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

ObjectiveTo estimate postmortem interval （PMI） by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods.MethodsRats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points （0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d） after death. Protein expression profile data with relative molecular mass of 14 000-230 000 were obtained. Principal component analysis （PCA） and orthogonal partial least squares （OPLS） were used for data analysis. Fisher discriminant model and back propagation （BP） neural network model were constructed to classify and preliminarily estimate the PMI. In addition, the protein expression profiles data of human skeletal muscles at different time points after death were collected, and the relationship between them and PMI was analyzed by heat map and cluster analysis.ResultsThe protein peak of rat skeletal muscle changed with PMI. The result of PCA combined with OPLS discriminant analysis showed statistical significance in groups with different time points （P