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The analysis of postmortem protein degradation has become of large interest for the estimation of the postmortem interval (PMI). Although several techniques have been published in recent years, protein degradation-based techniques still largely did not exceed basic research stages. Reasons include impractical and complex sampling procedures, as well as highly variable protocols in the literature, making it difficult to compare results. Following a three-step procedure, this study aimed to establish an easily replicable standardized procedure for sampling and processing, and further investigated the reliability and limitations for routine application. Initially, sampling and processing were optimized using a rat animal model. In a second step, the possible influences of sample handling and storage on postmortem protein degradation dynamics were assessed on a specifically developed human extracorporeal degradation model. Finally, the practical application was simulated by the collection of tissue in three European forensic institutes and an international transfer to our forensic laboratory, where the samples were processed and analyzed according to the established protocol.
The measurement of protein dynamics by proteomics to study cell remodeling has seen increased attention over the last years. This development is largely driven by a number of technological advances in proteomics methods. Pulsed stable isotope labeling in cell culture (SILAC) combined with tandem mass tag (TMT) labeling has evolved as a gold standard for profiling protein synthesis and degradation. While the experimental setup is similar to typical proteomics experiments, the data analysis proves more difficult: After peptide identification through search engines, data extraction requires either custom scripted pipelines or tedious manual table manipulations to extract the TMT-labeled heavy and light peaks of interest. To overcome this limitation, which deters researchers from using protein dynamic proteomics, we developed a user-friendly, browser-based application that allows easy and reproducible data analysis without the need for scripting experience. In addition, we provide a python package that can be implemented in established data analysis pipelines. We anticipate that this tool will ease data analysis and spark further research aimed at monitoring protein translation and degradation by proteomics.