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19 protocols using human gene 1.0 st

1

Comparative Gene Expression Analysis

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The list of differentially genes in the present sprint study was compared to a list of genes from a ‘control’ experiment in order to find overlapping genes. Biopsies were performed with an interval of 2.5 h in a non-exercised and fasting condition [15 (link)]. We obtained normalized and processed data files from Vissing et al [15 (link)] and cut-off values for fold change and FDR were chosen to be 1.2 and 10%, i.e. the same as in the present study. Thirty-nine upregulated and 52 downregulated genes were identified. The selected study was chosen to match the study populations as far as possible with regard to subject characteristics (6 healthy young men), the time interval between the biopsies (2.5 h) and array platform (Affymetrix Human Gene 1.0 ST). Statistical significance of overlap was analysed by using the hypergeometric test. Seven genes were upregulated and 14 genes were downregulated in common between controls and sprint exercise (S5 Table).
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2

Microarray-based Transcriptomic Profiling

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Whole genome gene expression of the samples was measured by the Human Gene 1.0 ST Affymetrix microarray. 300 ng of total RNA were used for microarray analysis following the manufacturer’s instructions. Both the WT Expression Kit by Ambion and the GeneChip Hybridization Wash and Stain kit by Affymetrix were used in the process. Briefly, during the three-day protocol, complementary single-strand DNA was synthesized from RNA, to be later fragmented, labeled and hybridized during 16 hours. The hybridized microarrays were washed and stained in a GeneChip Fluidics Station 450 and scanned in a GeneChip 7G Scanner afterwards.
The data of the .cel files were normalized with the Robust Multichip Average (RMA) in the Expression Console software by Affymetrix. As multiple-testing correction makes the group-wise comparisons more stringent as more genes are included in the analysis, we filtered the data to remove the least informative genes using the BRB-Array Tools software [BRB-ArrayTools Development Team, version 4.2.1] implemented in Microsoft Excel [Microsoft Corporation, Microsoft Office Professional Edition 2003]. The genes with a 90th percentile value smaller than 55.7 (the mean intensity of the negative control probes) were removed and 6451 probesets were left for the subsequent analysis.
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3

Whole Genome Gene Expression Profiling

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Whole genome gene expression of the samples was measured by the Human Gene 1.0 ST Affymetrix microarray. First, RNA integrity was checked with the Agilent RNA 6000 Nano kit and the samples with an RNA Integrity Value (RIN) above 6 were accepted to be further processed. 300 ng of total RNA were used for microarray analysis following the manufacturer’s instructions. Both the WT Expression Kit by Ambion and the GeneChip Hybridization Wash and Stain kit by Affymetrix were used in the process. Briefly, during the three-day protocol, complementary single-strand DNA was synthesized from RNA, to be later fragmented, labeled and hybridized during 16 hours. The hybridized microarrays were washed and stained in a GeneChip Fluidics Station 450 and scanned in a GeneChip 7G Scanner afterwards. Two of the relapse samples failed to provide any signal and, thus, the 73 files created from the scanning were stored for subsequent analysis.
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4

Microarray Analysis of Alveolar Epithelial Cells

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RNA from human alveolar epithelial cells exposed to CSE and controls was extracted with Trizol (Invitrogen Life Technologies, Grand Island, NY) and was hybridized to Whole-Transcript Microarrays (Human Gene 1.0 ST, Affymetrix, Santa Clara, CA) according to the Affymetrix protocol.
Microarray data was pre-processed using R and Bioconductor. Raw intensity values were background corrected, log2 transformed and then RMA normalized [18 (link)], using algorithms coded in the “oligo” package in Bioconductor. To identify differentially expressed genes we fit a linear model using the limma package [19 ]. Finally, lists of differentially expressed genes for each of the three comparisons were created selecting those genes that are statistically significant according to two summary statistics: the log fold-change and the B-statistic. The cutoff for the fold-change was chosen to be equal to 1 combined with a confidence measure based on the B-statistic greater than zero. The microarray analysis was performed in 3 individual experiments.
The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE77942. (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77942).
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5

