4x44k oligonucleotide microarrays

Manufactured by Agilent Technologies

The 4x44K oligonucleotide microarrays are a product offered by Agilent Technologies. These microarrays contain 44,000 oligonucleotide probes designed for gene expression analysis. The core function of these microarrays is to provide a platform for the simultaneous measurement of the expression levels of thousands of genes in a biological sample.

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Lab products found in correlation

Dynabeads mrna purification kit, by Thermo Fisher Scientific (2 mentions) Feature extraction software, by Agilent Technologies (1 mentions) Hiseq 2000, by Illumina (1 mentions) Feature extraction software version 9.5.1, by Agilent Technologies (1 mentions) Hiseq 2000 platform, by Illumina (1 mentions)

Close spelling variants of this product

Oligonucleotides (6 citations) Oligonucleotide microarrays (2 citations) 4x44K oligonucleotide-based microarrays (2 citations) Whole human genome 4x44K oligonucleotide microarrays (2 citations)

4 protocols using 4x44k oligonucleotide microarrays

Comparative Genomic Hybridization Microarray

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4x44K oligonucleotide microarrays (Agilent, Santa Clara, CA) were used for direct array comparative genomic hybridization (aCGH) as described previously to compare indicated cell lines to normal human reference DNA as published.18 Labeling and hybridization of genomic DNA was performed according to the manufacturer's recommendations.

Englinger B., Laemmerer A., Moser P., Kallus S., Röhrl C., Pirker C., Baier D., Mohr T., Niederstaetter L., Meier‐Menches S.M., Gerner C., Gabler L., Gojo J., Timelthaler G., Senkiv J., Jäger W., Kowol C.R., Heffeter P, & Berger W. (2020). Lipid droplet‐mediated scavenging as novel intrinsic and adaptive resistance factor against the multikinase inhibitor ponatinib. International Journal of Cancer, 147(6), 1680-1693.

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Microarray and RNA-seq for Expression Profiling

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For microarray analysis, the gene expression profiles were generated using customized 4 x 44 k oligonucleotide microarrays (Agilent Technologies). Sample preparation, labeling, and hybridization were performed according to the manufacturer's protocol. The microarray expression profiles were generated using Agilent's Feature Extraction software (Version 9.5.1). For RNA sequencing, the Dynabeads® mRNA Purification Kit (Invitrogen) was used to purify mRNA from total RNA, and ERCC RNA spike-in was added according to the user guide. Then, library construction was performed according to the non-stranded TruSeqs™ protocol, and clusters were generated according to the TruSeq PE cluster Kit v3 reagent preparation guide (for cBot-HiSeq/HiScanSQ). The high-throughput shotgun sequencing was performed on the Illumina HiSeq 2000 platform. Paired-end reads with lengths of 90 and 100 nucleotides were generated for 12 samples and 486 samples, respectively.

Lu Y., Xiao Y., Yin M.Z., Zhou X.C., Wu L.S., Chen W.Q., Luo Y., Kuang Y.H, & Zhu W. (2021). Polyethylene Glycol Ointment Alleviates Psoriasis-Like Inflammation Through Down-Regulating the Function of Th17 Cells and MDSCs. Frontiers in Medicine, 7, 560579.

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Tumor Profiling via Microarray and RNA-Seq

Cited 1 time

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Tumor material preparation was performed as described previously [16 (link)]. For microarray analysis, gene expression profiles were generated using customized 4x44k oligonucleotide microarrays (Agilent Technologies). Sample preparation, labeling, and hybridization were performed according to the manufacturer’s protocol. Microarray expression profiles were generated using Agilent’s Feature Extraction software (Version 9.5.1) [35 (link)]. For RNA sequencing, Dynabeads® mRNA Purification Kit (Invitrogen) was used to purify mRNA from total RNA, and ERCC RNA spike-in was added according to the user guide. Library construction was performed according to the non-stranded TruSeqs™ protocol. Clusters were generated according to the TruSeq PE cluster Kit v3 reagent preparation guide (for cBot-HiSeq/HiScanSQ). High-throughput shotgun sequencing was performed on the Illumina HiSeq 2000 platform. Paired-end reads with lengths of 90 and 100 nucleotides were generated for 12 samples and 486 samples, respectively.

Zhang W., Yu Y., Hertwig F., Thierry-Mieg J., Zhang W., Thierry-Mieg D., Wang J., Furlanello C., Devanarayan V., Cheng J., Deng Y., Hero B., Hong H., Jia M., Li L., Lin S.M., Nikolsky Y., Oberthuer A., Qing T., Su Z., Volland R., Wang C., Wang M.D., Ai J., Albanese D., Asgharzadeh S., Avigad S., Bao W., Bessarabova M., Brilliant M.H., Brors B., Chierici M., Chu T.M., Zhang J., Grundy R.G., He M.M., Hebbring S., Kaufman H.L., Lababidi S., Lancashire L.J., Li Y., Lu X.X., Luo H., Ma X., Ning B., Noguera R., Peifer M., Phan J.H., Roels F., Rosswog C., Shao S., Shen J., Theissen J., Tonini G.P., Vandesompele J., Wu P.Y., Xiao W., Xu J., Xu W., Xuan J., Yang Y., Ye Z., Dong Z., Zhang K.K., Yin Y., Zhao C., Zheng Y., Wolfinger R.D., Shi T., Malkas L.H., Berthold F., Wang J., Tong W., Shi L., Peng Z, & Fischer M. (2015). Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biology, 16(1), 133.

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Neuroblastoma Gene Expression Profiling

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Gene expression profiles of all 695 neuroblastoma samples had been generated previously using customized 4x44K oligonucleotide microarrays (Agilent Technologies) [23] (link). Gene expression–based classifiers had been developed on a cohort of 75 tumors (discovery set) from patients with maximally divergent clinical courses as described elsewhere [23] (link) (see Supplementary Material). Expression data and basic clinical information are available through ArrayExpress (http://www.ebi.ac.uk/arrayexpress; accession: E-MTAB-1781). As the classifiers led to discrepant results for a number of patients in the training set (Figure 2A), we considered all four predictors for risk score building.Figure 2

(A) Venn diagram of concordant and discordant classification results of the four different gene expression classifiers (NB-th10, -th24, -th26, and -th44) within the training set. The numbers of tumors classified as favorable or unfavorable are highlighted in green and red, respectively. (B) Schematic representation of the NB-Risk Score that considers classification results and hazard ratios of the two gene expression–based classifiers, NB-th24 and NB-th44. Favorable classification, 0; unfavorable classification, 1; LR, low risk; IR, intermediate risk; HR, high risk.

Figure 2

Rosswog C., Schmidt R., Oberthuer A., Juraeva D., Brors B., Engesser A., Kahlert Y., Volland R., Bartenhagen C., Simon T., Berthold F., Hero B., Faldum A, & Fischer M. (2017). Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment. Neoplasia (New York, N.Y.), 19(12), 982-990.

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