Dataset Information

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

ABSTRACT: The rise in throughput and quality of long-read sequencing should allow unambiguous identification of full-length transcript isoforms. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here we develop and characterize long-read Split-seq (LR-Split-seq), which uses combinatorial barcoding to sequence single cells with long reads. Applied to the C2C12 myogenic system, LR-split-seq associates isoforms to cell types with relative economy and design flexibility. We find widespread evidence of changing isoform expression during differentiation including alternative transcription start sites (TSS) and/or alternative internal exon usage. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells.

ORGANISM(S): Mus musculus

PROVIDER: GSE168776 | GEO | 2021/03/12

REPOSITORIES: GEO

ACCESS DATA

Shared Molecules

Only show the datasets with similarity scores above: 0.5

Threshold

0.5

Similar Datasets

Project description:Transcription and translation are intertwined processes where mRNA isoforms are crucial intermediaries. However, methodological limitations in analyzing translation at the mRNA isoform level have impaired our ability to comprehensively establish links between the full-length transcripts and the translatome. This has left gaps in our understanding of critical biological processes, regulatory mechanisms, and disease progression. To address this, we develop an integrated computational and experimental framework called long-read Ribo-STAMP (LR-Ribo-STAMP). LR-Ribo-STAMP capitalizes on advancements in long-read sequencing and RNA-base editing-mediated technologies to simultaneously and scalably profile translation and transcription at both gene and mRNA isoform levels for the first time. In this report, we show agreement between gene-level translation profiles obtained with LR-Ribo-STAMP and those from previously validated short-read Ribo-STAMP data in unperturbed cells. At the mRNA isoform level, we show that LR-Ribo-STAMP successfully profiles translation in unperturbed cells and links mRNA isoforms and regulatory features, such as upstream ORFs (uORFs) and regulatory sequences, to translation measurements. We further demonstrate the method’s effectiveness in profiling disease models by profiling translation at gene and isoform levels in a triple-negative breast cancer cell line under normoxia and hypoxia. Here, we find that LR-Ribo-STAMP effectively delineates orthogonal transcriptional and translation shifts between conditions at gene and isoform levels. At the isoform level, LR-Ribo-STAMP uniquely identifies key regulatory elements and shifts in mRNA isoform transcription that correlate with changes in translational, providing an example of insight that can inform the development of novel therapeutics. Overall, LR-Ribo-STAMP is a significant advancement in translation methods and can have profound implications for basic research and clinical applications.