Transcription profiling of human healthy peripheral blood cells isolated from male participants at rest
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Sandy Mosig
PROVIDER: E-GEOD-5237 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Exercise has an measureable impact on gene expression profiles of white blood cells. While subpopulations of white blood cells undergo specific changes in answer to physical load we focused on pan T-lymphocytes and Monocytes. These cells are in direct contact to organs influenced by exercise and additionally should be affected by alterations of nutrient and gas composition in blood, which occur as a consequence of exercise. We screened the gene expression profiles of top athletes at the beginning and the end of the Tour de France 2005 and at at the annual rest period in order to find functional processes that are differentially expressed in answer to the longtime and high load training and competition. Therefor we filtered out genes with significant changes in gene expression more than 1.5fold between two of the three conditions and build overlap lists of this list and GO (gene ontology) lists and KEGG (kyoto encyclopedia of genes and genomes) lists. We observed a strong decline in immune functions and an equal decline in metabolic processes. Experiment Overall Design: 12 professional cyclists from two german top teams gave written agreement to participate to this study. We collected 12ml of whole blood at each time point and isolated T-lymphocytes out of 4ml and monocytes out of 8ml whole blood. The experimental groups are divided into rest (6 replicates in Tcells/ 7 replicates in Monocytes), start (10 replicates in each cell type) and final (8 replicates in Tcells/ 9 replicates in Monocytes). This means that we collected samples at the beginning and the end of the Tour de France and during the annual rest period of the athletes.
2008-06-13 | E-GEOD-5105 | biostudies-arrayexpress
Project description:White blood cells (WBCs) express tens of thousands of genes. Their expression levels are modified by genetic and external factors. In further study we found that gene expression profiles of these cellls can serve as surrogate markers for monitoring exercise and training load.The purpose of the present study was to investigate the effects of acute exercise on gene expression profiles (GEPs) of PBMCs and T-lymphocytes in order to re-detect the patterns in subpopulations. The use of WBC subpopulations is necessary because of the well known subpopulation shifts that occur during physical activities. Three male probands performed an exhaustive treadmill test (ET) at 80% of their VO2max. PBMCs were isolated using BD Vacutainer CPT tubes containig Ficoll. T-lymphocytes were isolated from the PBMCs via rosetting technology. Gene expression profiles were measured using the Affymetrix GeneChip® technology. After scaling, normalisation, and filtering groupwise and pairwise comparisons of gene expression intensities were performed. We found that sorting increases the detection sensitivity and enables the researcher to observe regulation that is hidden in heterogenous populations. We therefore suggest not to work on mixed nbut instead on sorted cells when performing gene expression analyses. Experiment Overall Design: Three healthy male probands executed an exhaustive treadmill test (80% VO2max) until individual exhaustion. Blood samples (16ml) were drawn before and one hour past the tests. PBMCs were isolated using the Ficoll density centrifugation methode. T-lymphocytes were isolated by adding rosetting anibodies zo the cells before centrifugation. Cells were lysed using Trizol and worked up with Qiagen RNeasy Micro columns. RNA was processed and hybridised on U133A 2.0 Affymetrix GeneChips. Samples were grouped and gene expression changes were detected via multiple algorithms included in GeneSpring 7.2 (Agilent). Experiment Overall Design: The four groups contained three samples related to the cell type "PBMC" or "T-cell" and "before exercise" or "1hour past exercise". TTest, GOs and KEGG classification, principal component analysis and hierarchical clstering were used to scan for gene expression profiles induced through the different exercise conditions.
