This dissertation presents an effort to optimize human hair sample preparation and instrumental analysis for forensic individual identification and biogeographic classification.
This paper lays the groundwork for using proteomic methodologies, applied to human hair, for the generation of investigative leads, identification of criminal suspects, and the classification of a skin and hair disease. It is organized into the following sections: first, it introduces proteomic techniques as applied to the human hair shaft, including a historical overview of the use of protein in a forensic context, biology of the human hair shaft, and rationale for the current research; chapter two analyzes the optimal processing for proteomic genotyping of single human hairs, reviewing the research materials, methodology, results, and a discussion of findings; chapter three provides a discussion of alternative LC-MS platforms and data acquisition strategies for forensic proteomic genotyping; chapter four presents biogeographic classification of European and African individuals from hair shaft protein, using proteomic genotyping, with a review of the research materials and methodology, results, and a discussion of findings. The dissertation provides details about how sample processing was optimized, and the various mass spectrometry data acquisition strategies that were assessed. The dissertation also presents the research findings and their implications, including that population genetics software such as STRUCTURE and ADMIXTURE and likelihood ratio (LR) are able to estimate ancestry from genetically variant peptides with a statistically significant relationship; LR is especially able to distinguish African and European individuals. The dissertation’s appendix discusses the disease trichothiodystrophy, noting that the link between brittle hair phenotype and the disease mechanism is not fully understood but that the research shows that proteins high in cysteine content have a low abundance in patients controlled to normal control hairs.