Circulating extracellular RNAs (exRNAs) have the potential to serve as biomarkers for a wide range of medical conditions. However, limitations in existing exRNA isolation methods and a lack of knowledge on parameters affecting exRNA variability in human samples may hinder their successful discovery and clinical implementation. Using combinations of denaturants, reducing agents, proteolysis, and revised organic extraction, researchers from The Rockefeller University developed an automated, high-throughput approach for recovery of exRNAs and exDNA from the same biofluid sample. They applied this method to characterize exRNAs from 312 plasma and serum samples collected from 13 healthy volunteers at 12 time points over a 2-month period. Small RNA cDNA library sequencing identified nearly twofold increased epithelial-, muscle-, and neuroendocrine-cell-specific miRNAs in females, while fasting and hormonal cycle showed little effect. External standardization helped to detect quantitative differences in erythrocyte and platelet-specific miRNA contributions and in miRNA concentrations between biofluids. It also helped to identify a study participant with a unique exRNA phenotype featuring a miRNA signature of up to 20-fold elevated endocrine-cell-specific miRNAs and twofold elevated total miRNA concentrations stable for over 1 year. Collectively, these results demonstrate an efficient and quantitative method to discern exRNA phenotypes and suggest that plasma and serum RNA profiles are stable over months and can be routinely monitored in long-term clinical studies.
Unsupervised clustering of miRNAs from human biofluid samples reveal distinct miRNA signatures for serum and plasma and segregate subject P12 from otherwise indistinguishable study participants.
For each sample, individual calibrator and miRNA read frequencies are log2-transformed and color-coded. Study subject metadata and library preparation details are color-coded and indicated as annotation. This figure features samples with ≥2,000,000 hg19-mapped reads (A) The calibrator heatmap reports unsupervised clustering of two sets of 10 synthetic 22-nt 5′P/3′OH-containing oligoribonucleotides spiked into samples at the beginning of RNA isolation (Set1) and before small RNA cDNA library preparation (Set2). (B) The miRNA heatmap is based on the union of the top 90% of miRNAs present in any sample. Sample order (columns) and miRNA arrangement (rows) were determined by hierarchical clustering. All biofluid samples from study subject P12 (green) clustered together yet were different from the rest of samples.