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Articoli di riviste sul tema "Tissue microarray"

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Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 11 marzo 2023

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1

Yang, Jun, Mingjuan Zhang, Baoshan Su, XiaoLi Chen e AnJing Kang. "A novel tissue microarray instrumentation:The HT-1 tissue microarrayer". Indian Journal of Pathology and Microbiology 55, n. 3 (2012): 314. http://dx.doi.org/10.4103/0377-4929.101736.

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Barrette, Kathleen, Joost J. van den Oord e Marjan Garmyn. "Tissue Microarray". Journal of Investigative Dermatology 134, n. 9 (settembre 2014): 1–4. http://dx.doi.org/10.1038/jid.2014.277.

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3

Roszkowiak, Lukasz, e Carlos Lopez. "PATMA: parser of archival tissue microarray". PeerJ 4 (1 dicembre 2016): e2741. http://dx.doi.org/10.7717/peerj.2741.

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The tissue microarrays are commonly used in modern pathology for cancer tissue evaluation, as it is a very potent technique. Tissue microarray slides are often scanned to perform computer-aided histopathological analysis of the tissue cores. For processing the image, splitting the whole virtual slide into images of individual cores is required. The only way to distinguish cores corresponding to specimens in the tissue microarray is through their arrangement. Unfortunately, distinguishing the correct order of cores is not a trivial task as they are not labelled directly on the slide. The main aim of this study was to create a procedure capable of automatically finding and extracting cores from archival images of the tissue microarrays. This software supports the work of scientists who want to perform further image processing on single cores. The proposed method is an efficient and fast procedure, working in fully automatic or semi-automatic mode. A total of 89% of punches were correctly extracted with automatic selection. With an addition of manual correction, it is possible to fully prepare the whole slide image for extraction in the mean time of 2 min per tissue microarray. The proposed technique requires minimum skill and time to parse big array of cores from tissue microarray whole slide image into individual core images.

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Kaur, Rashmeet, Nagaraja A, Richa Bansal, Sujata Saxena e Bhavana Rai. "Tissue microarray- A review". International Journal of Oral Health Dentistry 4, n. 3 (15 ottobre 2018): 152–55. http://dx.doi.org/10.18231/2395-499x.2018.0035.

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5

Kim, Woo Ho. "High-Density Tissue Microarray". American Journal of Surgical Pathology 26, n. 9 (settembre 2002): 1236–37. http://dx.doi.org/10.1097/00000478-200209000-00017.

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6

Rubin, Mark A., e Rodney L. Dunn. "High-Density Tissue Microarray". American Journal of Surgical Pathology 26, n. 9 (settembre 2002): 1237–38. http://dx.doi.org/10.1097/00000478-200209000-00018.

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Page, Robert N., Roy King e Paul B. Googe. "Tissue Microarray in Melanoma". Journal of Histotechnology 26, n. 4 (dicembre 2003): 271–74. http://dx.doi.org/10.1179/his.2003.26.4.271.

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8

Packeisen, J. "Demystified ... Tissue microarray technology". Molecular Pathology 56, n. 4 (1 agosto 2003): 198–204. http://dx.doi.org/10.1136/mp.56.4.198.

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9

Jiang, Hui-Yong, Xue-Feng Zhang, Li Liu, Hui-Ling Li e Tong Zhao. "A novel tissue array technique for high-throughput tissue microarray analysis — microarray groups". In Vitro Cellular & Developmental Biology - Animal 43, n. 3-4 (21 maggio 2007): 109–12. http://dx.doi.org/10.1007/s11626-007-9019-3.

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10

Rangel, Catherine Starrs. "The Tissue Microarray: Helpful Hints!" Journal of Histotechnology 25, n. 2 (giugno 2002): 93–100. http://dx.doi.org/10.1179/his.2002.25.2.93.

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11

Marinelli, R. J., K. Montgomery, C. L. Liu, N. H. Shah, W. Prapong, M. Nitzberg, Z. K. Zachariah et al. "The Stanford Tissue Microarray Database". Nucleic Acids Research 36, Database (23 dicembre 2007): D871—D877. http://dx.doi.org/10.1093/nar/gkm861.

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12

Wollenberg, B. "Wertigkeit der Tissue-Microarray-Technik". HNO 52, n. 5 (1 maggio 2004): 394. http://dx.doi.org/10.1007/s00106-004-1092-2.

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13

Becich, MichaelJ, HyunseokP Kang, CharlesD Borromeo e JulesJ Berman. "The tissue microarray OWL schema: An open-source tool for sharing tissue microarray data". Journal of Pathology Informatics 1, n. 1 (2010): 9. http://dx.doi.org/10.4103/2153-3539.65347.

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14

Nilbert, M., e J. Engellau. "Experiences from tissue microarray in soft tissue sarcomas". Acta Orthopaedica Scandinavica 75, sup311 (gennaio 2004): 29–34. http://dx.doi.org/10.1080/00016470410001708300.

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15

Swanson, Benjamin J., Martha M. Yearsley, William Marsh e Wendy L. Frankel. "A Triple Stain of Reticulin, Glypican-3, and Glutamine Synthetase: A Useful Aid in the Diagnosis of Liver Lesions". Archives of Pathology & Laboratory Medicine 139, n. 4 (1 aprile 2015): 537–42. http://dx.doi.org/10.5858/arpa.2013-0645-oa.

