Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

Rebboah, Elisabeth; Reese, Fairlie; Williams, Katherine; Balderrama-Gutierrez, Gabriela; McGill, Cassandra; Trout, Diane; Rodriguez, Isaryhia; Liang, Heidi Yahan; Wold, B; Mortazavi, A

doi:10.1186/s13059-021-02505-w

Cited by 40 publications

(37 citation statements)

References 64 publications

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“…Together, these issues make accurate mammalian TL annotation particularly challenging, and complicate the study of TL functional elements and conservation. Ongoing efforts to sequence full length transcripts (39, 40), integrated with annotated promoters and transcription start sites, should eventually resolve such issues and greatly aid the study of TL functions in mammals.…”

Section: Discussionmentioning

confidence: 99%

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False-Positive IRESes from Hoxa9 and other genes resulting from errors in mammalian 5’ UTR annotations

Akirtava

May

McManus

2022

Preprint

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Hyperconserved genomic sequences have great promise for understanding core biological processes. It has been recently proposed that scores of hyperconserved transcript leaders (hTLs) encode Internal Ribosome Entry Sites (IRESes) that drive cap-independent translation in part via interactions with ribosome expansion segments. However, the direct functional significance of such interactions has not yet been definitively demonstrated. We provide evidence that the putative IRESes previously reported in Hoxa gene hTLs are not transcribed. Instead, these regions function independently as transcriptional promoters. In addition, we find the proposed RNA structure of the putative Hoxa9 IRES is not conserved. Instead, sequences previously shown to be essential for putative IRES activity encode a hyperconserved transcription factor binding site (E-box) that contributes to its promoter activity by binding to the transcription factors USF1 and USF2. Similar E-box sequences enhance the promoter activities of other putative Hoxa gene IRESes. Moreover, we provide evidence that the vast majority of hTLs with putative IRES activity overlap transcriptional promoters, enhancers, and 3′ splice sites that are most likely responsible for their reported IRES activities. These results argue strongly against recently reported widespread IRES-like activities from hTLs and contradict proposed interactions between ribosomal expansion segment ES9S and putative IRESes. Our work underscores the importance of accurate transcript annotations, controls in bicistronic reporter assays, and the power of synthesizing publicly available data from multiple sources.

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Section: Discussionmentioning

confidence: 99%

“…Ongoing efforts to sequence full length transcripts (81,82), integrated with annotated promoters and TSSs, should eventually resolve such issues and greatly aid the study of TL functions in mammals.…”

Section: Discussionmentioning

confidence: 99%

False-Positive IRESes from Hoxa9 and other genes resulting from errors in mammalian 5’ UTR annotations

Akirtava

May

McManus

2022

Preprint

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“…Contrary to other approaches, combinatorial barcoding does not require any complex microfluidics or other specialized equipment. Over the past few years, this approach has been widely adopted to study developmental processes and disease progression at scale [13][14][15][16][17][18][19][20][21][22][23][24][25][26] .…”

Section: Introductionmentioning

confidence: 99%

High sensitivity single cell RNA sequencing with split pool barcoding

Tran¹,

Papalexi²,

Schroeder³

et al. 2022

Preprint

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Single cell RNA sequencing (scRNA-seq) has become a core tool for researchers to understand biology. As scRNA-seq has become more ubiquitous, many applications demand higher scalability and sensitivity. Split-pool combinatorial barcoding makes it possible to scale projects to hundreds of samples and millions of cells, overcoming limitations of previous droplet based technologies. However, there is still a need for increased sensitivity for both droplet and combinatorial barcoding based scRNA-seq technologies. To meet this need, here we introduce an updated combinatorial barcoding method for scRNA-seq with dramatically improved sensitivity. To assess performance, we profile a variety of sample types, including cell lines, human peripheral blood mononuclear cells (PBMCs), mouse brain nuclei, and mouse liver nuclei. When compared to the previously best performing approach, we find up to a 2.6-fold increase in unique transcripts detected per cell and up to a 1.8-fold increase in genes detected per cell. These improvements to transcript and gene detection increase the resolution of the resulting data, making it easier to distinguish cell types and states in heterogeneous samples. Split-pool combinatorial barcoding already enables scaling to millions of cells, the ability to perform scRNA-seq on previously fixed and frozen samples, and access to scRNA-seq without the need to purchase specialized lab equipment. Our hope is that by combining these previous advantages with the dramatic improvements to sensitivity presented here, we will elevate the standards and capabilities of scRNA-seq for the broader community.

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“…We therefore designed the initial version of BLAZE to facilitate 10x barcode identification. Recent developments in throughput and accuracy for PacBio HiFi sequencing are increasing the applicability of PacBio for LR scRNA-seq, while LR nanopore protocols for other scRNA-seq modalities such as Split-seq are also now available [14,[31][32][33][34]. Although BLAZE is currently limited to identification of 10x barcodes from nanopore reads, we see potential to expand BLAZE to process both PacBio HiFi reads and reads from other scRNA-seq methods in the future.…”

Section: Discussionmentioning

confidence: 99%

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

You

Prawer

Paoli-Iseppi

et al. 2022

Preprint

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Single-cell RNA sequencing (scRNA-seq) has revolutionised our ability to profile gene expression. However, short-read (SR) scRNAseq methodologies such as 10x are restricted to sequencing the 3' or 5' ends of transcripts, providing accurate gene expression but little information on the RNA isoforms expressed in each cell. Newly developed long-read (LR) scRNA-seq enables the quantification of RNA isoforms in individual cells but LR scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes and allow single cell analysis. Here we introduce BLAZE (Barcode identification from long-reads for AnalyZing single-cell gene Expression), which accurately and efficiently identifies 10x cell barcodes using only nanopore LR scRNA-seq data. We compared BLAZE to existing tools, including cell barcodes identified from matched SR scRNA-seq, on differentiating stem cells and 5 cancer cell lines. BLAZE outperforms existing tools and provides a more accurate representation of the cells present in LR scRNA-seq than using matched short-reads. BLAZE provides accurate cell barcodes over a wide range of experimental read depths and sequencing accuracies, while other methodologies commonly identify false-positive barcodes and cell clusters, disrupting biological interpretation of LR scRNA-seq results. In conclusion, BLAZE eliminates the requirement for matched SR scRNA-seq to interpret LR scRNA-seq, simplifying procedures and decreasing costs while also improving LR scRNA-seq results. BLAZE is compatible with downstream tools accepting a cell barcode whitelist file and is available at https://github.com/shimlab/BLAZE.

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Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

Cited by 40 publications

References 64 publications

False-Positive IRESes from Hoxa9 and other genes resulting from errors in mammalian 5’ UTR annotations

False-Positive IRESes from Hoxa9 and other genes resulting from errors in mammalian 5’ UTR annotations

High sensitivity single cell RNA sequencing with split pool barcoding

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

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