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Advanced Science
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Advanced Science
Article . 2024
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Pan‐Cancer Single‐Nucleus Total RNA Sequencing Using snHH‐Seq

Authors: Haide Chen; Xiunan Fang; Jikai Shao; Qi Zhang; Liwei Xu; Jiaye Chen; Yuqing Mei; +26 Authors

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

Abstract

AbstractTumor 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.

Related Organizations
Keywords

Sequence Analysis, RNA, Science, Q, full‐length RNA, pan‐cancer, total RNA, Neoplasms, Humans, RNA, single‐nucleus RNA sequencing, high‐throughput and high‐sensitivity, RNA-Seq, Research Articles, Ecosystem

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    11
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
Green
gold
Related to Research communities
Cancer Research