2009-04-23 | E-GEOD-6053 | biostudies-arrayexpress
Project description:Lung cancers are a heterogeneous group of diseases with respect to biology and clinical behavior. So far, diagnosis and classification are based on histological morphology and immunohistological methods for discrimination between two main histologic groups: small cell lung cancer (SCLC) and non-small cell lung cancer which account for 20% and 80% of lung carcinomas, respectively. While SCLCs express properties of neuroendocrine cells, NSCLCs, which are divided into the three major subtypes adenocarcinoma, squamous cell carcinoma and dedifferentiated large cell carcinoma, show different characteristics such as the expression of certain keratins or production of mucin and lack neuroedocrine differentiation. The molecular pathogenesis of lung cancer involves the accumulation of genetic und epigenetic alterations including the activation of proto-oncogenes and inactivation of tumor suppressor genes which are different for lung cancer subgroups. The development of microarray technologies opened up the possibility to quantify the expression of a large number of genes simultaneously in a given sample. There are several recent reports on expression profiling on lung cancers but the analysis interpretation of the results might be difficult because of the heterogeneity of cellular components. A contamination of the tumor sample with normal epithelia, blood vessels, stromal cells, leucocytes and tumor necrosis may confound the true expression profile of the tumor. The use of laser capture microdissection (LCM) greatly improves the sample preparation for microarray expression analysis. Consequently, we used advanced technology including LCM and microarray analysis. In detail, we examined gene expression profiles of tumor cells from 29 previously untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), 9 small cell lung cancer (SCLC)) in comparison to normal lung tissue (LT) of 5 control patients without tumor. Bronchoscopical biopsies from the primary lung tumor were taken before treatment. Biopsies were cut into 8µm sections and from each section cancer cells were isolated using laser capture microdissection in order to obtain pure samples of tumor cells. Total RNA was extracted, reversely transcribed, in-vitro transcribed, labelled and hybridized to the array. For expression analysis, microarrays covering 8793 defined genes (Human HG Focus Array, Affymetrix) were used. Following quality control, array data were normalized and analysed for significant differences using variance stabilizing transformation (VSN) and significance analysis of microarrays (SAM), respectively. Based on differentially expressed genes cancer samples could be clearly separated from non cancer samples using hierarchical clustering. Comparing AC, SCC and SCLC with normal lung tissue, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed with an estimated false discovery rate < 2.6%. Each histological subtype showed a distinct expression profile. Further, using a genetic programming approach we constructed a classificator to discriminate AC, SCC, NT and SCLC. To this end, the 50 genes with the greatest signal-to-noise ratio were selected to train the classificator. By leave-one-out cross validation all 34 samples were correctly classified in this training set. In order to validate the 50-gene-classificator on a test set, further 13 microdissected lung cancer samples were used and correctly classified in concordance to pathologic finding. In conclusion, the different lung cancer subtypes have distinct molecular phenotypes which reflect biological characteristics of the tumor cells and which might be the basis for development of targeted therapy. Moreover, gene expression profiling and genetic programming is a suitable tool for classification and discrimination of different histological subtypes in lung cancer in comparison to normal lung tissue. Experiment Overall Design: Comparison of gene expression profiles of normal lung tissues, adenocarcinomas, squamous cell carcinomas and small cell lung cancers.
