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Impact of blood collection and processing on peripheral blood gene expression profiling in type 1 diabetes.

ABSTRACT: The natural history of type 1 diabetes (T1D) is challenging to investigate, especially as pre-diabetic individuals are difficult to identify. Numerous T1D consortia have been established to collect whole blood for gene expression analysis from individuals with or at risk to develop T1D. However, with no universally accepted protocol for their collection, differences in sample processing may lead to variances in the results. Here, we examined whether the choice of blood collection tube and RNA extraction kit leads to differences in the expression of genes that are changed during the progression of T1D, and if these differences could be minimized by measuring gene expression directly from the lysate of whole blood.Microarray analysis showed that the expression of 901 genes is highly influenced by sample processing using the PAXgene versus the Tempus system. These included a significant number of lymphocyte-specific genes and genes whose expression has been reported to differ in the peripheral blood of at-risk and T1D patients compared to controls. We showed that artificial changes in gene expression occur when control and T1D samples were processed differently. The sample processing-dependent differences in gene expression were largely due to loss of transcripts during the RNA extraction step using the PAXgene system. The majority of differences were not observed when gene expression was measured in whole blood lysates prepared from blood collected in PAXgene and Tempus tubes.We showed that the gene expression profile of samples processed using the Tempus system is more accurate than that of samples processed using the PAXgene system. Variation in sample processing can result in misleading changes in gene expression. However, these differences can be minimized by measuring gene expression directly in whole blood lysates.

SUBMITTER: Yip L

PROVIDER: S-EPMC5563008 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Impact of blood collection and processing on peripheral blood gene expression profiling in type 1 diabetes.

Yip Linda L Fuhlbrigge Rebecca R Atkinson Mark A MA Fathman C Garrison CG

BMC genomics 20170818 1

<h4>Background</h4>The natural history of type 1 diabetes (T1D) is challenging to investigate, especially as pre-diabetic individuals are difficult to identify. Numerous T1D consortia have been established to collect whole blood for gene expression analysis from individuals with or at risk to develop T1D. However, with no universally accepted protocol for their collection, differences in sample processing may lead to variances in the results. Here, we examined whether the choice of blood collect ...[more]

PMID: 28821222

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Project description:Background and Objectives: Visceral obesity is associated with chronic low-grade inflammation that predisposes to metabolic syndrome. Indeed, infiltration of adipose tissue with immune-inflammatory cells, including 'classical' inflammatory M1 and anti-inflammatory 'alternative' M2 macrophages, causes the release of a variety of bioactive molecules, resulting in the metabolic complications of obesity. This study examined the relative expression of macrophage phenotypic surface markers, cholesterol efflux proteins, scavenger receptors, and adenosine receptors in human circulating peripheral blood mononuclear cells (PBMCs), isolated from patients with type 2 diabetes mellitus (T2DM), with the aim to phenotypically characterize and identify biomarkers for these ill-defined cells. Materials and Methodology: PBMCs were isolated from four groups of adults: Normal-weight non-diabetic, obese non-diabetic, newly diagnosed with T2DM, and T2DM on metformin. The mRNA expression levels of macrophage phenotypic surface markers (interleukin-12 (IL-12), C-X-C motif chemokine ligand 10 (CXCL10), C-C motif chemokine ligand 17 (CCL17), and C-C motif receptor 7 (CCR7)), cholesterol efflux proteins (ATP-binding cassette transporter-1 (ABCA1), ATP binding cassette subfamily G member 1 (ABCG1), and sterol 27-hydroxylase (CYP27A)), scavenger receptors (scavenger receptor-A (SR-A), C-X-C motif ligand 16 (CXCL16), and lectin-like oxidized LDL receptor-1 (LOX-1)), and adenosine receptors (adenosine A2A receptor (A2AR) and adenosine A3 receptor (A3R)) were measured using qRT-PCR. Results: In PBMCs from T2DM patients, the expression of IL-12, CCR7, ABCA1, and SR-A1 was increased, whereas the expression of CXCL10, CCL17, ABCG1,27-hydroxylase, LOX-1, A2AR and A3R was decreased. On the other hand, treatment with the antidiabetic drug, metformin, reduced the expression of IL-12 and increased the expression of 27-hydroxylase, LOX-1, CXCL16 and A2AR. Conclusions: PBMCs in the circulation of patients with T2DM express phenotypic markers that are different from those typically present in adipose tissue M1 and M2 macrophages and could be representative of metabolically activated macrophages (MMe)-like cells. Our findings suggest that metformin alters phenotypic markers of MMe-like cells in circulation.

