Bovine blood gamma/delta T cell stimulation study
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ABSTRACT:
ORGANISM(S): Bos taurus
SUBMITTER: Jin Liu
PROVIDER: E-GEOD-1870 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Blood gamma/delta T cells were purfiied from holstein calves less than six months old. Blood from 4 individual calves were pooled and gamma/delta T cells were purified by immunomagnetic separation, rested in RPMI medium for 12h, and underwent ConA & hIL-2 stimulation. Total RNAs were extracted by using Trizol reagents. 15 ug of total RNA was used for cDNA synthesis and microarray hybridization. Table 1: regulated genes These genes were identified, in GeneSpring software, via filtering of genes with relative expression ratio of 1.5 and over or 0.667 or below, equivalent to up- and down-regulation by fold change 1.5 or more, in both experiments. Table 2: down-regulated genes These genes were identified, in GeneSpring software, via filtering of genes with relative expression ratio of 0.667 or below, equivalent to down-regulation by fold change 1.5 or more, in both experiments. The avergae fold change was then converted to negative value. Gene annotation was performed by using GeneSpring's "Build Simplified Ontology" constructor. Table 3: up-regulated genes These genes were identified, in GeneSpring software, via filtering of genes with relative expression ratio of 1.5 or over, equivalent to up-regulation by fold change 1.5 or more, in both samples. Gene annotation was performed by using GeneSpring's "Build Simplified Ontology" constructor. Keywords: other
2004-10-28 | GSE1870 | GEO
Project description:Neutrophil gene transcription following lipopolysaccharide exposure. Microarray analysis of lipopolysaccharide-treated human neutrophils. Neutrophils respond to infection by degranulation, release of reactive oxygen intermediates, and secretion of chemokines and cytokines; however, activation of neutrophil transcriptional machinery has been little appreciated. Recent findings suggest that gene expression may represent an additional neutrophil function after exposure to lipopolysaccharide (LPS). We performed microarray gene expression analysis of 4,608 mostly nonredundant genes on LPS-stimulated human neutrophils. Analysis of three donors indicated some variability but also a high degree of reproducibility in gene expression. Twenty-eight verifiable, distinct genes were induced by 4 h of LPS treatment, and 13 genes were repressed. Genes other than cytokines and chemokines are regulated; interestingly, genes involved in cell growth regulation and survival, transcriptional regulation, and interferon response are among those induced, whereas genes involved in cytoskeletal regulation are predominantly repressed. In addition, we identified monocyte chemoattractant protein-1 as a novel LPS-regulated chemokine in neutrophils. Included in these lists are five clones with no defined function. These data suggest molecular mechanisms by which neutrophils respond to infection and indicate that the transcriptional potential of neutrophils is greater than previously thought.
2010-06-05 | E-GEOD-105 | biostudies-arrayexpress
Project description:Experimental studies confirmed that seleium induced a global change of gene expression in HuH7 cells. Several novally induced secretory growth factors may contribute to the autocrine mode of growth elicited by selenium treatment. Keywords: Treatment HuH7 were cultured in 0.1 μM sodium selenite-containing medium for 15 passages (SELT) or in serum-free medium for 30 h (SF30) or in the regular complete medium containing 10 % fetal calf serum (HUH7) for 30 h. The total RNA were reverse transcried and hybridized to Human 8K-1 (Egenomix Technology Corp., Taiwan). The hybridization time and temperature was 16 h and 62â??, respectively. SELT and HUH7 samples were labeled with Cy5 while SF30 sample was labeled with Cy3. Local background correction and interchip global normalization were accomplished by GenePix Pro 5.0 (Molecular Devices, Sunnyvale, CA ). Genes with differential up- or down-regulation by 1.5 fold with respect to the SF30 cells were obtained using GeneSpring software 7.0 (Silicon Genetics, Agilent Technologies).
2010-06-30 | E-GEOD-4593 | biostudies-arrayexpress
Project description:human bronchial smooth muscle cells were growth arrested. One group was stimulated with 10ng/mL of IL-1beta, TNF-alfa, and gamma IFN for 20 hours. For every cDNA sample, 2 dyeflipped slides were hybridized (these have the same date in the titles). All cy3 (channel 2) measurements were normalized to the dye coupling control before a ratio of the values was taken.
