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

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.

Landscape and functional repertoires of long noncoding RNAs in the pan-cancer tumor microenvironment using single-nucleus total RNA sequencing

“…In our previous studies [19][20][21] , we developed a random-primed single nucleus total RNA sequencing method for FFPE and frozen tissues, and applied it to profile 32 samples derived from nine distinct cancer types 20 and 17 samples obtained from gliomas patients 21 , resulting in a combined dataset encompassing a total of 801,879 high-quality single-nucleus full-length total transcriptome data originating from 49 patients representing ten different cancer types. Leveraging this extended dataset, we construct an updated and refined transcriptional atlas of pan-cancer microenvironment (which was not certified by peer review) is the author/funder.…”

“…Despite the numerous advantages of scRNA-seq technologies, their applicability in studying lncRNAs has been limited. Unlike many other scRNA-seq methods, the random-primed technologies enable the detection of total RNAs, making it particularly suitable for the analysis of lncRNAs 19,20 . Through this approach, we bridge the gap and generate novel insights (which was not certified by peer review) is the author/funder.…”

“…The processed snRNA-seq datasets in the form of raw counts are acquired from our previous studies 20,21 . In total, 49 datasets comprising 49 samples are obtained encompassing ten distinct cancer types (Fig.…”

“…These methods rely on the hybridization of barcoded oligo-dT primers to the poly(A) sequences of polyadenylated transcripts for RNA capture and complementary DNA (cDNA) synthesis, which suffer from low capture efficiency for lncRNAs 16 . Nevertheless, these limitations can be overcome using random-primed single-cell/nucleus total RNA sequencing technologies such as MATQ-seq, VASA-seq, snRandom-seq, and snHH-seq, which offer high sensitivity in detecting lncRNAs 17–20 .…”

“…Nevertheless, these limitations can be overcome using random-primed single-cell/nucleus total RNA sequencing technologies such as MATQ-seq, VASA-seq, snRandom-seq, and snHH-seq, which offer high sensitivity in detecting lncRNAs [17][18][19][20] .…”