Microarrays最新文献

{"title":"\"Upstream Analysis\": An Integrated Promoter-Pathway Analysis Approach to Causal Interpretation of Microarray Data.","authors":"Jeannette Koschmann,&nbsp;Anirban Bhar,&nbsp;Philip Stegmaier,&nbsp;Alexander E Kel,&nbsp;Edgar Wingender","doi":"10.3390/microarrays4020270","DOIUrl":"https://doi.org/10.3390/microarrays4020270","url":null,"abstract":"<p><p>A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowledge-based analysis of the upstream pathway that control the activity of these TFs is shown to lead to hypothetical master regulators. This strategy was implemented as a workflow in a comprehensive bioinformatic software platform. We applied this workflow to gene sets that were identified by a novel triclustering algorithm in naphthalene-induced gene expression signatures of murine liver and lung tissue. As a result, tissue-specific master regulators were identified that are known to be linked with tumorigenic and apoptotic processes. To our knowledge, this is the first time that genes of expression triclusters were used to identify upstream regulators. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"270-86"},"PeriodicalIF":0.0,"publicationDate":"2015-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34422045","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 Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks.","authors":"Alina Sîrbu,&nbsp;Martin Crane,&nbsp;Heather J Ruskin","doi":"10.3390/microarrays4020255","DOIUrl":"https://doi.org/10.3390/microarrays4020255","url":null,"abstract":"<p><p>Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"255-69"},"PeriodicalIF":0.0,"publicationDate":"2015-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34422044","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":"Re-Punching Tissue Microarrays Is Possible: Why Can This Be Useful and How to Do It.","authors":"Aurélien Lacombe,&nbsp;Vincenza Carafa,&nbsp;Sandra Schneider,&nbsp;Melanie Sticker-Jantscheff,&nbsp;Luigi Tornillo,&nbsp;Serenella Eppenberger-Castori","doi":"10.3390/microarrays4020245","DOIUrl":"https://doi.org/10.3390/microarrays4020245","url":null,"abstract":"<p><p>Tissue microarray (TMA) methodology allows the concomitant analysis of hundreds of tissue specimens arrayed in the same manner on a recipient block. Subsequently, all samples can be processed under identical conditions, such as antigen retrieval procedure, reagent concentrations, incubation times with antibodies/probes, and escaping the inter-assays variability. Therefore, the use of TMA has revolutionized histopathology translational research projects and has become a tool very often used for putative biomarker investigations. TMAs are particularly relevant for large scale analysis of a defined disease entity. In the course of these exploratory studies, rare subpopulations can be discovered or identified. This can refer to subsets of patients with more particular phenotypic or genotypic disease with low incidence or to patients receiving a particular treatment. Such rare cohorts should be collected for more specific investigations at a later time, when, possibly, more samples of a rare identity will be available as well as more knowledge derived from concomitant, e.g., genetic, investigations will have been acquired. In this article we analyze for the first time the limits and opportunities to construct new TMA blocks using tissues from older available arrays and supplementary donor blocks. In summary, we describe the reasons and technical details for the construction of rare disease entities arrays. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"245-54"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34422043","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":"NAPPA as a Real New Method for Protein Microarray Generation.","authors":"Paula Díez,&nbsp;María González-González,&nbsp;Lucía Lourido,&nbsp;Rosa M Dégano,&nbsp;Nieves Ibarrola,&nbsp;Juan Casado-Vela,&nbsp;Joshua LaBaer,&nbsp;Manuel Fuentes","doi":"10.3390/microarrays4020214","DOIUrl":"https://doi.org/10.3390/microarrays4020214","url":null,"abstract":"<p><p>Nucleic Acid Programmable Protein Arrays (NAPPA) have emerged as a powerful and innovative technology for the screening of biomarkers and the study of protein-protein interactions, among others possible applications. The principal advantages are the high specificity and sensitivity that this platform offers. Moreover, compared to conventional protein microarrays, NAPPA technology avoids the necessity of protein purification, which is expensive and time-consuming, by substituting expression in situ with an in vitro transcription/translation kit. In summary, NAPPA arrays have been broadly employed in different studies improving knowledge about diseases and responses to treatments. Here, we review the principal advances and applications performed using this platform during the last years. