Comparison of N-Glycopeptide to Released N-Glycan Abundances and the Influence of Glycopeptide Mass and Charge States on N-Linked Glycosylation of IgG Antibodies.

We report the comparison of mass-spectral-based abundances of tryptic glycopeptides to fluorescence abundances of released labeled glycans and the effects of mass and charge state and in-source fragmentation on glycopeptide abundances. The primary glycoforms derived from Rituximab, NISTmAb, Evolocumab, and Infliximab were high-mannose and biantennary complex galactosylated and fucosylated N-glycans. Except for Evolocumab, in-source ions derived from the loss of HexNAc or HexNAc-Hex sugars are prominent for other therapeutic IgGs. After excluding in-source fragmentation of glycopeptide ions from the results, a linear correlation was observed between fluorescently labeled N-glycan and glycopeptide abundances over a dynamic range of 500. Different charge states of human IgG-derived glycopeptides containing a wider variety of abundant attached glycans were also investigated to examine the effects of the charge state on ion abundances. These revealed a linear dependence of glycopeptide abundance on the mass of the glycan with higher charge states favoring higher-mass glycans. Findings indicate that the mass spectrometry-based bottom-up approach can provide results as accurate as those of glycan release studies while revealing the origin of each attached glycan. These site-specific relative abundances are conveniently displayed and compared using previously described glycopeptide abundance distribution spectra "GADS" representations. Mass spectrometry data are available from the MAssIVE repository (MSV000093562).

Peptide ancestry informative markers in uterine neoplasms from women of European, African, and Asian ancestry.

Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with disease pathogenesis. Nonsynonymous single-nucleotide polymorphisms encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. In silico analysis identified that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R2 = 0.99). Multiplexed proteomic analysis of >100 human endometrial cancer cell lines and uterine leiomyoma tissues combined resulted in the quantitation of 62 pAIMs that correlate with patient race and genotype-confirmed ancestry. Candidates include a D451E substitution in GC vitamin D-binding protein previously associated with altered vitamin D levels in African and European populations. pAIMs will support generalized proteoancestry assessment as well as efforts investigating the impact of ancestry on the human proteome and how this relates to the pathogenesis of uterine neoplasms.

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.

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.

Proteomic genotyping of fingermark donors with genetically variant peptides.

Proteomic genotyping detects single amino acid polymorphisms to infer the genotype of corresponding non-synonymous SNPs. Like any DNA genotype, these inferences can be used to estimate random match probability. Fingermarks are a common source of biological evidence that is sample limited and a highly variable source of identifying DNA. Genetically variant peptides from fingermarks, that contain single amino acid polymorphisms, are an additional source of identifying genetic information. To discover these peptide biomarkers epidermal corneocytes from 9 subjects were isolated, processed, digested with trypsin and applied to mass spectrometry. The resulting proteomic and matching exome datasets were used to discover, characterize and validate 60 genetically variant peptides. An average of 28.8 ± 4.4 genetically variant peptides were detected from each subject resulting in a total of 264 SNP allele inferences with 260 true and 4 false positives, a false discovery rate of 1.5%. Random match probabilities were estimated using the genotype frequencies from the matching major populations in the 1000 Genomes Project. Estimates ranged up to a value of 1 in 1.7 × 108, with a median probability of 1 in 2.4 × 106. Furthermore, the proteomically-inferred genotypes are likely to be compatible with the STR-based random match probability estimates since the closest STR locus was 2.2 Mb from the nearest GVP-inferred SNP. This project represents a novel mode of genetic information that can be obtained from fingermarks and has the potential to complement other methods of human identification including analysis of ridge patterns or touch DNA.

Comparison of Quantifiler(®) Trio and InnoQuant™ human DNA quantification kits for detection of DNA degradation in developed and aged fingerprints.

The development techniques employed to visualize fingerprints collected from crime scenes as well as post-development ageing may result in the degradation of the DNA present in low quantities in such evidence samples. Amplification of the DNA samples with short tandem repeat (STR) amplification kits may result in partial DNA profiles. A comparative study of two commercially available quantification kits, Quantifiler(®) Trio and InnoQuant™, was performed on latent fingerprint samples that were either (i) developed using one of three different techniques and then aged in ambient conditions or (ii) undeveloped and then aged in ambient conditions. The three fingerprint development techniques used were: cyanoacrylate fuming, dusting with black powder, and the columnar-thin-film (CTF) technique. In order to determine the differences between the expected quantities and actual quantities of DNA, manually degraded samples generated by controlled exposure of DNA standards to ultraviolet radiation were also analyzed. A total of 144 fingerprint and 42 manually degraded DNA samples were processed in this study. The results indicate that the InnoQuant™ kit is capable of producing higher degradation ratios compared to the Quantifiler(®) Trio kit. This was an expected result since the degradation ratio is a relative value specific for a kit based on the length and extent of amplification of the two amplicons that vary from one kit to the other. Additionally, samples with lower concentrations of DNA yielded non-linear relationships of degradation ratio with the duration of aging, whereas samples with higher concentrations of DNA yielded quasi-linear relationships. None of the three development techniques produced a noticeably different degradation pattern when compared to undeveloped fingerprints, and therefore do not impede downstream DNA analysis.