Dataset Information

Alternative LC-MS/MS Platforms and Data Acquisition Strategies for Proteomic Genotyping

ABSTRACT: 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 forensic evidence. The utility of the inferred genotype increases as the detection of genetically variant peptides increases and as the technology is transferred to mass spectrometry platforms available to forensic practitioners. 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 and multiple reaction monitoring (p < 0.05). Targeted methods of analysis resulted in a 1.3-fold to 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 billion to 1 in 10 trillion from a single hair using targeted proteomics. 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 by forensic practitioners.

ORGANISM(S): Homo Sapiens

SUBMITTER: Glendon Parker

PROVIDER: PXD024651 | panorama | Fri Sep 10 00:00:00 BST 2021

REPOSITORIES: PanoramaPublic

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

Goecker Zachary C ZC Legg Kevin M KM Salemi Michelle R MR Herren Anthony W AW Phinney Brett S BS McKiernan Heather E HE Parker Glendon J GJ

Journal of proteome research 20210907 10

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 t ...[more]

PMID: 34491751

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Project description:This dataset results from the discovery, characterization and validation of 74 genetically variant peptides from fingermark protein. These peptides contain single amino acid polymorphisms, the result of non-synonymous SNPs. Detection of these peptide markers in fingermark proteomic datasets allows inferences to be made about the genotype status of corresponding SNP alleles. These inferences provide an individual genetic profile that may be used to calculate random match probabilities and provide information about potential genetic background. Epidermal corneocytes from five European and four South Asian subjects were isolated, processed, proteolytically digested with trypsin and 0.75 µg applied to a Q ExactiveTM hybrid quadrupole-Orbitrap mass spectrometer. The resulting datasets were used for discovery of genetically variant peptides by a “bottom-up” analysis of potential variants identified in X!Tandem datasets (thegpm.org) or by a “top-down” proteomic confirmation of non-synonymous SNP variants shown to be present in corresponding subject exome datasets. The later method resulted in discovery of a rare allele that was not observed in the 1000 Genome Project. The cumulative profiles of detected genetically variant peptides were obtained for each subject. Candidate genetically variant peptides were then validated by comparing proteomically inferred SNP alleles with matching genotypes in exome datasets. An average of 28.8 ± 4.4 genetically variant peptides were detected from each subject. Across the 9 subjects a total of 264 SNP allele inferences were made resulting in 260 true positives and 4 false positives, a false positive rate of 1.5%. Random match probabilities were calculated using the genotype frequencies of common genetically variant peptides from the matching major populations in the 1000 genome project. Estimates ranged up to a value of 1 in 1.7 x 108, with a median probability of 1 in 2.4 x 106. When probabilities were recalculated using values from other major genetic population groups African values were considerably less conservative, with resulting likelihood ratios (AFR/other populations) ranging up to 6 orders of magnitude. Conversely there was no resolution among estimates obtained from all non-African population groups, where resulting random match probabilities were within an order of magnitude. These genetically variant peptides are a starting point for the development of targeted mass spectrometry-based proteomic analyses that will increase the sensitivity of peptide detection. This project represents a novel mode of genetic information that can be obtained from fingermarks and has the potential to complement or confirm other methods of human identification including analysis of ridge patterns or touch DNA.

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Dataset Information

Alternative LC-MS/MS Platforms and Data Acquisition Strategies for Proteomic Genotyping

Publications

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

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