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"214-27"},"PeriodicalIF":0.0,"publicationDate":"2015-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34422041","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":"Label and Label-Free Detection Techniques for Protein Microarrays.","authors":"Amir Syahir,&nbsp;Kenji Usui,&nbsp;Kin-Ya Tomizaki,&nbsp;Kotaro Kajikawa,&nbsp;Hisakazu Mihara","doi":"10.3390/microarrays4020228","DOIUrl":"https://doi.org/10.3390/microarrays4020228","url":null,"abstract":"<p><p>Protein microarray technology has gone through numerous innovative developments in recent decades. In this review, we focus on the development of protein detection methods embedded in the technology. Early microarrays utilized useful chromophores and versatile biochemical techniques dominated by high-throughput illumination. Recently, the realization of label-free techniques has been greatly advanced by the combination of knowledge in material sciences, computational design and nanofabrication. These rapidly advancing techniques aim to provide data without the intervention of label molecules. Here, we present a brief overview of this remarkable innovation from the perspectives of label and label-free techniques in transducing nano‑biological events. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"228-44"},"PeriodicalIF":0.0,"publicationDate":"2015-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34422042","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":"Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies.","authors":"Stefanie Brezina,&nbsp;Regina Soldo,&nbsp;Roman Kreuzhuber,&nbsp;Philipp Hofer,&nbsp;Andrea Gsur,&nbsp;Andreas Weinhaeusel","doi":"10.3390/microarrays4020162","DOIUrl":"https://doi.org/10.3390/microarrays4020162","url":null,"abstract":"<p><p>New minimal invasive diagnostic methods for early detection of lung cancer are urgently needed. It is known that the immune system responds to tumors with production of tumor-autoantibodies. Protein microarrays are a suitable highly multiplexed platform for identification of autoantibody signatures against tumor-associated antigens (TAA). These microarrays can be probed using 0.1 mg immunoglobulin G (IgG), purified from 10 µL of plasma. We used a microarray comprising recombinant proteins derived from 15,417 cDNA clones for the screening of 100 lung cancer samples, including 25 samples of each main histological entity of lung cancer, and 100 controls. Since this number of samples cannot be processed at once, the resulting data showed non-biological variances due to \"batch effects\". Our aim was to evaluate quantile normalization, \"distance-weighted discrimination\" (DWD), and \"ComBat\" for their effectiveness in data pre-processing for elucidating diagnostic immune‑signatures. \"ComBat\" data adjustment outperformed the other methods and allowed us to identify classifiers for all lung cancer cases versus controls and small-cell, squamous cell, large-cell, and adenocarcinoma of the lung with an accuracy of 85%, 94%, 96%, 92%, and 83% (sensitivity of 0.85, 0.92, 0.96, 0.88, 0.83; specificity of 0.85, 0.96, 0.96, 0.96, 0.83), respectively. These promising data would be the basis for further validation using targeted autoantibody tests. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"162-87"},"PeriodicalIF":0.0,"publicationDate":"2015-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34421570","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":"Tissue Microarray Technology for Molecular Applications: Investigation of Cross-Contamination between Tissue Samples Obtained from the Same Punching Device.","authors":"Erik Vassella,&nbsp;José A Galván,&nbsp;Inti Zlobec","doi":"10.3390/microarrays4020188","DOIUrl":"https://doi.org/10.3390/microarrays4020188","url":null,"abstract":"<p><strong>Background: </strong>Tissue microarray (TMA) technology allows rapid visualization of molecular markers by immunohistochemistry and in situ hybridization. In addition, TMA instrumentation has the potential to assist in other applications: punches taken from donor blocks can be placed directly into tubes and used for nucleic acid analysis by PCR approaches. However, the question of possible cross-contamination between samples punched with the same device has frequently been raised but never addressed.</p><p><strong>Methods: </strong>Two experiments were performed. (1) A block from mycobacterium tuberculosis (TB) positive tissue and a second from an uninfected patient were aligned side-by-side in an automated tissue microarrayer. Four 0.6 mm punches were cored from each sample and placed inside their corresponding tube. Between coring of each donor block, a mechanical cleaning step was performed by insertion of the puncher into a paraffin block. This sequence of coring and cleaning was repeated three times, alternating between positive and negative blocks. A fragment from the 6110 insertion sequence specific for mycobacterium tuberculosis was analyzed; (2) Four 0.6 mm punches were cored from three KRAS mutated colorectal cancer blocks, alternating with three different wild-type tissues using the same TMA instrument (sequence of coring: G12D, WT, G12V, WT, G13D and WT). Mechanical cleaning of the device between each donor block was made. Mutation analysis by pyrosequencing was carried out. This sequence of coring was repeated manually without any cleaning step between blocks.</p><p><strong>Results/discussion: </strong>In both analyses, all alternating samples showed the expected result (samples 1, 3 and 5: positive or mutated, samples 2, 4 and 6: negative or wild-type). Similar results were obtained without cleaning step. These findings suggest that no cross-contamination of tissue samples occurs when donor blocks are punched using the same device, however a cleaning step is nonetheless recommended. Our result supports the use of TMA technology as an accessory to PCR applications.