Alternative LC-MS/MS Platforms and Data Acquisition Strategies for Proteomic Genotyping of Human Hair Shafts.

Protein is a major component of all biological evidence. Proteomic genotyping is the use of genetically variant peptides (GVPs) that contain single-amino-acid polymorphisms to infer the genotype of matching nonsynonymous single-nucleotide polymorphisms for the individual from whom the protein sample originated. This can be used to statistically associate an individual to evidence found at a crime scene. The utility of the inferred genotype increases as the detection of GVPs increases, which is the direct result of technology transfer to mass spectrometry platforms typically available. Digests of single (2 cm) human hair shafts from three European and two African subjects were analyzed using data-dependent acquisition on a Q-Exactive Plus Hybrid Quadrupole-Orbitrap system, data-independent acquisition and a variant of parallel reaction monitoring (PRM) on an Orbitrap Fusion Lumos Tribrid system, and multiple reaction monitoring (MRM) on an Agilent 6495 triple quadrupole system. In our hands, average GVP detection from a selected panel of 24 GVPs increased from 6.5 ± 1.1 and 3.1 ± 0.8 using data-dependent and -independent acquisition to 9.5 ± 0.7 and 11.7 ± 1.7 using PRM and MRM (p < 0.05), respectively. PRM resulted in a 1.3-fold increase in detection sensitivity, and MRM resulted in a 1.6-fold increase in detection sensitivity. This increase in biomarker detection has a functional impact on the statistical association of a protein sample and an individual. Increased biomarker sensitivity, using Markov Chain Monte Carlo modeling, produced a median-estimated random match probability of over 1 in 10 trillion from a single hair using targeted proteomics. For PRM and MRM, detected GVPs were validated by the inclusion of stable isotope-labeled peptides in each sample, which served also as a detection trigger. This research accomplishes two aims: the demonstration of utility for alternative analytical platforms in proteomic genotyping and the establishment of validation methods for the evaluation of inferred genotypes.

Elucidation of familial relationships using hair shaft proteomics.

This study examines the potential of hair shaft proteomic analysis to delineate genetic relatedness. Proteomic profiling and amino acid sequence analysis provide information for quantitative and statistically-based analysis of individualization and sample similarity. Protein expression levels are a function of cell-specific transcriptional and translational programs. These programs are greatly influenced by an individual's genetic background, and are therefore influenced by familial relatedness as well as ancestry and genetic disease. Proteomic profiles should therefore be more similar among related individuals than unrelated individuals. Likewise, profiles of genetically variant peptides that contain single amino acid polymorphisms, the result of non-synonymous SNP alleles, should behave similarly. The proteomically-inferred SNP alleles should also provide a basis for calculation of combined paternity and sibship indices. We test these hypotheses using matching proteomic and genetic datasets from a family of two adults and four siblings, one of which has a genetic condition that perturbs hair structure and properties. We demonstrate that related individuals, compared to those who are unrelated, have more similar proteomic profiles, profiles of genetically variant peptides and higher combined paternity indices and combined sibship indices. This study builds on previous analyses of hair shaft protein profiling and genetically variant peptide profiles in different real-world scenarios including different human hair shaft body locations and pigmentation status. It also validates the inclusion of proteomic information with other biomolecular substrates in forensic hair shaft analysis, including mitochondrial and nuclear DNA.

Forensic proteomics.

Protein is a major component of all biological evidence, often the matrix that embeds other biomolecules such as polynucleotides, lipids, carbohydrates, and small molecules. The proteins in a sample reflect the transcriptional and translational program of the originating cell types. Because of this, proteins can be used to identify body fluids and tissues, as well as convey genetic information in the form of single amino acid polymorphisms, the result of non-synonymous SNPs. This review explores the application and potential of forensic proteomics. The historical role that protein analysis played in the development of forensic science is examined. This review details how innovations in proteomic mass spectrometry have addressed many of the historical limitations of forensic protein science, and how the application of forensic proteomics differs from proteomics in the life sciences. Two more developed applications of forensic proteomics are examined in detail: body fluid and tissue identification, and proteomic genotyping. The review then highlights developing areas of proteomics that have the potential to impact forensic science in the near future: fingermark analysis, species identification, peptide toxicology, proteomic sex estimation, and estimation of post-mortem intervals. Finally, the review highlights some of the newer innovations in proteomics that may drive further development of the field. In addition to potential impact, this review also attempts to evaluate the stage of each application in the development, validation and implementation process. This review is targeted at investigators who are interested in learning about proteomics in a forensic context and expanding the amount of information they can extract from biological evidence.

Age-Related Changes in Hair Shaft Protein Profiling and Genetically Variant Peptides.

