Dataset Information

Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis.

ABSTRACT: Conventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available genome annotation by performing scRNA-seq analysis on any region in the genome for which transcriptional products are detected. Our tool generates a single-cell expression matrix for all transcriptionally active regions (TARs), performs single-cell TAR expression analysis to identify biologically significant TARs, and then annotates TARs using gene homology analysis. This procedure uses single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

SUBMITTER: Wang MFZ

PROVIDER: S-EPMC8042062 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis.

Wang Michael F Z MFZ Mantri Madhav M Chou Shao-Pei SP Scuderi Gaetano J GJ McKellar David W DW Butcher Jonathan T JT Danko Charles G CG De Vlaminck Iwijn I

Nature communications 20210412 1

Conventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available geno ...[more]

PMID: 33846360

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Project description: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.

Dataset Information

Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis.

Publications

Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis.

Similar Datasets

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