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bioRxiv : the preprint server for biology · 2025

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

СтатьяПрепринтНазвание и аннотация: Перевод в очереди
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Johnson Benjamin K, Majewski Mary F, Jang H Josh, Siwicki Rebecca A, Wegener Marc, Semwal Ayush, Morrison Jacob, Gallik Kristin, Hagemann-Jensen Michael, Foy Kelly, Rossell Larissa L, Siegwald Emily J, Chesla Dave W, Teixeira Jose M, Adams Marie, Drapkin Ronny, Esquibel Corinne, Sheridan Rachael T C, Wang Ting, Sandberg Rickard, Triche Timothy J, Shen Hui

Аннотация

Despite significant advances, current single-cell RNA sequencing (scRNA-seq) technologies often struggle with accurately detecting non-coding transcripts, achieving full-length RNA coverage, and/or resolving transcript-level complexity. Many are also difficult to implement or inaccessible without specialized liquid handlers, further limiting their utility. We present Single-cell TOtal RNA-seq Miniaturized (STORM-seq), a random-hexamer primed, ribo-reduced single-cell total RNA sequencing (sc-total-RNA-seq) protocol using standard laboratory equipment. Adapted as a kit, STORM-seq constructs sequence-ready libraries in one working day, producing the highest complexity scRNA-seq libraries to-date, robustly measuring transcript isoforms and clinically relevant gene fusions in single cells. STORM-seq faithfully reconstructs expression profiles of locus-level transposable elements (TEs), and provides high-resolution profiling of transient, low-abundance enhancer RNAs (eRNAs), offering a powerful tool to dissect single-cell gene regulatory networks in unprecedented detail. Applied to human fallopian tube epithelium, the improved transcriptional resolution reveals a putative progenitor-like population and intermediate cell states, shaped by TEs and non-coding RNAs.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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