Endogenous and Exogenous Small RNA Signatures as Novel Tools for Postmortem Interval Determination

Accurate estimation of the postmortem interval (PMI), the time elapsed between death and body discovery, is a critical challenge in forensic science due to the complex interplay of factors affecting decomposition. Traditional methods based on macroscopic changes often lack precision, especially in later postmortem stages.

This study aimed to develop a novel PMI estimation framework by integrating the dynamics of endogenous small non-coding RNAs (sncRNAs) and exogenous bacterial-derived small RNAs (sRNAs) using sRNA transcriptomics and machine learning.

Cardiac RNA degradation strongly correlated with PMI, with a random forest (RF) model achieving high accuracy (coefficient of determination (R²) = 0.939, mean absolute error (MAE) = 2.987 h). Employing PANDORA-seq, we profiled temporal changes in sncRNAs (miRNAs, tsRNAs and piRNAs) in postmortem cardiac tissue within 30 h in a mouse model, while simultaneously assessing RNA integrity (RIN) across eight organs. PANDORA-seq revealed stable sncRNA landscapes with specific dynamic shifts, leading to the identification of seven novel biomarkers (four tsRNAs, three piRNAs) for PMI prediction (R² = 0.760, MAE = 158.990 min). Bacterial-derived sRNAs, predominantly from Staphylococcus aureus, were upregulated at 30 h postmortem, suggesting complementary biomarker potential. Bioinformatics analysis indicated that host miRNAs may target bacterial mRNAs, hinting at cross-kingdom interactions.

These findings highlight the potential of integrated endogenous and exogenous sRNA analysis in PMI estimation, providing a high-precision, rapid diagnostic tool and revealing complex postmortem molecular processes.