Agilent arrays

Manufactured by Agilent Technologies

Sourced in United States

Agilent arrays are high-density microarray platforms used for genetic analysis. They provide a comprehensive solution for DNA, RNA, and protein research, enabling the simultaneous measurement of thousands of analytes in a single experiment.

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Lab products found in correlation

Spss 20, by IBM (1 mentions) Feature extraction software version 10, by Agilent Technologies (1 mentions) Agilent microarray scanner, by Agilent Technologies (1 mentions) Cgh analytics v4, by Agilent Technologies (1 mentions)

Close spelling variants of this product

Agilent Whole Mouse Genome arrays Agilent Whole Mouse Genome 4×44 multiplex format oligo arrays ULS Fluorescent Labeling Kit for Agilent arrays Agilent Mouse GE 8×60K v2 arrays Agilent Mouse miRNA 8×60K v21.0 arrays Agilent Whole Human Genome 4×44K arrays Agilent sure print G3 Hmn CGH 2x 400 K arrays Agilent Human v2 GE 4x44K arrays Agilent mouse whole-genome expression arrays Agilent 4x44k arrays

7 protocols using agilent arrays

Intrinsic Subtyping of Breast Cancer

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Gene expression data were generated using custom-designed Agilent arrays containing approximately 32.1K probes, representing approximately 21.5K unique genes from FFPE breast cancer tumor tissue. Approximately 90% (or 652 breast cancer tumors) passed the RNA quality check (according to the diagnostic quality model) and were used in the intrinsic subtype analysis. Tumors were assigned to one of five molecular subtypes (luminal A, luminal B, HER2-enriched, basal-like, normal-like) using the PAM50 classifier, as described in Parker et al. (Supplementary Material, available online) (26 (link)).

Lindström L.S., Yau C., Czene K., Thompson C.K., Hoadley K.A., van't Veer L.J., Balassanian R., Bishop J.W., Carpenter P.M., Chen Y.Y., Datnow B., Hasteh F., Krings G., Lin F., Zhang Y., Nordenskjöld B., Stål O., Benz C.C., Fornander T., Borowsky A.D, & Esserman L.J. (2018). Intratumor Heterogeneity of the Estrogen Receptor and the Long-term Risk of Fatal Breast Cancer. JNCI Journal of the National Cancer Institute, 110(7), 726-733.

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Differential Gene Expression Analysis

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Normalized gene expression data for tumor and normal samples, either from an Illumina sequencing platform or Agilent arrays depending on availability for each cancer type, were obtained from TCGA. We compared expression levels for each gene between tumors and matched normal samples using paired Wilcoxon tests and corrected nominal p-values using the Benjamini-Hochberg procedure. If a gene was significantly (FDR < 0.05) differentially expressed (lower or higher) in tumor samples, it received a +1 towards tumor suppressor or oncogene score, respectively.

Schlicker A., Michaut M., Rahman R, & Wessels L.F. (2016). OncoScape: Exploring the cancer aberration landscape by genomic data fusion. Scientific Reports, 6, 28103.

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Gastric Cancer Gene Expression Analysis

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GC gene expression data was obtained from the NCBI GEO, (http://www.ncbi.nlm.nih.gov/geo/). One data set GSE51575 consisted of 26 paired primary gastric adenocarcinoma tissues and surrounding normal fresh frozen tissues was included. All the tissues were obtained after curative resection and pathologic confirmation at Samsung Medical Center (Korea cohort). The raw CEL files from the Agilent arrays (Agilent, Santa Clara, CA, USA) for GSE51575 were processed and normalized using the Robust Multichip Average as previously described.^{25 (link)} All statistical analyses were performed using SPSS 20.0 software (IBM, SPSS, Chicago, IL, USA). The significance of differences between groups was estimated using the Student’s t-test, χ2 test, Fisher’s exact test, Mann-Whitney test, Kruskal–Wallis test or Wilcoxon test, as appropriate. A ROC curve was established to evaluate the diagnostic value for differentiating between GC and benign diseases. FP survival (FPS) and OS rates were calculated by the Kaplan–Meier method with the log-rank test applied for comparison. Pearson correlation analysis was performed to investigate the correlation between TINCR and E2F1 mRNA expression. Two-sided P-values were calculated, and a probability level of 0.05 was chosen for statistical significance.

Xu T.P., Wang Y.F., Xiong W.L., Ma P., Wang W.Y., Chen W.M., Huang M.D., Xia R., Wang R., Zhang E.B., Liu Y.W., De W, & Shu Y.Q. (2017). E2F1 induces TINCR transcriptional activity and accelerates gastric cancer progression via activation of TINCR/STAU1/CDKN2B signaling axis. Cell Death & Disease, 8(6), e2837-.

