Advanced Science (Feb 2024)

Pan‐Cancer Single‐Nucleus Total RNA Sequencing Using snHH‐Seq

  • Haide Chen,
  • Xiunan Fang,
  • Jikai Shao,
  • Qi Zhang,
  • Liwei Xu,
  • Jiaye Chen,
  • Yuqing Mei,
  • Mengmeng Jiang,
  • Yuting Wang,
  • Zhouyang Li,
  • Zihang Chen,
  • Yang Chen,
  • Chengxuan Yu,
  • Lifeng Ma,
  • Peijing Zhang,
  • Tianyu Zhang,
  • Yuan Liao,
  • Yuexiao Lv,
  • Xueyi Wang,
  • Lei Yang,
  • Yuting Fu,
  • Daobao Chen,
  • Liming Jiang,
  • Feng Yan,
  • Wei Lu,
  • Gao Chen,
  • Huahao Shen,
  • Jingjing Wang,
  • Changchun Wang,
  • Tingbo Liang,
  • Xiaoping Han,
  • Yongcheng Wang,
  • Guoji Guo

DOI
https://doi.org/10.1002/advs.202304755
Journal volume & issue
Vol. 11, no. 5
pp. n/a – n/a

Abstract

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Abstract Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single‐cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA‐seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high‐throughput and high‐sensitivity method called snHH‐seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full‐length RNA‐seq data is also established. snHH‐seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan‐cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full‐length RNA at the single‐nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

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