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Ht 12 array

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The HT-12 array is a high-throughput gene expression microarray platform developed by Illumina. It is designed to analyze the expression levels of thousands of genes simultaneously. The HT-12 array provides a comprehensive coverage of well-characterized genes, enabling researchers to perform large-scale, genome-wide expression profiling studies.

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Lab products found in correlation

Gene expression arrays, by Illumina (1 mentions) Tempus tube, by Thermo Fisher Scientific (1 mentions) Bioanalyzer, by Agilent Technologies (1 mentions)

7 protocols using ht 12 array

1

Transcriptomic Analysis of Differential Gene Expression

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Total RNA was isolated from cells grown under different assay conditions. The quality and quantity of RNA was assessed spectrophotometrically by a Nanodrop and Bioanalyzer. Equal amounts of RNA samples were used to prepare cDNA library and processed for transcriptomic analysis using Illumina Gene Expression arrays. The raw data obtained from hybridization to illumina HT-12 array, was quantile normalized followed by baseline transformation to median of all the samples using GeneSpring GX 12.5 software. The differentially expressed genes were identified across the samples using volcano plot with a fold change threshold of 1.5 and t test p value threshold adjusted for false discovery rate less than 0.001 for statistical significance. Hierarchical clustering of differentially expressed genes in treated vs. control conditions was done using Euclidian algorithm with Centroid linkage rule to identify gene clusters whose expression levels were significantly reproduced across the replicates. Differentially expressed genes were subjected to biological significance analysis by GOElite tool to determine enriched biological pathways. Over representation Analysis (ORA) of significant biological categories (GeneOntology and Pathway) involving differentially expressed transcripts was performed and a network was modeled. GEO accession number for the microarray data: GSE125317.
Behera A.K., Kumar M., Shanmugam M.K., Bhattacharya A., Rao V.J., Bhat A., Vasudevan M., Gopinath K.S., Mohiyuddin A., Chatterjee A., Sethi G, & Kundu T.K. (2019). Functional interplay between YY1 and CARM1 promotes oral carcinogenesis. Oncotarget, 10(38), 3709-3724.
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2

Microarray Analysis of Drug-Resistant Cell Lines

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RNA from cell lines and controls was prepared, labeled, and hybridized to Illumina HT-12 array. For analysis, the sample probe profile data in the GeneSpring export format was transformed to log-2 scale and normalized using quantile method by applying beadarray package31 (link) in BioConductor 2.11. Four duplicated samples were averaged to reduce the total sample size to 9 across three groups. The comparison between drug sensitive and drug resistant groups was performed using limma package. The significantly changed probes were identified by moderated t statistics. The p-values from moderated t-tests were adjusted by Benjamini and Hochberg's method to control false discovery rate. The microarray data reported in this article have been deposited in NCBI's Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (Accession no. GSE49508, URL: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49508).
Lovly C.M., McDonald N.T., Chen H., Ortiz-Cuaran S., Heukamp L.C., Yan Y., Florin A., Ozretić L., Lim D., Wang L., Chen Z., Chen X., Lu P., Paik P.K., Shen R., Jin H., Buettner R., Ansén S., Perner S., Brockmann M., Bos M., Wolf J., Gardizi M., Wright G.M., Solomon B., Russell P.A., Rogers T.M., Suehara Y., Red-Brewer M., Tieu R., de Stanchina E., Wang Q., Zhao Z., Johnson D.H., Horn L., Wong K.K., Thomas R.K., Ladanyi M, & Pao W. (2014). Rationale for co-targeting IGF-1R and ALK in ALK fusion positive lung cancer. Nature medicine, 20(9), 1027-1034.
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3

Microarray Analysis of Drug-Resistant Cell Lines

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RNA from cell lines and controls was prepared, labeled, and hybridized to Illumina HT-12 array. For analysis, the sample probe profile data in the GeneSpring export format was transformed to log-2 scale and normalized using quantile method by applying beadarray package31 (link) in BioConductor 2.11. Four duplicated samples were averaged to reduce the total sample size to 9 across three groups. The comparison between drug sensitive and drug resistant groups was performed using limma package. The significantly changed probes were identified by moderated t statistics. The p-values from moderated t-tests were adjusted by Benjamini and Hochberg's method to control false discovery rate. The microarray data reported in this article have been deposited in NCBI's Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (Accession no. GSE49508, URL: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49508).
Lovly C.M., McDonald N.T., Chen H., Ortiz-Cuaran S., Heukamp L.C., Yan Y., Florin A., Ozretić L., Lim D., Wang L., Chen Z., Chen X., Lu P., Paik P.K., Shen R., Jin H., Buettner R., Ansén S., Perner S., Brockmann M., Bos M., Wolf J., Gardizi M., Wright G.M., Solomon B., Russell P.A., Rogers T.M., Suehara Y., Red-Brewer M., Tieu R., de Stanchina E., Wang Q., Zhao Z., Johnson D.H., Horn L., Wong K.K., Thomas R.K., Ladanyi M, & Pao W. (2014). Rationale for co-targeting IGF-1R and ALK in ALK fusion positive lung cancer. Nature medicine, 20(9), 1027-1034.
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4

