Optimal processing for proteomic genotyping of single human hairs

Area of Science:

• Forensic Science
• Proteomics
• Genetics

Background:

• Traditional hair analysis relies on microscopy, lacking quantitative identification power.
• Proteomic genotyping uses protein sequencing for more accurate random match probability (RMP) estimates.
• Single human scalp hairs offer potential forensic evidence but require optimized analysis methods.

Purpose of the Study:

• To optimize proteomic genotyping for individual identification from a single human scalp hair (∼100 μg).
• To enhance genetically variant peptide yield and maximize ancestral information extraction.
• To develop a robust protocol for forensic investigators.

Main Methods:

• Experiments focused on optimizing reduction, alkylation, and protein digestion specific to hair shaft chemistry.
• High-resolution mass spectrometry was used to detect genetically variant peptides from hair shaft proteins.
• Two algorithms, GVP Finder and GVP Scout, were developed for data analysis and discovery.

Main Results:

• Optimized conditions increased the average number of genetically variant peptides from 20 ± 5 to 73 ± 5 (p = 1 × 10⁻¹³).
• Random match probabilities improved significantly, reaching up to 1 in 620 million from a single hair.
• Ancestral information was successfully extracted, with substantial differences in RMPs between European and African reference populations.

Conclusions:

• Optimized proteomic genotyping provides a powerful tool for individual identification from minimal hair evidence.
• The enhanced method offers improved accuracy and valuable ancestral information for forensic applications.
• This research standardizes results across diverse biogeographic backgrounds, advancing peptide analysis in forensics.