Microarrays最新文献

{"title":"Integrated Microfluidic Nucleic Acid Isolation, Isothermal Amplification, and Amplicon Quantification.","authors":"Michael G Mauk,&nbsp;Changchun Liu,&nbsp;Jinzhao Song,&nbsp;Haim H Bau","doi":"10.3390/microarrays4040474","DOIUrl":"https://doi.org/10.3390/microarrays4040474","url":null,"abstract":"<p><p>Microfluidic components and systems for rapid (<60 min), low-cost, convenient, field-deployable sequence-specific nucleic acid-based amplification tests (NAATs) are described. A microfluidic point-of-care (POC) diagnostics test to quantify HIV viral load from blood samples serves as a representative and instructive example to discuss the technical issues and capabilities of \"lab on a chip\" NAAT devices. A portable, miniaturized POC NAAT with performance comparable to conventional PCR (polymerase-chain reaction)-based tests in clinical laboratories can be realized with a disposable, palm-sized, plastic microfluidic chip in which: (1) nucleic acids (NAs) are extracted from relatively large (~mL) volume sample lysates using an embedded porous silica glass fiber or cellulose binding phase (\"membrane\") to capture sample NAs in a flow-through, filtration mode; (2) NAs captured on the membrane are isothermally (~65 °C) amplified; (3) amplicon production is monitored by real-time fluorescence detection, such as with a smartphone CCD camera serving as a low-cost detector; and (4) paraffin-encapsulated, lyophilized reagents for temperature-activated release are pre-stored in the chip. Limits of Detection (LOD) better than 10³ virons/sample can be achieved. A modified chip with conduits hosting a diffusion-mode amplification process provides a simple visual indicator to readily quantify sample NA template. In addition, a companion microfluidic device for extracting plasma from whole blood without a centrifuge, generating cell-free plasma for chip-based molecular diagnostics, is described. Extensions to a myriad of related applications including, for example, food testing, cancer screening, and insect genotyping are briefly surveyed. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"474-89"},"PeriodicalIF":0.0,"publicationDate":"2015-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Synthetic Kinome Microarray Data Generator.","authors":"Farhad Maleki,&nbsp;Anthony Kusalik","doi":"10.3390/microarrays4040432","DOIUrl":"https://doi.org/10.3390/microarrays4040432","url":null,"abstract":"<p><p>Cellular pathways involve the phosphorylation and dephosphorylation of proteins. Peptide microarrays called kinome arrays facilitate the measurement of the phosphorylation activity of hundreds of proteins in a single experiment. Analyzing the data from kinome microarrays is a multi-step process. Typically, various techniques are possible for a particular step, and it is necessary to compare and evaluate them. Such evaluations require data for which correct analysis results are known. Unfortunately, such kinome data is not readily available in the community. Further, there are no established techniques for creating artificial kinome datasets with known results and with the same characteristics as real kinome datasets. In this paper, a methodology for generating synthetic kinome array data is proposed. The methodology relies on actual intensity measurements from kinome microarray experiments and preserves their subtle characteristics. The utility of the methodology is demonstrated by evaluating methods for eliminating heterogeneous variance in kinome microarray data. Phosphorylation intensities from kinome microarrays often exhibit such heterogeneous variance and its presence can negatively impact downstream statistical techniques that rely on homogeneity of variance. It is shown that using the output from the proposed synthetic data generator, it is possible to critically compare two variance stabilization methods. