C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution

Kouno, Tsukasa; Moody, Jonathan; Kwon, Andrew Tae-Jun; Shibayama, Youtaro; Kato, Sachi; Huang, Yi; Böttcher, Michael E.; Motakis, Efthymios; Mendez, Mickaël; Severin, Jessica; Luginbühl, Joachim; Abugessaisa, Imad; Hasegawa, Akira; Takizawa, Satoshi; Arakawa, Takahiro; Furuno, Masaaki; Ramalingam, Naveen; West, Jay; Suzuki, Harukazu; Kasukawa, Takeya; Lassmann, Timo; Hon, Chung-Chau; Arner, Erik; Carninci, Piero; Plessy, Charles; Shin, Jay W.

doi:10.1038/s41467-018-08126-5

Cited by 83 publications

(45 citation statements)

References 69 publications

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“…New technologies, such as single cell sequencing, will allow annotation of cell-specific expressed and regulatory regions of the genome at unprecedented resolution (Papatheodorou et al, 2019). C1 CAGE now offers the opportunity to detect TSS and enhancer activity at single-cell resolution (Kouno et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Global Analysis of Transcription Start Sites in the New Ovine Reference Genome (Oar rambouillet v1.0)

et al. 2020

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The overall aim of the Ovine FAANG project is to provide a comprehensive annotation of the new highly contiguous sheep reference genome sequence (Oar rambouillet v1.0). Mapping of transcription start sites (TSS) is a key first step in understanding transcript regulation and diversity. Using 56 tissue samples collected from the reference ewe Benz2616, we have performed a global analysis of TSS and TSS-Enhancer clusters using Cap Analysis Gene Expression (CAGE) sequencing. CAGE measures RNA expression by 5 cap-trapping and has been specifically designed to allow the characterization of TSS within promoters to single-nucleotide resolution. We have adapted an analysis pipeline that uses TagDust2 for clean-up and trimming, Bowtie2 for mapping, CAGEfightR for clustering, and the Integrative Genomics Viewer (IGV) for visualization. Mapping of CAGE tags indicated that the expression levels of CAGE tag clusters varied across tissues. Expression profiles across tissues were validated using corresponding polyA+ mRNA-Seq data from the same samples. After removal of CAGE tags with <10 read counts, 39.3% of TSS overlapped with 5 ends of 31,113 transcripts that had been previously annotated by NCBI (out of a total of 56,308 from the NCBI annotation). For 25,195 of the transcripts, previously annotated by NCBI, no TSS meeting stringent criteria were identified. A further 14.7% of TSS mapped to within 50 bp of annotated promoter regions. Intersecting these predicted TSS regions with annotated promoter regions (±50 bp) revealed 46% of the predicted TSS were "novel" and previously un-annotated. Using whole-genome bisulfite sequencing data from the same tissues, we were able to determine that a proportion of these "novel" TSS were

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Section: Discussionmentioning

confidence: 99%

Global Analysis of Transcription Start Sites in the New Ovine Reference Genome (Oar rambouillet v1.0)

et al. 2020

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“…13,14 Recent single-cell genome-wide data of nascent transcription challenges some of the typical characteristics of eRNAs as it was shown that enhancer transcription is unidirectional, and therefore within a cellular population, some cells transcribe the sense strand and others the antisense strand. 15 In the cases where both strands of eRNAs were detected in the same cell, single-molecule fluorescence in situ hybridization (FISH) experiments showed that colocalization of both RNA molecules was rare. Because typical RNA expression methodologies show an average for total cells, that would explain why eRNAs have been described as bidirectional.…”

Section: General Features and Dynamics Of Ernasmentioning

confidence: 99%

“…Importantly, eRNAs were expressed at similar levels compared with gene promoters in single cells and were detected in just a subset of cells, ie, displaying transcriptional bursting. 15 More single-cell measurements of nascent transcription in different conditions are needed to clarify the real nature of transcripts originating from enhancers.…”

