In the reconstruction of a crime, several fundamental questions exist and need to be resolved in order to find conclusion as well as criminal justice. The time when a crime had occurred is an often hard-to-solve piece of the puzzle and can be of crucial relevance in the context of a criminal trial. A biological trace left at a crime scene could result in the recreation of when it happened - and consequentially the time since deposition (TsD) of when such a biological trace was left at a crime scene could be useful in a forensic investigation. Yet, despite its significance and a general desire in the forensic community, no routinely applied, feasible and validated method for the estimation of the TsD exists as of today. In recent years, research to determine or estimate the TsD received noticeable updraft and attention from the scientific community and advancements in the instrumental and bioinformatical landscape have opened up new possible ways to study the TsD and its related phenomena.
The work presented here introduces modern liquid-chromatography high-resolution mass-spectrometry (LC-HR-MS) based proteomics and metabolomics methods to study and exploit time- and storage-dependent changes in both the proteomes and metabolomes of biological traces in order to determine or approximate the TsD. Dried matrix spots (DMS) where created to resemble authentic, dried biological fluids found at a crime-scene and aged for various time-periods and under different storage conditions. The aged DMS were then prepared and subjected to LC-HR-MS analysis. Both, the proteomes and metabolomes exhibited stark, global differences attributed to both timeand storage condition after data analysis and evaluation. Multivariate statistical analysis was applied to, in most cases, discriminate between (larger) time-periods from samples of the same storage condition. Comparisons between storage conditions were significantly more challenging to form, as differences between the observed changes between the storage conditions were large and extensive. On the other hand, potential marker peptides and metabolites exhibiting traceable time-dependent changes were identified and studied in more detail. Several peptides showed promising time-dependent changes in their modification patterns (i.e., the level of oxidization of the peptide chain) which could then be used to build a prediction model for the passed TsD and approximate the deposition time. Analogous, key metabolites were also found to exhibit strong timedependent changes, yet the main bottleneck in (untargeted) metabolomics analysis - the conclusive identification of unknown metabolic features - could not be resolved in the context of this study thus far.
Overall, these studies mainly serve to lay the foundation for further, future studies and bring new context to existing methods regarding TsD-estimation by adding large-scale untargeted methodologies utilizing LC-MS based metabolomics and proteomics approaches to the forensic toolkit, but application into routine forensic casework is still yet to come.
