Dataset Information

Gene expression analysis of whole blood, peripheral blood mononuclear cells, and lymphoblastoid cell lines from the Framingham Heart Study.

ABSTRACT: Despite a growing number of reports of gene expression analysis from blood-derived RNA sources, there have been few systematic comparisons of various RNA sources in transcriptomic analysis or for biomarker discovery in the context of cardiovascular disease (CVD). As a pilot study of the Systems Approach to Biomarker Research (SABRe) in CVD Initiative, this investigation used Affymetrix Exon arrays to characterize gene expression of three blood-derived RNA sources: lymphoblastoid cell lines (LCL), whole blood using PAXgene tubes (PAX), and peripheral blood mononuclear cells (PBMC). Their performance was compared in relation to identifying transcript associations with sex and CVD risk factors, such as age, high-density lipoprotein, and smoking status, and the differential blood cell count. We also identified a set of exons that vary substantially between participants, but consistently in each RNA source. Such exons are thus stable phenotypes of the participant and may potentially become useful fingerprinting biomarkers. In agreement with previous studies, we found that each of the RNA sources is distinct. Unlike PAX and PBMC, LCL gene expression showed little association with the differential blood count. LCL, however, was able to detect two genes related to smoking status. PAX and PBMC identified Y-chromosome probe sets similarly and slightly better than LCL.

SUBMITTER: Joehanes R

PROVIDER: S-EPMC3289123 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Gene expression analysis of whole blood, peripheral blood mononuclear cells, and lymphoblastoid cell lines from the Framingham Heart Study.

Joehanes Roby R Johnson Andrew D AD Barb Jennifer J JJ Raghavachari Nalini N Liu Poching P Woodhouse Kimberly A KA O'Donnell Christopher J CJ Munson Peter J PJ Levy Daniel D

Physiological genomics 20111101 1

Despite a growing number of reports of gene expression analysis from blood-derived RNA sources, there have been few systematic comparisons of various RNA sources in transcriptomic analysis or for biomarker discovery in the context of cardiovascular disease (CVD). As a pilot study of the Systems Approach to Biomarker Research (SABRe) in CVD Initiative, this investigation used Affymetrix Exon arrays to characterize gene expression of three blood-derived RNA sources: lymphoblastoid cell lines (LCL) ...[more]

PMID: 22045913

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Project description:BackgroundHeart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.MethodsWe collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (? 4 points), intermediate (5-11), and high (? 12) risk categories correlating with GEP.ResultsResults indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.ConclusionGEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.

Project description:Gene expression profiling using blood samples is a valuable tool for biomarker discovery in clinical studies. Different whole blood RNA collection and processing methods are highly variable and might confound comparisons of results across studies. The main aim of the current study is to compare how blood storage, extraction methodologies, and the blood components themselves may influence gene expression profiling. Whole blood and peripheral blood mononuclear cell (PBMC) samples were collected in triplicate from five healthy donors. Whole blood was collected in RNAgard® and PAXgene® Blood RNA Tubes, as well as in collection tubes with anticoagulants such as dipotassium ethylenediaminetetraacetic acid (K2EDTA) and Acid Citrate Dextrose Solution A (ACD-A). PBMCs were separated using sodium citrate Cell Preparation Tubes (CPT™), FICOLL™, magnetic separation, and the LeukoLOCK™ methods. After blood collection, the LeukoLOCK™, K2EDTA and ACD-A blood tubes were shipped overnight using cold conditions and samples from the rest of the collection were immediately frozen with or without pre-processing. The RNA was isolated from whole blood and PBMCs using a total of 10 different experimental conditions employing several widely utilized RNA isolation methods. The RNA quality was assessed by RNA Integrity Number (RIN), which showed that all PBMC procedures had the highest RIN values when blood was stabilized in TRIzol® Reagent before RNA extraction. Initial data analysis showed that human blood stored and shipped at 4°C overnight performed equally well when checked for quality using RNA integrity number when compared to frozen stabilized blood. Comparisons within and across donor/method replicates showed signal-to-noise patterns which were not captured by RIN value alone. Pathway analysis using the top 1000 false discovery rate (FDR) corrected differentially expressed genes (DEGs) showed frozen vs. cold shipping conditions greatly impacted gene expression patterns in whole blood. However, the top 1000 FDR corrected DEGs from PBMCs preserved after frozen vs. cold shipping conditions (LeukoLOCK™ preserved in RNAlater®) revealed no significantly affected pathways. Our results provide novel insight into how RNA isolation, various storage, handling, and processing methodologies can influence RNA quality and apparent gene expression using blood samples. Careful consideration is necessary to avoid bias resulting from downstream processing. Better characterization of the effects of collection method idiosyncrasies will facilitate further research in understanding the effect of gene expression variability in human sample types.

Dataset Information

Gene expression analysis of whole blood, peripheral blood mononuclear cells, and lymphoblastoid cell lines from the Framingham Heart Study.

Publications

Gene expression analysis of whole blood, peripheral blood mononuclear cells, and lymphoblastoid cell lines from the Framingham Heart Study.

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