Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution
Cao, J.; Zeng, J.; Meng, Y.; Huang, S.; Zhang, J.; Fang, X.; Guo, Y.; Shi, W.; Liu, Y.; Zheng, Z.; Fang, J.; Xiong, X.; Chen, G.; Wu, L.; Yang, C.
Show 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. Here, we present a dendrimeric DNA coordinate-barcoding design for FFPE-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 2.54-4.25-fold sensitivity increase in mouse brain transcriptomic profiling over similar assays with an available protocol, successfully mapping rare genes and sparse cell types. It robustly resolves tumor microenvironments (TMEs) in clinical archives preserved 6-20 years. With expanded probe libraries, Decoder-FFPE-seq captures long non-coding and viral RNAs, revealing spatial co-expression networks with mRNAs in oral squamous cell carcinomas (OSCCs). In an OSCC immunotherapy cohort, it delineates TME heterogeneity across pathology response groups and tracks spatial cellular dynamics pre- and post-immuno-chemotherapy. Decoder-FFPE-seq establishes a robust, high-resolution framework for sensitive ST profiling in fundamental and translational studies of archival tissues.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- YAP-Driven Oral Epithelial Stem Cell Malignant Reprogramming at Single Cell Resolution 97%
- Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response 97%
- Reconstructing disease dynamics for mechanistic insights and clinical benefit 97%
"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.