2010-06-09 | E-GEOD-1288 | biostudies-arrayexpress
Project description:Data processing included: 1. Filtering of those bad quality image spots (these includes alterations on the shape or hybridization) 2. Filtering of low intensity which was done flagging with -50 those spots whose feature intensity was lower in any of both channels than the average of the local background in the corresponding channel for all spots. However, those genes whose feature intensity in one channel was under the limit but in the other channel was 5 times over limit were considered as on-off genes and were not filtered. 3. Filtering of low reproducibility intrachip replicated spots. Normal distribution was created with the value obtained from the formula: log2(Rax1/Rb) which means logarithm (two-based) of one ratio multiplied by the inverse of its replicate. This value for each gene shoud keep between the range of average ± 3SD. Those genes out of these range are flagged as -25 4. Normalization of ratios was done by Lowess mathematical method. Correction factor was 0.33
2010-06-10 | E-GEOD-2105 | biostudies-arrayexpress
Project description:Data processing included: 1. Filtering of those bad quality image spots (these includes alterations on the shape or hybridization) 2. Filtering of low intensity which was done flagging with -50 those spots whose feature intensity was lower in any of both channels than the average of the local background in the corresponding channel for all spots. However, those genes whose feature intensity in one channel was under the limit but in the other channel was 5 times over limit were considered as on-off genes and were not filtered. 3. Filtering of low reproducibility intrachip replicated spots. Normal distribution was created with the value obtained from the formula: log2(Rax1/Rb) which means logarithm (two-based) of one ratio multiplied by the inverse of its replicate. This value for each gene shoud keep between the range of average ± 3SD. Those genes out of these range are flagged as -25 4. Normalization of ratios was done by Lowess mathematical method. Correction factor was 0.33 This SuperSeries is composed of the following subset Series: GSE960: wild type 0 hours exposure to congo red GSE961: wild type 2 hours exposure to congo red GSE962: wild type 4 hours exposure to congo red GSE963: wild type 6 hours exposure to congo red GSE964: slt2 mutant 4 hours exposure to congo red GSE965: rlm1 mutant 4 hours exposure to congo red GSE966: wild type two hours exposure to zymolyase Keywords: SuperSeries Refer to individual Series
2010-06-09 | E-GEOD-959 | biostudies-arrayexpress
Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
2022-11-24 | E-MTAB-9167 | biostudies-arrayexpress
Project description:LncRNA expression profiling for liver tissues of mice fed for a normal diet (NFD, 3mice) and a high-fat diet (HFD, 3mice) Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
2022-11-23 | E-MTAB-8730 | biostudies-arrayexpress
Project description:Human histomonocytic U937 cells exhibit macrophage-like properties after phorbol ester stimulation. Accumulating evidence demonstrated that remarkable number of transcripts changed in their amount at total RNA levels. However, post-transcriptional regulation has not been fully elucidated in this model. Thus, we compared expression profiles between polysomal and cytoplasmic RNAs before and after phorbol 12-myristate 13-acetate stimulation (48h, 32nM). Table 1: Detailed description of the expression data of human histomonocytic cell line U937 cells. Data of total cytoplasmic and polysomal fractions before and after PMA stimulation is shown. This table contains all spot data except: 1) saturated spots, 2) undetectable spots, 3) stained spots, and 4) spots only detected in less than one samples. Column A: Gene symbol (âI_xxxxxxâ is corresponding to EST clones); Column B: Genbank accession no. Column C: Description; Column D: Average value of expression levels in total cytoplasmic fraction in the absence of PMA; Column E: Average value of expression levels in total cytoplasmic fraction in the presence of PMA (32nM, 48h); Column F: Average value of expression levels in polysomal fraction in the absence of PMA; Column G: Average value of expression levels in polysomal fraction in the presence of PMA; Column H: Expression ratio of polysome, PMA (-) to cytoplasm, PMA(-); Column I: Expression ratio of polysome, PMA (+) to cytoplasm, PMA(+); Column J: Expression ratio of cytoplasm, PMA (+) to cytoplasm, PMA(-); Column K: Expression ratio of polysome, PMA (+) to polysome, PMA(-); Column L: Different expression between polysome, PMA (-) and cytoplasm, PMA(-): different=1; Column M: Different expression between polysome, PMA (+) and cytoplasm, PMA(+): different=1; Column N: Different expression between cytoplasm, PMA (+) and cytoplasm, PMA(-): different=1; Column O: Different expression between polysome, PMA (+) and polysome, PMA(-): different=1; ; Table 2: Candidates post-transcriptionally regulated by PMA. We extracted 105 transcripts whose expression levels were altered only in either fraction accompanied by changes in polysome/cytoplasm expression ratio. Column A: Gene symbol (âI_xxxxxxâ is corresponding to EST clones); Column B: Genbank accession no. Column C: Description; Column D: Average value of expression levels in total cytoplasmic fraction in the absence of PMA; Column E Average value of expression levels in total cytoplasmic fraction in the presence of PMA; Column F: Average value of expression levels in polysomal fraction in the absence of PMA; Column G: Average value of expression levels in polysomal fraction in the presence of PMA; Column H: Expression ratio of polysome, PMA (-) to cytoplasm, PMA(-); Column I: Expression ratio of polysome, PMA (+) to cytoplasm, PMA(+); Column J: Expression ratio of cytoplasm, PMA (+) to cytoplasm, PMA(-); Column K: Expression ratio of polysome, PMA (+) to polysome, PMA(-)
2008-06-11 | E-GEOD-1783 | biostudies-arrayexpress
Project description:This series includes the four major subtypes of pituitary adenomas and normal post-mortem pituitary tissue Data Transformation Using Affymetrix Microarray Suite 5.0 global scaling was applied to the quantification data to adjust the average recorded to a target intensity of 100. Data were then exported into the bioinformatics software GeneSpring 6.0 (Silicon Genetics, Redwood City, CA) for further analysis. Data normalization was performed to scale the data so that the average intensity value on each array was 1 by dividing each expression value by the median of the expression levels on each chip. The individual gene expression levels for each of the 4 pituitary adenoma subtype arrays was divided by the expression level in the normal pituitary array. Thus, the data are presented as relative to the expression in normal pituitary tissue. Filtering was then performed to identify genes over-expressed or under-expressed at least 2.0 fold in tumours compared to normal pituitary. TABLE 1: The genes / ESTs differentially overexpressed >= 2-fold in at least one pituitary adenoma subtype compared to normal pituitary. Negative values represent underexpression. Where genes are represented by more than one probe set, individual probe data sets are given. TABLE 2: The genes / ESTs differentially underexpressed >= 2-fold in at least one pituitary adenoma subtype compared to normal pituitary. Negative values represent underexpression. Where genes are represented by more than one probe set, individual probe data sets are given. Keywords = pituitary tumor Keywords: other
2005-05-23 | GSE2175 | GEO