CAMP Gene Expression Analysis of Asthma and Obesity

Cited 1 time
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The cross-sectional CAMP gene expression data have been previously
described (12 (link)). Briefly, 620 CAMP
subjects with whole blood samples collected only at early adulthood were assayed
with the Illumina HT-12 (version 3) platform with 47,009 high-quality probes.
Quality control filters included removal of failed arrays, probes with low
outlying log2 intensities (<5), and probes with poor
signal-to-noise ratios (95th percentile /5th percentile).
These expression data were quantile-normalized and log2-transformed.
A set of 106 subjects who were with obesity at enrollment in CAMP (body mass
index (BMI) ≥ 95th percentile for age and sex) were excluded
to focus the analysis on asthma complicated by incident obesity (10 ). Following the exclusion of probes with low
variances (15 (link)), the final dataset
comprised 514 subjects and 10,448 probes. For replication, cross-sectional whole
blood gene expression data were also assayed with the Illumina HT-12 array
(version 4): 47,009 high-quality probes for 91 adult subjects in CHS (12 (link)); and 47,256 high-quality probes for 329
pediatric subjects in GACRS (16 ). A
subset of 10,448 probes in CHS and 10,437 probes in GACRS were matched to the
CAMP data by their universal nucleotide identifiers (17 (link)).
Croteau-Chonka D.C., Chen Z., Barnes K.C., Barraza-Villarreal A., Celedón J.C., Gauderman W.J., Gilliland F.D., Krishnan J.A., Liu A.H., London S.J., Martinez F.D., Millstein J., Naureckas E.T., Nicolae D.L., White S.R., Ober C., Weiss S.T, & Raby B.A. (2018). Gene co-expression networks in whole blood implicate multiple interrelated molecular pathways in obese asthma. Obesity (Silver Spring, Md.), 26(12), 1938-1948.
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5

Comprehensive Breast Cancer Tissue Analysis

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Hematoxylin and eosin (H&E) sections of 1,992 untreated primary invasive breast carcinomas described in the METABRIC study [15 (link)] were quality assessed. These sections are from female patients diagnosed between 1980 and 2005 from consecutive series from five contributing hospitals in the UK and Canada with clinical annotations and matched DNA and RNA profiling data. Of these, H&E samples from two hospitals were highly fragmented, leaving in total 1,026 cases from the remaining three hospitals, which were split into a test set of 510 samples (hospital 1 and 2; Cohort 1) and an independent validation set of 516 samples (hospital 3; Cohort 2) for retrospective analysis (Fig 1A; S1 Table). On average, three sections (top, middle, and bottom) were taken from the single frozen tumor aliquot included in the METABRIC study in order to represent the morphological profile of the tumor [15 (link),16 (link)]. Tumor sections were stained independently in different laboratories according to the hospital site. Whole-tumor section images, copy number profiled using Affymetrix SNP6, gene expression profiled using Illumina HT-12 array, and long-term follow-up data (median 68.3 mo) were obtained.
Natrajan R., Sailem H., Mardakheh F.K., Arias Garcia M., Tape C.J., Dowsett M., Bakal C, & Yuan Y. (2016). Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis. PLoS Medicine, 13(2), e1001961.
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6

Integrated Transcriptomic Analysis of Breast Cancer

Cited 3 times
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TCGA BRCA RNA-seq RSEM gene-level counts of 1211 cases, including 1097 primary solid tumor tissues and 114 solid tissue normal, aligned to the hg19 reference genome were downloaded from GDC’s legacy archive. The 192 TNBC samples identified in the previous section were extracted and normalized (TCGAanalyze_Normalization) using the R/Bioconductor package TCGAbiolinks (ver.2.9.5)62 (link). Next, we performed sample normalization adjusting for GC-content and upper-quantile between-lane by applying and implementing the EDASeq protocol63 (link). For the 122 prospective CPTAC BRCA samples64 (link), we obtained the median-normalized gene expression data (log2 FPKM) from linkedomics (http://linkedomics.org/data_download/CPTAC-BRCA/). RNA expression for the 28 TNBC tumors identified using the “genomic-guided identification of TNBC specimens” section were normalized and subtyped as detailed in methods section “TNBC subtyping” below. MET500 (FPKM) RNAseq data (n = 868) was retrieved from https://xenabrowser.net/16 (link). We identified 92 unique breast tumors within the MET500 dataset, among them 40 were classified as TNBC samples that underwent TNBC subtyping. Complete normalized expression data for 1981 METABRIC BRCA samples profiled with Illumina HT 12 arrays were obtained through Synapse (https://www.synapse.org/#!Synapse:syn1757063).
Lehmann B.D., Colaprico A., Silva T.C., Chen J., An H., Ban Y., Huang H., Wang L., James J.L., Balko J.M., Gonzalez-Ericsson P.I., Sanders M.E., Zhang B., Pietenpol J.A, & Chen X.S. (2021). Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes. Nature Communications, 12, 6276.
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7

Whole Blood RNA Expression Profiling

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Whole blood samples were collected in Tempus tubes (Applied Biosystems, Foster City, California, USA) and stored at −80°C. RNA was extracted according to the manufacturer’s protocol. RNA quality was assessed using Bioanalyzer (Agilent Genomics, Santa Clara, California, USA) and those with RNA integrity numbers >7 were examined. Global gene expression was assessed using Illumina HT-12 arrays (Illumina, San Diego, California, USA) in SCOT samples and Illumina Human Ref8 V.3 arrays in UT Houston Divisional Repository samples. The gene expression data are deposited on Gene Expression Omnibus (GSE130953). Details regarding gene expression normalisation, filtering and initial analysis, as well as the modular analysis,5 (link) are mentioned in the online supplemental methods.
Assassi S., Wang X., Chen G., Goldmuntz E., Keyes-Elstein L., Ying J., Wallace P.K., Turner J., Zheng W.J., Pascual V., Varga J., Hinchcliff M.E., Bellocchi C., McSweeney P., Furst D.E., Nash R.A., Crofford L.J., Welch B., Pinckney A., Mayes M.D, & Sullivan K.M. (2019). Myeloablation followed by autologous stem cell transplantation normalises systemic sclerosis molecular signatures. Annals of the rheumatic diseases, 78(10), 1371-1378.
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