The Forensic Science Unit, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom.
Amsterdam Centre for Ancient Studies and Archaeology (ACASA) - Department of Archaeology, Faculty of Humanities, University of Amsterdam, Amsterdam, Netherlands.
Elife. 2022 Dec 30;11:e83658. doi: 10.7554/eLife.83658.
The combined use of multiple omics allows to study complex interrelated biological processes in their entirety. We applied a combination of metabolomics, lipidomics and proteomics to human bones to investigate their combined potential to estimate time elapsed since death (i.e., the postmortem interval [PMI]). This 'ForensOMICS' approach has the potential to improve accuracy and precision of PMI estimation of skeletonized human remains, thereby helping forensic investigators to establish the timeline of events surrounding death. Anterior midshaft tibial bone was collected from four female body donors before their placement at the Forensic Anthropology Research Facility owned by the Forensic Anthropological Center at Texas State (FACTS). Bone samples were again collected at selected PMIs (219-790-834-872days). Liquid chromatography mass spectrometry (LC-MS) was used to obtain untargeted metabolomic, lipidomic, and proteomic profiles from the pre- and post-placement bone samples. The three omics blocks were investigated independently by univariate and multivariate analyses, followed by Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), to identify the reduced number of markers describing postmortem changes and discriminating the individuals based on their PMI. The resulting model showed that pre-placement metabolome, lipidome and proteome profiles were clearly distinguishable from post-placement ones. Metabolites in the pre-placement samples suggested an extinction of the energetic metabolism and a switch towards another source of fuelling (e.g., structural proteins). We were able to identify certain biomolecules with an excellent potential for PMI estimation, predominantly the biomolecules from the metabolomics block. Our findings suggest that, by targeting a combination of compounds with different postmortem stability, in the future we could be able to estimate both short PMIs, by using metabolites and lipids, and longer PMIs, by using proteins.
多种组学的联合使用可以研究复杂的相互关联的生物过程。我们将代谢组学、脂质组学和蛋白质组学应用于人体骨骼，以研究它们联合估计死后时间（即死后间隔[PMI]）的潜力。这种“ForensOMICS”方法有可能提高对骨骼化人类遗骸 PMI 估计的准确性和精度，从而帮助法医调查人员建立与死亡相关的事件时间表。在将尸体捐赠者的遗体放置在德克萨斯州立大学法医人类学研究设施（FACTS）所属法医人类学中心之前，从四位女性尸体捐赠者的胫骨中轴前侧采集了骨样本。在选定的 PMI 时再次采集了骨样本（219-790-834-872 天）。使用液相色谱-质谱（LC-MS）从放置前和放置后的骨样本中获得非靶向代谢组学、脂质组学和蛋白质组学图谱。通过单变量和多变量分析独立研究了这三个组学模块，然后使用 Latent variable approaches for Omics studies（DIABLO）进行数据集成分析以发现生物标志物，以确定描述死后变化并基于 PMI 区分个体的标记数量减少。结果模型表明，放置前的代谢组、脂质组和蛋白质组图谱明显与放置后的图谱区分开来。放置前样本中的代谢物表明能量代谢的枯竭和向另一种燃料来源的转变（例如，结构蛋白）。我们能够识别出具有出色 PMI 估计潜力的某些生物分子，主要是来自代谢组学模块的生物分子。我们的研究结果表明，通过针对具有不同死后稳定性的化合物组合，我们将来有可能通过使用代谢物和脂质来估计短 PMI，通过使用蛋白质来估计长 PMI。
