1 research outputs found
Improved Precursor Characterization for Data-Dependent Mass Spectrometry
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 (<i>m</i>/<i>z</i>) 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 <i>m</i>/<i>z</i> 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