2008-06-14 | E-GEOD-6044 | biostudies-arrayexpress
Project description:To identify genes associated with lung cancer progression, we examined gene expression profiles of tumor cells from 20 patients with primary, untreated non-small cell lung cancer (10 adenocarcinomas (AC) and 10 squamous cell carcinomas (SCC)) in comparison to lung tissue of 23 patients with stage IIIB or stage IV non-small cell lung cancer (15 AC and 8 SCC). Bronchoscopical biopsies from patient with recurrent lung tumor were taken after initial treatment. Cancer cells were isolated using laser capture microdissection in order to obtain pure samples of tumor cells. For expression analysis, microarrays covering 8793 defined genes (Human HG Focus Array, Affymetrix) were used. Array data were normalized and analysed for significant differences using variance stabilizing transformation (VSN) and significance analysis of microarrays (SAM), respectively. Genes were considered to be up- or down-regulated when the ratio between primary and recurrent tumor samples were at least 1.5-fold differentially expressed with an estimated false discovery rate: < 5%. Based on differentially expressed genes, primary cancer samples could be separated from recurrent tumor samples. We identified 115 and 124 significantly regulates genes in AC and SCC, respectively. For example, in recurrent AC we found increased expression of genes related to the wingless (FZD6, RYK, MYC) and calcium (CALM1, ATB2B1, S100A2) signalling pathways which might play a role in metastasis of tumor cells. Other differentially expressed genes were related to cell cycle (CCND1, CDK2), transcription factors (TTF1, TAF2, YY1), nuclear mRNA splicing and mRNA processing (SFRS1, HNRPL), protein-nucleus import (NUTF2, KPNB1, NUP50) and chromatin modification (HIST1H4C, SMARCC1). In SCC, we found an increased expression of CTNNB1, an important mediator in wingless signalling pathway. Among the down-regulated genes in SCC, the utmost fraction belonged to genes coding for ubiquitin mediated proteolysis (UCHL1, PSMA3, COPS6) and ribosomal proteins (RPS26, RPL7A, RPS15). Other down regulated genes were related to transcription factors (TCEA2, TAF10), nuclear mRNA splicing and mRNA processing (SNRPD2, HNRPM). In conclusion, a distinct pattern of gene expression is found during the progression from primary carcinoma to recurrent NSCLC. Our microarray-based expression profiling revealed interesting novel candidate genes and pathways that may contribute to lung cancer progression. Experiment Overall Design: - 20 patients with primary, untreated non-small cell lung cancer (10adenocarcinomas (AC) and 10 squamous cell carcinomas (SCC)) in comparison to lung tissue of 23 patients with stage IIIB or stage IV non-small cell lung cancer (15 AC and 8 SCC) Experiment Overall Design: - Human HG Focus Array, Affymetrix) were used Experiment Overall Design: - Array data were normalized and analysed for significant differences using variance stabilizing transformation (VSN) and significance analysis of microarrays (SAM) Experiment Overall Design: - Genes were considered to be up- or down-regulated when the ratio between primary and recurrent tumor samples were at least 1.5-fold differentially expressed with an estimated false discovery rate: < 5%
2008-06-15 | E-GEOD-7880 | biostudies-arrayexpress
Project description:Blood genomic profiling has been applied to disorders of the blood and various organ systems including brain to elucidate disease mechanisms and identify surrogate disease markers. Since most studies have not examined specific cell types, we performed a preliminary genomic survey of major blood cell types from normal individuals using microarrays. CD4+ T cells, CD8+ T cells, CD19+ B cells, CD56+ natural killer cells, and CD14+ monocytes were negatively selected using the RosetteSep antibody cocktail, while polymorphonuclear leukocytes were separated with density gradient media. Genes differentially expressed by each cell type were identified. To demonstrate the potential use of such cell subtype-specific genomic expression data, a number of the major genes previously reported to be regulated in ischemic stroke, migraine, and Tourette syndrome are shown to be associated with distinct cell populations in blood. These specific gene expression, cell-type-related profiles will need to be confirmed in larger data sets and could be used to study these and many other neurological diseases. This study includes data from major blood cell types from normal individuals. These cell types include CD4+ T cells, CD8+ T cells, CD19+ B cells, CD56+ natural killer cells, CD14+ monocytes and PMN (polymorphonuclear cells). The data were hybridized on 3 separate batches of gene chips.
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2008-06-15 | E-GEOD-6638 | biostudies-arrayexpress
Project description:Ribonucleoprotein immunoprecipitation microarray (RIp-chip) study using various myosin proteins and myosin-specific chaperones from the fission Schizosaccaromyces pombe. Two strains were used. In rng3TAP the rng3 protein has been tagged with TAP to allow its detection and immunoprecipitation. The TAP strain expresses the TAP sequence alone (without being attached to an other protein)
2008-12-23 | E-TABM-516 | biostudies-arrayexpress