Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution

Formalin-fixed paraffin-embedded (FFPE) tissue–compatible spatial transcriptomics (ST) is transforming biology and histopathology, but achieving high sensitivity, genome-wide coverage, single-cell resolution, and researcher accessibility remains challenging. To address these challenges, we here present a dendrimeric DNA coordinate barcoding design for FFPE tissue–adapted spatial RNA sequencing (Decoder-FFPE-seq) by integrating RNA-to-DNA probe conversion with blotting-assisted barcoding. Decoder-FFPE-seq provides single-cell resolution with a >2.6-fold sensitivity increase in whole-transcriptome mouse brain and archived human sample profiling over existing platforms, resolving potential cellular identity of low-abundance genes, finer tissue architecture, and capturing an epithelial-mesenchymal transition–associated signature for prognostic value. With expanded probe libraries, Decoder-FFPE-seq captures long noncoding and viral RNAs, revealing their spatial coregulatory networks with messenger RNAs in oral squamous cell carcinomas (OSCCs). In an OSCC immunotherapy cohort, two CD8 + T cell subpopulations were respectively identified in pre- and postchemoimmunotherapy samples, potentially linked to treatment response. Decoder-FFPE-seq establishes a robust, high-resolution, and accessible framework for sensitive ST profiling of archival tissues in fundamental and translational studies.

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Publication Details

Journal
Science Advances
Published
2026-10-30
DOI
https://doi.org/10.1126/sciadv.ady5085
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution

Lingling Wu, Yiwei Meng, Jian Chen Fang, Zhong Zheng et al.
4 links
Science Advances
Single-cell and spatial transcriptomics
article

Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution

Lingling Wu, Yiwei Meng, Jian Chen Fang, Zhong Zheng, Chaoyong James Yang, Xing Fang, Weixiong Shi, Gang Chen, Xuepeng Xiong, Jiao Cao, Yang Guo, Yutong Liu, Shanqing Huang, Jing Zhang, Jun Zeng
article en

Abstract

Formalin-fixed paraffin-embedded (FFPE) tissue–compatible spatial transcriptomics (ST) is transforming biology and histopathology, but achieving high sensitivity, genome-wide coverage, single-cell resolution, and researcher accessibility remains challenging. To address these challenges, we here present a dendrimeric DNA coordinate barcoding design for FFPE tissue–adapted spatial RNA sequencing (Decoder-FFPE-seq) by integrating RNA-to-DNA probe conversion with blotting-assisted barcoding. Decoder-FFPE-seq provides single-cell resolution with a >2.6-fold sensitivity increase in whole-transcriptome mouse brain and archived human sample profiling over existing platforms, resolving potential cellular identity of low-abundance genes, finer tissue architecture, and capturing an epithelial-mesenchymal transition–associated signature for prognostic value. With expanded probe libraries, Decoder-FFPE-seq captures long noncoding and viral RNAs, revealing their spatial coregulatory networks with messenger RNAs in oral squamous cell carcinomas (OSCCs). In an OSCC immunotherapy cohort, two CD8 + T cell subpopulations were respectively identified in pre- and postchemoimmunotherapy samples, potentially linked to treatment response. Decoder-FFPE-seq establishes a robust, high-resolution, and accessible framework for sensitive ST profiling of archival tissues in fundamental and translational studies.

Science AdvancesVol. 12(44)
Shanghai Jiao Tong University (CN), Xiamen University (CN), Renji Hospital (CN), Wuhan University (CN)
Innovative Research Team of High-level Local University in Shanghai, National Natural Science Foundation of China, Shanghai Rising-Star Program
Good health and well-being
Openalex Percentile: Top 22%
Single-cell and spatial transcriptomics
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