Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Gene expression in chronic heart failure patients

ABSTRACT: Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood cells is a useful way to study disease pathobiology and may help elucidate biomarkers and molecular mechanisms of disease. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, some cell types and some indication. Accurate enumeration of the many component cell types that make up peripheral whole blood can be costly, however, and may further complicate the sample collection process. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. We present a freely-available, and open-source, multiresponse Gaussian model capable of accurately inferring the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles. The model was developed on a cohort of patients with chronic obtructive pulmonary disease and tested in chronic heart failure patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE77343 | GEO | 2016/05/25

SECONDARY ACCESSION(S): PRJNA310098

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Gene expression in chronic obstructive pulmonary disease (COPD) patients

Project description:Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood cells is a useful way to study disease pathobiology and may help elucidate biomarkers and molecular mechanisms of disease. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, some cell types and some indication. Accurate enumeration of the many component cell types that make up peripheral whole blood can be costly, however, and may further complicate the sample collection process. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. We present a freely-available, and open-source, multiresponse Gaussian model capable of accurately inferring the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles. The model was developed on a cohort of patients with chronic obstructive pulmonary disease (COPD) and tested in chronic heart failure patients.

2016-05-25 | GSE77344 | GEO

Expression data from multiple sclerosis patients in remission and relapse

Project description:Whole-genome expression of peripheral blood leukocytes was measured in 22 patients and 24 controls using the Human Gene 1.0 ST array by Affymetrix

2013-04-01 | GSE41890 | GEO

A Cell-based Systems Biology Assessment of Human Blood to Monitor Immune Responses After Influenza Vaccination

Project description:Systems biology is an approach to comprehensively study complex interactions within a biological system. Most published systems vaccinology studies have utilized whole blood or peripheral blood mononuclear cells (PBMC) to monitor the immune response after vaccination. Because human blood is comprised of multiple hematopoietic cell types, the potential for masking responses of under-represented cell populations is increased when analyzing whole blood or PBMC. To investigate the contribution of individual cell types to the immune response after vaccination, we established a rapid and efficient method to purify human T and B cells, natural killer (NK) cells, myeloid dendritic cells (mDC), monocytes, and neutrophils from fresh venous blood. Purified cells were fractionated and processed in a single day. RNA-Seq and quantitative shotgun proteomics were performed to determine expression profiles for each cell type prior to and after inactivated seasonal influenza vaccination. Our results show that transcriptomic and proteomic profiles generated from purified immune cells differ significantly from PBMC. Differential expression analysis for each immune cell type also shows unique transcriptomic and proteomic expression profiles as well as changing biological networks at early time points after vaccination. This cell type-specific information provides a more comprehensive approach to monitor vaccine responses.

2015-01-08 | PXD001657 | Pride

RNA-Seq profiling of 29 immune cell types and peripheral blood mononuclear cells

Project description:We performed RNA-Seq transcriptome profiling on 29 immune cell types consituting peripheral blood mononuclear cells (PBMCs) sorted from 4 Singaporean-Chinese individuals (S4 cohort). We also performed RNA-Seq and microarray transcriptome profiling of PBMCs from an extended cohort of 13 individuals (S13 cohort). The data was used first to characterize the transcriptomic signatures and relationships among the 29 immune cell types. Then we explored the difference in mRNA composition in terms of transcripts proportions and abundance. Lastly, we performed deep deconvolution for both microarray and RNA-Seq technologies.

2019-02-06 | GSE107011 | GEO

Microarray profiling peripheral blood mononuclear cells

2019-02-06 | GSE106898 | GEO

Cord blood buffy coat DNA methylation is comparable to whole cord blood methylation

Project description:Cord blood DNA methylation is associated with numerous health outcomes and environmental exposures. Whole cord blood DNA reflects all nucleated blood cell types, while centrifuging whole blood separates red blood cells by generating a white blood cell buffy coat. Both sample types are used in DNA methylation studies. Cell types have unique methylation patterns and processing can impact cell distributions, which may influence comparability. To evaluate differences in cell composition and DNA methylation between buffy coat and whole cord blood samples, cord blood DNA methylation was measured with the Infinium EPIC BeadChip (Illumina) in 8 individuals, each contributing buffy coat and whole blood samples.

2018-01-01 | GSE103189 | GEO

Transcription profiling of human CD34+ treated with EPO at day 7 and 14 to investigate the effect of EPO on erythroid maturation.

Project description:To study transcriptional profiles in human CD34+ cell in EPO condition on day 7 and day 14. Experiment Overall Design: After obtaining informed consent, human CD34+ cells were isolated in high purity from the peripheral blood of normal human volunteers. The cells were cultured at a concentration of 104 -105 cells/mL in medium supplemented with 4 U/mL EPO (Amgen, Thousand Oaks, CA) as described previously.1 Cells were enumerated using an electronic cell counter (Coulter, Hialeah, FL). Adult and fetal hemoglobin was analysed by HPLC. RNA was extracted using QIAshredder and Rneasy Minikit.

2008-06-15 | E-GEOD-7874 | biostudies-arrayexpress

Peripheral blood transcriptome responses to Salmonella Typhimurium in pigs differing in Salmonella shedding phenotypes

Project description:Salmonella species infect many vertebrate species, and pigs colonized with Salmonella enterica serovar Typhimurium (ST) are usually asymptomatic, making detection of these Salmonella-carrier pigs difficult. The variable fecal shedding of this gram-negative bacteria in such pigs is an important cause of foodborne illness and zoonotic disease. To investigate gene pathways and biomarkers associated with the variance in Salmonella shedding following experimental inoculation, we have initiated the first analysis of the whole blood transcriptional response induced by Salmonella. A population of pigs (n=40) was inoculated with ST and the peripheral blood and feces were collected between 2 and 20 days post-inoculation. Two groups of pigs with either low shedding (LS) or persistent shedding (PS) phenotypes were identified. The global transcriptional changes in response to ST inoculation were identified by Affymetrix Genechip?analysis of peripheral blood RNA at day 0 and day 2 post-inoculation.

2011-05-28 | GSE27000 | GEO

Deconvolution of whole blood DNA methylomes reveals immune cell type-specific differential methylation in Multiple Sclerosis

Project description:Whole blood is a highly convenient and informative tissue from which to sample DNA and RNA in epigenomic and functional genomic studies, but it is comprised of multiple distinct cell types and this complexity significantly impairs our ability to interpret downstream differential methylation and/or differential expression results. In this multiple sclerosis (MS)-focused study we utilised an application of current statistical deconvolution methods to interrogate whole blood DNA methylation data thereby enabling the methylome of several immune cell types to be analysed independently. Methylome profiling on cell type-purified blood samples revealed optimal CpG sets for use as robust immune cell markers in the statistical deconvolution process. We show that it is possible to identify differentially methylated (DM) loci in a cell type specific manner using statistical deconvolution. Finally, we demonstrate that deconvolution improved the biological relevance and interpretability of our DM results, significantly enhancing concordance of the identified DM loci with loci previously shown to be genetically or epigenetically associated with MS.

2017-06-30 | GSE88824 | GEO

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

Project description:High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomics studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Several different quantification approaches have been proposed, ranging from simple counting of reads overlapping given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of both performance and interpretability. We also illustrate that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices, and incorporation of transcript-level abundance estimates improves the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package (tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.

2015-12-10 | E-MTAB-4119 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data