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

Yuntian Fu (), Heonseok Kim, Sharmili Roy, Sijia Huang, Jenea I. Adams, Susan M. Grimes, Billy T. Lau, Anuja Sathe, Hanlee P. Ji () and Nancy R. Zhang ()
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Yuntian Fu: University of Pennsylvania
Heonseok Kim: Stanford University School of Medicine
Sharmili Roy: Stanford University School of Medicine
Sijia Huang: University of Pennsylvania
Jenea I. Adams: University of Pennsylvania
Susan M. Grimes: Stanford University School of Medicine
Billy T. Lau: Stanford University School of Medicine
Anuja Sathe: Stanford University School of Medicine
Hanlee P. Ji: Stanford University School of Medicine
Nancy R. Zhang: University of Pennsylvania

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-60902-2

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