Plasma processing conditions substantially influence circulating microRNA biomarker levels

Abstract

Circulating, cell-free microRNAs (miRNAs) are promising candidate biomarkers, but optimal conditions for processing blood specimens for miRNA measurement remain to be established. Our previous work showed that the majority of plasma miRNAs are likely blood cell-derived. In the course of profiling lung cancer cases versus healthy controls, we observed a broad increase in circulating miRNA levels in cases compared to controls and that higher miRNA expression correlated with higher platelet and particle counts. We therefore hypothesized that the quantity of residual platelets and microparticles remaining after plasma processing might impact miRNA measurements. To systematically investigate this, we subjected matched plasma from healthy individuals to stepwise processing with differential centrifugation and 0.22 µm filtration and performed miRNA profiling. We found a major effect on circulating miRNAs, with the majority (72%) of detectable miRNAs substantially affected by processing alone. Specifically, 10% of miRNAs showed 4-30x variation, 46% showed 30-1,000x variation, and 15% showed >1,000x variation in expression solely from processing. This was predominantly due to platelet contamination, which persisted despite using standard laboratory protocols. Importantly, we show that platelet contamination in archived samples could largely be eliminated by additional centrifugation, even in frozen samples stored for six years. To minimize confounding effects in microRNA biomarker studies, additional steps to limit platelet contamination for circulating miRNA biomarker studies are necessary. We provide specific practical recommendations to help minimize confounding variation attributable to plasma processing and platelet contamination.

Figure 1. MicroRNA Biomarker Study Comparing Plasma from Lung Cancer Cases and Controls.

(A) Average miRNA cycle threshold (CT) of plasma from lung cancer cases on the x-axis and from controls on the y-axis. Dotted line represents the line of identity. (B) Bar graph of average particle counts (thousand/µL) in the platelet and microparticle size ranges of plasma from lung cancer cases and controls. Student's t-test, two-tailed, p = 0.02 and 0.13, respectively. Error bars represent standard deviation.

Figure 2. Stepwise Plasma Processing and Associated Particle Content.

(A) Left: Schematic of stepwise plasma processing steps. Right: Histogram content of platelets and microparticles in stepwise processed plasma samples. X-axis is particle diameter and Y-axis is number of particles/mL. (B) Platelet and microparticle content (in thousand/µL) of stepwise processed plasma samples as measured by particle counter and hematology analyzer.

Figure 3. MicroRNA Expression in Stepwise Processed Plasma Samples Using qRT-PCR Profiling.

(A) Heat map of relative expression (in CTs) of 282 detected miRNAs assayed in parallel using the Exiqon qRT-PCR array. Colors represent the greatest relative expression differences across the 5 different processing conditions for each individual miRNA. The processing condition(s) with the highest relative expression is shown in red and lowest in green for each miRNA. miRNAs are arranged left to right in order of decreasing difference between CTs across all 5 samples. Selected miRNAs are highlighted above. Ranges of average fold-differences are indicated along the bottom, where fold-differences are calculated as = 2^(ΔCT). (B) Validation of selected miRNAs by individual qRT-PCR assay. MicroRNAs are arranged in order of decreasing range between highest and lowest fold difference. Numbers in each box represent the normalized CT difference from the mean of all CT values for each miRNA evaluated in sample type. Color schema follows panel A. Average miRNA expression fold-differences between Plasma_RICH vs. Plasma_POOR, Plasma_STD vs. Plasma_FILT, and Plasma _POOR vs. Plasma_FILT is indicated in the bottom rows. Fold-differences are calculated as = 2^(ΔCT).

Figure 4. Additional Quality Control and Processing of Frozen, Archived Plasma Samples.

(A) Schematic of archival sample types: Plasma_STD, post-thaw processing to Plasma_POOR, and Serum_STD Samples were collected at each of five time points from six healthy donors in a prospective screening cohort between 2001 and 2007. (B) Microparticle and platelet content of 3 sample types from all 30 timed draws as measured by particle counter. (C) Normalized CT difference from the mean of selected miRNAs tested by individual qRT-PCR assay. Individual miRNAs are grouped into most affected, affected and unaffected by processing, as listed. T-test comparing Plasma_STD and Plasma_POOR was calculated for each miRNA and p values are listed on the bottom row. (D) Graphs depicting the CT change from baseline of the miRNAs most affected, affected and unaffected in frozen archival Plasma_STD and Plasma_POOR.

Figure 5. Serum Processing and Associated Particle Content.

(A) Left: Schematic of Serum_STD processed with a 0.22 um filtration step to form Serum_FILT. Right: Histogram content of platelets and microparticles in stepwise processed plasma samples. X-axis is particle diameter and Y-axis is number of particles/mL. (B) Platelet and microparticle content (in thousand/µL) of serum samples as measured by particle counter and hematology analyzer. (B) Validation of selected miRNAs by individual qRT-PCR assay. Normalized CT difference from mean of all 7 samples (including 5 plasma samples) is shown. MicroRNAs are arranged as in Figure 3. Average fold difference between Serum_STD vs. Serum_FILT is indicated below.