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Context The correct histologic diagnosis of mass lesions of the liver can be difficult, especially in biopsy samples. Reticulin, glypican-3, and glutamine synthetae are stains that can help distinguish hepatocellular carcinoma, hepatic adenoma, and focal nodular hyperplasia. Objective To evaluate the utility of a triple stain of reticulin, glypican-3, and glutamine synthetae in distinguishing hepatocellular carcinoma, hepatic adenoma, and focal nodular hyperplasia. Design Whole tissue sections and tissue microarrays were evaluated with a triple stain of reticulin, followed by glutamine synthetae (diaminobenzidine, brown chromogen) and glypican-3 (alkaline phosphatase, red chromogen). The 109 cases evaluated included whole tissue section hepatocellular carcinoma (n = 16), tissue microarray hepatocellular carcinoma (n = 19), whole tissue section hepatic adenoma (n = 15), tissue microarray hepatic adenoma (n = 13), whole tissue section focal nodular hyperplasia (n = 13; 12%), tissue microarray focal nodular hyperplasia (n = 13), as well as nonmalignant liver parenchyma adjacent to hepatocellular carcinoma (n = 20). All cases were scored for reticulin being intact or lost, positive or negative staining for glypican-3, and diffuse, maplike, perivenular, or negative staining for glutamine synthetae. Results The combination of intact reticulin with either glypican-3 negativity or negative glutamine synthetae was 92% sensitive and 95% specific in the distinction of tissue microarray hepatic adenoma from hepatocellular carcinoma. For the distinction of tissue microarray focal nodular hyperplasia and hepatic adenoma, maplike glutamine synthetae was most useful and was 85% sensitive and 100% specific. Conclusions The triple stain of reticulin, glypican-3, and glutamine synthetae is useful in the differentiation of hepatocellular carcinoma, hepatic adenoma, and focal nodular hyperplasia on biopsy specimens. Furthermore, this triple stain is advantageous to single stains and can help when aberrant staining patterns are observed.

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Jacobsen, M., D. Repsilber, A. Gutschmidt, A. Neher, K. Feldmann, H. J. Mollenkopf, S. H. E. Kaufmann e A. Ziegler. "Deconfounding Microarray Analysis". Methods of Information in Medicine 45, n. 05 (2006): 557–63. http://dx.doi.org/10.1055/s-0038-1634118.

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Summary Objectives: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type. Methods: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes. Results: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results. Conclusions: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.

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Spisák, Sándor, Barnabás Galamb, Barnabás Wichmann, Ferenc Sipos, Orsolya Galamb, Norbert Solymosi, Balázs Nemes, Zsolt Tulassay e Béla Molnár. "Tissue microarray (TMA) validated progression markers in colorectal cancer using antibody microarrays". Orvosi Hetilap 150, n. 34 (1 agosto 2009): 1607–13. http://dx.doi.org/10.1556/oh.2009.28697.

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Háttér: A vastagbéldaganatok kialakulásának és progressziójának molekuláris folyamata, valamint az ennek hátterében álló fehérjeszintű változások nem ismertek. A microarray-rendszerek alkalmazása több száz vagy ezer fehérjekomponens párhuzamos vizsgálatával újabb információkat adhat ezeknek a kérdéseknek a megválaszolásához. Célkitűzés: A daganatkialakulás és -progresszió fehérjeszintű markereinek azonosítása ellenanyagcsipekkel, valamint validálása szöveti microarray-rendszerrel. Célunk továbbá olyan fehérjeszintű markerkombinációk azonosítása, amelyek lehetővé teszik a korai és kései vastagbéldaganatok molekuláris módszerekkel történő elkülönítését és diagnosztizálását. Anyag és módszer: Tíz Dukes B, valamint 6 Dukes D stádiumú beteg sebészi úton eltávolított, friss fagyasztott daganatos és ép mintáit vizsgáltuk meg. A homogenizált mintákból nyert nyers fehérjepreparátumokat Clontech AB500 array-re hibridizáltuk. Tizenkét kiválasztott gén validálása szöveti microarray (TMA) -technológiával történt. Eredmények: A makroszkóposan ép és daganatos területek között 67 szignifikáns, fehérjeszinten eltérő expressziót mutató gént azonosítottunk (p < 0,05), amelyek az apoptózis (5), sejtciklus-szabályozás (7), transzkripciószabályozás (4) és DNS-replikáció (6), illetve más olyan, mint transzport, sejtadhézió (45) sejtfunkcióiban vesznek részt. Immunohisztokémiai alapú TMA-val igazoltuk 12 marker: a CYCA1, HSP60, TOP1, APC, CBP, ERK, EGFR, C-myc, Cald, DARPP32, MRE11A, AR, EPS8 génekben kapott eltéréseket morfológiai szinten is. Megbeszélés: Eredményeink szerint a tumoros kialakulás fehérjecsippel meghatározott, morfológiai szinten ellenőrzött markerei a sejtfunkció széles spektrumát érintik. A daganat progressziós markerei az apoptózis, sejtciklus szabályozása és a jelátviteli utak sejtfunkcióit érintik. Sikerült egy olyan 6 fehérjéből álló markerkombinációt meghatároznunk, amely lehetővé teszi a korai és késői daganatok elkülönítését.

