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

• NCJ Number: 303191
• Author(s): Zachary C. Goecker; Kevin M. Legg; Michelle R. Salemi; Anthony W. Herren; Brett S. Phinney; Heather E. McKiernan; Glendon J. Parker
• Date Published: September 2021
• Length: 12 pages
• Grant Number(s): 2015-DN-BX-K065; 2019-R2-CX-0051
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Use of Genetically Variant Peptides to Statistically Estimate the Genetic Background of Hair Shafts; Personal Identification and Ancestral Characterization from Genetically Variant Peptides in Human Hair

Annotation

In the current study, 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. 

Abstract

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. In the current study, 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. (publisher abstract modified)