Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq
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ABSTRACT:
PROVIDER: PRJNA713904 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13948564_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR13948565_1.fastq.gz | Fastqsanger.gz | |||
| SRR13948565_2.fastq.gz | Fastqsanger.gz | |||
| SRR13948566_1.fastq.gz | Fastqsanger.gz | |||
| SRR13948566_2.fastq.gz | Fastqsanger.gz |
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Project description: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.
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Project description:Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing methods using either short-read (SR) or long-read (LR) RNA sequencing have significant limitations: SR sequencing provides high depth but struggles with isoform deconvolution, while LR sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. Applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of mRNA abundance determinants, reveals the role of untranslated regions (UTRs) in isoform regulation through isoform-specific interactions with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.
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