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Genome-wide Transcriptomic Profiling of Arthralgia Patients

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Within the framework of another research study⁴³ (manuscript in preparation), an explorative genome-wide transcriptional profiling study was performed on synovial tissue biopsies obtained from 13 autoantibody-positive (IgM rheumatoid factor and/or anti-citrullinated protein antibody) arthralgia patients using Agilent arrays (Agilent technologies, Amstelveen, The Netherlands). The study was approved by the institutional review board of the Academic Medical Center (Amsterdam, The Netherlands), and all study subjects gave written informed consent. At the time of synovial biopsy of a knee joint by mini-arthroscopy these individuals did not display any evidence of arthritis as assessed by an experienced rheumatologist. However, they are “at risk” for developing RA and therefore prospectively followed over time to study the possible development of arthritis^{42 (link)}. Expression levels of genes of interest were extracted from this dataset and investigated for their possible association with arthritis development. To this end, RA-risk individuals were stratified based on their mRNA levels into relative low or high expressers compared to the median values of the whole population, after which arthritis-free survival was compared to each other using a log-rank (Mantel–Cox) test.

Perucha E., Melchiotti R., Bibby J.A., Wu W., Frederiksen K.S., Roberts C.A., Hall Z., LeFriec G., Robertson K.A., Lavender P., Gerwien J.G., Taams L.S., Griffin J.L., de Rinaldis E., van Baarsen L.G., Kemper C., Ghazal P, & Cope A.P. (2019). The cholesterol biosynthesis pathway regulates IL-10 expression in human Th1 cells. Nature Communications, 10, 498.

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Identifying de novo copy number variations

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A detailed description of how de novo CNVs were identified in this sample is provided elsewhere [27 (link)]. Briefly, CNV calls were excluded if they had a frequency >1% in the sample, were covered by <15 probes, overlapped segmental duplications by >50% of their length, or were smaller than 15KB. All de novo CNVs included in the current study were validated using custom Agilent arrays [27 (link)]. For transmitted and non-transmitted CNVs, we applied a more stringent CNV size threshold that excluded CNVs <100 KB as these were not independently validated [27 (link), 31 (link)]. Transmitted CNVs were defined as those observed in a proband that had any overlap with a CNV observed in at least one of their parents. Non-transmitted CNVs were those observed in a parent but not their child.

Rammos A., Kirov G., Hubbard L., Walters J.T., Holmans P., Owen M.J., O’Donovan M.C, & Rees E. (2023). Family-based analysis of the contribution of rare and common genetic variants to school performance in schizophrenia. Molecular Psychiatry, 28(5), 2081-2087.

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aCGH Analysis of Somatic Deletions

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aCGH was performed following manufacturer protocol modified by Hostetter et al. (Hostetter et al., 2010 (link)). One microgram of genomic DNA extracted from archival material and 400 ng of reference DNA were labeled with Cyanine 5 and 3, as described previously (Perot et al., 2012 (link)). Labeled DNA was hybridized to Agilent arrays (Agilent Technologies) with a 60k resolution across the genome. Slides were scanned on Agilent microarray scanner and analyzed using Feature extraction software, version 10.5.1.1 (Agilent Technologies) and Agilent genomic workbench lite 6.5.0.18. The ADM-2 algorithm was used to identify DNA copy number anomalies at the probe level. Homozygous deletion was considered when log2 ratios of targeting probes were below 1. Intermediate log2 ratios values between −1 and −0.25 do not allow to be conclusive as to whether the deletion is homo or heterozygous. A low-level copy number gain was defined as a log 2 ratio > 0.25. To further characterize the 22q11 somatic deletion identified in case ES#6, we used custom-designed aCGH 180k Agilent array with high density coverage of 22q11 locus (in which 200 oligonucleotide probes target SMARCB1).

Le Loarer F., Zhang L., Fletcher C.D., Ribeiro A., Singer S., Italiano A., Neuville A., Houlier A., Chibon F., Coindre J.M, & Antonescu C.R. (2014). Consistent SMARCB1 Homozygous Deletions in Epithelioid Sarcoma and in a Subset of Myoepithelial Carcinomas can be Reliably Detected by FISH in Archival Material. Genes, chromosomes & cancer, 53(6), 475-486.

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Genomic Profiling of Copy Number Changes

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DNA was hybridized to 8 Â 60 K whole-genome Agilent arrays (G4450A; Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer's protocol. The ADM-2 algorithm of the CGH Analytics v4.0.76 software (Agilent Technologies) was used to identify DNA copy number anomalies at the probe level. A low-level copy number gain was defined as a log 2 ratio 40.25 and a copy number loss was defined as a log 2 ratio o0.25. A high-level gain or amplification was defined as a log 2 ratio41.5 and a homozygous deletion was suspected when the ratio was o À 1.
The genomic index was calculated for each profile as follows: genomic index ¼ A 2 /C, where A is the total number of alterations (segmental gains and losses) and C is the number of involved chromosomes.

Croce S., Ribeiro A., Brulard C., Noel J.C., Amant F., Stoeckle E., Devouassoux-Shisheborah M., Floquet A., Arnould L., Guyon F., Mishellany F., Garbay D., Cuppens T., Zikan M., Leroux A., Frouin E., Duvillard P., Terrier P., Farre I., Valo I., MacGrogan G.M, & Chibon F. (2015). Uterine smooth muscle tumor analysis by comparative genomic hybridization: a useful diagnostic tool in challenging lesions. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 28(7).

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