The quality control of the scanned data was first estimated by confirming the order of the signal intensities of the Poly-A and Hybridization controls using an Affymetrix GeneChip Expression Console (Affymetrix). The raw expression values were imported as Affymetrix.CEL files into a Partek Genomics Suite 6.6 (Partek Inc., St. Louis, MO, USA). The data were analyzed and normalized including the Preprocessing, Differentially Expressed Genes (DEGs) Finding, and Clustering modules. The RMA algorithm was used to eliminate all signals related to non-specific hybridization and to normalize the microarray data. The mean fluorescence intensities of all genes were obtained comparing two samples (one vs. one) for each “sample type” condition; the analysis was performed comparing the HIV+ twin to the HIV− twin. After normalization, the DEGs satisfying the conditions of the Fold Change (FC) cut-off of 2 (>+2 or <−2) from all the genes probed in the strip were selected. Hierarchical cluster analysis was also performed to see how data aggregated and to generate heat maps. For DEGs selection, only the FC method was used as there are only two samples, one for type and it was not possible to apply the statistical analysis procedure.
Genechip expression console
Manufactured by Thermo Fisher Scientific
The GeneChip Expression Console is a software tool designed for the analysis of gene expression data generated from Affymetrix GeneChip microarray experiments. It provides a user-friendly interface for importing, normalizing, and analyzing gene expression data.
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Lab products found in correlation
Fluidics station 450, by Thermo Fisher Scientific (2 mentions)
Ht 3 ivt plus kit, by Thermo Fisher Scientific (2 mentions)
Genechip mogene1.0 st arrays, by Thermo Fisher Scientific (1 mentions)
Gcs 3000 7g, by Thermo Fisher Scientific (1 mentions)
Biomek fxp laboratory automation workstation, by Beckman Coulter (1 mentions)
Genetitan, by Thermo Fisher Scientific (1 mentions)
Genechip scanner 3000, by Thermo Fisher Scientific (1 mentions)
Rneasy mini kit, by Qiagen (1 mentions)
Genetitan u219 array plates, by Thermo Fisher Scientific (1 mentions)
Genechip mirna 3.0 array, by Thermo Fisher Scientific (1 mentions)
10 protocols using genechip expression console
1
Differentially Expressed Genes in HIV Twins
2
Transcriptome Profiling of Mouse Genes
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Fragmented cDNA was hybridized for 17hr at 45°C to GeneChip® MoGene 1.0 ST Arrays (Affymetrix, Santa Clara CA). The MoGene 1.0 ST arrays contain oligonucleotide probe sets that are specifically designed to interrogate and detect more than 28,000 coding and 7,000 non-coding gene transcripts (including ~2,000 long intergenic non-coding transcripts). Arrays were washed and stained using a Fluidics Station 450 (Affymetrix) according to the manufacturer's recommended procedures. The arrays were stained with phycoerythrin-conjugated streptavidin (Invitrogen, Carlsbad, CA) and the fluorescence intensities were determined using a GCS 3000 7G high-resolution confocal laser scanner and analyzed using programs resident in the Affymetrix GeneChip Expression Console (Affymetrix).
3
Gene Expression Analysis of Tumor Immune Quiescence
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After QC was performed, the expression profile of each sample was generated by Gene Chip Expression Console (Affymetrix, CA) and further processed and analyzed in dChip per the instructions [17 (link)]. Briefly, the dChip algorithm was used to summarize the gene expression profile on each sample, where each chip was compared to a reference chip for normalization in overall chip hybridization intensity so as to allow for comparison between different chips. Hierarchical clustering was performed using the same dChip software to examine the similarity of gene expression between different samples. The gene expression profile of each sample was filtered according to the default criteria of dChip. The expression values of genes of interest were listed and compiled. The filtered gene expression profile of each sample was imported into BRB Array Tool [18 (link)] for gene enrichment analysis in order to identify statistically significant gene sets that might be involved in the development of immune quiescence in tumors.
4
Transcriptome Analysis via Affymetrix Microarray
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Messenger RNA analysis was performed on purified RNA converted to biotin-labeled complementary RNA copies with the Affymetrix HT 3’ IVT Plus kit (Affymetrix, Santa Clara, CA) per protocol provided at a Beckman Biomek FXP Laboratory Automation Workstation (Beckman, Indianapolis, IN). Briefly, 250 ng of total RNA were reverse-transcribed into complementary DNA copies using oligo-dT primers and reverse transcriptase followed by second-strand synthesis using DNA polymerase I [2 (link)]. After purification, the cDNA library was used as a template to generate biotin-labeled cRNA copies using T7 RNA polymerase and biotinylated deoxyuridine triphosphate. Biotinylated cRNA was fragmented by limited alkaline hydrolysis and then hybridized overnight to Affymetrix GeneTitan U219 array plates using the Affymetrix GeneTitan instrument and protocol. After processing, chip images were converted to numeric data with the probe logarithmic intensity error algorithm as executed in the Affymetrix GeneChip Expression Console. All data were MIAME compliant.
