Phospho-RNA-Seq Highlights Specific Small RNA Profiles in Plasma Extracellular Vesicles

Abstract

Small RNAs (sRNAs) are bioactive molecules that can be detected in biofluids, reflecting physiological and pathological states. In plasma, sRNAs are found within extracellular vesicles (EVs) and in extravesicular compartments, offering potential sources of highly sensitive biomarkers. Deep sequencing strategies to profile sRNAs favor the detection of microRNAs (miRNAs), the best-known class of sRNAs. Phospho-RNA-seq, through the enzymatic treatment of sRNAs with T4 polynucleotide kinase (T4-PNK), has been recently developed to increase the detection of thousands of previously inaccessible RNAs. In this study, we investigated the value of phospho-RNA-seq on both the EVs and extravesicular plasma subfractions. Phospho-RNA-seq increased the proportion of sRNAs used for alignment and highlighted the diversity of the sRNA transcriptome. Unsupervised clustering analysis using sRNA counts matrices correctly classified the EVs and extravesicular samples only in the T4-PNK treated samples, indicating that phospho-RNA-seq stresses the features of sRNAs in each plasma subfraction. Furthermore, T4-PNK treatment emphasized specific miRNA variants differing in the 5'-end (5'-isomiRs) and certain types of tRNA fragments in each plasma fraction. Phospho-RNA-seq increased the number of tissue-specific messenger RNA (mRNA) fragments in the EVs compared with the extravesicular fraction, suggesting that phospho-RNA-seq favors the discovery of tissue-specific sRNAs in EVs. Overall, the present data emphasizes the value of phospho-RNA-seq in uncovering RNA-based biomarkers in EVs.

Keywords: T4-PNK; biomarkers; extracellular vesicles; plasma; small RNA.

Figure 1

Specific small RNA profiles are found in the extracellular vesicles and protein-enriched fractions upon T4-PNK treatment. (A) Schematic illustration of the workflow followed in this study. (B) Fold change in the MFI values for the EV markers CD9, CD63, and CD81 in the EV-enriched fraction pooled after SEC. MFI fold changes were calculated as referenced to the negative control. (C) NTA size distribution profiles of the pooled EV-enriched fractions for the three different samples. (D) Representative cryo-TEM image of the EV- and protein-enriched fractions obtained after plasma SEC subfractionation. (E) Evaluation of EV markers by Western blot. SH-SY5Y lysate was included in the analysis as the positive control. (F) Fraction of short RNA reads mapping to distinct classes of small RNAs as annotated using the ExceRpt tool for the EV- and protein-enriched fractions, both after treatment with T4-PNK and without T4-PNK treatment. (G) Heatmap of hierarchical clustering analysis of sRNA profiles in EV- and protein- enriched pools using the ExceRpt tool. (H) Volcano plot showing differentially expressed sRNAs in T4-PNK treated samples versus non-treated samples, both in the EV- and protein-enriched fractions as annotated using ExceRpt. Colored dots represent the significantly deregulated sRNAs (|log2FoldChange| > 0.58, adjusted p < 0.05). miRNAs, tRNA, and gene fragments are specifically highlighted. (I) Heatmap of hierarchical clustering analysis of sRNAs identified in the No-T4-PNK treated and T4-PNK-treated samples. (J) Normalized abundance of sequences belonging to tRNA-Leu-TAG-1 (SeqCluster tool) from the EV- and protein-enriched fractions, both treated and not treated with T4-PNK. (K) Normalized abundance of sequence mapping onto tRNA-Ser-GTC-4 (SeqCluster tool) from the EV- and protein-enriched fractions, both treated and not treated with T4-PNK. Data in (B) are represented as mean ± SEM (n = 3 samples per group).

Figure 2

Phospho-RNA-Seq stresses the detection of specific miRNAs in the EV- and protein-enriched plasma fractions. (A) Fraction of reads that align to miRNAs found in the T4-PNK-treated and non-treated samples using the ExceRpt tool. (B) Absolute number of miRNAs identified using ExceRpt in the EV- and protein-enriched fractions treated and not treated with T4-PNK. (C) Venn diagrams of the miRNAs identified by ExceRpt in both the treated and non-treated EV- (left panel) and protein-enriched fractions (right panel). miRNAs identified with >0 RPM in each sample were considered. (D) Heatmap hierarchical clustering analysis of the miRNAs identified using the SeqBuster tool in the non-treated and treated samples. (E) Radar plots showing the number of the different types of isomiRs (left plots) and the abundance of each type of isomiR (right plots) in the EV- and protein-enriched fractions as analyzed with SeqBuster. Each type of isomiR is labelled as either Iso 5p (miRNAs that vary in the 5′ end); Iso 3p (miRNAs that vary in the 3′ end); Iso add (miRNAs varying in the 3′end due to nucleotide additions); and Iso sn (miRNAs that vary because of a nucleotide substitution), respectively. Ref indicates the canonical (most abundant) reference miRNA. (F) Hierarchical clustering analysis of the miRNAs identified using the SeqBuster tool in the EV- and protein-enriched fractions. Data in (A,B) are represented as mean ± SEM (n = 3 per group).

Figure 3

T4-PNK treatment in EVs highlights tissue-specific mRNA fragments. (A) Genes with a TSS > 3 overlapping with sRNAs mapped onto protein coding (with >5 reads in each of the samples) specifically found only in the EV-enriched fractions treated with T4-PNK. (B) Percentage of genes expressed (sRNAs that mapped onto protein coding with >5 reads in each of the samples) in the EV- and protein-enriched fractions treated with T4-PNK that overlap with the top 1000 expressed genes in each tissue. The dataset from the ExceRpt tool was used (n = 3 per group). The chi-squared test was used to evaluate whether T4-PNK treatment resulted in significant differences in the proportions of tissue-enriched protein coding gene fragments between the EV- and protein-enriched fractions (p * < 0.05).