrays 2015, 4(3), 370-388; https://doi.org/10.3390/microarrays4030370 - 17 Aug 2015
Cited by 8 | Viewed by 1892
Abstract
Post translational modifications have been recognized as chemical signals that create docking sites for evolutionary conserved effector modules, allowing for signal integration within large networks of interactions. Lysine acetylation in particular has attracted attention as a regulatory modification, affecting chromatin structure and linking [...] Read more.
Post translational modifications have been recognized as chemical signals that create docking sites for evolutionary conserved effector modules, allowing for signal integration within large networks of interactions. Lysine acetylation in particular has attracted attention as a regulatory modification, affecting chromatin structure and linking to transcriptional activation. Advances in peptide array technologies have facilitated the study of acetyl-lysine-containing linear motifs interacting with the evolutionary conserved bromodomain module, which specifically recognizes and binds to acetylated sequences in histones and other proteins. Here we summarize recent work employing SPOT peptide technology to identify acetyl-lysine dependent interactions and document the protocols adapted in our lab, as well as our efforts to characterize such bromodomain-histone interactions. Our results highlight the versatility of SPOT methods and establish an affordable tool for rapid access to potential protein/modified-peptide interactions involving lysine acetylation. Full article
(This article belongs to the Special Issue Peptide Microarrays)
Show Figures

Figure 1

Open AccessArticle
Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology
Microarrays 2015, 4(3), 339-369; https://doi.org/10.3390/microarrays4030339 - 12 Aug 2015
Cited by 5 | Viewed by 2949
Abstract
DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the [...] Read more.
DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer. Full article
(This article belongs to the Special Issue Advanced Methods in Microarrays for Cancer Research)
Show Figures

Figure 1

Open AccessArticle
Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer
Microarrays 2015, 4(3), 324-338; https://doi.org/10.3390/microarrays4030324 - 17 Jul 2015
Cited by 2 | Viewed by 2698
Abstract
The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a [...] Read more.
The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two carefully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy. Full article
Show Figures

Figure 1

Open AccessReview
Microintaglio Printing for Soft Lithography-Based in Situ Microarrays
Microarrays 2015, 4(3), 311-323; https://doi.org/10.3390/microarrays4030311 - 14 Jul 2015
Cited by 2 | Viewed by 2465
Abstract
Advances in lithographic approaches to fabricating bio-microarrays have been extensively explored over the last two decades. However, the need for pattern flexibility, a high density, a high resolution, affordability and on-demand fabrication is promoting the development of unconventional routes for microarray fabrication. This [...] Read more.
Advances in lithographic approaches to fabricating bio-microarrays have been extensively explored over the last two decades. However, the need for pattern flexibility, a high density, a high resolution, affordability and on-demand fabrication is promoting the development of unconventional routes for microarray fabrication. This review highlights the development and uses of a new molecular lithography approach, called “microintaglio printing technology”, for large-scale bio-microarray fabrication using a microreactor array (µRA)-based chip consisting of uniformly-arranged, femtoliter-size µRA molds. In this method, a single-molecule-amplified DNA microarray pattern is self-assembled onto a µRA mold and subsequently converted into a messenger RNA or protein microarray pattern by simultaneously producing and transferring (immobilizing) a messenger RNA or a protein from a µRA mold to a glass surface. Microintaglio printing allows the self-assembly and patterning of in situ-synthesized biomolecules into high-density (kilo-giga-density), ordered arrays on a chip surface with µm-order precision. This holistic aim, which is difficult to achieve using conventional printing and microarray approaches, is expected to revolutionize and reshape proteomics. This review is not written comprehensively, but rather substantively, highlighting the versatility of microintaglio printing for developing a prerequisite platform for microarray technology for the postgenomic era. Full article
(This article belongs to the Special Issue New and Old Technologies for Generation of Microarrays)
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop