sc-rDSeq: Droplet-based single-cell full-length total RNA-seq method

Abstract This protocol describes sc‑rDSeq, a scalable, droplet‑based method for full‑length, strand‑specific total RNA sequencing at single‑cell resolution. The protocol uses a refined set of 220 ribosomal‑depleted sequences (rDS) primers that selectively exclude ribosomal RNA during initial reverse transcription, enabling capture of both polyadenylated and non‑polyadenylated RNAs such as histone RNAs, non‑coding RNAs, and enhancer RNAs, without requiring costly post‑amplification depletion steps. This method is useful for researchers who would like to detect not only gene expression variations, but also alternative splicing events and single nucleotide variations in complex heterogeneous cellular systems, providing a more complete view of cellular heterogeneity and regulatory programs that remain invisible to conventional polyadenylated‑only sequencing approaches. Compared with existing full‑length protocols, which are often limited by high reagent costs or reliance on complex multi‑step microfluidics, sc-rDSeq provides a simpler, single-step microfluidic workflow compatible with standard inDrops platforms, which may reduce experimental complexity and cost relative to existing full-length total-RNA methods. A key improvement is the tenfold increase in unique molecular identifiers per cell relative to 3′ end‑based methods, at a reported reagent cost of approximately $0.08 per cell, making deep total transcriptome analysis more accessible. The protocol includes three major parts: sc‑rDSeq barcode synthesis, single‑cell co‑encapsulation, and library construction. For complete details on the use and execution of this protocol, please refer to Sun et al., 2026 (DOI: 10.1093/nar/gkag312).

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Publication Details

Journal
Biology Methods and Protocols
Published
2026-05-28
DOI
https://doi.org/10.1093/biomethods/bpag027
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

sc-rDSeq: Droplet-based single-cell full-length total RNA-seq method

Oren Ram, Xue Sun
Biology Methods and Protocols
Single-cell and spatial transcriptomics
article

sc-rDSeq: Droplet-based single-cell full-length total RNA-seq method

Oren Ram, Xue Sun
article en

Abstract

Abstract This protocol describes sc‑rDSeq, a scalable, droplet‑based method for full‑length, strand‑specific total RNA sequencing at single‑cell resolution. The protocol uses a refined set of 220 ribosomal‑depleted sequences (rDS) primers that selectively exclude ribosomal RNA during initial reverse transcription, enabling capture of both polyadenylated and non‑polyadenylated RNAs such as histone RNAs, non‑coding RNAs, and enhancer RNAs, without requiring costly post‑amplification depletion steps. This method is useful for researchers who would like to detect not only gene expression variations, but also alternative splicing events and single nucleotide variations in complex heterogeneous cellular systems, providing a more complete view of cellular heterogeneity and regulatory programs that remain invisible to conventional polyadenylated‑only sequencing approaches. Compared with existing full‑length protocols, which are often limited by high reagent costs or reliance on complex multi‑step microfluidics, sc-rDSeq provides a simpler, single-step microfluidic workflow compatible with standard inDrops platforms, which may reduce experimental complexity and cost relative to existing full-length total-RNA methods. A key improvement is the tenfold increase in unique molecular identifiers per cell relative to 3′ end‑based methods, at a reported reagent cost of approximately $0.08 per cell, making deep total transcriptome analysis more accessible. The protocol includes three major parts: sc‑rDSeq barcode synthesis, single‑cell co‑encapsulation, and library construction. For complete details on the use and execution of this protocol, please refer to Sun et al., 2026 (DOI: 10.1093/nar/gkag312).

Biology Methods and Protocols
Hebrew University of Jerusalem (IL)
Openalex Percentile: Top 8%
Single-cell and spatial transcriptomics
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