Reduction of systematic bias in transcriptome data from human peripheral blood mononuclear cells for transportation and biobanking

Abstract

Transportation of samples is essential for large-scale biobank projects. However, RNA degradation during pre-analytical operations prior to transportation can cause systematic bias in transcriptome data, which may prevent subsequent biomarker identification. Therefore, to collect high-quality biobank samples for expression analysis, specimens must be transported under stable conditions. In this study, we examined the effectiveness of RNA-stabilizing reagents to prevent RNA degradation during pre-analytical operations with an emphasis on RNA from peripheral blood mononuclear cells (PBMCs) to establish a protocol for reducing systematic bias. To this end, we obtained PBMCs from 11 healthy volunteers and analyzed the purity, yield, and integrity of extracted RNA after performing pre-analytical operations for freezing PBMCs at -80°C. We randomly chose 7 samples from 11 samples individually, and systematic bias in expression levels was examined by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR), RNA sequencing (RNA-Seq) experiments and data analysis. Our data demonstrated that omission of stabilizing reagents significantly lowered RNA integrity, suggesting substantial degradation of RNA molecules due to pre-analytical freezing. qRT-PCR experiments for 19 selected transcripts revealed systematic bias in the expression levels of five transcripts. RNA-Seq for 25,223 transcripts also suggested that about 40% of transcripts were systematically biased. These results indicated that appropriate reduction in systematic bias is essential in protocols for collection of RNA from PBMCs for large-scale biobank projects. Among the seven commercially available stabilizing reagents examined in this study, qRT-PCR and RNA-Seq experiments consistently suggested that RNALock, RNA/DNA Stabilization Reagent for Blood and Bone Marrow, and 1-Thioglycerol/Homogenization solution could reduce systematic bias. On the basis of the results of this study, we established a protocol to reduce systematic bias in the expression levels of RNA transcripts isolated from PBMCs. We believe that these data provide a novel methodology for collection of high-quality RNA from PBMCs for biobank researchers.

Figure 1. Workflow of the study design.

Figure 2. qRT-PCR analysis of 19 target transcripts.

GAPDH and the Ctrl1 condition were used as a reference transcript and condition, respectively. ΔΔCt values are shown by vertical axes. Horizontal axes represent pre-analytical conditions in the following order: Ctrl2, Without stab, Protect, Lock, Stab, SDS, and 1-Thio. *, p<0.05; **, p<0.01 (Wilcoxon signed rank test).

Figure 3. Quality assessment and control of sequencing data from HiSeq2500.

A. Quality values of sequence reads calculated by Cufflinks (Cuffdiff) in each sample. B. The blue bars show the number of sequence reads mapped to the human genome (hs37d5) with TopHat, and the red line with squares indicates the mapped percentage in each sample (B).

Figure 4. Data bias of transcriptome analysis in each condition.

A. Correlation analysis of the average of FPKM under eight conditions for each sample. B. Cluster analysis of 56 transcriptomes: eight conditions for each of seven volunteers. C. Pair-wise comparisons of significant differences in gene expression for each sample. The number in each box shows the number of differentially expressed genes (p<0.05, Wilcoxon signed rank test).

Figure 5. Protocol to reduce systematic bias for transcriptome analysis of PBMCs collected in remote assessment centers.

A protocol for pre-analytical operations to mediate the effects of systematic bias in transcriptome data of PBMCs for transportation and biobanking is shown.