Genespring gx v14

Manufactured by Agilent Technologies

Sourced in United States

GeneSpring GX v14.9 is a software application for data analysis and visualization of gene expression data. It provides tools for importing, normalizing, and analyzing data from various gene expression platforms, including microarrays and RNA-sequencing. The software enables users to perform statistical analyses, identify differentially expressed genes, and visualize the data through various plotting options.

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19 protocols using genespring gx v14

Microarray Gene Expression Profiling

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Total RNA was isolated from minced tissue samples using a miRNeasy Mini Kit (217004; Qiagen) as described previously (25 (link)). RNA samples with an RNA integrity number ≥6.0 underwent gene expression profiling (GEP) using a one-color Low Input Quick Amp Labeling Kit (5190-2305; Agilent Technologies) for amplification/fluorescence-labeling and a SurePrint G3 Human Gene Expression 8 × 60K v2 Microarray kit (G4851B; Agilent Technologies) for detecting gene expression. The microarray kit contains 50,599 probes capable of detecting 29,833 genes registered in the Entrez Gene Database of the National Center for Biotechnology Information. Hybridization signals were detected using a DNA Microarray C Scanner (Agilent Technologies), and the scanned images were analyzed using Agilent Feature Extraction v10.7.3.1 software. Microarray analysis was performed in accordance with Minimum Information About a Microarray Experiment guidelines (26 (link)). Data analysis was performed using GeneSpring GX v14.9.1 (Agilent Technologies), the Subio Platform, Excel 2021, and GraphPad Prism v8.3.0. We selected probes to be analyzed according to the reference genome sequence hg19, obtained from the UCSC Genome Browser (27 (link)). Raw signal intensity values were log₂ transformed and normalized to the 75th percentile.

Ohshima K., Nagashima T., Fujiya K., Hatakeyama K., Watanabe Y., Morimoto K., Kamada F., Shimoda Y., Ohnami S., Naruoka A., Serizawa M., Ohnami S., Kenmotsu H., Shiomi A., Tsubosa Y., Bando E., Sugiura T., Sugino T., Terashima M., Uesaka K., Urakami K., Akiyama Y, & Yamaguchi K. (2023). Whole-genome and Epigenomic Landscapes of Malignant Gastrointestinal Stromal Tumors Harboring KIT Exon 11 557–558 Deletion Mutations. Cancer Research Communications, 3(4), 684-696.

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Cluster Analysis of miRNA Expression

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Cluster analysis was performed using the normalized expression (50th percentile) of the 89 miRNAs in (a) individual patients of the reference set of primary tumors and (b) averaged levels within each tumor class of the reference set. The hierarchical cluster analyses were performed using genespring gx v.14.9.1 software (Agilent Technologies) using complete‐linkage rule and Manhattan correlation distance. Standard deviation on the average expression of each miRNA within each class was also assessed.

Laprovitera N., Riefolo M., Porcellini E., Durante G., Garajova I., Vasuri F., Aigelsreiter A., Dandachi N., Benvenuto G., Agostinis F., Sabbioni S., Berindan Neagoe I., Romualdi C., Ardizzoni A., Trerè D., Pichler M., D'Errico A, & Ferracin M. (2021). MicroRNA expression profiling with a droplet digital PCR assay enables molecular diagnosis and prognosis of cancers of unknown primary. Molecular Oncology, 15(10), 2732-2751.

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Microarray Data Processing Workflow

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Microarray data was processed in GeneSpring GX v14.8 (Agilent Technologies). Flags were used to filter out the probe sets that did not result in a “present” call in at least 10% of the samples, with the “present” lower cut-off of 0.99. Signal values were then set to a threshold level of 10, log₂ transformed, and per-chip normalized using 75th percentile shift algorithm. Next, per-gene normalization was applied by dividing each messenger RNA transcript by the median intensity of all the samples. Next, transcripts were filtered to select the most variable probes: those that had a minimum of 1.5-fold expression change compared with the median intensity across all samples, in greater than 10% of all samples. For modular fold enrichment analysis, Illumina IDs were converted to Ensembl IDs using the annotation file available from Illumina, retaining IDs with one to one mapping.

