Extracellular vesicles carry transcriptional 'dark matter' revealing tissue-specific information.
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.
Dogra N
，Chen TY
，Gonzalez-Kozlova E
，Miceli R
，Cordon-Cardo C
，Tewari AK
，Losic B
，Stolovitzky G
... -
《Journal of Extracellular Vesicles》
Global transcriptomic changes occur in uterine fluid-derived extracellular vesicles during the endometrial window for embryo implantation.
Are uterine fluid-derived extracellular vesicles (UF-EVs) a 'liquid biopsy' reservoir of biomarkers for real-time monitoring of endometrial status?
The transcriptomic cargo of UF-EVs reflects the RNA profile of the endometrial tissue as well as changes between the non-receptive and the receptive phase, possibly supporting its use for a novel endometrial receptivity test.
EVs have been previously isolated from uterine fluid, where they likely contribute to the embryo-endometrium crosstalk during implantation. Based on a meta-analysis of studies on endometrial tissue implantation-associated genes and the human exosomes database, 28 of the 57 transcripts considered as receptivity markers refer to proteins present in human exosomes. However, the specific transcriptomic content of receptive phase UF-EVs has yet to be defined.
Two experimental series were set up. First, we simultaneously sequenced RNA species derived from paired UF-EVs and endometrial tissue samples collected from physiologically cycling women. Second, we analyzed RNA species of UF-EVs collected during the non-receptive (LH + 2) and receptive (LH + 7) phase of proven fertile women and from the receptive (LH + 7) phase of a population of women undergoing ART and transfer of euploid blastocysts.
For paired UF-endometrial tissue sampling, endometrial tissue biopsies were obtained with the use of a Pipelle immediately after UF collection performed by lavage of the endometrial cavity. Overall, n = 87 UF samples were collected and fresh-processed for EV isolation and total RNA extraction, while western blotting was used to confirm the expression of EV protein markers of the isolated vesicles. Physical characterization of UF-EVs was performed by Nanoparticle Tracking Analysis. To define the transcriptomic cargo of UF-EV samples, RNA-seq libraries were successfully prepared from n = 83 UF-EVs samples and analyzed by RNA-seq analysis. Differential gene expression (DGE) analysis was used to compare RNA-seq results between different groups of samples. Functional enrichment analysis was performed by gene set enrichment analysis with g:Profiler. Pre-ranked gene set enrichment analysis (GSEA) with WebGestalt was used to compare RNA-seq results with the gene-set evaluated in a commercially available endometrial receptivity array.
A highly significant correlation was found between transcriptional profiles of endometrial biopsies and pairwise UF-EV samples (Pearson's r = 0.70 P < 0.0001; Spearman's ρ = 0.65 P < 0.0001). In UF-EVs from fertile controls, 942 gene transcripts were more abundant and 1305 transcripts less abundant in the LH + 7 receptive versus the LH + 2 non-receptive phase. GSEA performed to evaluate concordance in transcriptional profile between the n = 238 genes included in the commercially available endometrial receptivity array and the LH + 7 versus LH + 2 UF-EV comparison demonstrated an extremely significant and consistent enrichment, with a normalized enrichment score (NES)=9.38 (P < 0.001) for transcripts up-regulated in LH + 7 in the commercial array and enriched in LH + 7 UF-EVs, and a NES = -5.40 (P < 0.001) for transcripts down-regulated in LH + 7 in the commercial array and depleted in LH + 7 UF-EVs. When analyzing LH + 7 UF-EVs of patients with successful versus failed implantation after transfer of one euploid blastocyst in the following cycle, we found 97 genes whose transcript levels were increased and 64 genes whose transcript levels were decreased in the group of women who achieved a pregnancy. GSEA performed to evaluate concordance in transcriptional profile between the commercially available endometrial receptivity array genes and the comparison of LH + 7 UF-EVs of women with successful versus failed implantation, demonstrated a significant enrichment with a NES = 2.14 (P = 0.001) for transcripts up-regulated in the commercial array in the receptive phase and enriched in UF-EVs of women who conceived, and a not significant NES = -1.18 (P = 0.3) for transcripts down-regulated in the commercial array and depleted in UF-EVs. In terms of physical features, UF-EVs showed a homogeneity among the different groups analyzed except for a slight but significant difference in EV size, being smaller in women with a successful implantation compared to patients who failed to conceive after euploid blastocyst transfer (mean diameter ± SD 205.5± 22.97 nm vs 221.5 ± 20.57 nm, respectively, P = 0.014).
