Single cell and spatial alternative splicing analysis with Nanopore long read sequencing

This is the authors' abstract. We don't add key points for this paper.

Long-read sequencing boosts alternative splicing analysis but faces technical and computational barriers in single-cell and spatial settings. High Nanopore error rates compromise cell barcode and UMI recovery, while read truncation and misalignment undermine isoform quantification. Downstream, a statistical framework to assess splicing variation within and between cells or spatial spots is lacking. We introduce Longcell, a statistical and computational pipeline for isoform quantification from single-cell and spatially barcoded Nanopore long reads. Longcell efficiently recovers cell barcodes and UMIs, corrects sequencing errors, and models splicing diversity within and between cells or spots. Applied across multiple datasets, Longcell allows accurate identification of spatial isoform switching. Longcell also reveals widespread high intra-cell isoform heterogeneity for highly expressed genes. Finally, on a perturbation experiment for 9 splicing factors, Longcell identifies regulatory targets that are validated by targeted sequencing. Single-cell isoform quantification using Nanopore long reads remains limited by sequencing errors. Here, authors present Longcell, a computational framework that corrects these errors and uncovers splicing variation across cell populations and spatial contexts.