Singhania A., Graham C.M., Gabryšová L., Moreira-Teixeira L., Stavropoulos E., Pitt J.M., Chakravarty P., Warnatsch A., Branchett W.J., Conejero L., Lin J.W., Davidson S., Wilson M.S., Bancroft G., Langhorne J., Frickel E., Sesay A.K., Priestnall S.L., Herbert E., Ioannou M., Wang Q., Humphreys I.R., Dodd J., Openshaw P.J., Mayer-Barber K.D., Jankovic D., Sher A., Lloyd C.M., Baldwin N., Chaussabel D., Papayannopoulos V., Wack A., Banchereau J.F., Pascual V.M, & O’Garra A. (2019). Transcriptional profiling unveils type I and II interferon networks in blood and tissues across diseases. Nature Communications, 10, 2887.

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Comprehensive Transcriptome Analysis of PDX Models

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Data were normalized and analyzed using GeneSpring GX v.14.8 software (Agilent Technologies). Data transformation was applied to set all the negative raw values at 1.0, then the quintile normalization was applied. The probes detected in at least one sample were used for statistical analyses. Unsupervised principal component analysis and correlation analysis (Pearson’s correlation) were performed to assess sample similarity and to assess the global gene expression profile of PDX models. Differentially expressed genes were selected to have a ≥2-fold expression difference between matching PDX and primaries and an adjusted p-value ≤ 0.05 at paired t-test, with Benjamini and Hoechberg correction for false positive reduction. Hierarchical clustering was performed for OS samples with GeneSpring clustering tool using the list of differentially expressed genes and the Manhattan correlation as a measure of similarity. Pathway and network analysis of differentially expressed genes was determined using the web-based software MetaCore (GeneGo, Thomson Reuters).

Nanni P., Landuzzi L., Manara M.C., Righi A., Nicoletti G., Cristalli C., Pasello M., Parra A., Carrabotta M., Ferracin M., Palladini A., Ianzano M.L., Giusti V., Ruzzi F., Magnani M., Donati D.M., Picci P., Lollini P.L, & Scotlandi K. (2019). Bone sarcoma patient-derived xenografts are faithful and stable preclinical models for molecular and therapeutic investigations. Scientific Reports, 9, 12174.

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Microarray Data Preprocessing and Analysis

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External microarray datasets retrieved from GEO as non-normalised matrices were processed in GeneSpring GX v14.8 (Agilent Technologies). Flags were used to filter out the probe sets that did not result in a ‘present’ call in at least 10% of the samples, with the ‘present’ lower cut-off of 0.8. Signal values were then set to a threshold level of 10, log₂ transformed, and per-chip normalised using 75th percentile shift algorithm. Next, per-gene normalisation was applied by dividing each messenger RNA transcript by the median intensity of all the samples. The training, test and validation sets in Bloom et al.^{10 (link)} were combined and the batch effects were removed using sva⁶³. In Kaforou et al.^{24 (link)}, HIV+/− groups were combined and analysed as one dataset. In all datasets, multiple probes mapping to the same gene were removed and the probe with the highest inter-quartile range across all samples was retained to match with the RNA-seq data. Differentially expressed genes were identified using the bioconductor package limma^{64 (link)} in R and only the genes with FDR p-value < 0.05 corrected for multiple testing using the BH method⁶² were considered significant.

Singhania A., Verma R., Graham C.M., Lee J., Tran T., Richardson M., Lecine P., Leissner P., Berry M.P., Wilkinson R.J., Kaiser K., Rodrigue M., Woltmann G., Haldar P, & O’Garra A. (2018). A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection. Nature Communications, 9, 2308.

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Microarray Data Normalization Protocol

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Microarray data was processed in GeneSpring GX v14.8 (Agilent
Technologies). Flags were used to filter out the probe sets that did not result
in a “present” call in at least 10% of the samples, with the
“present” lower cut-off of 0.99. Signal values were then
normalized using neqc function with default parameters from limma package (v
3.38.3) in R. This function performs background correction, quantile
normalization and log2 transformation of intensity signals. For modular fold
enrichment analysis, Illumina IDs were converted to Ensembl IDs using both the
annotation file available from Illumina and biomaRt package (2.38.0) in R. Next,
transcripts were filtered to select the 50% most variable probes across all
samples.

