Researchers at the Johannes Gutenberg University Mainz have devised a data-independent acquisition mass spec method that uses ion mobility drift times to improve precursor fragmentation efficiency and, consequently, protein identifications.
Applying the approach to an analysis of HeLa cell lysate on a Waters Synapt G2-S instrument, the team identified 36,551 peptides and 3,795 proteins with a 90-minute nanoLC gradient. Notably, given the traditional superiority of data-dependent acquisition methods for making peptide IDs, this surpassed the performance of a comparable DDA analysis done on a Thermo Fisher Scientific Q Exactive.
Traditionally, shotgun proteomics experiments have used data-dependent acquisition wherein the mass spectrometer performs an initial scan of precursor ions entering the instrument and selects a sampling of those ions for fragmentation and generation of MS/MS spectra. Because instruments can't scan quickly enough to acquire all the precursors entering at a given moment, however, many ions – particularly low-abundance ions – are never selected for MS/MS fragmentation and so are not detected.
In DIA, on the other hand, the mass spec selects broad windows of ions and fragments all precursors in that window, allowing the machine to collect MS/MS spectra on all ions in a sample. Generally speaking, this makes for more reproducible data across different runs and samples, as the instrument is sampling the same complete set of precursor and fragment ions each time.
DDA methods, however, have typically proven superior in terms of the overall number of peptide IDs made per experiment. In the case of Waters' DIA method, called MSE, this is in significant part due to inefficient fragmentation of precursor ions, JGU researcher Stefan Tenzer, the leader of the effort, told ProteoMonitor.
Launched in 2006, Waters' MSE workflow was the first commercially available DIA method. Unlike AB Sciex's Swath or DIA approaches being developed for Thermo Fisher instruments that divide samples into m/z-based fragmentation windows, the Waters method uses the co-elution times to match precursor ions to the fragment ions generated, allowing them to be searched against databases to make peptide IDs.
In this approach, all co-eluting ions of both low and high m/z are fragmented in parallel. However, low m/z ions typically fragment best at low collision energies, while high m/z ions fragment best at high collision energies. This means that in order to fragment both types of ions, the instrument must ramp the collision energy over the course of the scan cycle so that it fragments both at low and high collision energy.
This solution, however, is less than ideal, Tenzer noted. For instance, if an experiment starts off at low collision energy then ramps up to moderate collision energy and then to high collision energy, in the first third of the scan, only the low m/z ions will be fragmented effectively; in the second third of the scan, only medium m/z ions will be fragmented well; and in the final third, only the high m/z ions will be fragmented well. Essentially, at every portion of the scan, a large portion of the ions will be fragmented inefficiently.
To get around this problem, the researchers needed a way to assign m/z to specific ions, which would then let them more closely control the collision energy under which they were fragmented.
To do this, Tenzer and his colleagues harnessed the Synapt G2-S's ion mobility separation technology, using the specific behavior of ions in the instrument's upfront IMS component as a method for establishing their approximate m/z.
"If you have [IMS], the small ions will travel faster [through it] because they have a higher ion mobility," Tenzer said. This makes it possible to predict when ions of a certain m/z will reach the mass spec's collision cell based on their drift time, allowing a user to adjust the collision energies for optimal fragmentation.
Applying this method, which they named UDMSE, in a study published this week in Nature Methods, the researchers improved fragmentation efficiency by roughly two-fold compared to the conventional MSE approach, Tenzer said. Running the method on a 90-minute gradient, they achieved a 47 percent increase in protein IDs compared to Waters HDMSE approach, an advanced form of MSE that uses IMS-based information for correlating precursor and fragment ions but not for optimizing collision energies.
Tenzer and his colleagues also presented in the paper a new open source software , called ISOQuant, for analysis of DIA data generated using the method. Intended for in-depth analysis of data processed by Waters' ProteinLynx GlobalServer software, the package uses cross-annotation of mass spec ion signals from different runs on the basis of mass, retention time, and drift time to improve the reproducibility of peptide IDs and quantitative data across runs.
"If we see an ion with the same mass, the same retention time, and the same drift time in two different technical replicates, then we're pretty sure that it's the same ion," Tenzer said. "And if this ion is identified in more than one replicate, we cross-assign the annotation... which makes [the data] very reproducible."
In an analysis of UDMSE from three technical replicates, the researchers found that applying the ISOQuant package improved the overlap of proteins identified in all three replicates from 48.5 percent to 97.8 percent.
In addition to comparing their UDMSE approach to conventional Waters' DIA techniques, Tenzer and his team also compared the technique to DDA analysis on a Thermo Scientific Q Exactive instrument.
Running a HeLa cell digest on both machines, the researchers found that the UDMSE method provided 36 percent more protein IDs and 95 percent more peptide IDs than the DDA-based workflow on the Q Exactive.
As Tenzer noted, at the time of the study, the Q Exactive was considered the top of the line Orbitrap. Since then, Thermo Fisher has released a new flagship instrument, its Orbitrap Fusion. In a study published in Molecular & Cellular Proteomics in October, University of Wisconsin-Madison researcher Josh Coon demonstrated this instrument's ability to analyze the full yeast proteome in one hour, identifying roughly 4,000 proteins.
Even compared to the Fusion, though, the UDMSE on the Synapt G2-S appears fairly competitive, Tenzer noted. In their 90-minute HeLa cell analyses, the researchers identified 3,795 proteins and demonstrated roughly the same rate of peptide IDs per second, he said. They have not yet done a one-hour analysis using the method, but are currently working on such a study, he added.