Image: The LTQ Orbitrap XL hybrid ion trap mass spectrometer (Photo courtesy of Thermo Fisher Scientific).
A major challenge for proteomics, particularly in studies using complex samples with high dynamic ranges, is that only a small proportion of peptides in a sample are selected for analysis.
This means that high-abundance peptides are overwhelmingly selected for, making identification and quantification of lower-abundance molecules challenging and highly variable across different samples. One approach to tackling this problem is improving MS2-level analysis by developing methods capable of fragmenting and analyzing a higher proportion of the precursor ions introduced into the mass spectrometer (MS).
Scientists at the Max Planck Institute of Biochemistry (Martinsried, Germany) developed an approach, named the BoxCar acquisition method that adjusts the sampling of ions at the MS1 level during MS analysis to expand the instrument's dynamic range and sampling depth. This approach significantly boosts the sensitivity and reproducibility of conventional data-dependent MS acquisition.
In the BoxCar method, the team focused on this MS1 level, and the limitations of the ion storage device (C-trap) used in some of Orbitrap instruments, specifically. In these instruments, ions are generated by electrospray and then passed into the C-trap before moving into the Orbitrap for analysis. However, according to the authors, the C-trap can store only one million charges at a time, which, they noted, is around 1% of the ions generated during its fill time, meaning that around 99% of ions generated are never analyzed. Because high-abundance ions are overrepresented in the overall sample, they will also be overrepresented in the 1% of the ions that are ultimately analyzed, crowding out lower-abundance molecules.
The investigators applied the method to human plasma and found that it provided an additional order of magnitude of dynamic range, a notable boost in performance, given that plasma can exceed ten orders of dynamic range. They also found it helped substantially with the "missing data" problem that has limited the usefulness of shotgun proteomics in work like clinical biomarker research, where reproducible quantification across large numbers of samples is key. In an analysis of 10 HeLa cell digests using 45-minute MS runs, the scientists found the BoxCar method quantified 7,222 proteins per run, 6,216 of which were quantified in all 10 runs. An equivalent study using a conventional shotgun method quantified 5,050 proteins, 4,180 of them in all 10 runs. In one hour analyses, the method provided MS1-level evidence for more than 90% of the proteome of a human cancer cell line that had previously been identified in 24 fractions.
Florian Meier, MSc, the lead author of the study, said, “It's now no longer a big deal to generate a very deep library of a sample by fractionating. If you have a lot of samples, then the time you spend on generating libraries is not a lot compared to the time you can spend on actually quantifying your samples. That's the workflow we have set up for clinical samples in particular.” The study was published on May 7, 2018, in the journal Nature Methods.
Max Planck Institute of Biochemistry