Moreira-Teixeira L., Tabone O., Graham C.M., Singhania A., Stavropoulos E., Redford P.S., Chakravarty P., Priestnall S.L., Suarez-Bonnet A., Herbert E., Mayer-Barber K.D., Sher A., Fonseca K.L., Sousa J., Cá B., Verma R., Haldar P., Saraiva M, & O’Garra A. (2020). Mouse transcriptome reveals potential signatures of protection and pathogenesis in human tuberculosis. Nature immunology, 21(4), 464-476.

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Microarray Data Normalization Protocol

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Microarray data was processed in GeneSpring GX v14.8 (Agilent
Technologies). Flags were used to filter out the probe sets that did not result
in a “present” call in at least 10% of the samples, with the
“present” lower cut-off of 0.99. Signal values were then
normalized using neqc function with default parameters from limma package (v
3.38.3) in R. This function performs background correction, quantile
normalization and log2 transformation of intensity signals. For modular fold
enrichment analysis, Illumina IDs were converted to Ensembl IDs using both the
annotation file available from Illumina and biomaRt package (2.38.0) in R. Next,
transcripts were filtered to select the 50% most variable probes across all
samples.

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Differential Gene Expression in Normoxia and Hypoxia

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To investigate differential gene expression after the TNF-α stimulation of MDA-MB-435-pFDR1 and M13SV1-Cre-Puro cells under normoxia and hypoxia, a single colour microarray analysis (Agilent Human 4×44K v2 Microarray (Agilent Technologies, Waldbronn, Germany)) was performed. The total RNA of the cells (three independent experiments were pooled) matching the RIN criteria of 8 to 10 were sent on dry ice to Source BioSciences (Nottingham, UK), which performed synthesis and Cy3 labelling of the cDNA and hybridisation of the microarrays. Microarray gene expression data were analysed using GeneSpring GX v14.8 software (Agilent Technologies, Waldbronn, Germany). Expression data were normalized based on quantiles with a threshold of 1 and no baseline transformation was performed. For further analysis, data were filtered by flags (not detected flags and compromised spots were removed) and fold changes (2-fold up and down). All microarray data were deposited in the ArrayExpress database at EMBL-EBI (https://www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-6084.

Weiler J., Mohr M., Zänker K.S, & Dittmar T. (2018). Matrix metalloproteinase-9 (MMP9) is involved in the TNF-α-induced fusion of human M13SV1-Cre breast epithelial cells and human MDA-MB-435-pFDR1 cancer cells. Cell Communication and Signaling : CCS, 16, 14.

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Retinoblastoma miRNA Expression Analysis

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Total RNA was extracted and purified from 3 normal retina tissues and 3 retinoblastoma tissues using mirVana™ miRNA Isolation kit (Ambion; Thermo Fisher Scientific, Inc.). The samples were hybridized on Agilent human miRNA microarray v21 (Agilent Technologies, Santa Clara, CA, USA). Microarray data were normalized and analyzed using GeneSpring GX v.14.8 software (Agilent Technologies). The threshold set for differentially expressed miRNAs was P < 0.05 and fold-change ≥2.0.

Yang L., Zhang L., Lu L, & Wang Y. (2020). miR-214-3p Regulates Multi-Drug Resistance and Apoptosis in Retinoblastoma Cells by Targeting ABCB1 and XIAP. OncoTargets and therapy, 13, 803-811.

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Differential miRNA Expression Analysis

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Array images were analyzed using Agilent Feature Extraction software (version 11.0.1.1). The GeneSpring GX v14.9 software package (Agilent Technologies) was used to normalize quantile and process subsequent data acquired from the miRNA chip. Processed data were verified and optimized by alignment to human miRNA transcriptome, which represented all known miRNAs. miRNAs that at least three out of nine samples have flags in detected were chosen for subsequently analysis after normalization. Differentially expressed miRNAs between the two samples were filtered using the characteristic log 2 (Fold Change) ≥ 1.0, P ≤ 0.05, and FDR ≤ 1.0. Differentially expressed miRNAs with statistical significance between the two groups were presented through Volcano Plot and Hierarchical Clustering. R scripts were used to draw the hierarchical clustering.

Zeng J., Liu W., Liang J., Peng J., Wang F., Tang J., Yang Q., Zhuang L., Huang D, & Li L. (2021). Analysis of miRNA Profiles and the Regulatory Network in Congenital Pulmonary Airway Malformations. Frontiers in Pediatrics, 9, 671107.

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