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. 2024 Aug;13(8):e12481.
doi: 10.1002/jev2.12481.

Extracellular vesicles carry transcriptional 'dark matter' revealing tissue-specific information

Navneet Dogra  1   2   3 , Tzu-Yi Chen  1 , Edgar Gonzalez-Kozlova  4 , Rebecca Miceli  1 , Carlos Cordon-Cardo  1 , Ashutosh K Tewari  5 , Bojan Losic  2 , Gustavo Stolovitzky  2   6
Affiliations

Extracellular vesicles carry transcriptional 'dark matter' revealing tissue-specific information

Navneet Dogra et al. J Extracell Vesicles. 2024 Aug.

Abstract

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid-biopsy markers. Yet, the complete transcriptomic landscape of EV-associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20-50 nt), and uncharacterized genome annotations-often denoted as the 'dark matter' of the genome. In this study, we investigate the EV-associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre- & post-prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data-based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV-RNA expression patterns emerging from the un-annotated genomic regions (UGRs) of the transcriptomes associated with tissue-specific phenotypes. We have named these novel EV-associated small RNAs as 'EV-UGRs' or "EV-dark matter". Here, we demonstrate that EV-UGR gene expressions are downregulated by ∼100 fold (FDR < 0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV-UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow-up patients. Finally, we developed a stem-loop RT-qPCR assay that validated prostate cancer-specific EV-UGRs for selective fluid-based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV-transcriptome and demonstrate that EV-UGRs carry tissue-specific Information that significantly alters pre- and post-prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV-RNAs hold promise in liquid-biopsy by avoiding highly invasive biopsy procedures in prostate cancer.

