A single-cell transcriptomic method for the deconvolution of multi-donor blood mixtures.

Authors

Jiayan Li, Liangze Li, Yiwei Guo, Jiaxin Ji, Huan Yu, Haiqiang Xue, Wenrui Tian, Zidong Liu, Gengqian Zhang

Published in

Briefings in bioinformatics. Volume 27. Issue 5. Sep 01, 2026.

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.

PMID:
42949537
Bibliographic data and abstract were imported from PubMed on 28 Oct 2026.

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