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Korbelik, J., M. Cardeno, J. P. Matisic, A. C. Carraro e C. MacAulay. "Cytology Microarrays". Analytical Cellular Pathology 29, n. 5 (1 gennaio 2007): 435–42. http://dx.doi.org/10.1155/2007/258297.

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The use of high throughput genetic and expression platforms are generating many candidate diagnostic markers and therapeutic targets for a wide variety of clinical conditions. Tissue microarrays can be used for the evaluation of the utility of many of these markers. However, tissue microarrays can suffer from the limitations associated with sampling and sectioning tissues. We introduce a novel microarray technique based on cell suspensions. Multiple slides can be made, all of which are equally representative of the initial sample. A robotic device was designed that can deposit 60 distinct spots of cytological material on a glass slide. Each spot of cells deposited in this manner may correspond to a unique source. Controlling the number of cells per spot, their distribution within the spot and the size of the spot can be achieved by modifying the viscosity of the cell solution or regulating the amount of fluid deposited. A fully automated analysis of quantitatively stained microarray samples has been performed to quantify the number of cells per spot, the size of spots and the DNA amount per cell in each spot. The reproducibility of these parameters was found to be high.

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Croner, Roland S., Berthold Lausen, Vera Schellerer, Isabel Zeittraeger, Axel Wein, Claus Schildberg, Thomas Papadopoulos et al. "Comparability of Microarray Data between Amplified and Non Amplified RNA in Colorectal Carcinoma". Journal of Biomedicine and Biotechnology 2009 (2009): 1–9. http://dx.doi.org/10.1155/2009/837170.

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Microarray analysis reaches increasing popularity during the investigation of prognostic gene clusters in oncology. The standardisation of technical procedures will be essential to compare various datasets produced by different research groups. In several projects the amount of available tissue is limited. In such cases the preamplification of RNA might be necessary prior to microarray hybridisation. To evaluate the comparability of microarray results generated either by amplified or non amplified RNA we isolated RNA from colorectal cancer samples (stage UICC IV) following tumour tissue enrichment by macroscopic manual dissection (CMD). One part of the RNA was directly labelled and hybridised to GeneChips (HG-U133A, Affymetrix), the other part of the RNA was amplified according to the “Eberwine” protocol and was then hybridised to the microarrays. During unsupervised hierarchical clustering the samples were divided in groups regarding the RNA pre-treatment and 5.726 differentially expressed genes were identified. Using independent microarray data of 31 amplified vs. 24 non amplified RNA samples from colon carcinomas (stage UICC III) in a set of 50 predictive genes we validated the amplification bias. In conclusion microarray data resulting from different pre-processing regarding RNA pre-amplification can not be compared within one analysis.

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20

Hartmann, Arndt. "Tissue microarray studies in bladder cancer". Scandinavian Journal of Urology and Nephrology 42, sup218 (gennaio 2008): 141–46. http://dx.doi.org/10.1080/03008880802291840.

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Kim, Woo Young, Jae Bok Lee, Seung Pil Jung, Hoon Yub Kim, Sang Uk Woo, Gil Soo Son e Jeoung Won Bae. "Gene Expression Profiles of Papillary Thyroid Microcarcinoma". International Surgery 102, n. 1-2 (1 gennaio 2017): 39–46. http://dx.doi.org/10.9738/intsurg-d-17-00049.1.

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The objective was to identify gene expression profile of papillary thyroid microcarcinoma. To help improve diagnosis of papillary thyroid microcarcinoma, we performed gene expression profiling and compared it to pair normal thyroid tissues. We performed microarray analysis with 6 papillary thyroid microcarcinoma and 6 pair normal thyroid tissues. Differentially expressed genes were selected using paired t test, linear models for microarray data, and significance analysis of microarrays. Real-time quantitative reverse transcription–polymerase chain reaction was used to validate the representative 10 genes (MET, TIMP1, QPCT, PROS1, LRP4, SDC4, CITED1, DPP4, LRRK2, RUNX2). We identified 91 differentially expressed genes (84 upregulated and 7 downregulated) in the gene expression profile and validated 10 genes of the profile. We identified a significant genetic difference between papillary thyroid microcarcinoma and normal tissue by 10 upregulated genes greater than 2-fold (P < 0.05).

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22

Simon, Ronald, e Guido Sauter. "Tissue microarray (TMA) applications: implications for molecular medicine". Expert Reviews in Molecular Medicine 5, n. 26 (21 ottobre 2003): 1–12. http://dx.doi.org/10.1017/s1462399403006781.

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Modern expression-screening platforms such as complementary DNA (cDNA) arrays allow for high-throughput lead discovery in cancer and other diseases. For evaluation of promising candidate genes, however, in situ analysis of high numbers of clinical tissues samples – for example, by immunohistochemistry or fluorescence in situ hybridisation – is mandatory. Tissue microarray (TMA) technology greatly facilitates such analysis. Minute tissue cores (diameter 0.6 mm) are removed from up to a thousand different conventional paraffin blocks and re-assembled in a single empty paraffin block at predefined positions. Sections of the resulting TMA can be utilised for the range of research applicable to conventional tissue sections. Important advantages of the TMA technology are speed (parallel analysis of up to a thousand tissues), cost efficiency (the same amount of reagents required for a single large-section analysis is sufficient for a thousand samples), and standardisation (the same experimental conditions are applied to all samples). Because of the high numbers of samples usually included in TMAs, they are optimally suited to detect genotype–phenotype associations with high statistical power. Thus, TMA technology will markedly accelerate the transition from basic research to clinical applications.

