Abstract

Optimal determination of the postmortem interval (PMI) is one of the cornerstones of forensic analysis but oftentimes the dependence and variability of the conventional methods like entomology, histology and biochemical techniques most commonly experience sudden limitations since they are sensitive to the environment. New also cites the human microbiome as an active and quantifiable state of an after death postmortem clock, and post mortal microbial succession is an excellent clock. The integration of Next-Generation Sequencing (NGS) technologies and Machine Learning (ML) algorithms, though, is revolutionizing the field and is now able to profile microorganisms at high resolution and model and predict PMI with high accuracy. NGS technologies such as 16S rRNA sequencing, shotgun metagenomics and whole-genome sequencing can potentially lead organizations to find various types of microbial communities which were not used before with higher accuracy. None of them are capable, however, of bulking up these enormous data sets with superior computing methods. Machine learning algorithms like rule-based systems Rain Forest, the Support Vector Machines and deep learning systems have shown the potential to recognize patterns of microbial succession regimes and translate them into useful prediction of PMI. The answer to this challenge lies in switched NGS and ML: with sequencing, the biological signal is provided, and predictive models are utilized in order to refine the temporal resolution and minimize the margin of error. Even with such progress, there are more challenging problems. In this instance variability between settings, the absence of standard procedures, small databases of forensic microbiomes, and its potential legal inadmissibility all pose hurdles to its application on a large scale. The development of portable sequencing machines, global forensic databases on microbes, and the explicable AI to suit the needs of the judiciary are the way forward. The article is a review of the current conditions of microbiome-based PMI estimation with a focus on highlighting how both NGS and ML can be valuable in developing an effective and standardized postmortem clock in forensic scientists.

• 1.   Javan GT, Finley SJ, Can I, Wilkinson JE, Hanson JD, Tarone AM, et al. Human thanatomicrobiome succession and time since death. Sci Rep. 2016;6:29598. DOI: 10.1038/ srep29598.
• 2.   Johnson HR, Trinidad D, Guzman S, Khan Z, Parziale JV, DeBruyn JM, et al. A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval. PLoS One. 2016;11(12):e0167370. DOI: 10.1371/journal.pone.0167370.
• 3.   Hu S, Zhang X, Yang F, Nie H, Lu X, Guo Y, Zhao X, et al. Multimodal Approaches Based on Microbial Data for Accurate Postmortem Interval Estimation. Microorganisms. 2024;12(11):2193. DOI: 10.3390/ microorganisms12112193.
• 4.   Tozzo P, Amico I, Delicati A, Toselli F, Caenazzo L. Post-Mortem Interval and Microbiome Analysis through 16S rRNA Analysis: A Systematic Review. Diagnostics. 2022;12:2641. DOI: 10.3390/diagnostics12112641.
• 5.   Moitas B, Caldas IM, Sampaio-Maia B. Microbiology and postmortem interval: a systematic review. Forensic Science, Medicine, and Pathology. 2024;20:696-715. DOI: 10.1007/ s12024-023-00733-z.
• 6.   ß Case (rabbit study) — Effect of temperature and time on the thanatomicrobiome of the rabbit: American Journal of Veterinary Research. 2019;80(12):1161-1171. DOI: 10.1177/1040638719828412.
• 7.   Daily thanatomicrobiome changes in soil as an approach of PMI estimation (Tennessee study). Forensic Science International. 2017;273: 109-119.
• 8.   Mason AR, McKee-Zech HS, Steadman DW, DeBruyn JM. Environmental predictors impact microbial-based postmortem interval (PMI) estimation models within human decomposition soils. PLoS One. 2024;19(10):e0311906. DOI: 10.1371/journal. pone.0311906.
• 9.   Li N, et al. Artificial intelligence-based microbiome analysis for PMI estimation. Front Microbiol. 2024;
• 10.   Bruna Moitas, Inês Morais Caldas, Benedita Sampaio-Maia. Microbiology and postmortem interval: a systematic review. Forensic Sci Med Pathol. 2024;20:696-715. DOI: 10.1007/s12024- 023-00733-z.
• 11.   Chongqing Medical University group. Predicting the postmortem interval using human intestinal microbial communities. Science & Justice. 2021; DOI: 10.1016/j. scijus.2021.06.006.
• 12.   Oliveira M, Marszałek K, Kowalski M, Frolova A, Łabaj PP, Branicki W, da Silva DD, DinisOliveira RJ. Sequencing Technologies in Forensic Microbiology: Current Trends and Advancements. Forensic Sci. 2024;4(4):523-545. DOI: 10.3390/forensicsci4040035.
• 13.   “Application of Microbiome in Forensics” – Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35:833-844. DOI: 10.1038/nbt.3935.
• 14.   Estimating the postmortem interval using microbes: Knowledge gaps… Genes. 2018;9(2):104. DOI: 10.3390/genes9020104.
• 15.   “Microbial Forensics: A Present to Future Perspective on Genomic …” (review) – Forensic. (2022); DOI: 10.1089/forensic.2022.0007.
• 16.   Exploring the role of the human microbiome in forensic identification: opportunities and challenges. Int J Legal Med. 2024; (issue). DOI: 10.1007/s00414-024-03217-z.
• 17.   Emerging methods of human microbiome analysis and its forensic applications. (ScienceDirect review, 2024).

Data Sharing Statement

Funding