Chiappetta, Maria Elena
ORCID: 0009-0005-0458-671X, Roggia, Elisa, Alladio, Eugenio
ORCID: 0000-0001-9362-6907, Bonicelli, Andrea
ORCID: 0000-0002-9518-584X and Procopio, Noemi
ORCID: 0000-0002-7461-7586
(2025)
Forensic Metabolomics: Enhancing PMI Estimation through Porcine Bone Tissue Profiling.
Journal of Proteome Research, 24
(11).
pp. 5498-5510.
ISSN 1535-3893
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Official URL: https://doi.org/10.1021/acs.jproteome.5c00250
Abstract
The estimation of the post-mortem interval (PMI) in forensic skeletal remains is extremely challenging, as traditional morphological methods lose their effectiveness and accuracy as decomposition progresses. To address this issue, this study utilizes metabolomics to investigate the biochemical changes affecting bone tissue during the decomposition process. Fragments of pig mandibles were buried in an open grassland field at varying depths (0, 10, 30, and 50 cm) and collected every month up to 6 months. Bone metabolites were extracted using a single-phase methanol–water protocol, and both gas chromatography–mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied for their analysis. The primary goal of this study is to identify specific metabolic shifts associated with increasing post-mortem intervals to identify potential bone metabolomic biomarkers for PMI and to assess the impact of burial depth on these changes. The generated regression models using LC-MS/MS data were able to estimate the PMI of the skeletal fragments with an accuracy of 14 days over 6 months, an outstanding result, particularly considering the current lack of methodologies to estimate PMI from bones. Burial depth, instead, did not play a significant role on the metabolomic bone signature. This research deepens our understanding of post-mortem biochemical processes in bones, making a significant contribution to the advancing field of forensic metabolomics, and highlights the potential of metabolomics for investigating buried skeletal remains and enhancing post-mortem interval assessments.
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