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Wu, Q., W. Du e X. Ling. "EAAT16 PROTEIN EXPRESSION IN HUMAN TISSUES DETECTED BY TISSUE MICROARRAY". Shock 26, Supplement 1 (ottobre 2006): 28. http://dx.doi.org/10.1097/00024382-200610001-00086.

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Shangkuan, Wei-Chuan, Hung-Che Lin, Yu-Tien Chang, Chen-En Jian, Hueng-Chuen Fan, Kang-Hua Chen, Ya-Fang Liu et al. "Risk analysis of colorectal cancer incidence by gene expression analysis". PeerJ 5 (15 febbraio 2017): e3003. http://dx.doi.org/10.7717/peerj.3003.

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Background Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction. Objective Microarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples. Methods We collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues. Results Ten genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method. Conclusions Six genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.

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Fernebro, Eva, Michael Dictor, Pär-Ola Bendahl, Mårten Fernö e Mef Nilbert. "Evaluation of the Tissue Microarray Technique for Immunohistochemical Analysis in Rectal Cancer". Archives of Pathology & Laboratory Medicine 126, n. 6 (1 giugno 2002): 702–5. http://dx.doi.org/10.5858/2002-126-0702-eottmt.

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Abstract Background.—Immunohistochemical staining for tumor-associated proteins is widely used for the identification of novel prognostic markers. Recently, a tissue-conserving, high-throughput technique, tissue microarray, has been introduced. This technique uses 0.6-mm tissue core biopsy specimens, 500 to 1000 of which are brought into a new paraffin array block, which can be sectioned up to 100 times. Methods.—We evaluated the tissue microarray technique for immunohistochemical analysis in 20 rectal cancers. Immunohistochemical staining was performed for the proliferation marker Ki-67 and the tumor suppressor protein p53 in whole tissue sections and in tissue core biopsy specimens. Results.—The whole tissue sections were assessed by counting all cells in 10 high-power fields (×40), which resulted in a mean fraction of Ki-67–expressing tumor cells of 0.81 (range, 0.54–1.0). p53 expression assessed in whole tissue sections showed nuclear staining in 15 (75%) of 20 rectal carcinomas. For the tissue microarray technique, a median of 3 (range, 3–5) 0.6-mm tissue core biopsy specimens were studied from each of the 20 tumor specimens. The tissue microarray method gave a mean Ki-67 expression of 0.85 (range, 0.50–1.0) in tumor cell nuclei and showed p53 protein expression in the same 15 of 20 tumors as in the whole tissue sections. Conclusion.—We conclude that the tissue microarray technique for immunohistochemical staining in rectal cancer yields staining of good quality and expression data for Ki-67 and p53 comparable to those obtained with whole tissue staining. The feasibility of tissue microarray thus enables time- and tissue-preserving studies of multiple markers in large tumor series.

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Schachter, Pinhas P., Suhail Ayesh, Imad Matouk, Tamar Schneider, Abraham Czerniak e Abraham Hochberg. "Differential Expression of Kinase Genes in Primary Hyperparathyroidism: Adenoma Versus Normal and Hyperplastic Parathyroid Tissue". Archives of Pathology & Laboratory Medicine 131, n. 1 (1 gennaio 2007): 126–30. http://dx.doi.org/10.5858/2007-131-126-deokgi.

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Abstract Context.—Differentiation between adenoma and hyperplasia or even normal parathyroid tissue is difficult and based mainly on the surgeon's skill. Exploration of genes that express differentially in these various tissues using microarrays and other sophisticated research tools will enable identification and perhaps development of new methods of perioperative diagnosis. Objective.—To assemble a panel of kinase genes to differentiate parathyroid adenoma from normal and hyperplastic parathyroid tissue. Design.—RNA was extracted from adenoma, hyperplasia, and normal parathyroid tissue and hybridized to a microarray containing 359 human cDNAs of known kinase genes. Signals of exposure were scanned and quantified with software for digital image analysis. Semiquantitative reverse transcriptase polymerase chain reaction analysis of sample genes was performed, up-regulated or down-regulated, to validate the microarray results. Results.—The ratio values considered significant (<0.5 or >1.5) suggest that genes up-regulated in parathyroid adenoma are those responsible for blood vessel angiogenesis and genes belonging to the cyclin-dependent kinase inhibitor groups. Genes down-regulated in parathyroid adenoma are related to cellular growth and apoptosis—genes from the mitogen-activated protein kinase group and DNA-dependent protein kinase group. An interesting gene down-regulated in the parathyroid adenoma samples is related to the serine/threonine protein kinases that exert a key function in calcium handling. A panel of 5 genes was defined: p19, p21 and the gene for vascular endothelial growth factor from the up-regulated group, and the gene for protein kinase C and SGK from the down-regulated group. Reverse transcriptase polymerase chain reaction confirmed the microarray results for these genes. Conclusions.—The kinase genes panel presented can be used to differentiate parathyroid adenoma from normal and hyperplastic parathyroid tissue in particular when histopathology fails to provide a decisive diagnosis.

