A single-cell transcriptomic method for the deconvolution of multi-donor blood mixtures.
- 1School of Forensic Medicine, Shanxi Medical University, No. 55 Wenhua Street, Yuci District, Jinzhong, Shanxi 030619, China.
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.
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BibTeX
@article{li2026singlecell,
title = {{A single-cell transcriptomic method for the deconvolution of multi-donor blood mixtures.}},
author = {Jiayan Li and Liangze Li and Yiwei Guo and Jiaxin Ji and Huan Yu and Haiqiang Xue and Wenrui Tian and Zidong Liu and Gengqian Zhang},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag597},
url = {https://europepmc.org/article/MED/42949537},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - A single-cell transcriptomic method for the deconvolution of multi-donor blood mixtures.
AU - Jiayan Li
AU - Liangze Li
AU - Yiwei Guo
AU - Jiaxin Ji
AU - Huan Yu
AU - Haiqiang Xue
AU - Wenrui Tian
AU - Zidong Liu
AU - Gengqian Zhang
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag597
UR - https://europepmc.org/article/MED/42949537
AB - 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.
SN - 1467-5463
SN - 1477-4054
ER -