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"432-53"},"PeriodicalIF":0.0,"publicationDate":"2015-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic-Wide Analysis with Microarrays in Human Oncology.","authors":"Kenichi Inaoka,&nbsp;Yoshikuni Inokawa,&nbsp;Shuji Nomoto","doi":"10.3390/microarrays4040454","DOIUrl":"https://doi.org/10.3390/microarrays4040454","url":null,"abstract":"<p><p>DNA microarray technologies have advanced rapidly and had a profound impact on examining gene expression on a genomic scale in research. This review discusses the history and development of microarray and DNA chip devices, and specific microarrays are described along with their methods and applications. In particular, microarrays have detected many novel cancer-related genes by comparing cancer tissues and non-cancerous tissues in oncological research. Recently, new methods have been in development, such as the double-combination array and triple-combination array, which allow more effective analysis of gene expression and epigenetic changes. Analysis of gene expression alterations in precancerous regions compared with normal regions and array analysis in drug-resistance cancer tissues are also successfully performed. Compared with next-generation sequencing, a similar method of genome analysis, several important differences distinguish these techniques and their applications. Development of novel microarray technologies is expected to contribute to further cancer research. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 4","pages":"454-73"},"PeriodicalIF":0.0,"publicationDate":"2015-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4040454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aptamer-Based Screens of Human Body Fluids for Biomarkers.","authors":"Dania Albaba,&nbsp;Sanam Soomro,&nbsp;Chandra Mohan","doi":"10.3390/microarrays4030424","DOIUrl":"https://doi.org/10.3390/microarrays4030424","url":null,"abstract":"<p><p>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. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"424-31"},"PeriodicalIF":0.0,"publicationDate":"2015-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Constitutional Copy Number Variants in Breast Cancer.","authors":"Logan C Walker,&nbsp;George A R Wiggins,&nbsp;John F Pearson","doi":"10.3390/microarrays4030407","DOIUrl":"https://doi.org/10.3390/microarrays4030407","url":null,"abstract":"<p><p>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. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"407-23"},"PeriodicalIF":0.0,"publicationDate":"2015-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery.","authors":"Christopher J Walsh,&nbsp;Pingzhao Hu,&nbsp;Jane Batt,&nbsp;Claudia C Dos Santos","doi":"10.3390/microarrays4030389","DOIUrl":"https://doi.org/10.3390/microarrays4030389","url":null,"abstract":"<p><p>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. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"389-406"},"PeriodicalIF":0.0,"publicationDate":"2015-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology.","authors":"Javier Arsuaga,&nbsp;Tyler Borrman,&nbsp;Raymond Cavalcante,&nbsp;Georgina Gonzalez,&nbsp;Catherine Park","doi":"10.3390/microarrays4030339","DOIUrl":"10.3390/microarrays4030339","url":null,"abstract":"<p><p>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. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"339-69"},"PeriodicalIF":0.0,"publicationDate":"2015-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer.","authors":"Clare Coveney,&nbsp;David J Boocock,&nbsp;Robert C Rees,&nbsp;Suha Deen,&nbsp;Graham R Ball","doi":"10.3390/microarrays4030324","DOIUrl":"https://doi.org/10.3390/microarrays4030324","url":null,"abstract":"<p><p>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. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"324-38"},"PeriodicalIF":0.0,"publicationDate":"2015-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microintaglio Printing for Soft Lithography-Based in Situ Microarrays.","authors":"Manish Biyani,&nbsp;Takanori Ichiki","doi":"10.3390/microarrays4030311","DOIUrl":"https://doi.org/10.3390/microarrays4030311","url":null,"abstract":"<p><p>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. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"311-23"},"PeriodicalIF":0.0,"publicationDate":"2015-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer.","authors":"Enery Lorenzo,&nbsp;Katia Camacho-Caceres,&nbsp;Alexander J Ropelewski,&nbsp;Juan Rosas,&nbsp;Michael Ortiz-Mojer,&nbsp;Lynn Perez-Marty,&nbsp;Juan Irizarry,&nbsp;Valerie Gonzalez,&nbsp;Jesús A Rodríguez,&nbsp;Mauricio Cabrera-Rios,&nbsp;Clara Isaza","doi":"10.3390/microarrays4020287","DOIUrl":"https://doi.org/10.3390/microarrays4020287","url":null,"abstract":"<p><p>Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"287-310"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34087601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}