Ge Jianye, King Jonathan L, Smuts Amy, Budowle Bruce
Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX 76107, USA.
Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, USA.
Genes (Basel). 2021 Oct 20;12(11):1649. doi: 10.3390/genes12111649.
Wet-lab based studies have exploited emerging single-cell technologies to address the challenges of interpreting forensic mixture evidence. However, little effort has been dedicated to developing a systematic approach to interpreting the single-cell profiles derived from the mixtures. This study is the first attempt to develop a comprehensive interpretation workflow in which single-cell profiles from mixtures are interpreted individually and holistically. In this approach, the genotypes from each cell are assessed, the number of contributors (NOC) of the single-cell profiles is estimated, followed by developing a consensus profile of each contributor, and finally the consensus profile(s) can be used for a DNA database search or comparing with known profiles to determine their potential sources. The potential of this single-cell interpretation workflow was assessed by simulation with various mixture scenarios and empirical allele drop-out and drop-in rates, the accuracies of estimating the NOC, the accuracies of recovering the true alleles by consensus, and the capabilities of deconvolving mixtures with related contributors. The results support that the single-cell based mixture interpretation can provide a precision that cannot beachieved with current standard CE-STR analyses. A new paradigm for mixture interpretation is available to enhance the interpretation of forensic genetic casework.
基于湿实验室的研究利用新兴的单细胞技术来应对解释法医混合证据的挑战。然而,在开发一种系统的方法来解释从混合物中获得的单细胞图谱方面投入的精力很少。本研究首次尝试开发一种全面的解释工作流程,其中对混合物中的单细胞图谱进行单独和整体的解释。在这种方法中,评估每个细胞的基因型,估计单细胞图谱的贡献者数量(NOC),然后生成每个贡献者的一致图谱,最后可以将一致图谱用于DNA数据库搜索或与已知图谱进行比较,以确定其潜在来源。通过模拟各种混合场景以及实际的等位基因缺失和插入率、估计NOC的准确性、通过共识恢复真实等位基因的准确性以及解卷积相关贡献者混合物的能力,评估了这种单细胞解释工作流程的潜力。结果支持基于单细胞的混合解释能够提供当前标准CE-STR分析无法实现的精度。一种用于混合解释的新范式可用于加强法医遗传案件工作的解释。