Bioinformatics Analysis of GEO Datasets

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Data sets required for bioinformatics analysis from Gene Expression Omnibus database (GEO) (https://www.ncbi.nlm.nih.gov/geo/). GSE135917[20 (link)] and GSE38792[21 (link)] microarray datasets had the same gene annotation platform (HuGene-1_0-st; Affymetrix Human Gene 1.0 ST). GSE135917 dataset had 18 samples of subcutaneous adipose tissue along with normal controls (n = 8) and untreated OSAHS patients (n = 10). GSE38792 dataset had 18 samples of visceral adipose tissue along with normal controls (n = 8) and untreated OSAHS patients (n = 10). Table S1, Supplemental Digital Content, http://links.lww.com/MD/J823 showed the clinical data and basic information related to the patients in both datasets.
The “GEOquery” software package was employed to get the original data that was analyzed using the oligo software package in R software (version 4.1.0). The probe name was changed to the gene name following the manufacturer-provided annotation file. Probes without corresponding gene names were deleted. The “affy” software package in R software was used to preprocess and normalize the chip dataset. The “select” function in R software was used to extract the expression of IL-10 mRNA in each sample of the above 2 datasets.
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6

Nasal Epithelial Cell Transcriptome and Methylation

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Nasal epithelial samples were collected at two time points from each subject: (1) in the ED (T0) and (2) on the inpatient floor 18–24 hours after receiving corticosteroids in the ED (T1). The procedure, characterization of cell types, sample processing, and RNA isolation have been described previously10 (link). Nasal samples collected contained >90% epithelial cells, similar to our previous findings10 (link). Expression profiles were generated on the Affymetrix Human Gene 1.0 ST platform. Quantitative real time-PCR was used to validate and replicate candidate genes (see Online Repository). DNA isolation, bisulphite treatment, and pyrosequencing analysis of methylation levels of 5 CpG sites within the VNN1 promoter for the methylation cohort is detailed in the Online Repository.
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7

Analyzing Adipose Tissue Transcriptomics in OSA

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The datasets were obtained from the Gene Expression Omnibus database (GEO) (http://www.ncbi.nlm.nih.gov/geo/). GSE135917 (Gharib et al., 2020 (link)) and GSE38792 (Gharib et al., 2013 (link)) microarray datasets were performed on the same platform GPL6244 (HuGene-1_0-st; Affymetrix Human Gene 1.0 ST). GSE135917 contained fifty subcutaneous adipose tissue samples including normal controls (n = 8) and OSA patients without treatment (n = 34). GSE38792 contained eighteen visceral adipose tissue samples including normal controls (n = 8) and OSA patients without treatment (n = 10). We used GSE135917 as the training set and GSE38792 as the testing set. The clinical and demographic characteristics of the study patients in GSE135917 was shown in Table 1.
Raw data were downloaded using the GEOquery package (Davis and Meltzer, 2007 (link)) and analyzed using the oligo package (Carvalho and Irizarry, 2010 (link)) of Bioconductor in R version 4.1.0. The data were normalized with the RMA method and probe IDs were converted into gene names according to the platform annotation information.
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8

Genome-wide Microarray Analysis Protocol

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The genome-wide microarrays Human Genome U133 Plus 2.0 and Human Gene 1.0 ST (both from Affymetrix) were labeled and analyzed as previously described [32 (link)]. These datasets have previously been deposited at the NCBI GEO database (GSE17938 and GSE36331).
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9

Heparinized Blood Profiling for Hyperlipidemia

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Heparinized blood samples (5 mL) were collected once from healthy donors and from all patients before hyperlipidemia treatment or coronary bypass grafting. Plasma (2 mL) was immediately collected by centrifugation of whole blood, and an aliquot for lipid measurement was kept at −70 °C for detection of HNP 1–3 by ELISA (Hycult Biotech). Packed blood cells were resuspended in D-PBS (Wisent Inc.) and used to isolate mononuclear cells. Approximately 2 million peripheral blood mononuclear cells (PBMCs) in TRIzol (Invitrogen™) were kept at −70 °C for gene expression profiling by DNA microarray analysis using Affymetrix GeneChip® Human Gene 1.0 ST. α-defensin expression was validated by qRT-PCR.
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10

Regulation of USP7 in Chronic Lymphocytic Leukemia

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Expression analysis of USP7 was assessed in a panel of 217 CLL relative to 12 control samples available at GEO (GSE51528) [35 (link)]. Samples were assayed on Affymetrix Human Gene 1.0 ST and have been normalized by the RMA algorithm. Differential Expression analysis has been performed using GEO2R tool. The correlation between the gene USP7 and its putative targeting miRNAs was evaluated on 210 matching samples. miRNAs were assayed on Agilent-019118 Human miRNA Microarray 2.0 G4470B available at GEO website (GSE51527) [35 (link)]. A log2 transformation has been applied to the miRNAs expression levels. The Pearson coefficient has been used to evaluate the correlation between the USP7 gene and miR-338-3p or miR181b.
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