Recent reports highlight possible improvements in individual identification using proteomic information from human hair evidence. These reports have stimulated investigation of parameters that affect the utility of proteomic information. In addition to variables already studied relating to processing technique and anatomic origin of hair shafts, an important variable is hair ageing. Present work focuses on the effect of age on protein profiling and analysis of genetically variant peptides (GVPs). Hair protein profiles may be affected by developmental and physiological changes with age of the donor, exposure to different environmental conditions and intrinsic processes, including during storage. First, to explore whether general trends were evident in the population at different ages, hair samples were analyzed from groups of different subjects in their 20's, 40's and 60's. No significant differences were seen as a function of age, but consistent differences were evident between European American and African American hair profiles. Second, samples collected from single individuals at different ages were analyzed. Mostly, these showed few protein expression level differences over periods of 10 years or less, but samples from subjects at 44 and 65 year intervals were distinctly different in profile. The results indicate that use of protein profiling for personal identification, if practical, would be limited to decadal time intervals. Moreover, batch effects were clearly evident in samples processed by different staff. To investigate the contribution of storage (at room temperature) in affecting the outcomes, the same proteomic digests were analyzed for GVPs. In samples stored over 10 years, GVPs were reduced in number in parallel with the yield of identified proteins and unique peptides. However, a very different picture emerged with respect to personal identification. Numbers of GVPs sufficed to distinguish individuals despite the age differences of the samples. As a practical matter, three hair samples per person provided nearly the maximal number obtained from 5 or 6 samples. The random match probability (where the log increased in proportion to the number of GVPs) reached as high as 1 in 108. The data indicate that GVP results are dependent on the single nucleotide polymorphism profile of the donor genome, where environmental/processing factors affect only the yield, and thus are consistent despite the ages of the donors and samples and batchwise effects in processing. This conclusion is critical for application to casework where the samples may be in storage for long periods and used to match samples recently collected.

Optimal processing for proteomic genotyping of single human hairs.

The use of hair evidence for human identification is undergoing considerable improvement through the adoption of proteomic genotyping. Unlike traditional microscopic comparisons, protein sequencing provides quantitative and empirically based estimates for random match probability. Non-synonymous SNPs are translated as single amino acid polymorphisms and result in genetically variant peptides. Using high resolution mass spectrometry, these peptides can be detected in hair shaft proteins and used to infer the genotypes of corresponding SNP alleles. We describe experiments to optimize the proteomic genotyping approach to individual identification from a single human scalp hair 2 cm in length (∼100 µg). This is a necessary step to develop a protocol that will be useful to forensic investigators. To increase peptide yield from hair, and to maximize genetically variant peptide and ancestral information, we examined the conditions for reduction, alkylation, and protein digestion that specifically address the distinctive chemistry of the hair shaft. Results indicate that optimal conditions for proteomic analysis of a single human hair include 6 h of reduction with 100 mM dithiothreitol at room temperature, alkylation with 200 mM iodoacetamide for 45 min, and 6 h of digestion with two 1:50 (enzyme:protein) additions of stabilized trypsin at room temperature, with stirring incorporated into all three steps. Our final conditions using optimized temperatures and incubation times increased the average number of genetically variant peptides from 20 ±â€¯5 to 73 ±â€¯5 (p = 1 × 10-13), excluding intractable hair samples. Random match probabilities reached up to 1 in 620 million from a single hair with a median value of 1 in 1.1 million, compared to a maximum random match probability of 1 in 1380 and a median value of 1 in 24 for the original hair protein extraction method. Ancestral information was also present in the data. While the number of genetically variant peptides detected were equivalent for both European and African subjects, the estimated random match probabilities for inferred genotypes of European subjects were considerably smaller in African reference populations and vice versa, resulting in a difference in likelihood ratios of 6.8 orders of magnitude. This research will assure uniformity in results across different biogeographic backgrounds and enhance the use of novel peptide analysis in forensic science by helping to optimize genetically variant peptide yields and discovery. This work also introduces two algorithms, GVP Finder and GVP Scout, which facilitate searches, calculate random match probabilities, and aid in discovery of genetically variant peptides.

Proteomic genotyping: Using mass spectrometry to infer SNP genotypes in pigmented and non-pigmented hair.

Proteomic genotyping uses genetically variant peptides that contain single amino acid polymorphisms to infer the genotype of corresponding non-synonymous SNP alleles. We have focused on hair proteins as a source of protein-based genetic information in a forensic context. An optimized sample processing protocol for hair shafts has been developed for use on a single hair that allows us to conduct validation protocols on real world samples. This includes whether the inferred SNP genotypes are robust and not systematically affected by biological or chemical variation in hair proteomes that might be obtained from a crime scene. To this end we analyzed the hair of 4 mature individuals with a mixture of pigmented and non-pigmented hair. We demonstrate significant changes in the proteomes of grey versus pigmented hair. Vesicle specific proteins and lipid catabolism proteins were enriched in pigmented hair, and housekeeping proteins and lipid anabolic enzymes were enriched in grey, non-pigmented hair. The resulting profiles of genetically variant peptides, however, were more correlated with profiles from the same individuals regardless of pigmentation status. Together with other published evidence, this finding indicates that profiles of genetically variant peptides are robust and more correlated with other genetically variant peptide profiles from the same individual irrespective of changes occurring in the hair protein profile. Based on this small sample, investigators using profiles of genetically variant peptides to infer random match probabilities should not expect to observe differences based on the pigmentation of the hair shaft.

