Going a Layer Deeper: Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) to Analyze
Jerika Ho1,2,3, Naomi L Stock3, Vaughn Mangal4
1University of Windsor, 401 Sunset Ave, Windsor Ontario N9B 3P4, Canada.
Summary
Molecular analysis of distinct tissue layers reveals unique decomposition patterns. This layer-resolved mass spectrometry approach identifies persistent biomarkers for estimating the post-mortem interval (PMI) even as tissues degrade.
Area of Science:
- Forensic Science
- Biochemistry
- Molecular Biology
Background:
- Soft tissue degradation obscures morphological analysis for post-mortem change.
- Existing molecular methods often homogenize tissues, losing spatial information.
- Distinct tissue layers (skin, adipose, muscle) exhibit unique decomposition dynamics.
Purpose of the Study:
- To develop and apply a layer-resolved mass spectrometry framework for analyzing soft tissue decomposition.
- To investigate molecular changes within specific tissue layers over time.
- To identify tissue-specific molecular signatures for post-mortem interval (PMI) estimation.
Main Methods:
- Utilized matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) profiling across multiple donors.
- Employed MALDI MS-imaging (MSI) for a detailed case study.
- Combined MSI with Hematoxylin and eosin (H&E) staining for structural assessment and layer identification.
Main Results:
- Distinct molecular differences were observed among skin, adipose, and muscle layers.
- Each tissue layer maintained a unique lipid profile and showed unique accumulated degree days (ADD)-correlated features.
- MALDI-MSI revealed spatial redistribution of decomposition molecules across layers, offering insights beyond morphology.
Conclusions:
- Layer-resolved MALDI-based profiling and imaging reveal persistent molecular signatures during soft tissue decomposition.
- This approach provides a refined framework for identifying tissue-specific biomarkers.
- The findings have potential value for improving post-mortem interval (PMI) estimation.
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