Section: General Features and Dynamics Of Ernasmentioning

confidence: 99%

Enhancer RNAs: Insights Into Their Biological Role

2019

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Enhancers play a central role in the transcriptional regulation of metazoans. Almost a decade ago, the discovery of their pervasive transcription into noncoding RNAs, termed enhancer RNAs (eRNAs), opened a whole new field of study. The presence of eRNAs correlates with enhancer activity; however, whether they act as functional molecules remains controversial. Here we review direct experimental evidence supporting a functional role of eRNAs in transcription and provide a general pipeline that could help in the design of experimental approaches to investigate the function of eRNAs. We propose that induction of transcriptional activity at enhancers promotes an increase in its activity by an RNA-mediated titration of regulatory proteins that can impact different processes like chromatin accessibility or chromatin looping. In a few cases, transcripts originating from enhancers have acquired specific molecular functions to regulate gene expression. We speculate that these transcripts are either nonannotated long noncoding RNAs (lncRNAs) or are evolving toward functional lncRNAs. Further work will be needed to comprehend better the biological activity of these transcripts.

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“…The t-SNE plot in the middle panel of ( Fig. 5a In the single-cell dataset PRJDB5282 [13], generated by C1 CAGE protocol ( Fig. 5c), the skewed cells clustered separately on t-SNE plot (top of Fig.…”

Section: Effect Of Skewed Cells On Downstream Analysismentioning

confidence: 99%

“…1c). The tag-based sequencing of 5' or 3' ends methods [11][12][13][14] should have the peak coverage at either the 5' or 3' end of the gene with low/no coverage in the middle region of the gene body. In the second type, there was high coverage in the middle of the gene for 5'-end and 3'-end sequence protocols (Fig.…”

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Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

Abugessaisa

Noguchi

Cardon

et al. 2019

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Analysis and interpretation of single-cell RNA-sequencing (scRNA-seq) experiments are compromised by the presence of poor quality cells. For meaningful analyses, such poor quality cells should be excluded to avoid biases and large variation. However, no clear guidelines exist.We introduce SkewC, a novel quality-assessment method to identify poor quality single-cells in scRNA-seq experiments. The method is based on the assessment of gene coverage for each single cell and its skewness as a quality measure. To validate the method, we investigated the impact of poor quality cells on downstream analyses and compared biological differences 2 between typical and poor quality cells. Moreover, we measured the ratio of intergenic expression, suggesting genomic contamination, and foreign organism contamination of single-cell samples.SkewC is tested in 37,993 single-cells generated by 15 scRNA-seq protocols. We envision SkewC as an indispensable QC method to be incorporated into scRNA-seq experiment to preclude the possibility of scRNA-seq data misinterpretation. Results Wide discrepancies in gene body coverage among scRNA-seq protocolsGene body (full transcript length) coverage considers the distribution of the sequence tags over the entire transcripts. We computed and analyzed the gene body coverage of different protocols (Online Methods). Remarkably, the gene body coverage shows wide differences among the data-sets generated by different scRNA-seq protocols ( Fig. 1a and b; Panels a and b in Supplementary Figs. 1-6). This indicates major variation and differences in scRNA-seq datasets produced by different protocols.We investigated the pattern of the gene body coverage for each single cell in individual datasets. The visualization of the gene body coverage profile revealed two patterns of gene body coverage. The first set of single-cells show well clustered gene coverage distribution according to the target sequence of the protocol. The second set of single-cells showed skewed gene body coverage distributions. The skewness in the distribution could be observed in term of coverage bias towards specific gene region.In one type, there was bias towards the 3'-end of the gene body in case of the 5'-end sequence and full-length sequence protocols. The bias towards the 3'-end indicated by high coverage at the 3'-end of the gene body (Fig. 1c). The tag-based sequencing of 5' or 3' ends methods [11][12][13][14] should have the peak coverage at either the 5' or 3' end of the gene with low/no coverage in the middle region of the gene body. In the second type, there was high coverage in the middle of the gene for 5'-end and 3'-end sequence protocols (Fig. 1c), in contrast to the full-length sequencing protocols. In the third type, there was low coverage in the middle of the gene for full-length sequence protocols. This indicated by low coverage at mid-point of 5'-3'-end of gene body (Fig. 1d).

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C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution

Cited by 83 publications

References 69 publications

Global Analysis of Transcription Start Sites in the New Ovine Reference Genome (Oar rambouillet v1.0)

Global Analysis of Transcription Start Sites in the New Ovine Reference Genome (Oar rambouillet v1.0)

Enhancer RNAs: Insights Into Their Biological Role

Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

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