Pan-cancer single-nucleus total RNA sequencing using HH-seq
Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing has been 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 during tumor processing. Additionally, frozen tumor samples constitute a vast and valuable material bank for cancer research. Therefore, we developed a high-throughput and high-sensitivity method called HH-seq, which combines random primers and a pre-index strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. We also established a robust pipeline to facilitate the analysis of full-length RNA-seq data. We applied HH-seq to more than 730,000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables us to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns and clone dynamic, etc. We identified new malignant cell subclusters and explored their specific function across cancers. Furthermore, we investigated the malignant status of epithelial cells 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. Overall design: Single nuclei RNA-sequencing of cell lines and mouse tissues using HH-seq, benchmark analysis was performed. 7 mouse brain samples were analyzed in four conditions of snHH-seq: different reverse transcription primer (No_index, RT primer without cell barcode 2; Index, RT primer with cell barcode 2), different sample storage (frozen, nuclei isolated from frozen sample; fresh, nuclei isolated from fresh sample). The MIX samples (human-mouse mix experiment) show the effect of cell concentration on contamination rate.