</p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"188-95"},"PeriodicalIF":0.0,"publicationDate":"2015-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34421571","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":"Reverse Phase Protein Arrays-Quantitative Assessment of Multiple Biomarkers in Biopsies for Clinical Use.","authors":"Stefanie Boellner,&nbsp;Karl-Friedrich Becker","doi":"10.3390/microarrays4020098","DOIUrl":"https://doi.org/10.3390/microarrays4020098","url":null,"abstract":"<p><p>Reverse Phase Protein Arrays (RPPA) represent a very promising sensitive and precise high-throughput technology for the quantitative measurement of hundreds of signaling proteins in biological and clinical samples. This array format allows quantification of one protein or phosphoprotein in multiple samples under the same experimental conditions at the same time. Moreover, it is suited for signal transduction profiling of small numbers of cultured cells or cells isolated from human biopsies, including formalin fixed and paraffin embedded (FFPE) tissues. Owing to the much easier sample preparation, as compared to mass spectrometry based technologies, and the extraordinary sensitivity for the detection of low-abundance signaling proteins over a large linear range, RPPA have the potential for characterization of deregulated interconnecting protein pathways and networks in limited amounts of sample material in clinical routine settings. Current aspects of RPPA technology, including dilution curves, spotting, controls, signal detection, antibody validation, and calculation of protein levels are addressed. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"98-114"},"PeriodicalIF":0.0,"publicationDate":"2015-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34367634","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 Microarrays-Current Status and Future Prospects.","authors":"Martin Witt,&nbsp;Johanna-Gabriela Walter,&nbsp;Frank Stahl","doi":"10.3390/microarrays4020115","DOIUrl":"https://doi.org/10.3390/microarrays4020115","url":null,"abstract":"<p><p>Microarray technologies are state of the art in biological research, which requires fast genome, proteome and transcriptome analysis technologies. Often antibodies are applied in protein microarrays as proteomic tools. Since the generation of antibodies against toxic targets or small molecules including organic compounds remains challenging the use of antibodies may be limited in this context. In contrast to this, aptamer microarrays provide alternative techniques to circumvent these limitations. In this article we review the latest developments in aptamer microarray technology. We discuss similarities and differences between DNA and aptamer microarrays and shed light on the post synthesis immobilization of aptamers including corresponding effects on the microarray performance. Finally, we highlight current limitations and future prospects of aptamer microarray technology. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 2","pages":"115-32"},"PeriodicalIF":0.0,"publicationDate":"2015-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4020115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34367635","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":"In Silico Genomic Fingerprints of the Bacillus anthracis Group Obtained by Virtual Hybridization.","authors":"Hueman Jaimes-Díaz,&nbsp;Violeta Larios-Serrato,&nbsp;Teresa Lloret-Sánchez,&nbsp;Gabriela Olguín-Ruiz,&nbsp;Carlos Sánchez-Vallejo,&nbsp;Luis Carreño-Durán,&nbsp;Rogelio Maldonado-Rodríguez,&nbsp;Alfonso Méndez-Tenorio","doi":"10.3390/microarrays4010084","DOIUrl":"https://doi.org/10.3390/microarrays4010084","url":null,"abstract":"<p><p>In this study we evaluate the capacity of Virtual Hybridization to identify between highly related bacterial strains. Eight genomic fingerprints were obtained by virtual hybridization for the Bacillus anthracis genome set, and a set of 15,264 13-nucleotide short probes designed to produce genomic fingerprints unique for each organism. The data obtained from each genomic fingerprint were used to obtain hybridization patterns simulating a DNA microarray. Two virtual hybridization methods were used: the Direct and the Extended method to identify the number of potential hybridization sites and thus determine the minimum sensitivity value to discriminate between genomes with 99.9% similarity. Genomic fingerprints were compared using both methods and phylogenomic trees were constructed to verify that the minimum detection value is 0.000017. Results obtained from the genomic fingerprints suggest that the distribution in the trees is correct, as compared to other taxonomic methods. Specific virtual hybridization sites for each of the genomes studied were also identified. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":"4 1","pages":"84-97"},"PeriodicalIF":0.0,"publicationDate":"2015-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4010084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34367633","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}