{"doi":"10.1101/2022.03.14.484332","title":"Efficient profiling of total RNA in single cells with STORM-seq","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>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.</jats:p>","journal":null,"year":null,"id":629153,"datarank":0.4335557636844247,"base_score":2.8903717578961645,"endowment":2.8903717578961645,"self_citation_contribution":0.4335557636844247,"citation_network_contribution":0.0,"self_endowment_contribution":0.4335557636844247,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":8,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":69088,"name":"Mary F. Majewski","orcid":null,"position":1,"is_corresponding":false},{"id":280738,"name":"H. Josh Jang","orcid":"0000-0001-9654-4448","position":2,"is_corresponding":false},{"id":1041906,"name":"Rebecca A. Siwicki","orcid":"0000-0002-2061-3782","position":3,"is_corresponding":false},{"id":1338578,"name":"Marc Wegener","orcid":null,"position":4,"is_corresponding":false},{"id":1028106,"name":"Ayush Semwal","orcid":"0000-0002-7598-6834","position":5,"is_corresponding":false},{"id":638458,"name":"Jacob Morrison","orcid":"0000-0001-8592-4744","position":6,"is_corresponding":false},{"id":1081098,"name":"Kristin L. Gallik","orcid":"0000-0003-3587-1028","position":7,"is_corresponding":false},{"id":859529,"name":"Michael Hagemann-Jensen","orcid":"0000-0002-6423-8216","position":8,"is_corresponding":false},{"id":285621,"name":"Kelly K. Foy","orcid":"0000-0002-7695-4922","position":9,"is_corresponding":false},{"id":639501,"name":"Larissa L. Rossell","orcid":null,"position":10,"is_corresponding":false},{"id":639502,"name":"Emily J. Siegwald","orcid":null,"position":11,"is_corresponding":false},{"id":669224,"name":"Dave Chesla","orcid":"0000-0002-4612-2586","position":12,"is_corresponding":false},{"id":353083,"name":"Jose M. Teixeira","orcid":"0000-0002-6438-5064","position":13,"is_corresponding":false},{"id":397506,"name":"Marie Adams","orcid":"0000-0001-7909-2339","position":14,"is_corresponding":false},{"id":1436,"name":"Ronny Drapkin","orcid":"0000-0002-6912-6977","position":15,"is_corresponding":false},{"id":481028,"name":"Corinne R. Esquibel","orcid":"0000-0001-6543-8386","position":16,"is_corresponding":false},{"id":703562,"name":"Rachael Sheridan","orcid":"0000-0002-7934-7888","position":17,"is_corresponding":false},{"id":24564,"name":"Ting Wang","orcid":"0000-0002-6800-242X","position":18,"is_corresponding":false},{"id":229162,"name":"Rickard Sandberg","orcid":"0000-0001-6473-1740","position":19,"is_corresponding":false},{"id":1837,"name":"Timothy J. Triche","orcid":"0000-0001-5665-946X","position":20,"is_corresponding":false},{"id":14040,"name":"Hui Shen","orcid":"0000-0001-9767-4084","position":21,"is_corresponding":false},{"id":486008,"name":"Benjamin K. Johnson","orcid":"0000-0002-1482-1032","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Efficient profiling of total RNA in single cells with STORM-seq","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>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.</jats:p>","is_dataset_classified":null,"base_score":2.8903717578961645,"endowment":2.8903717578961645,"datacite_reuse_total":8,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40475517","pmcid":null,"openalex_id":"https://openalex.org/W4220838582","authors":[],"funders":[{"funder_name":"NCI","grant_id":"R37CA230748","title":null},{"funder_name":"NCI","grant_id":"R03CA290259","title":null},{"funder_name":"Ovarian Cancer Research Alliance","grant_id":"891749","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01AI171984-02","title":"The roles of genetics, hormones, and gender in sexually dimorphic immune response"},{"funder_name":"National Institutes of Health","grant_id":"4R37CA230748-06","title":"High-throughput Epigenomic Mapping of Regulatory Elements in Ovarian Cancer at Basepair Resolution"}],"total_grants":5,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2022,"count":2},{"year":2023,"count":1},{"year":2024,"count":4},{"year":2025,"count":9},{"year":2026,"count":1}],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/03/14/2022.03.14.484332.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/03/14/2022.03.14.484332.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2022.03.14.484332","host_type":"publisher"},{"url":"https://doi.org/10.1101/2022.03.14.484332","host_type":"repository"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40475517","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12139823","host_type":"repository"},{"url":"http://dx.doi.org/10.1101/2022.03.14.484332","host_type":""}],"fields_of_study":["Ovarian cancer diagnosis and treatment","Single-cell and spatial transcriptomics","0301 basic medicine","0303 health sciences","03 medical and health sciences"],"mesh_terms":[],"keywords":["Fallopian tube","Epithelium","Cell biology","Biology","RNA-Seq","RNA","Cell","Anatomy","Transcriptome","Gene","Genetics","Gene expression","Article"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Life below water"}],"linked_datasets":[{"doi":"10.5281/zenodo.18340827","title":"Imaging and Flow Cytometry data for \"Efficient profiling of total RNA in single cells with STORM-seq\"","publisher":"Zenodo","resource_type":"Text"},{"doi":"10.5281/zenodo.18512862","title":"Code supporting \"Efficient profiling of total RNA in single cells with STORM-seq\"","publisher":"Zenodo","resource_type":"Software"},{"doi":"10.5281/zenodo.18515238","title":"Additional data supporting \"Efficient profiling of total RNA in single cells with STORM-seq\"","publisher":"Zenodo","resource_type":"Dataset"},{"doi":"10.5281/zenodo.18515237","title":"Additional data supporting \"Efficient profiling of total RNA in single cells with STORM-seq\"","publisher":"Zenodo","resource_type":"Dataset"},{"doi":"10.5281/zenodo.18512861","title":"Code supporting \"Efficient profiling of total RNA in single cells with STORM-seq\"","publisher":"Zenodo","resource_type":"Software"},{"doi":"10.5281/zenodo.18340825","title":"Imaging and Flow Cytometry data for \"Efficient profiling of total RNA in single cells with STORM-seq\"","publisher":"Zenodo","resource_type":"Text"},{"doi":"10.5281/zenodo.10908626","title":"STORMqc version 0.1.0","publisher":"Zenodo","resource_type":"Software"},{"doi":"10.5281/zenodo.10908625","title":"STORMqc version 0.1.0","publisher":"Zenodo","resource_type":"Software"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T17:09:54.302793Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}