A holistic approach to understanding biochemical degradation of human tissues using high resolution MALDI MS
Jerika Ho1,2,3, Naomi L Stock3, Vaughn Mangal4
1Department of Chemistry and Biochemistry, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada. hojerik@uwindsor.ca.
Abstract:
Estimating the postmortem interval (PMI) is crucial in medico-legal death investigations, but existing methods are highly influenced by environmental and physiological factors. This study proposes a novel biochemical strategy for PMI estimation using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) to detect molecular changes in decomposing human soft tissues. Samples from the quadriceps and shoulder were collected from 13 human donors at the Research in Experimental and Social Thanatology (REST) facility in Quebec, Canada, spanning accumulated degree days (ADD) ranging from 9.75 to 11 455.86. Tissues (14 μm) were cryosectioned, mounted on carbon tape, dried under nitrogen gas, and coated with a 2,5-dihydroxybenzoic acid matrix solution. Hematoxylin and eosin staining provided histological context for the sampled tissues. MALDI MS data were acquired using a Bruker SolariX XR Fourier-transform Ion Cyclotron Resonance MS. Van Krevelen diagrams and principal component analysis revealed decomposition-linked trends, with shifts in biochemical profiles over time. N/C and N/O ratios increased, reflecting protein and peptide degradation and microbial activity, while O/C ratios declined, likely due to the loss of oxygen-rich compounds. Mixed-effects models showed moderate associations (R2 marginal = 0.06-0.49) between elemental ratios and ADD, with conditional R2 values up to 0.55. Spearman's rank correlation identified compounds significantly associated with ADD. Six candidate biomarkers were identified, with variable model fit (R2 marginal = 0.003-0.80) and unresolved donor-level effects. This study demonstrates the novel application of MALDI MS for PMI biomarker discovery and contributes one of the largest datasets in forensic decomposition chemistry research.
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