Transcriptomic data were deposited in NCBI Gene Expression Omnibus (GEO) and can be retrieved using GEO series accession number: GSE158958.
Separation of RNA species associated with EV membranes might have been incomplete, and membrane-bound RNA species-rather than the internal RNA content of EVs-might have contributed to our RNA-seq results. Also, we cannot definitely distinguish the relative contribution of exosomes, microvesicles and apoptotic bodies to our findings. When considering patients undergoing ART, we did not collect UFs in the same cycle of the euploid embryo transfer but in the one immediately preceding. We considered this approach as the most appropriate in relation to the novel, explorative nature of our study. Based on our results, a validation of UF-EV RNA-seq analyses in the same cycle in which embryo transfer is performed could be hypothesized.
On the largest sample size of human EVs ever analyzed with RNA-seq, this study establishes a gene signature to use for less-invasive endometrial receptivity tests. This report is indeed the first to show that the transcriptome of UF-EVs correlates with the endometrial tissue transcriptome, that RNA signatures in UF-EVs change with endometrial status, and that UF-EVs could serve as a reservoir for potential less-invasive collection of receptivity markers. This article thus represents a step forward in the design of less-invasive approaches for real-time monitoring of endometrial status, necessary for advancing the field of reproductive medicine.
The study was funded by a competitive grant from European Society of Human Reproduction and Embryology (ESHRE Research Grant 2016-1). The authors have no financial or non-financial competing interests to disclose.
NA.
Giacomini E
，Scotti GM
，Vanni VS
，Lazarevic D
，Makieva S
，Privitera L
，Signorelli S
，Cantone L
，Bollati V
，Murdica V
，Tonon G
，Papaleo E
，Candiani M
，Viganò P
... -
《-》
Defining candidate mRNA and protein EV biomarkers to discriminate ccRCC and pRCC from non-malignant renal cells in vitro.
Renal cell carcinoma (RCC) accounts for over 400,000 new cases and 175,000 deaths annually. Diagnostic RCC biomarkers may prevent overtreatment in patients with early disease. Extracellular vesicles (EVs) are a promising source of RCC biomarkers because EVs carry proteins and messenger RNA (mRNA) among other biomolecules. We aimed to identify biomarkers and assess biological functions of EV cargo from clear cell RCC (ccRCC), papillary RCC (pRCC), and benign kidney cell lines. EVs were enriched from conditioned cell media by size exclusion chromatography. The EV proteome was assessed using Tandem Mass Tag mass spectrometry (TMT-MS) and NanoString nCounter technology was used to profile 770 cancer-related mRNA present in EVs. The heterogeneity of protein and mRNA abundance and identification highlighted the heterogeneity of EV cargo, even between cell lines of a similar pathological group (e.g., ccRCC or pRCC). Overall, 1726 proteins were quantified across all EV samples, including 181 proteins that were detected in all samples. In the targeted profiling of mRNA by NanoString, 461 mRNAs were detected in EVs from at least one cell line, including 159 that were present in EVs from all cell lines. In addition to a shared EV cargo signature, pRCC, ccRCC, and/or benign renal cell lines also showed unique signatures. Using this multi-omics approach, we identified 34 protein candidate pRCC EV biomarkers and 20 protein and 8 mRNA candidate ccRCC EV biomarkers for clinical validation.
Zieren RC
，Dong L
，Clark DJ
，Kuczler MD
，Horie K
，Moreno LF
，Lih TM
，Schnaubelt M
，Vermeulen L
，Zhang H
，de Reijke TM
，Pienta KJ
，Amend SR
... -
《-》
ResearchGate
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钛学术
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