Optimal processing for proteomic genotyping of single human hairs

Goecker, Zachary C.; Salemi, Michelle; Karim, Noreen; Phinney, Brett S.; Rice, Robert H.; Parker, Glendon J.

doi:10.1016/j.fsigen.2020.102314

Cited by 23 publications

(65 citation statements)

References 41 publications

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“…Samples used in this study were prepared as part of an earlier study [17]. Briefly, five individuals were analyzed: three subjects of European (Davis, CA) and two subjects of African genetic background, respectively (Sorenson Forensics LLC, Salt Lake City, UT).…”

Section: Hair Collection and Processingmentioning

confidence: 99%

“…The length of hair on the head before cutting was roughly 10 cm. Hair shafts were further cut to a length of 20 mm before continuing with protein extraction [17]. The African hair samples weighed almost half of the weight of the European hair samples due to differences in hair shaft width and shape (data not shown).…”

Section: Hair Collection and Processingmentioning

confidence: 99%

“…Initial development of proteomic genotyping has focused on hair shafts as a source of proteinbased genetic information. Over 400 genetically variant peptides from hair have been identified that can predict the corresponding SNP allele [17]. Optimization to this point has focused on chemical processing to maximize peptide production from a single hair shaft [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Over 400 genetically variant peptides from hair have been identified that can predict the corresponding SNP allele [17]. Optimization to this point has focused on chemical processing to maximize peptide production from a single hair shaft [17][18][19]. Aside from a shift to more modern mass spectrometry instruments for data dependent acquisition, relatively little has been done to optimize data acquisition at the mass spectrometry level.…”

Section: Introductionmentioning

confidence: 99%

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

Goecker¹,

Legg²,

Salemi

et al. 2021

Preprint

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Protein is a major component of all biological evidence. Proteomic genotyping is the use of genetically variant peptides that contain single amino acid polymorphisms to infer the genotype of matching non-synonymous single nucleotide polymorphisms for the individual who originated the protein sample. 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 genetically variant peptides 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 on a Orbitrap Fusion Lumos Tribrid system, and multiple reaction monitoring on an Agilent 6495 triple quadrupole system. In our hands, average genetically variant peptide detection from a selected 24 genetically variant peptide panel 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 parallel reaction monitoring and multiple reaction monitoring (p < 0.05). Parallel reaction monitoring resulted in a 1.3-fold increase in detection sensitivity, and multiple reaction monitoring 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 parallel reaction monitoring and multiple reaction monitoring, detected genetically variant peptides 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.

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Section: Hair Collection and Processingmentioning

confidence: 99%

Section: Hair Collection and Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Goecker¹,

Legg²,

Salemi

et al. 2021

Preprint

Self Cite

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show abstract

“…This method was adapted with the addition of an initial mechanical homogenization step to extract proteins from minimal amounts of hair tissue (1-2 cm), which was necessary to analyze multiple hair segments along the same HS. After incubation, the hair was reduced with DTT and then alkylated with iodoacetamide (IAA), as per previous methods 100 . Table S1) used for downstream analyses includes downregulated (<0.8-fold, n=23) and upregulated (>1.5-fold, n=67) proteins.…”

Section: Hair Shaft Proteomicsmentioning

confidence: 99%

Quantitative Mapping of Human Hair Graying and Reversal in Relation to Life Stress

Rosenberg

Rausser

Ren

et al. 2020

Preprint

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Hair graying is a universal hallmark of aging 1 but its mechanisms are insufficiently understood 2 and its reversibility in humans remains uncertain. Moreover, while psychological stress accelerates human biological aging 3,4 and triggers hair graying in animals 5 , no prior study has longitudinally examined the stress-to-hair graying connection in humans. Here we develop an approach to quantitatively profile natural graying events and their associated proteomic signatures along individual human hairs, resulting in a quantifiable physical timescale of aging.Using this approach, we identify white hairs that naturally regain pigmentation within days to weeks in healthy young individuals across sex, ethnicities, ages, and body regions, demonstrating that human hair graying is naturally reversible. Proteomic analysis of matched dark and white hairs replicated across two independent experiments show that graying is marked by the upregulation of proteins related to energy metabolism, mitochondria, and antioxidant defenses. Coordinated graying and reversal also occur simultaneously across multiple scalp hair follicles of a person, suggesting that unknown systemic factors influence hair graying patterns. Combining hair pigmentation profiling and proteomics at the single hair shaft level, we also report hair graying and reversal occurring in parallel with behavioral and psychological stressors. A computational simulation of life-long and stress-induced hair graying suggests a threshold-based mechanism for the rapid reversibility of graying. Together, these findings document the reversibility of hair graying in humans and provide a new model to examine the modifiability of human aging.

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