Isoform-resolved spatial transcriptomics on a lab-made high-density array via a single-chip NGS-TGS workflow
Yue, Z.; Liu, M.; Liu, Y.; Lu, D.; Zhang, M.; Wang, Y.; Shi, Y.; Miao, Y.; Wang, S.; Jiang, Y.; Wang, Y.; Zhao, J.; Liu, N.; Lv, C.; Zhai, J.; Li, B.
Show abstract
High-resolution spatial transcriptomics is still cost-prohibitive and dominated by short-read sequencing, limiting in situ detection of transcript structures. Here we present a low-cost, lab-made, high-density bead-in-microwell chip assembled by routine centrifuge and decoded by a tri-part combinatorial long-barcode strategy. The design provides 5.6x107 barcode combinations across 4.1x10{square} capture sites, reducing collisions while remaining tolerant to third-generation sequencing raw errors. Using a single-chip, dual-platform workflow, we split full-length cDNA for next-generation and third-generation sequencing within the same spatial coordinates. In an incompatible tomato-pepper graft, we identified interface-associated splicing reprogramming, including thousands of unannotated isoforms, enriched novel CaGRP1 isoforms, and SlPIP2 intron retention, supported by individual long-read sequences. In mouse embryos, we further demonstrate cross-species applicability by resolving unannotated Col1a2 isoforms in oligodendrocyte progenitors and spatially restricted Dalrd3 intron retention. This cost-efficient system broadens access to spatial full-length transcriptomics and may support diverse biological studies.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Multiplexed spatial mapping of chromatin features, transcriptome, and proteins in tissues 97%
- ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells 97%
- A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines 96%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.