5
RNA Preparation and Affymetrix Microarray Analysis
6
Gene Expression Analysis of Tumor Immune Quiescence
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After QC was performed, the expression profile of each sample was generated by Gene Chip Expression Console (Affymetrix, CA) and further processed and analyzed in dChip per the instructions [17 (link)]. Briefly, the dChip algorithm was used to summarize the gene expression profile on each sample, where each chip was compared to a reference chip for normalization in overall chip hybridization intensity so as to allow for comparison between different chips. Hierarchical clustering was performed using the same dChip software to examine the similarity of gene expression between different samples. The gene expression profile of each sample was filtered according to the default criteria of dChip. The expression values of genes of interest were listed and compiled. The filtered gene expression profile of each sample was imported into BRB Array Tool [18 (link)] for gene enrichment analysis in order to identify statistically significant gene sets that might be involved in the development of immune quiescence in tumors.
7
Affymetrix HTA Microarray Protocol
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Total cellular RNA was isolated with RNeasy® Mini Kit (74,104, Qiagen) and treated with DNase I for 10 min. Global gene expression profile analysis was done using Affymetrix HTA microarrays at the Yale Center for Genomic Analysis. The hybridization patterns and signal intensities were analyzed and interpreted using the Affymetrix GeneChip Expression Console.
8
Transcriptome Analysis of Pigmentation
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Transcriptome analysis was performed by using Affymetrix GeneTitan U219 array plates (Affymetrix) with the Affymetrix GeneTitan instrument and protocol provided. Following processing, chip images were converted to numeric data using the PLIER algorithm as executed in the Affymetrix Gene Chip Expression Console. R packages (R Core Team: http://www.R-project.org/ ) were used for statistical analyses. Differentially expressed probe sets for each spot versus non‐spot adjacent tissue were analysed using One‐way ANOVA model implemented in the lima R package. Hierarchical cluster analysis of spot gene expression data (log2 fold changes of spot vs. non‐spot adjacent tissue) was done using heatmap.2 R package. Only probe sets which are significantly changed (p < 0.05) in at least one spot type were included in the clustering analysis. Gene set enrichment analysis (GSEA) was done using GAGE20 implemented in R. Graphs of regulation of human pigmentation genes from The International Federation of Pigment Cell Societies (IFPCS: https://www.ifpcs.org/colorgenes/ ) were generated for each spot type using Cytoscape.21 Details of LCM and RNA isolation processes, histomorphometry, immunohistology, as well as mRNA target labelling, processing, and analysis are provided in Supporting Information S1 .
9
RNA Labeling and Microarray Analysis
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Purified RNA was converted to biotin-labelled cRNA copies using the HT 3' IVT Plus kit (Affymetrix, Santa Clara, CA) and protocol provided as executed on a Biomek â FXp Laboratory Automation Workstation (Beckman). In brief, 250 ng of total RNA was reverse-transcribed into cDNA copies using oligo-dT primers and reverse transcriptase followed by second strand synthesis using DNA polymerase I. Following purification, the cDNA library was used as a template for generating biotin-labelled cRNA copies using T7 RNA polymerase and biotinylated dUTP.
Biotinylated cRNA was fragmented by limited alkaline hydrolysis and then hybridized overnight to GeneTitan â U219 array plates (Affymetrix) using the GeneTitan â instrument and protocol provided. Following processing, chip images were converted to numeric data using the PLIER algorithm as executed in the Affymetrix GeneChip Expression Console software as now included in their TAC software.
Biotinylated cRNA was fragmented by limited alkaline hydrolysis and then hybridized overnight to GeneTitan â U219 array plates (Affymetrix) using the GeneTitan â instrument and protocol provided. Following processing, chip images were converted to numeric data using the PLIER algorithm as executed in the Affymetrix GeneChip Expression Console software as now included in their TAC software.
10
Analyzing Small RNA Expression in Myasthenia Gravis
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The quality control of the scanned data was first estimated by confirming the order of the signal intensities of the Poly-A and hybridization controls using the Affymetrix GeneChip Expression Console. Then raw expression values were generated as ".CEL files" and ".CHP files" and were analyzed into the Transcriptome Analysis Console Software (TAC). The average for the intensity of fluorescence for each small RNA is calculated with the Tukey's Biweight method that calculates a robust average unaffected by outliers. The Tukey's Biweight average values are given in tables 2A-B in a log2 scale. TAC computes and summarizes a traditional unpaired One-Way Analysis of Variance (ANOVA) for each pair of condition groups. The fold changes (FCs) are given in linear values for MG versus control data. We selected small human RNAs that were dysregulated with a FC over 1.5 or -1.5 and p-value ≤ 0.05. Other miRNAs of interest could have also been extracted if we had lowered the fold change threshold and these miRNAs can be found in the supplemental table S1.
To search for miRNA clusters susceptible to be dysregulated in MG patients versus controls, we compared for each chromosome the number of dysregulated miRNAs with a χ2 test in table 2A to the number of mature human miRNAs spotted on the Affymetrix GeneChip miRNA 3.0 Array.
To search for miRNA clusters susceptible to be dysregulated in MG patients versus controls, we compared for each chromosome the number of dysregulated miRNAs with a χ2 test in table 2A to the number of mature human miRNAs spotted on the Affymetrix GeneChip miRNA 3.0 Array.
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