Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods.

Area of Science:

• Forensic Science
• Biochemistry
• Proteomics

Background:

• Estimating postmortem interval (PMI) is crucial in forensic investigations.
• Skeletal muscle tissue undergoes proteomic changes after death that correlate with time elapsed.

Purpose of the Study:

• To develop a method for estimating PMI using protein expression profiles in skeletal muscle.
• To evaluate the efficacy of protein chip technology and multivariate analysis for PMI determination.

Main Methods:

• Water-soluble proteins were extracted from rat skeletal muscle at 10 time points postmortem.
• Protein expression profiles were analyzed using Principal Component Analysis (PCA) and Orthogonal Partial Least Squares (OPLS).
• Fisher discriminant and Back Propagation (BP) neural network models were constructed for PMI estimation, with validation on human skeletal muscle data.

Main Results:

• Protein expression profiles in rat skeletal muscle varied significantly with PMI.
• The BP neural network model achieved high accuracy (98.2% internal, 95.8% external validation) for PMI estimation.
• Cluster analysis of human skeletal muscle samples revealed significant protein expression differences between 4 days and 25 hours postmortem.

Conclusions:

• Protein chip technology provides a rapid and reliable method for obtaining skeletal muscle protein profiles.
• Multivariate analysis models, particularly the BP neural network, show promise for accurate PMI estimation.
• This approach offers a novel strategy for forensic determination of postmortem interval.