Improved Precursor Characterization for Data-Dependent Mass Spectrometry.
Alexander S HebertChristian ThöingNicholas M RileyNicholas W KwiecienEvgenia ShiskovaRomain HuguetHelene L CardasisAndreas KuehnShannon EliukVlad ZabrouskovMichael S WestphallGraeme C McAlisterJoshua J CoonPublished in: Analytical chemistry (2018)
Modern ion trap mass spectrometers are capable of collecting up to 60 tandem MS (MS/MS) scans per second, in theory providing acquisition speeds that can sample every eluting peptide precursor presented to the MS system. In practice, however, the precursor sampling capacity enabled by these ultrafast acquisition rates is often underutilized due to a host of reasons (e.g., long injection times and wide analyzer mass ranges). One often overlooked reason for this underutilization is that the instrument exhausts all the peptide features it identifies as suitable for MS/MS fragmentation. Highly abundant features can prevent annotation of lower abundance precursor ions that occupy similar mass-to-charge (m/z) space, which ultimately inhibits the acquisition of an MS/MS event. Here, we present an advanced peak determination (APD) algorithm that uses an iterative approach to annotate densely populated m/z regions to increase the number of peptides sampled during data-dependent LC-MS/MS analyses. The APD algorithm enables nearly full utilization of the sampling capacity of a quadrupole-Orbitrap-linear ion trap MS system, which yields up to a 40% increase in unique peptide identifications from whole cell HeLa lysates (approximately 53 000 in a 90 min LC-MS/MS analysis). The APD algorithm maintains improved peptide and protein identifications across several modes of proteomic data acquisition, including varying gradient lengths, different degrees of prefractionation, peptides derived from multiple proteases, and phosphoproteomic analyses. Additionally, the use of APD increases the number of peptides characterized per protein, providing improved protein quantification. In all, the APD algorithm increases the number of detectable peptide features, which maximizes utilization of the high MS/MS capacities and significantly improves sampling depth and identifications in proteomic experiments.
Keyphrases
- ms ms
- mass spectrometry
- liquid chromatography
- high performance liquid chromatography
- amino acid
- machine learning
- deep learning
- electronic health record
- liquid chromatography tandem mass spectrometry
- ultra high performance liquid chromatography
- high resolution
- multiple sclerosis
- healthcare
- protein protein
- gas chromatography
- tandem mass spectrometry
- neural network
- capillary electrophoresis
- primary care
- computed tomography
- dna methylation
- small molecule
- single cell
- mesenchymal stem cells
- stem cells
- binding protein
- cell therapy
- genome wide
- label free
- solid phase extraction
- data analysis
- contrast enhanced
- wastewater treatment
- magnetic resonance
- bone marrow
- antibiotic resistance genes