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.

Project description:BackgroundGene expression profiling from peripheral blood 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 study was to compare genome-wide gene expression profiles obtained from the two widely used commercially available whole blood RNA collection systems - PAXgene™ and Tempus™ tubes. Comparisons of present call rates, variances, correlations and influence of globin reduction across the two collection systems was performed using in vivo glucocorticoid stimulation in 24 peripheral blood samples from three individuals.ResultsRNA quality, yield and numbers of detected transcripts from the two RNA collection systems was comparable, with no significant differences between the tube types. Globin reduction resulted in a significant increase in present call rates (p = 8.17 × 10-5 and p = 1.95 × 10-3 in PAXgene™ and Tempus™ tubes respectively) and significant decrease in gene expression variance in both RNA collection tubes (p = 0.0025 and p = 0.041 in PAXgene™ and Tempus™ tubes respectively). Comparisons of glucocorticoid receptor-stimulated gene expression profiles between the two collection tube systems revealed an overlap of only 17 to 54%, depending on the stringency level of the statistical thresholds. This overlap increased by 1-8% when the RNA samples were processed to remove the globin mRNA.ConclusionRNA obtained from PAXgene™ and Tempus™ tubes was comparable in terms of quality and yield, however, detectable gene expression changes after glucocorticoid receptor stimulation were distinct, with an overlap of only up to 46% between the two collection systems. This overlap increased to 54% when the samples were depleted of globin mRNA and drastically reduced to 17-18% when only gene expression differences with a fold change greater than 2.0 were assessed. These results indicate that gene expression profiles obtained from PAXgene™ and Tempus™ differ drastically and should not be analyzed together. These data suggest that researchers must exert caution while interpreting expression profiles obtained through different RNA collection tubes.

Project description:OBJECTIVES:The aim of present study was to find genetic pathways activated during infection with bacterial meningitis (BM) and potentially influencing the course of the infection using genome-wide RNA expression profiling combined with pathway analysis and functional annotation of the differential transcription. METHODS:We analyzed 21 patients with BM hospitalized in 2008. The control group consisted of 18 healthy subjects. The RNA was extracted from whole blood, globin mRNA was depleted and gene expression profiling was performed using GeneChip Human Gene 1.0 ST Arrays which can assess the transcription of 28,869 genes. Gene expression profile data were analyzed using Bioconductor packages and Bayesian modeling. Functional annotation of the enriched gene sets was used to define the altered genetic networks. We also analyzed whether gene expression profiles depend on the clinical course and outcome. In order to verify the microarray results, the expression levels of ten functionally relevant genes with high statistical significance (CD177, IL1R2, IL18R1, IL18RAP, OLFM4, TLR5, CPA3, FCER1A, IL5RA, and IL7R) were confirmed by quantitative real-time (qRT) PCR. RESULTS:There were 8569 genes displaying differential expression at a significance level of p < 0.05. Following False Discovery Rate (FDR) correction, a total of 5500 genes remained significant at a p-value of < 0.01. Quantitative RT-PCR confirmed the differential expression in 10 selected genes. Functional annotation and network analysis indicated that most of the genes were related to activation of humoral and cellular immune responses (enrichment score 43). Those changes were found in both adults and in children with BM compared to the healthy controls. The gene expression profiles did not significantly depend on the clinical outcome, but there was a strong influence of the specific type of pathogen underlying BM. CONCLUSION:This study demonstrates that there is a very strong activation of immune response at the transcriptional level during BM and that the type of pathogen influences this transcriptional activation.

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Impact of blood collection and processing on peripheral blood gene expression profiling in type 1 diabetes.

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Impact of blood collection and processing on peripheral blood gene expression profiling in type 1 diabetes.

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