Session: Parallel session 5 - Forensic and cultural heritage

Identification of human bodily fluids in forensic traces using a proteomic approach

This research project aims to advance forensic science, on which the expert reports carried out by the IRCGN are based. Traces found at crime scenes are essential for solving an investigation, as they provide a wealth of information. While it is possible to identify an individual by their DNA profile, routine characterization of the human biological fluid from which types it comes remains a significant challenge. Proteomic analysis can provide precise complementary information to resolve ambiguities. This identification is particularly crucial in the context of forensic investigations, particularly in cases of sexual assault.



In order to address this analytical challenge, we have developed a method to discriminate between different human bodily fluids (blood, saliva, urine, semen, and vaginal fluid) in a sensitive and specific nanoLC-MS/MS bottom-up proteomics analysis. This strategy is based on the initial identification of a panel of proteotypic peptides selected from the literature and experimentally validated. Each bodily fluid is thus characterized by a particular and unambiguous peptide fingerprint that serves as its signature. To ensure the reliability of the results, method validation was carried out following the ISO/IEC 17025 standard. Thus, repeatability, precision and accuracy, stability over time, and different supports effects on peptide detection were studied.



Subsequently, a more extensive cohort study (minimum 60) enabled the selecting of a new panel of robust biomarkers. All of the parameters and this new panel were finally validated on a significant population. Establishing a well-defined list of molecular signatures has made it possible to propose the development of rapid, specific, and sensitive targeted analyses, which can be exploited in the context of complex matrices, that is to say, samples collected in the field. Finally, different data processing strategies, including artificial intelligence, were employed to refine the study