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Biology subjects

Phinney, B. S.

Publications and source records attributed to Phinney, B. S..

3 recordsLinked to original sources

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 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. HighlightsO_LITest four mass spectrometry configurations to optimize detection of genetically variant peptides C_LIO_LITechnology transfer of proteomic genotyping assays C_LIO_LIImproved sensitivity results in higher level of forensic discrimination for human identification using multiple reaction monitoring C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/435505v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1001cfaorg.highwire.dtl.DTLVardef@6e8aa1org.highwire.dtl.DTLVardef@14f8076org.highwire.dtl.DTLVardef@7ad930_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology

2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Study

Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample, as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of four exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analysed by focusing on two datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings and skill levels.

bioinformatics

Deep learning neural network prediction method improves proteome profiling of vascular sap of grapevines during Pierce's disease development

Plant secretome studies have shown the importance of plant defense proteins in the vascular system against pathogens. Studies on Pierces disease of grapevines caused by the xylem-limited bacteria Xylella fastidiosa (Xf) have detected proteins and pathways associated to its pathobiology. Despite the biological importance of the secreted proteins in the extracellular space to plant survival and development, proteome studies are scarce due to technical and technological challenges. Deep learning neural network prediction methods can provide powerful tools for improving proteome profiling by data-independent acquisition (DIA). We aimed to explore the potential of this strategy by combining it with in silico spectral library prediction tool, Prosit, to analyze the proteome of vascular leaf sap of grapevines with Pierces disease. The results demonstrate that the combination of DIA and Prosit increased the total number of identified proteins from 145 to 360 for grapevines and 18 to 90 for Xf. The new proteins increased the range of molecular weight, assisted on the identification of more exclusive peptides per protein, and increased the identification of low abundance proteins. These increases allowed the identification of new functional pathways associated with cellular responses to oxidative stress to be further investigated.

genomics