Efficient profiling of total RNA in single cells with STORM-seq.

Area of Science:

• Molecular Biology
• Genomics
• Transcriptomics

Background:

• Current single-cell RNA sequencing (scRNA-seq) methods face challenges in detecting non-coding transcripts, achieving full-length RNA coverage, and resolving transcript complexity.
• Many scRNA-seq protocols require specialized equipment, limiting accessibility and broad implementation.

Purpose of the Study:

• To introduce Single-cell TOtal RNA-seq Miniaturized (STORM-seq), a novel, accessible protocol for comprehensive single-cell total RNA sequencing.
• To demonstrate STORM-seq's capability to enhance transcript detection, including non-coding RNAs, isoforms, and gene fusions, using standard laboratory equipment.

Main Methods:

• STORM-seq utilizes a random-hexamer primed, ribo-reduced single-cell total RNA sequencing approach.
• The protocol is designed for rapid library construction (one working day) and can be adapted as a kit, requiring standard laboratory equipment.

Main Results:

• STORM-seq generates high-complexity libraries, enabling robust measurement of transcript isoforms and clinically relevant gene fusions in single cells.
• The method accurately profiles locus-level transposable elements (TEs) and low-abundance enhancer RNAs (eRNAs), providing high-resolution gene regulatory network insights.
• Application to human fallopian tube epithelium revealed a progenitor-like population and intermediate cell states influenced by TEs and non-coding RNAs.

Conclusions:

• STORM-seq offers a powerful, accessible tool for detailed single-cell transcriptomic analysis, overcoming key limitations of existing scRNA-seq technologies.
• The protocol's ability to resolve complex transcriptomes, including non-coding RNAs and TEs, provides unprecedented resolution for dissecting single-cell gene regulatory networks.