The genetic characterization and identification of individuals who contributed to biological mixtures are complex and mostly unresolved tasks. These tasks are relevant in various fields, particularly in forensic investigations, which frequently encounters crime scene stains generated by more than one person. Currently, forensic mixture deconvolution is mostly performed subsequent to forensic DNA profiling at the level of the mixed DNA profiles, which comes with several limitations. Some previous studies attempted at separating single cells prior to forensic DNA profiling. However, these approaches are biased at selection of the cells and, due to their targeted DNA analysis on low template DNA, provide incomplete and unreliable forensic DNA profiles. We recently demonstrated the feasibility of performing mixture deconvolution prior to forensic DNA profiling through the utilization of a non-targeted single-cell transcriptome sequencing (scRNA-seq). In addition to individual-specific mixture deconvolution, this approach also allowed accurate characterisation of biological sex, biogeographic ancestry and individual identification of the separated mixture contributors. However, RNA has the forensic disadvantage of being prone to degradation, and sequencing RNA - focussing on coding regions - limits the number of single nucleotide polymorphisms (SNPs) utilized for genetic mixture deconvolution, characterization, and identification. These limitations can be overcome by performing single-cell sequencing on the level of DNA instead of RNA. Here, for the first time, we applied non-targeted single-cell DNA sequencing (scDNA-seq) by applying the scATAC-seq (Assay for Transposase-Accessible Chromatin with sequencing) technique to address the challenges of mixture deconvolution in the forensic context. We demonstrated that scATAC-seq, together with our recently developed De-goulash data analysis pipeline, is capable of deconvoluting complex blood mixtures of five individuals from both sexes with varying biogeographic ancestries. We further showed that our approach achieved correct genetic characterization of the biological sex and the biogeographic ancestry of each of the separated mixture contributors and established their identity. Furthermore, by analysing generated scATAC-seq data mixtures, we demonstrated successful individual-specific mixture deconvolution of i) highly complex mixtures of 11 individuals, ii) balanced mixtures containing as few as 20 cells (10 per each individual), and iii) imbalanced mixtures with a ratio as low as 1:80. Overall, our proof-of-principle study demonstrates the general feasibility of scDNA-seq in general, and scATAC-seq in particular, for mixture deconvolution, genetic characterization and individual identification of the separated mixture contributors. Furthermore, it shows that compared to scRNA-seq, scDNA-seq detects more SNPs from fewer cells, providing higher sensitivity, that is valuable in forensic genetics.

中文翻译：

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使用非靶向单细胞 DNA 测序对多人生物混合物进行解卷积并准确表征和识别分离的贡献者

对生物混合物做出贡献的个体的遗传特征和识别是复杂且大多未解决的任务。这些任务与各个领域相关，特别是在法医调查中，经常会遇到由多个人产生的犯罪现场污点。目前，法医混合反卷积主要是在混合 DNA 图谱水平上的法医 DNA 分析之后进行的，这存在一些局限性。之前的一些研究尝试在法医 DNA 分析之前分离单个细胞。然而，这些方法在细胞选择方面存在偏差，并且由于它们对低模板 DNA 进行靶向 DNA 分析，因此提供的法医 DNA 谱不完整且不可靠。我们最近证明了通过利用非靶向单细胞转录组测序 (scRNA-seq) 在法医 DNA 分析之前进行混合物反卷积的可行性。除了个体特定的混合物反卷积之外，该方法还可以准确表征生物性别、生物地理祖先以及分离的混合物贡献者的个体识别。然而，RNA 的法医缺点是易于降解，而 RNA 测序（重点关注编码区域）限制了用于遗传混合物反卷积、表征和鉴定的单核苷酸多态性 (SNP) 的数量。通过在 DNA（而不是 RNA）水平上进行单细胞测序可以克服这些限制。在这里，我们首次通过应用 scATAC-seq（转座酶可及染色质测序分析）技术来应用非靶向单细胞 DNA 测序 (scDNA-seq)，以解决法医环境中混合物反卷积的挑战。我们证明了 scATAC-seq 与我们最近开发的 De-goulash 数据分析管道一起，能够对具有不同生物地理血统的五名男女的复杂血液混合物进行去卷积。我们进一步表明，我们的方法实现了每个分离的混合物贡献者的生物性别和生物地理祖先的正确遗传特征，并确定了他们的身份。此外，通过分析生成的 scATAC-seq 数据混合物，我们证明了成功的个体特异性混合物反卷积：i) 11 个个体的高度复杂混合物，ii) 包含少至 20 个细胞（每个个体 10 个）的平衡混合物，以及 iii) 不平衡的混合物比例低至 1:80 的混合物。总体而言，我们的原理验证研究证明了 scDNA-seq，特别是 scATAC-seq 对于混合物反卷积、遗传表征和分离混合物贡献者的个体识别的一般可行性。此外，它表明与 scRNA-seq 相比，scDNA-seq 可以从更少的细胞中检测到更多的 SNP，从而提供更高的灵敏度，这在法医遗传学中很有价值。