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Shaknovich, Rita, Ashlyn Celestine, Lin Yang e Giorgio Cattoretti. "Novel Relational Database for Tissue Microarray Analysis". Archives of Pathology & Laboratory Medicine 127, n. 4 (1 aprile 2003): 492–94. http://dx.doi.org/10.5858/2003-127-0492-nrdftm.

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Obermann, Ellen C., Joerg Marienhagen, Robert Stoehr, Peter H. Wuensch e Ferdinand Hofstaedter. "Tissue microarray construction from bone marrow biopsies". BioTechniques 39, n. 6 (dicembre 2005): 822–26. http://dx.doi.org/10.2144/000112073.

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Torlakovic, Emina Emilia. "Quality control by tissue microarray in immunohistochemistry". Journal of Clinical Pathology 65, n. 10 (15 giugno 2012): 961. http://dx.doi.org/10.1136/jclinpath-2012-200945.

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Seicean, Andrada, Daniel Popa, Radu Seicean e Ovidiu Balacescu. "S1815 Tissue Microarray Analysis in Pancreatic Adenocarcinoma". Gastroenterology 136, n. 5 (maggio 2009): A—276. http://dx.doi.org/10.1016/s0016-5085(09)61257-9.

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Simon, R., S. Panussis, K. Glatz, C. Tapia, M. Mirlacher, M. Mihatsch e G. Sauter. "Evaluation of tissue microarray based HER2 diagnosis". Pathology - Research and Practice 200, n. 4 (gennaio 2004): 305. http://dx.doi.org/10.1016/s0344-0338(04)80594-6.

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Liu, Xueli, Vladimir Minin, Yunda Huang, David B. Seligson e Steve Horvath. "Statistical Methods for Analyzing Tissue Microarray Data". Journal of Biopharmaceutical Statistics 14, n. 3 (29 dicembre 2004): 671–85. http://dx.doi.org/10.1081/bip-200025657.

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Hsing, Chung-Hsi, Chung-Liang Ho, Lih-Yun Chang, Yi-Lin Lee, Shih-Sung Chuang e Ming-Shi Chang. "Tissue microarray analysis of interleukin-20 expression". Cytokine 35, n. 1-2 (luglio 2006): 44–52. http://dx.doi.org/10.1016/j.cyto.2006.07.006.

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Lal, Sean, Lisa Nguyen, Rhenan Tezone, Fredrik Ponten, Jacob Odeberg, Amy Li e Cristobal dos Remedios. "Tissue microarray profiling in human heart failure". PROTEOMICS 16, n. 17 (11 agosto 2016): 2319–26. http://dx.doi.org/10.1002/pmic.201600135.

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Kim, Se Hoon, Dong-Min Kim, Hyosup Shim, Junjeong Choi, Seong Hwan Park, Seung Min Song, Young Ho Shin e Donghoon Choi. "Application of tissue microarray for atherectomized tissues from peripheral arterial disease". Pathology - Research and Practice 207, n. 9 (settembre 2011): 568–72. http://dx.doi.org/10.1016/j.prp.2011.06.007.

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Lee, Hye Won, Yu Rang Park, Jaehyun Sim, Rae Woong Park, Woo Ho Kim e Ju Han Kim. "The Tissue Microarray Object Model: A Data Model for Storage, Analysis, and Exchange of Tissue Microarray Experimental Data". Archives of Pathology & Laboratory Medicine 130, n. 7 (1 luglio 2006): 1004–13. http://dx.doi.org/10.5858/2006-130-1004-ttmoma.

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Abstract Context.—Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathologic information related to TMA data analysis. Objective.—To create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathologic data elements. Design.—We designed a tissue microarray object model (TMA-OM). Both the array information and the experimental procedure models are created by referring to the microarray gene expression object model, minimum information specification for in situ hybridization and immunohistochemistry experiments, and the TMA data exchange specifications. The clinical and histopathologic information model is created by using College of American Pathologists cancer protocols and National Cancer Institute common data elements. Microarray Gene Expression Data Ontology, the Unified Medical Language System, and the terms extracted from College of American Pathologists cancer protocols and NCI common data elements are used to create a controlled vocabulary for unambiguous annotation. Result.—The TMA-OM consists of 111 classes in 17 packages to represent clinical and histopathologic information as well as experimental data for any type of cancer. We implemented a Web-based application for TMA-OM, supporting data export in XML format conforming to the TMA data exchange specifications or the document type definition derived from TMA-OM. Conclusions.—The TMA-OM provides a comprehensive data model for storage, analysis, and exchange of TMA data and facilitates model-level integration of other biological models.

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Pillai, R., R. Deeter, C. T. Rigl, M. Halks-Miller, W. D. Henner e L. Buturovic. "Validation of a microarray-based gene expression test for tumors with uncertain origins using formalin-fixed paraffin-embedded (FFPE) specimens". Journal of Clinical Oncology 27, n. 15_suppl (20 maggio 2009): e22015-e22015. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e22015.

