Potential of Proteomics in Forensic Phenotyping

A Focus on Biological Sex Estimation

Journal Article (2025)
Author(s)

Shirin Alex (Nederlands Forensisch Instituut (NFI))

Ruben Almey (Universiteit Gent)

Rachel Sian Dennis (Universiteit Gent)

Olivier Tytgat (Universiteit Gent)

Robbin Bouwmeester (Universiteit Gent)

Dieter Deforce (Universiteit Gent)

Marcel De Puit (TU Delft - Applied Sciences, Nederlands Forensisch Instituut (NFI))

Maarten Dhaenens (Universiteit Gent)

Laura De Clerck (Universiteit Gent)

Research Group
ChemE/Advanced Soft Matter
DOI related publication
https://doi.org/10.1021/acs.jproteome.5c00098 Final published version
More Info
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Publication Year
2025
Language
English
Research Group
ChemE/Advanced Soft Matter
Journal title
Journal of Proteome Research
Issue number
10
Volume number
24
Pages (from-to)
4888-4899
Downloads counter
44
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Abstract

Forensic DNA analysis is well established for phenotyping, providing valuable investigative leads. Proteomics, the large-scale study of proteins, is emerging as a complementary tool to DNA analysis, particularly for enhancing the evidential value of traces. This study explores the potential of proteomics to extract phenotypic traits from whole blood, using the estimation of biological sex as a starting point. Using LC–MS/MS, proteomes from 100 whole blood samples of known sex were analyzed to develop a biological sex classifier. Cross-validation of the model highlighted the model’s ability to achieve accurate classification, identifying key peptides, such as those from pregnancy zone protein and ceruloplasmin, as critical markers. To test real-world applicability, mock case samples were generated, bringing attention to the need for model robustness. Overall, our results suggest that using proteomics to infer phenotypic traits from whole blood samples in the context of forensics, while feasible, is hindered by hard-to-overcome technical challenges. We therefore recommend that future forensic proteomics research be directed toward areas where it can be most informative, such as source attribution and estimating the timeline of events, rather than focusing on extracting phenotypic traits for donor profiling.