Institute of Legal Medicine, Medical Faculty, University Hospital Cologne, Cologne, Germany.
Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany.
Forensic Sci Int Genet. 2023 Nov;67:102915. doi: 10.1016/j.fsigen.2023.102915. Epub 2023 Aug 3.
Obtaining forensically relevant information beyond who deposited a biological stain on how and under which circumstances it was deposited is a question of increasing importance in forensic molecular biology. In the past few years, several studies have been produced on the potential of gene expression analysis to deliver relevant contextualizing information, e.g. on nature and condition of a stain as well as aspects of stain deposition timing. However, previous attempts to predict the time-of-day of sample deposition were all based on and thus limited by previously described diurnal oscillators. Herein, we newly approached this goal by applying current sequencing technologies and statistical methods to identify novel candidate markers for forensic time-of-day predictions from whole transcriptome analyses. To this purpose, we collected whole blood samples from ten individuals at eight different time points throughout the day, performed whole transcriptome sequencing and applied biostatistical algorithms to identify 81 mRNA markers with significantly differential expression as candidates to predict the time of day. In addition, we performed qPCR analysis to assess the characteristics of a subset of 13 candidate predictors in dried and aged blood stains. While we demonstrated the general possibility of using the selected candidate markers to predict time-of-day of sample deposition, we also observed notable variation between different donors and storage conditions, highlighting the relevance of employing accurate quantification methods in combination with robust normalization procedures.This study's results are foundational and may be built upon when developing a targeted assay for time-of-day predictions from forensic blood samples in the future.
在法医分子生物学中,获取有关除了谁在何时何地沉积生物痕迹之外的法医学相关信息是一个日益重要的问题。在过去的几年中,已经有几项关于基因表达分析潜力的研究,以提供相关的情境化信息,例如关于痕迹的性质和状况以及痕迹沉积时间方面的信息。然而,之前预测样本沉积时间的尝试都基于并因此受到先前描述的昼夜节律振荡器的限制。在此,我们通过应用当前的测序技术和统计方法,从全转录组分析中识别用于法医时间预测的新候选标记物,来重新解决这一目标。为此,我们从十个人在一天中的八个不同时间点收集了全血样本,进行了全转录组测序,并应用生物统计学算法来识别 81 个具有显著差异表达的 mRNA 标记物作为候选物,以预测一天中的时间。此外,我们进行了 qPCR 分析,以评估一组 13 个候选预测因子在干燥和老化血液痕迹中的特征。虽然我们证明了使用所选候选标记物来预测样本沉积时间的可能性,但我们也观察到不同供体和储存条件之间存在显著差异,突出了在开发未来从法医血液样本中进行时间预测的靶向测定时使用准确的定量方法和稳健的归一化程序的重要性。这项研究的结果是基础性的,将来可能会在此基础上开发针对法医血液样本时间预测的靶向测定。