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‐...

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Published inJournal of extracellular vesicles Vol. 13; no. 8; pp. e12481 - n/a
Main Authors Dogra, Navneet, Chen, Tzu‐Yi, Gonzalez‐Kozlova, Edgar, Miceli, Rebecca, Cordon‐Cardo, Carlos, Tewari, Ashutosh K., Losic, Bojan, Stolovitzky, Gustavo
Format Journal Article
LanguageEnglish
Published United States John Wiley & Sons, Inc 01.08.2024
Wiley
Subjects
Annotations
biofluids
Biopsy
Cancer
Cell Line, Tumor
Clinical trials
Dark matter
Datasets
Electron microscopes
Epithelial cells
Extracellular vesicles
Extracellular Vesicles - metabolism
Gene expression
Gene Expression Regulation, Neoplastic
Genomes
Genomics
Homeostasis
Humans
liquid biopsy
Male
Organ Specificity - genetics
Phenotypes
Prokaryotes
Prostate cancer
Prostate carcinoma
Prostatectomy
Prostatic Neoplasms - genetics
Prostatic Neoplasms - metabolism
Prostatic Neoplasms - pathology
RNA sequencing
small RNA
Transcriptome
Transcriptomes
Transcriptomics
Transmission electron microscopy
RNA sequencing
biofluids
cancer
extracellular vesicles
small RNA
liquid biopsy
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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‐UGRsʼ 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.
AbstractList 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‐UGRsʼ 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.
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.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.
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‐UGRsʼ 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.
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.
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‐UGRsʼ 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.
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‐UGRsʼ 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.
Author Tewari, Ashutosh K.
Losic, Bojan
Cordon‐Cardo, Carlos
Miceli, Rebecca
Stolovitzky, Gustavo
Gonzalez‐Kozlova, Edgar
Chen, Tzu‐Yi
Dogra, Navneet
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  organization: Icahn School of Medicine at Mount Sinai
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  organization: Icahn School of Medicine at Mount Sinai
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  organization: DREAM Challenges
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39148266$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1080_20565623_2025_2461940
crossref_primary_10_3390_ijms251910338
crossref_primary_10_1002_jev2_70005
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Copyright 2024 The Author(s). published by Wiley Periodicals LLC on behalf of International Society for Extracellular Vesicles.
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Copyright_xml – notice: 2024 The Author(s). published by Wiley Periodicals LLC on behalf of International Society for Extracellular Vesicles.
– notice: 2024 The Author(s). Journal of Extracellular Vesicles published by Wiley Periodicals LLC on behalf of International Society for Extracellular Vesicles.
– notice: 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Issue 8
Keywords RNA sequencing
biofluids
cancer
extracellular vesicles
small RNA
liquid biopsy
Language English
License Attribution
http://creativecommons.org/licenses/by/4.0
2024 The Author(s). Journal of Extracellular Vesicles published by Wiley Periodicals LLC on behalf of International Society for Extracellular Vesicles.
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MergedId FETCHMERGED-LOGICAL-c4591-5ea2c548bd32811c0442926eea4ffb25d07b022159dbd820ffcb3f80d97811633
Notes Navneet Dogra, Tzu‐Yi Chen and Edgar Gonzalez‐Kozlova have contributed equally to the work.
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PublicationTitle Journal of extracellular vesicles
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Snippet From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo...
Abstract From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and...
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SubjectTerms Annotations
biofluids
Biopsy
Cancer
Cell Line, Tumor
Clinical trials
Dark matter
Datasets
Electron microscopes
Epithelial cells
Extracellular vesicles
Extracellular Vesicles - metabolism
Gene expression
Gene Expression Regulation, Neoplastic
Genomes
Genomics
Homeostasis
Humans
liquid biopsy
Male
Organ Specificity - genetics
Phenotypes
Prokaryotes
Prostate cancer
Prostate carcinoma
Prostatectomy
Prostatic Neoplasms - genetics
Prostatic Neoplasms - metabolism
Prostatic Neoplasms - pathology
RNA sequencing
small RNA
Transcriptome
Transcriptomes
Transcriptomics
Transmission electron microscopy
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Title Extracellular vesicles carry transcriptional ‘dark matter’ revealing tissue‐specific information
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Volume 13
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