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e22015 Background: Microarray-based gene expression has been validated as an aid in the diagnosis of tumors with uncertain origins when the specimen is frozen tissue. Microarray use has been largely limited to RNA derived from frozen specimens. This study evaluated performance of a microarray-based test in identifying the tumor type in FFPE specimens. Methods: ZFFPE human tumor specimens (n=405) representing the 15 tissue of origin sites on the Pathwork® Tissue of Origin Test panel were blinded and evenly distributed between two independent processing labs. All specimens consisted of a 10-μm-paraffin curl containing at least 60% viable tumor and were either metastatic or poorly differentiated primaries. Each specimen was processed through RNA extraction, amplification, labeling, hybridization to a Pathchip® microarray, and was scanned to generate a qualified data file. A pre-specified classification algorithm utilizing more than 1500 genes was applied to each data file to yield Similarity Scores corresponding to the 15 tissues on the test panel. Results were then unblinded and compared to the available diagnoses. Results: Of the 405 specimens, 352 yielded qualified data files (87%). Based on the top Similarity Score, the overall agreement with available diagnoses was 89% (95% CI, 85%-92%) and for each specimen an average of 12 out of 15 tissues could be ruled out with > 99% probability. Results for all tissue types were highly informative with diagnostic odds ratios ranging from 178 to 28509. Performance was similar for metastatic (n=150; 91% agreement) and poorly differentiated primary specimens (n=202; 87% agreement). Conclusions: The large size of this study allows an accurate estimate of the confidence of test predictions for both ruling in and ruling out tissues as likely sites of primary origin. The Pathwork Tissue of Origin Test makes the potential benefits of microarray-based gene expression tests for tumors with uncertain origins available for use with the most common type of histology specimen, FFPE. [Table: see text]

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Mathur, Sandeep Kumar, Priyanka Jain e Prashant Mathur. "Microarray Evidences the Role of Pathologic Adipose Tissue in Insulin Resistance and Their Clinical Implications". Journal of Obesity 2011 (2011): 1–16. http://dx.doi.org/10.1155/2011/587495.

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Clustering of insulin resistance and dysmetabolism with obesity is attributed to pathologic adipose tissue. The morphologic hallmarks of this pathology are adipocye hypertrophy and heightened inflammation. However, it's underlying molecular mechanisms remains unknown. Study of gene function in metabolically active tissues like adipose tissue, skeletal muscle and liver is a promising strategy. Microarray is a powerful technique of assessment of gene function by measuring transcription of large number of genes in an array. This technique has several potential applications in understanding pathologic adipose tissue. They are: (1) transcriptomic differences between various depots of adipose tissue, adipose tissue from obese versus lean individuals, high insulin resistant versus low insulin resistance, brown versus white adipose tissue, (2) transcriptomic profiles of various stages of adipogenesis, (3) effect of diet, cytokines, adipokines, hormones, environmental toxins and drugs on transcriptomic profiles, (4) influence of adipokines on transcriptomic profiles in skeletal muscle, hepatocyte, adipose tissue etc., and (5) genetics of gene expression. The microarray evidences of molecular basis of obesity and insulin resistance are presented here. Despite the limitations, microarray has potential clinical applications in finding new molecular targets for treatment of insulin resistance and classification of adipose tissue based on future risk of insulin resistance syndrome.

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Kaiser, Sergio, e Laura K. Nisenbaum. "Evaluation of common gene expression patterns in the rat nervous system". Physiological Genomics 16, n. 1 (16 dicembre 2003): 1–7. http://dx.doi.org/10.1152/physiolgenomics.00125.2003.

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In the postgenomic era, integrating data obtained from array technologies (e.g., oligonucleotide microarrays) with published information on eukaryotic genomes is beginning to yield biomarkers and therapeutic targets that are key for the diagnosis and treatment of disease. Nevertheless, identifying and validating these drug targets has not been a trivial task. Although a plethora of bioinformatics tools and databases are available, major bottlenecks for this approach reside in the interpretation of vast amounts of data, its integration into biologically representative models, and ultimately the identification of pathophysiologically and therapeutically useful information. In the field of neuroscience, accomplishing these goals has been particularly challenging because of the complex nature of nerve tissue, the relatively small adaptive nature of induced-gene expression changes, as well as the polygenic etiology of most neuropsychiatric diseases. This report combines published data sets from multiple transcript profiling studies that used GeneChip microarrays to illustrate a postanalysis approach for the interpretation of data from neuroscience microarray studies. By defining common gene expression patterns triggered by diverse events (administration of psychoactive drugs and trauma) in different nerve tissues (telencephalic brain areas and spinal cord), we broaden the conclusions derived from each of the original studies. In addition, the evaluation of the identified overlapping gene lists provides a foundation for generating hypotheses relating alterations in specific sets of genes to common physiological processes. Our approach demonstrates the significance of interpreting transcript profiling data within the context of common pathways and mechanisms rather than specific to a given tissue or stimulus. We also highlight the use of gene expression patterns in predictive biology (e.g., in toxicogenomics) as well as the utility of combining data derived from multiple microarray studies that examine diverse biological events for a broader interpretation of data from a particular microarray study.