Demonstration of a mitochondrial DNA-compatible workflow for genetically variant peptide identification from human hair samples.

Hair is an evidentiary sample that typically does not provide sufficient nuclear DNA for forensic analysis. Therefore, state-of-the-art forensic examination for hair samples include subjective microscopic evaluation, mitochondrial DNA (mtDNA) analysis, and more recently, proteomic genotyping that uses protein variation in the form of genetically variant peptides (GVPs) to infer single nucleotide polymorphism (SNP) alleles. Since many cases involve limited sample amounts (approximately 2 cm or less), any additional destructive testing (besides mtDNA) would be excluded. If a mtDNA-compatible protein extraction workflow could be developed, GVPs would provide additional forensic value without sacrificing any portion of the original hair sample. Here, we demonstrate an optimized method that can be used to obtain both whole genome mtDNA and putative GVP profiles from a single limited hair sample. The method involves urea-based extraction of proteins from hair, followed by buffer exchange and protease digestion. Peptides are eluted through a 30 kDa membrane and analyzed using traditional proteomic techniques. DNA is subsequently extracted from the filter and analyzed using whole mt-genome analysis. The method was verified with a range of hair sample types (head, pubic, and arm hair) from a diverse cohort of 22 individuals. Specifically, putative GVP profiles and mtDNA haplotypes concordant with buccal swab samples from the same donor were obtained from 22 individuals. Further, the utility of the method was verified across two different laboratories. The method is applicable for proteomic-based GVP analysis and mt-genome analysis for forensic research applications.

Comparison of protein expression levels and proteomically-inferred genotypes using human hair from different body sites.

The microanatomy of human hair differs as a function of the site of origin on the body. This was a major consideration when anatomical features of hair were used as a means of comparison and human identification. Recent advances have demonstrated that proteomics of the hair shaft can be used to develop profiles of protein abundance and genetically variant peptides, the latter in turn being used to infer genotypes of SNP alleles. Because the profile of proteins would be expected to change as hair anatomy changes, it is an open question if the profile of genetically variant peptides will also change. While some sample to sample variation is expected, a potential drawback of using genetically variant peptides to infer an individual genotype is that the proteomic profile might change as a function of body site origin as well as an individual's genotype. The hypothesis in this study is that the profile of hair shaft genetically variant peptides depends more on an individual's genotype than on the site of hair shaft origin. To test this an analysis of both protein expression levels and genetically variant peptides was conducted on 4 body sites (scalp, axillary, beard and pubic hair) from 5 individuals with 4 biological replicates. Levels of protein expression were estimated using label-free quantification on resulting proteomic mass spectrometry datasets. The same datasets were then also analyzed for the presence of genetically variant peptides. This study demonstrates that the protein profiles of hair shafts varied as a function of somatic origin. By contrast the profile of genetically variant peptides, and resulting inferred genotype of SNP alleles, were more dependent on the individual. In this study random match probabilities ranged up to 1 in 196. Individual identification based on genetically variant peptides therefore can be obtained from human hair without regard to the site of origin. If the site of hair shaft origin was legally relevant then microscopic analysis is still necessary. This study demonstrates the utility of proteomic analysis for extracting forensic information from hair shaft evidence.

Proteomic analysis of hair shafts from monozygotic twins: Expression profiles and genetically variant peptides.

Forensic association of hair shaft evidence with individuals is currently assessed by comparing mitochondrial DNA haplotypes of reference and casework samples, primarily for exclusionary purposes. Present work tests and validates more recent proteomic approaches to extract quantitative transcriptional and genetic information from hair samples of monozygotic twin pairs, which would be predicted to partition away from unrelated individuals if the datasets contain identifying information. Protein expression profiles and polymorphic, genetically variant hair peptides were generated from ten pairs of monozygotic twins. Profiling using the protein tryptic digests revealed that samples from identical twins had typically an order of magnitude fewer protein expression differences than unrelated individuals. The data did not indicate that the degree of difference within twin pairs increased with age. In parallel, data from the digests were used to detect genetically variant peptides that result from common nonsynonymous single nucleotide polymorphisms in genes expressed in the hair follicle. Compilation of the variants permitted sorting of the samples by hierarchical clustering, permitting accurate matching of twin pairs. The results demonstrate that genetic differences are detectable by proteomic methods and provide a framework for developing quantitative statistical estimates of personal identification that increase the value of hair shaft evidence.