Keywords: RNA sequencing; biofluids; cancer; extracellular vesicles; liquid biopsy; small RNA.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Overview of experimental design and data analysis. (A) Number of samples and subtype composition of each dataset included in the analysis. (B) Sources of sample types. (C) Biofluid profiles of all samples. (D) Workflow of the EV‐UGR analyses.
FIGURE 2
FIGURE 2
Characterization of the small EVs and their respective RNA cargo. (A) transmission electron microscopy (TEM) analyses of blood serum EVs from prostate cancer subjects. Scale bar is 100 nm. (B) Immunogold TEM analyses shows 6 nm gold‐CD81 (a canonical marker for EVs) on the surface of vesicles. (C) Nanoparticle tracking analyses (NTA) present particles between the size of ∼30–150 nm in diameter. Overall, ∼2 × 1011 particles/mL were detected in blood serum. (D) Immunofluorescence co‐localization study (nanoview) for canonical tetraspanins show EVs are enriched with CD9, CD63, and CD81. (E) Immunofluorescence co‐localization study shows EVs bound to the CD9 functionalized surface. (F) Minimum EV binding was observed when CD9 was not presented on the surface. (G & H) Capillary electrophoresis using pico chip for Bioanalyzer (Agilent) analyses revealed the size of small RNA in cells and EVs. (I) Capillary electrophoresis using small RNA chip for Bioanalyzer analyses revealed the size and distribution of small RNA in EVs.
FIGURE 3
FIGURE 3
Mass spectrometry and exRNA cargo type analyses of EVs (A) Workflow: Serum EV isolation using different platforms: ultracentrifugation (UC), UC in conjunction with density gradient (UC+DG), and nanoDLD, followed by proteomics and EV‐hallmark and pathway enrichment analyses. We isolated EVs from three different human subjects and compared between three different isolation methods (total n = 9). (B) Principal components analysis (PCA) to identify possible batch effects between technologies. (C) Venn diagram comparison of common and unique EV proteins among three isolation technologies. (D) MISEV 2023 recommended EV, NVEP, and EVP hallmark analyses. Heatmap includes proteins from MISEV 2023 (Welsh, et al., JEV 2023, Table 3. Protein content‐based EV characterization) recommended EV‐hallmarks for EVs and NVEPs markers. Lipoproteins, and other common co‐isolates, and EV‐proteins are presented. E) Gene ontology pathway analyses is performed, and top pathway enrichment scores are compared. All three technologies used for EV isolation demonstrated ontologies‐based pathways of Extracellular exosome. nanoDLD EVs were enriched with ontologies related to Extracellular vesicle, Extracellular organelle, Extracellular exosome (Figure 3E, Table S2). UC and UC+DG also displayed the presence of blood microparticle pathway ontology (due to lipoprotein contribution). Notably, the blood microparticles associated ontology was absent in the nanoDLD EVs proteome. (F) The top enriched proteins from our proteomics datasets are compared with Vesiclepedia database. (G) exRNA Atlas cargo type analyses to delineate cargo type proportions for EVs from Serum, urine, and tissue samples (bulk) are compared. Overall, the EVs isolated in our study enrich with high and low density vesicles associated RNA with minimum contribution from AGO2 and lipoprotein‐associated RNA. Total samples used for this study: nanoDLD (n = 17), UC (n = 14, urine = 7, serum = 7), bulk tissue samples (n = 10, tumor = 5, normal = 5.
FIGURE 4
FIGURE 4
Characterization of UGRs/small read clusters (smRCs). (A) Schematic drawing of reads aligning on the genome highlighting 3 representative examples that help define UGRs and their properties. Left image or UGR/smRC2, has a minimum expression threshold representative of at least 100 reads aligned in a tight genomic region. This region is represented by a read pile up and very few surrounding reads besides the peak. Thus, showcasing a peak dominant UGR without uniform tiling or low complexity. Central image or UGR/smRC2 represents a region without any peaks or no peak dominance with a uniform tiling composition or high complexity. Additionally, between smRC1 and smRC2 there is a minimum distance of 75 base pairs arbitrarily defined by the read size of our experiment that separates these two clusters. Right image or UGR/smRC7 represents a region with insufficient distance between a peak and a uniform tiling. Thus, both regions composed by the uniform tiling and the peak constitute the UGR 3, of moderate complexity. (B) The length of a UGR in our dataset varies from 14 to 10000 base pairs shows a function of density of all detected UGRs. (C) The distribution of complexity values for prostate cancer UGRs, has a median ∼0.1 highlighting that the majority of UGRs are of low complexity. (D) Density function of the number of reads in the peak of a UGR divided by the total number of reads in the same UGR, shows a median of ∼0.3. (E, F) The difference in length between annotated (blue) and unannotated UGRs (red) with statistical significance (FDR < 0.05) computed with wilcoxon rank test. Additionally, Uncharacterized, or not annotated UGRs (red) also have lower number of unique reads and lower complexity. (G) compared to annotated regions (blue). (H) The annotated UGRs correspond not only to protein genes but to a wide spectrum of different genomic elements such as pseudogenes, lincRNAs, snoRNAs, rRNAs and other non‐coding RNAs shown in blue. Additionally, the expression of uncharacterized UGRs (red) is also higher than well studied miRNAs (blue) or protein coding genes (blue).
FIGURE 5
FIGURE 5
Visualization of selected UGRs in prostate cancer patients (tumor, normal, serum EVs pre‐ & post prostatectomy) and in human prostate carcinoma epithelial cell line (22RV1) and their EVs. Three distinct examples of UGRs identified at Chromosome 2, 6, and 11 visualized in Integrated Genome Browser. Expression patterns emanating from the un‐annotated (A) and partially annotated (B, C) genomic regions from Chromosome 2, 6, and 11, respectively. EV‐UGRs expression is downregulated pre‐prostatectomy and upregulated post‐prostatectomy in all three examples. Inset: UGR expression pattens from 8 prostate cancer (pre‐ and post‐prostatectomy) subjects. (D) Human prostate carcinoma epithelial cell line (22RV1) and their EVs also show similar expression patterns in un‐annotated (D) and partially annotated (E, F) genomic regions from Chromosome 2, 6, and 11, respectively. To ensure reproducibility, EVs are isolated using two different technologies nanoDLD (A–C) and UC (D–F). Reference sequence (Refseq) shows no annotation (A, D) or partial annotations (B, C, E, F) for the selected genomic regions.
FIGURE 6
FIGURE 6
Cellular and EVs profiles reveal distinct new UGRs. (A) Variance partition analyses shows that origin of RNA is the main driver of variance in our data followed by age and PSA. (B) Principal component analysis (PCA) resulted in the complete separation of the samples based on origin of RNA by tissue, urine, or serum. (C) The differential expression between cellular and EVs UGRs colored by EVs (blue) or cell (red). (D) MAplot showing the average expression in log2CPM scale colored by EVs (blue) or cell (red). (E) Spearman correlation between cells and EVs in log2CPM scale showing no correlation between either UGR expression. (F) The complexity of UGRs is shown in the y axis while the percentage of reads in the peak compared to the total reads is shown in x axis. These properties of cells or EVs show that the centers of density (black lines) align completely with their differential expression profiles (colors red for cells and blue for EVs) indicating that UGR properties also are defined by their RNA origin. (G) A selection of top UGRs for tumor, adjacent normal, urine or serum are shown in a heatmap. The annotation for samples is shown in the columns on the left of the plot for isolation type, tissue and RNA origin. The annotation on top of the heatmap indicates the average expression levels, the differential expression status (high in either tumor, adjacent normal, serum or urine) and the gene biotype for each UGR.
FIGURE 7
FIGURE 7
Identification of UGRs in independent external datasets and development of a simple RT‐qPCR assay. (A) Principal component profiles for CRC cell lines colored based on the wild or mutant (KRAS) status for either cells or EVs, showing a separation between each phenotype. (B) differential expression volcano plot between cells (red) and EVs (blue). (C) Overlap between UGRs from either cohort (Our PCA, EERC CRC cell lines and EERC health plasma patients. (D) The common UGRs between either cohort separated by their logFC based on cell/EVs comparison. (E) Comparison using Wilcoxon rank test between the lengths of UGRs from either cell (red) or EVs (blue) from different cohorts. (F) Comparison using Wilcoxon rank test between the percentage of unique reads from either cells (red) or EVs (blue). (G) Comparison using Wilcoxon rank test for the complexity values of either cells (red) or EVs (blue). (H) Comparison using Wilcoxon rank test for percentage of reads in the peak between cells (red) and EVs (blue). (I) Gene biotype composition between cells or EVs, showing that EVs contain a larger non coding and unannotated RNA footprint than cells. (J) RNAseq quantification of UGRs selected from differential expression pool. (K) RT‐qPCR validation of select EV‐UGRs from RNAseq data. No significant differences were found between technologies of EV isolation. Normal: normal tissue, Tumor: tumor tissue, Serum: serum EVs, Urine: urine EVs, Control: no template control. Following UGRs were tested for RT‐qPCR: chr2:148881489‐148881928, chr2:222918489‐222918711, chr8:21329709‐21329879.
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