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Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens
Published on: May 5, 2018
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Single-nucleus total RNA sequencing of formalin-fixed paraffin-embedded samples using snRandom-seq
Ziye Xu1,2, Yuexiao Lyu1, Haide Chen1
1Department of Laboratory Medicine of The First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, China.
Nature Protocols
|April 25, 2025
Summary
snRandom-seq enables single-nucleus RNA sequencing from challenging formalin-fixed paraffin-embedded (FFPE) tissues. This novel method enhances RNA detection and offers a 4-day protocol for analyzing archived clinical specimens.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Formalin-fixed paraffin-embedded (FFPE) tissues are a rich source of clinical data but difficult for single-nucleus RNA sequencing.
- Existing high-throughput single-cell/single-nucleus RNA sequencing (sc/snRNA-seq) methods face challenges with FFPE sample quality and RNA integrity.
Purpose of the Study:
- To develop and validate a novel method for single-nucleus RNA sequencing (snRNA-seq) specifically optimized for FFPE tissues.
- To overcome limitations in RNA capture and detection in archived clinical samples.
Main Methods:
- Developed snRandom-seq, a droplet- and random primer-based technology for FFPE snRNA-seq.
- Protocol includes single-nucleus isolation, in situ DNA blocking, reverse transcription, dA tailing, barcoding, and library preparation.
- Utilizes random primers for comprehensive total RNA capture.
Main Results:
- snRandom-seq achieved a low doublet rate of 0.3%.
- Demonstrated increased RNA coverage and enhanced detection of non-coding and nascent RNAs compared to other methods.
- The entire protocol is completed within 4 days.
Conclusions:
- snRandom-seq is a powerful and efficient platform for snRNA-seq analysis of FFPE clinical specimens.
- This technology expands the utility of archived tissues for studying complex biological systems and diseases.
- Enables deeper insights into cellular heterogeneity and molecular mechanisms from historical patient cohorts.

