Single-cell transcriptome sequencing on the Nanopore platform with ScNapBar.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) platforms are expanding, yet robust full-length RNA sequencing solutions are lacking.
• Long-read sequencing from Nanopore offers potential for scRNA-seq integration with 10x Chromium, but high error rates complicate downstream analysis.
• Accurate cell barcode assignment is crucial for scRNA-seq data integrity.

Purpose of the Study:

• To develop a high-throughput solution for full-length RNA sequencing at the single-cell level.
• To address the challenge of high error rates in Nanopore sequencing for cell barcode assignment.
• To enable accurate analysis of mRNA dynamics in individual cells.

Main Methods:

• Proposed a hybrid sequencing approach combining Nanopore and Illumina platforms.
• Developed ScNapBar software for cell barcode assignment using unique molecular identifiers (UMIs) or Naive Bayes methods.
• Benchmarked ScNapBar on simulated and real Nanopore datasets, including analysis of nonsense-mediated RNA decay pathways.

Main Results:

• ScNapBar achieves high accuracy in cell barcode assignment, particularly with low sequencing saturation.
• The software effectively distinguishes cell populations based on their RNA decay pathway status.
• Identified characteristic nonsense-mediated mRNA decay events in Nanopore-based single-cell RNA sequencing data.

Conclusions:

• ScNapBar provides a robust solution for accurate cell barcode assignment in Nanopore-based scRNA-seq.
• The hybrid approach enhances data quality and enables detailed analysis of mRNA processing.
• This method facilitates the study of complex cellular processes like RNA decay at the single-cell level.