Wang Xi, Yuan Xi, Lin Yifeng, Lan Qiong, Mei Shuyan, Cai Meiming, Lei Fanzhang, Dong Bonan, Zhao Ming, Zhu Bofeng
Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Int J Legal Med. 2025 Apr 17. doi: 10.1007/s00414-025-03456-8.
In recent years, it has become a major research trend to obtain the microbial relative abundance in common body fluid stains at the crime scenes through 16S rRNA next generation sequencing to explore the effectiveness in forensic application. However, few scholars have combined the determination of tissue sources of body fluid stains with the inference of time since deposition (TsD) based on the relative and absolute abundance of microorganism in the same sample in a single study. Therefore, we preliminarily used the four abundant saliva-related bacteria to distinguish fresh saliva, saliva stains (exposure ≤60 days) from the four kinds of fresh body fluids and epidermal tissue, simultaneously assessed the temporal variation regularities in both microbial relative and absolute abundance in these saliva stains. Quantitative real-time PCR results demonstrated that fresh saliva samples and saliva stains exposed for up to 60 days still retained two or more abundant saliva-related bacteria, demonstrating sufficient discriminative power to identify saliva stain from other four kinds of body fluids and tissue. Microbial compositions and temporal analyses of 56 saliva samples revealed that many phyla and genera with abundance higher than 1% had different temporal variation regularities in relative and absolute abundance data, except for some genera such as Neisseria, etc. Beta diversity analysis indicated greater differences in absolute quantitative data among fresh saliva samples and saliva stains at different time points compared with relative quantitative data. The support vector machine (svm) model based on microbial relative or absolute abundance both have the prediction accuracy higher than 0.8 in classifying saliva stains deposited at 1 h, 1 day, and 7 to 60 days. This study combined the tissue origin identification and TsD inference of saliva stains, and the absolute quantitative technology was applied for the first time to the TsD inference of saliva stains. And the results indicated that using the absolute quantitative technology might be more suitable for early TsD inference (within 14 days) of saliva stains in this study, which helped to accurately infer the TsD of saliva stains, providing an important clue for forensic investigation.
近年来,通过16S rRNA下一代测序获取犯罪现场常见体液斑中的微生物相对丰度,以探索其在法医学应用中的有效性已成为一个主要研究趋势。然而,很少有学者在一项研究中,将体液斑的组织来源鉴定与基于同一样本中微生物相对和绝对丰度的沉积时间推断相结合。因此,我们初步利用四种丰富的唾液相关细菌,从四种新鲜体液和表皮组织中区分新鲜唾液、唾液斑(暴露时间≤60天),同时评估这些唾液斑中微生物相对和绝对丰度的时间变化规律。定量实时PCR结果表明,新鲜唾液样本和暴露长达60天的唾液斑仍保留两种或更多丰富的唾液相关细菌,显示出从其他四种体液和组织中识别唾液斑的足够判别能力。对56个唾液样本的微生物组成和时间分析表明,除了一些属如奈瑟菌等外,许多相对丰度和绝对丰度高于1%的门和属具有不同的时间变化规律。β多样性分析表明,与相对定量数据相比,不同时间点的新鲜唾液样本和唾液斑之间的绝对定量数据差异更大。基于微生物相对或绝对丰度的支持向量机(svm)模型在对沉积1小时、1天以及7至60天的唾液斑进行分类时,预测准确率均高于0.8。本研究将唾液斑的组织来源鉴定和沉积时间推断相结合,首次将绝对定量技术应用于唾液斑的沉积时间推断。结果表明,在本研究中使用绝对定量技术可能更适合唾液斑的早期沉积时间推断(14天内),这有助于准确推断唾液斑的沉积时间,为法医学调查提供重要线索。