Reduction of Systematic Bias in Transcriptome Data from Human Peripheral Blood Mononuclear Cells for Transportation and Biobanking

<div><p>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.</p></div

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

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

MOESM1 of Fibrinogen alpha C chain 5.9Â kDa fragment (FIC5.9), a biomarker for various pathological conditions, is produced in post-blood collection by fibrinolysis and coagulation factors

Additional file 1. Analysis of FIC5.9 releasing in coagulation factor-deficient plasma. (A) Mass spectrum of coagulation-depleted plasma reactivated using an APTT reagent. Synthesized FIC5.9 is indicated with a red line and SI-labeled FIC5.9 peptide is indicated with a blue line. (B) Quantification of FIC5.9 released by coagulation reactivation. The relative intensity of FIC5.9 was calculated by comparison with the intensity of the internal standard (SI-FIC5.9). The error bars represent the standard error of the mean (SEM) for three experiments

TaqMan probes for the 21 candidate genes analyzed by qRT-PCR.

<p>TaqMan probes for the 21 candidate genes analyzed by qRT-PCR.</p

RNA purity, yield, and integrity.

<p>RNA purity, yield, and integrity were measured for 11 individual samples. A260/A280 indicates the ratio of absorbance at 260 and 280 nm.</p><p>N.A.: not available (not measureable), RIN: RNA integrity number.</p><p>*<i>p</i><0.05; **<i>p</i><0.01; ***<i>p</i><0.001 (Wilcoxon signed rank test).</p>$<p>RNA yield could be measured for nine individual samples (failed in two samples).</p>#<p>RNA yield could be measured for 10 individual samples (failed in one sample).</p

Workflow of the study design.

<p>Workflow of the study design.</p

Apolipoprotein E resequencing and its application to serotyping.

<p>(<b>A</b>) ApoE resequencing. Figure shows a representative result of wild-type ApoE amino acid sequence determination (sequence coverage = 93.6%, excluding the 18-residue signal peptide) using Orbitrap LC-MS/MS. Black highlighting denotes the determined sequence. Amino acid residues C112 and R158, which demonstrate polymorphism in ApoE2 (C158) and ApoE4 (R112), are circled. Amino acids are represented by their one-letter codes. (<b>B</b>) Tryptic peptide polymorphisms and ion chromatograms. Mutations in amino acid residues 112 and 158, which were covered by protein resequencing, cause peptide fragment polymorphisms. The R158C mutation (ApoE2) results in the cLAVYQAGAR peptide, where the C112R mutation (ApoE4) yields the LGADMEDVR peptide. Figure shows representative chromatograms for the doubly charged ions extracted from subjects with E2/E3 and E3/E4 heterozygous combinations. The calculated and observed monoisotopic masses for each peptide are indicated. (<b>C</b>) Corresponding MS/MS spectra for each peptide in (<b>B</b>). Polymorphic peptide sequences from subjects with heterozygous combinations were confirmed by MS/MS. The b- and y-ions are labeled. In (<b>B</b>) and (<b>C</b>), lower-case “c” represents alkylated cysteine residues. C. mass = calculated mass; O. mass = observed mass; Da = dalton.</p

qRT-PCR analysis of 19 target transcripts.

<p><i>GAPDH</i> 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. *, <i>p</i><0.05; **, <i>p</i><0.01 (Wilcoxon signed rank test).</p

Quality assessment and control of sequencing data from HiSeq2500.

<p><b>A.</b> Quality values of sequence reads calculated by Cufflinks (Cuffdiff) in each sample. <b>B.</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).</p

Data bias of transcriptome analysis in each condition.

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