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Transcript assembly improves expression quantification of transposable elements in single-cell RNA-seq data.

ABSTRACT: Transposable elements (TEs) are an integral part of the host transcriptome. TE-containing noncoding RNAs (ncRNAs) show considerable tissue specificity and play important roles during development, including stem cell maintenance and cell differentiation. Recent advances in single-cell RNA-seq (scRNA-seq) revolutionized cell type-specific gene expression analysis. However, effective scRNA-seq quantification tools tailored for TEs are lacking, limiting our ability to dissect TE expression dynamics at single-cell resolution. To address this issue, we established a TE expression quantification pipeline that is compatible with scRNA-seq data generated across multiple technology platforms. We constructed TE-containing ncRNA references using bulk RNA-seq data and showed that quantifying TE expression at the transcript level effectively reduces noise. As proof of principle, we applied this strategy to mouse embryonic stem cells and successfully captured the expression profile of endogenous retroviruses in single cells. We further expanded our analysis to scRNA-seq data from early stages of mouse embryogenesis. Our results illustrated the dynamic TE expression at preimplantation stages and revealed 146 TE-containing ncRNA transcripts with substantial tissue specificity during gastrulation and early organogenesis.

SUBMITTER: Shao W

PROVIDER: S-EPMC7849386 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Transposable elements (TEs), also known as "jumping genes", are repetitive sequences with the capability of changing their location within the genome. They are key players in many different biological processes in health and disease. Therefore, a reliable quantification of their expression as transcriptional units is crucial to distinguish between their independent expression and the transcription of their sequences as part of canonical transcripts. TEs quantification faces difficulties of different types, the most important one being low reads mappability due to their repetitive nature preventing an unambiguous mapping of reads originating from their sequences. A large fraction of TEs fragments localizes within introns, which led to the hypothesis that intron retention (IR) can be an additional source of bias, potentially affecting accurate TEs quantification. IR occurs when introns, normally removed from the mature transcript by the splicing machinery, are maintained in mature transcripts. IR is a widespread mechanism affecting many different genes with cell type-specific patterns. We hypothesized that, in an RNA-seq experiment, reads derived from retained introns can introduce a bias in the detection of overlapping, independent TEs RNA expression. In this study we performed meta-analysis using public RNA-seq data from lymphoblastoid cell lines and show that IR can impact TEs quantification using established tools with default parameters. Reads mapped on intronic TEs were indeed associated to the expression of TEs and influence their correct quantification as independent transcriptional units. We confirmed these results using additional independent datasets, demonstrating that this bias does not appear in samples where IR is not present and that differential TEs expression does not impact on IR quantification. We concluded that IR causes the over-quantification of intronic TEs and differential IR might be confused with differential TEs expression. Our results should be taken into account for a correct quantification of TEs expression from RNA-seq data, especially in samples in which IR is abundant.

Project description:BackgroundRNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments.ResultsWe present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene.ConclusionsRSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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Transcript assembly improves expression quantification of transposable elements in single-cell RNA-seq data.

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