Stability of the skin microbiome during short-term in situ and in vitro conditions: Foundational support for the potential to trace skin sites and identify individuals.

Journal: International journal of legal medicine
Published Date: Jan 17, 2026

Abstract

As the largest human organ, the skin frequently interacts with the environment and retains abundant microbial information, making it a crucial source of forensic biological evidence. However, the temporal dynamics of microbial communities between in situ and in vitro samples, as well as the traceability of in vitro samples back to their donors based on corresponding in situ samples, remain unverified through longitudinal sampling and dynamic tracking. A total of 15 young adult volunteers participated in the study, during which skin microbiome samples were collected from their palms and cheeks. A short-term exposure experiment was designed, with a duration of between 0 and 72 hours. Fresh samples were collected at corresponding timepoints, in a synchronised manner. In this study, we utilised a combination of 16S rRNA gene (V3-V4 regions) sequencing and machine learning algorithms to analyse the environmental exposure effects on microbial community structure and their forensic applicability. The results indicated that the relative abundance of dominant genera remained largely stable, regardless of exposure status, with no significant temporal variations observed in the short term. Although individual lifestyles exerted an influence on microbiome composition, they did not affect significant alterations to the overall community architecture. The random forest model attained an accuracy of 91.33% in skin site identification, while the individual differentiation accuracy attained 97.33% when integrating palm and cheek data. These results indicate that the skin microbiome exhibits considerable structural stability under both in situ and in vitro conditions during short-term exposure and maintains high host specificity and site-specific characteristics.

Authors

  • Linying Ye
    Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
  • Jieyu Du
    Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
  • Litao Huang
    Department of Clinical Research Management, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
  • Mingyue Zhao
    Jiangsu Key Laboratory of Nano Technology, College of Engineering and Applied Sciences, Nanjing University, 22 Hankou Road, Nanjing, 210093, China.
  • Fanglan Tan
    Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
  • Xiaofeng Zhang
    College of Medicine, Xi'an International University, Shaanxi, P. R. China.
  • Xiaohui Chen
    School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.
  • Quyi Xu
    Guangzhou Forensic Science Institute & Key Laboratory of Forensic Pathology, Ministry of Public Security, Guangzhou, Guangdong, China.
  • Changhui Liu
    Guangzhou Forensic Science Institute, Guangzhou, P. R. China.
  • Yucong Lin
    Center for Statistical Science, Tsinghua University, Beijing, Beijing, China; Department of Industrial Engineering, Tsinghua University, Beijing, Beijing, China.
  • Xingchun Zhao
    Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China.
  • Chao Liu
    Anti-Drug Technology Center of Guangdong Province, National Anti-Drug Laboratory Guangdong Regional Center, Guangzhou 510230, China.
  • Ling Chen
    Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States.

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