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◇ bioRxiv2026-08-29· genomics

Decoding the Transcriptome Dark Matter: Construction of Single-Cell Whole-Transcriptome Regulatory Atlas by dropTotal

X. Liu, W. Cao, Y. Pan, Z. Luo, T. Wu, Y. Du, X. Xu, Z. Jin, C. Li, Y. Mu, Y. Liu, Q. Zhu

原始摘要(英文原文)· Original abstract
To profile unknown ncRNAs-"dark matter" in single cells, we developed dropTotal, a high-throughput droplet-based total RNA-seq method that uses dU-modified GAT primer with temperature-ramp hybridization and droplet merge barcoding to co-detect coding and non-coding transcripts with record sensitivity (>13,500 genes/cell, including >2,000 lncRNAs and >500 sncRNAs), compatible with fresh, frozen, fixed, and FFPE tissues. Applied to ~75,000 human glioma nuclei, it captured 60,313 genes (18,681 lncRNA, 19,859 mRNAs and 6,753 sncRNAs), enabling ncRNA-driven regulatory landscape construction. In oligodendroglioma, module analysis identified recurrence-associated ncRNA-centered modules linked to therapy resistance and invasion; in glioblastoma, six cellular states showed hundreds of state-specific unannotated ncRNAs with divergent functions, from MIR222HG-mediated immune modulation to SCIRT-driven hypoxia adaptation. Alternative splicing analysis identified 428 state-specific junction markers and mapped cell-state-specific alternative splicing regulation. dropTotal offers broad application for decoding the underlying ncRNA biology and single-cell whole transcriptome regulatory mechanisms in cellular identity and disease progression.
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Decoding the Transcriptome Dark Matter: Construction of Single-Cell Whole-Transcriptome Regulatory Atlas by dropTotal — 科研速览 Science Skim