{
  "abstract": "Identifying individual donors from multi-donor biological mixtures remains challenging in forensic science, especially for same-tissue samples. Single-cell RNA sequencing (scRNA-seq) preserves individual genetic variation through cellular isolation, offering a promising solution. However, there is a lack of systematic evaluation regarding procedures for analyzing forensic mixtures. Here, we present and systematically evaluate a donor-reference-free deconvolution framework across complex three-donor and five-donor peripheral blood mixtures. To overcome transcriptomic sparsity and technogenic noise, we implemented a two-stage filtering strategy combining population-level pseudo-bulk variant prescreening via FreeBayes with cell-level genotyping using CellSNP-lite. This approach effectively purges false-positive candidate single-nucleotide polymorphisms, doubling the single-cell matrix density and increasing high-depth loci by nearly seven-fold. When integrated with the Vireo algorithm, the optimized upstream data quality elevates downstream genetic divergence scores, enabling clear individual deconvolution. Independent whole-genome sequencing validated the framework, demonstrating high genotype concordance and generating powerful likelihood ratios that far exceed conventional forensic identification thresholds. Furthermore, the deconvolved single-cell profiles enable reliable cross-sample donor matching and demonstrate concordant biogeographic ancestry assignments consistent with the known East Asian background of all participating contributors. This proof-of-concept study establishes a validated, practical framework for resolving same-tissue blood mixtures, providing a foundational baseline for the application of single-cell transcriptomics in forensic genetics.",
  "abstract_shown": true,
  "affiliations": [
    "School of Forensic Medicine, Shanxi Medical University, No. 55 Wenhua Street, Yuci District, Jinzhong, Shanxi 030619, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiayan Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Liangze Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yiwei Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiaxin Ji"
    },
    {
      "affs": [
        0
      ],
      "name": "Huan Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Haiqiang Xue"
    },
    {
      "affs": [
        0
      ],
      "name": "Wenrui Tian"
    },
    {
      "affs": [
        0
      ],
      "name": "Zidong Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Gengqian Zhang",
      "orcid": "0000-0002-7466-1958"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/68vfd42sf4ad5.bib",
  "cited_by": 0,
  "doi": "10.1093/bib/bbag597",
  "fulltext_url": "https://europepmc.org/articles/PMC13714790",
  "id": "68vfd42sf4ad5",
  "issns": [
    "1467-5463",
    "1477-4054"
  ],
  "landing_url": "https://europepmc.org/article/MED/42949537",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/SCI01060"
    ],
    [
      "heading",
      "Problem Solving Protocol"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7224093413",
  "pdf_url": "https://europepmc.org/articles/PMC13714790?pdf=render",
  "pmcid": "PMC13714790",
  "pmid": "42949537",
  "published": "2026-09-01",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/68vfd42sf4ad5.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:35Z",
      "native_id": "MED:42949537",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42949537"
    },
    {
      "first_seen": "2026-11-01T12:22:13Z",
      "native_id": "PMC13714790",
      "source": "pmc",
      "url": "https://doi.org/10.1093/bib/bbag597"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "A single-cell transcriptomic method for the deconvolution of multi-donor blood mixtures.",
  "title_text": "A single-cell transcriptomic method for the deconvolution of multi-donor blood mixtures.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:22:13Z",
  "url": "https://research.draconitic.com/paper/68vfd42sf4ad5",
  "venue": "Briefings in bioinformatics",
  "versions": []
}