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Skotheim, Rolf I., Anne Kallioniemi, Bodil Bjerkhagen, Fredrik Mertens, Helge R. Brekke, Outi Monni, Spyro Mousses et al. "Topoisomerase-IIα Is Upregulated in Malignant Peripheral Nerve Sheath Tumors and Associated With Clinical Outcome". Journal of Clinical Oncology 21, n. 24 (15 dicembre 2003): 4586–91. http://dx.doi.org/10.1200/jco.2003.07.067.

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Purpose: To identify target genes of clinical significance for patients with malignant peripheral-nerve sheath tumor (MPNST), an aggressive cancer for which no consensus therapy exists. Materials and Methods: Biopsies and clinical data from 51 patients with MPNST were included in this study. Based on our previous research implicating chromosome arm 17q amplification in MPNST, we performed gene expression analyses of 14 MPNSTs using chromosome 17–specific cDNA microarrays. Copy numbers of selected gene probes and centromere probes were then determined by interphase fluorescence in situ hybridization in 16 MPNSTs. Finally, we generated a tissue microarray containing 79 samples from 44 MPNSTs, on which in situ protein expressions of candidate genes were examined and related to clinical end points. Results: Among several deregulated genes found by cDNA microarray analyses, topoisomerase IIα (TOP2A) was the most overexpressed gene in MPNSTs compared with benign neurofibromas. Excess copies of the TOP2A were also seen at the DNA level in 10 of 16 cases, and high expression of the TOP2A protein was seen in 83% of the tumors on the tissue microarray. The TOP2A-expressing tumors were associated with poor cancer-specific survival and presence of metastases. Conclusion: We have identified TOP2A as a target gene in MPNST, using a focused gene expression profiling followed by a DNA copy number evaluation and clinical validation of the encoded protein using a tissue microarray. This study is the first to suggest that TOP2A expression may be a predictive factor for adverse outcome in MPNST.

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Bertucci, François, Bruno Chetaille e Luc Xerri. "Gene Expression Profiling forIn SilicoMicrodissection of Hodgkin's Lymphoma Microenvironment and Identification of Prognostic Features". Advances in Hematology 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/485310.

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Gene expression profiling studies based on DNA microarrays have demonstrated their ability to define the interaction pathways between neoplastic and nonmalignant stromal cells in cancer tissues. During the past ten years, a number of approaches including microdissection have tried to resolve the variability in DNA microarray measurements stemming from cancer tissue sample heterogeneity. Another approach, designated as virtual orin silicomicrodissection, avoids the laborious and time-consuming step of anatomic microdissection. It consists of confronting the gene expression profiles of complex tissue samples to those of cell lines representative of different cell lineages, different differentiation stages, or different signaling pathways. This strategy has been used in recent studies aiming to analyze microenvironment alterations using gene expression profiling of nonmicrodissected classical Hodgkin lymphoma tissues in order to generate new prognostic factors. These recent contributions are detailed and discussed in the present paper.

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Haedicke, Wolfgang, Helmut H. Popper, Charles R. Buck e Kurt Zatloukal. "Automated evaluation and normalization of immunohistochemistry on tissue microarrays with a DNA microarray scanner". BioTechniques 35, n. 1 (luglio 2003): 164–68. http://dx.doi.org/10.2144/03351md04.

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Sampaio, João P. A., José R. Cavalcante, Francisco N. N. Furtado, Roberto C. P. Lima-Júnior, Ronaldo A. Ribeiro e Paulo R. C. Almeida. "A handcrafted tissue microarray for a matrix arrangement of tissue samples". Journal of Pharmacological and Toxicological Methods 70, n. 1 (luglio 2014): 70–72. http://dx.doi.org/10.1016/j.vascn.2014.05.005.

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Opest, A., e T. Freeman. "Tissue specific molecular pathology of osteoarthritis revealed by tissue microarray analysis". Osteoarthritis and Cartilage 20 (aprile 2012): S93. http://dx.doi.org/10.1016/j.joca.2012.02.094.

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Gunasekaran, Kumudhini Priya, e Anbu Lenin Kulandaivel. "Diagnostic Immunohistochemistry with Manual Tissue Microarray Technique: A Pilot Study on Non-Hodgkin Lymphoma". Annals of Pathology and Laboratory Medicine 5, n. 10 (26 ottobre 2018): A842–847. http://dx.doi.org/10.21276/apalm.2216.

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Song, Young Soo, Hye Won Lee, Yu Rang Park, Do Kyoon Kim, Jaehyun Sim, Hyunseok Peter Kang e Ju Han Kim. "TMA-TAB: A spreadsheet-based document for exchange of tissue microarray data based on the tissue microarray-object model". Journal of Biomedical Informatics 43, n. 3 (giugno 2010): 435–41. http://dx.doi.org/10.1016/j.jbi.2009.10.001.

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Day, Robert C., Les McNoe e Richard C. Macknight. "Evaluation of Global RNA Amplification and Its Use for High-Throughput Transcript Analysis of Laser-Microdissected Endosperm". International Journal of Plant Genomics 2007 (5 aprile 2007): 1–17. http://dx.doi.org/10.1155/2007/61028.

