{"entity": "publication", "iuid": "51b964e9a47043c58890db5f731459d4", "timestamp": "2026-09-29T02:15:36.492Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/51b964e9a47043c58890db5f731459d4.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/51b964e9a47043c58890db5f731459d4"}}, "title": "Single nucleus transcriptomics data integration recapitulates the major cell types in human liver.", "authors": [{"family": "Diamanti", "given": "Klev", "initials": "K", "orcid": "0000-0002-4922-8415", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8638ca04ea7f4ff8a191f3c6969b1394.json"}}, {"family": "Inda D\u00edaz", "given": "Juan Salvador", "initials": "JS", "orcid": "0000-0002-3735-8300", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eaba7e7cf3f4fdeb9883a16f5c6d9fe.json"}}, {"family": "Raine", "given": "Amanda", "initials": "A", "orcid": "0000-0002-2775-6516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/79424b142f554519a5ae5a169b71366e.json"}}, {"family": "Pan", "given": "Gang", "initials": "G", "orcid": "0000-0003-4243-1821", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8322e7f4f5a842d0930a69a5e2eaf425.json"}}, {"family": "Wadelius", "given": "Claes", "initials": "C", "orcid": "0000-0002-2033-7829", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f01aa16d2b3343e980a2f2a9eb93af91.json"}}, {"family": "Cavalli", "given": "Marco", "initials": "M", "orcid": "0000-0003-1143-1431", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c3104b875f2e459093b1e468ba4af556.json"}}], "type": "journal article", "published": "2021-02-00", "journal": {"title": "Hepatol. Res.", "issn": "1386-6346", "volume": "51", "issue": "2", "pages": "233-238", "issn-l": null}, "abstract": "The aim of this study was to explore the benefits of data integration from different platforms for single nucleus transcriptomics profiling to characterize cell populations in human liver.\n\nWe generated single-nucleus RNA sequencing data from Chromium 10X Genomics and Drop-seq for a human liver sample. We utilized state of the art bioinformatics tools to undertake a rigorous quality control and to integrate the data into a common space summarizing the gene expression variation from the respective platforms, while accounting for known and unknown confounding factors.\n\nAnalysis of single nuclei transcriptomes from both 10X and Drop-seq allowed identification of the major liver cell types, while the integrated set obtained enough statistical power to separate a small population of inactive hepatic stellate cells that was not characterized in either of the platforms.\n\nIntegration of droplet-based single nucleus transcriptomics data enabled identification of a small cluster of inactive hepatic stellate cells that highlights the potential of our approach. We suggest single-nucleus RNA sequencing integrative approaches could be utilized to design larger and cost-effective studies.", "doi": "10.1111/hepr.13585", "pmid": "33119937", "labels": [], "xrefs": [], "notes": [], "created": "2026-09-23T11:08:12.005Z", "modified": "2026-09-23T11:08:12.099Z"}