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Laser microdissection (LM) provides a useful method for isolating specific cells or tissues from biological samples. Here, we adapted microdissection protocols to allow high-resolution transcript analysis of different tissues from developing Arabidopsis seed. Sufficient RNA (∼50 ng) was extracted from endosperm tissue for RT-PCR. However, to obtain enough RNA for microarray analyses, it was necessary to amplify the RNA. PCR- and IVT-based amplification methods were investigated and several important technical aspects of amplification were identified (such as target truncation and alterations in signal intensity). We found that when starting from only 50 ng of RNA, amplification methods based on PCR and IVT produced sufficient product for reliable microarray hybridizations, with two-round IVT giving the best results. Microarray analyses, using endosperm-derived RNA amplified by two-round IVT, reproducibly identified endosperm enriched marker genes. Thus, when combined with RNA-amplification protocols, LM is a robust and reliable technique for high-throughput tissue-specific gene expression analysis.

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Lugli, Alessandro, Yvonne Forster, Philippe Haas, Antoinio Nocito, Christoph Bucher, Heidi Bissig, Martina Mirlacher, Martina Storz, Michael J. Mihatsch e Guido Sauter. "Calretinin expression in human normal and neoplastic tissues: a tissue microarray analysis on 5233 tissue samples". Human Pathology 34, n. 10 (ottobre 2003): 994–1000. http://dx.doi.org/10.1053/s0046-8177(03)00339-3.

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Nowakowska-Zajdel, E., U. Mazurek, J. Wierzgoń, T. Kokot, E. Fatyga, E. Ziółko, K. Klakla et al. "Expression of ADAM28 and IGFBP-3 Genes in Patients with Colorectal Cancer — A Preliminary Report". International Journal of Immunopathology and Pharmacology 26, n. 1 (gennaio 2013): 223–28. http://dx.doi.org/10.1177/039463201302600122.

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Adamalisynes ( ADAMs) play an important role in inter-membrane interactions, cell adhesion and fusion processes and protein shedding from the cell surface. Many reports indicate that members of the ADAMs family are overexpressed in human cancer. The aim of the present study was to evaluate ADAM28 and Insulin Like Growth Factor Binding Protein-3 ( IGFBP-3)) gene expression in colorectal carcinoma tissues with regard to the overweight or obese status of the patients using an oligonucleotide microarray technique. Fresh tissue specimens were obtained from colorectal cancer patients during surgical treatment. Eighteen specimens from tumour and 18 normal tissue specimens from colorectal cancer patients at clinical stages III and IV were analysed. The examined patients were divided into two groups; those with BMI≥25 and those with normal BMI. The control group consisted of 18 specimens of non-neoplastic colon tissues, which were divided between overweight/obese and normal body weight patients. The gene transcriptional activity from the specimens was analysed using an oligonucleotide microarray technique. Microarrays and rinsing and marking solutions were prepared according to the procedure in the Gene Expression Analysis Technical Manual. The following conclusions were made: i) change of ADAM28 and IGFBP-3 genes expression are present in the normal tissue in overweight/obese patients with colorectal cancer only; ii) the observed molecular variability of ADAM28 and IGFBP-3 expression may be an initial process of cancer proliferation; iii) the histopathologically normal surgical margin in this group of patients was not equal to the molecular margin.

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Schwers, Stephan, Elke Reifenberger, Mathias Gehrmann, Alexandre Izmailov e Kerstin Bohmann. "A High-Sensitivity, Medium-Density, and Target Amplification–Free Planar Waveguide Microarray System for Gene Expression Analysis of Formalin-Fixed and Paraffin-Embedded Tissue". Clinical Chemistry 55, n. 11 (1 novembre 2009): 1995–2003. http://dx.doi.org/10.1373/clinchem.2009.128215.

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Abstract Background: Many microarray platforms and their associated assay chemistries do not work properly with RNA extracted from formalin-fixed, paraffin-embedded (FFPE) tissue samples, a feature that severely hampers the use of microarrays in oncology applications, for which FFPE tissue is the routine specimen. Furthermore, the limited sensitivity of most microarray platforms requires time-consuming and costly amplification reactions of the target RNA, which negatively affects clinical laboratory work flow. Methods: We developed an approach for sensitively and reliably measuring mRNA abundances in FFPE tissue samples. This approach involves automated RNA extractions, direct hybridization of extracted RNA to immobilized capture probes, antibody-mediated labeling, and readout with an instrument applying the principle of planar waveguides (PWG). A 14-gene multiplex assay conducted with RNA isolated from 20 FFPE blocks was correlated to an analysis of the same with reverse-transcription quantitative real-time PCR (RT-qPCR). Results: The assay sensitivity for gene expression analysis obtained for the PWG microarray platform was <10 fmol/L, eliminating the need for target preamplification. We observed a correlation coefficient of 0.87 to state-of-the-art RT-qPCR technology with RNA isolated from FFPE tissue, despite a compressed dynamic range for the PWG system (a 2.9-log dynamic range for PWG in our test system vs 5.0 logs for RT-qPCR). The precision of the PWG platform was comparable to RT-qPCR (Pearson correlation coefficient of 0.9851 for PWG vs 0.9896 for RT-qPCR) for technical replicates. Conclusions: The presented PWG platform demonstrated excellent sensitivity and precision and is especially well suited for any application for which fast, simple, and robust multiplex assays of RNA in FFPE tissue are required.

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