Bruderer et al. (2015) recently published a paper detailing hyper reaction monitoring (HRM), a novel data-independent analysis (DIA) mass spectrometry (MS) technique. Built around SWATH-MS, another DIA approach that gives complete coverage of all fragment ions, the research team applied HRM to profile the proteome.1 Describing their technique as an improvement on shotgun proteomics, the team showed that the new workflow overcomes problems inherent in traditional data collection methodology.
As a high-throughput technique, shotgun proteomics is capable of delivering vast numbers of identifications for mass spectrometric peptide discovery in complex samples. As a quantitative technique, however, the problems of missing data—arising from peptides that are not present in sufficient abundancies for detection or that cannot be identified2—and lack of consistency between experimental runs (which hinders comparison) detract from the final analysis. Not only does the new HRM workflow, which works via retention-time-normalized spectral libraries, avoid the problem of missing data, but it is also an effective and reproducible method for protein quantitation.
In the validation presented, the authors reported an increase in the numbers of peptides identified and quantified, in addition to better than 98% reproducibility. The research team also demonstrated the new workflow’s usefulness by characterizing proteome changes resulting from sub-lethal acetaminophen treatment in 3D human liver microtissues.
The team used the human embryonic kidney cell line, HEK-293, to develop and optimize the HRM-DIA workflow, before moving on to examine hepatocyte response to sub-lethal acetaminophen treatment. They compared results gathered using the HRM workflow with those obtained via traditional shotgun proteomics, searching the spectral data gathered against the human UniProt fasta database.
Bruderer et al. carried out liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis using an Easy-nLC 1000 liquid chromatograph in conjunction with a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (both Thermo Scientific), for both HRM DIA and the traditional data-dependent workflows. In addition to this, the research team created a selected reaction monitoring (SRM) assay, analyzing samples spiked with synthetic, heavy-labeled peptides on a TSQ Vantage triple quadrupole mass spectrometer (Thermo Scientific) to generate the extensive retention-time-normalized spectral libraries for the HRM step.
First, the team carried out spiking studies using non-human proteins added to digested protein extracts obtained from cultures of the HEK-293 cells to optimize and validate the HRM workflow. Using these master mixes, termed “Profiling Standard Sample Set” by the researchers, they found that HRM detected 60% more peptides than shotgun proteomics, with only 1.6% of those peptides missing from the profile, as compared to 51% using the traditional method. Furthermore, reproducibility was more consistent for the new method when the researchers compared different experimental runs.
Once validated, the research team turned to characterizing proteome changes due to acetaminophen treatment of a commercial 3D human liver microtissue, a cell culture model containing hepatocytes capable of metabolizing drugs in vitro. After establishing a non-lethal dose by examining cell viability markers (ATP assay), the team treated the cultures with various concentrations of acetaminophen on day five of the culture, then harvested the cells three days later.
Using HRM profiling, the team found differential expression of eight proteins, including novel proteins not previously reported in toxicity studies. They also found induction of physiologically relevant mitochondrial protein adducts that could further explain toxicity pathways responsible for acetaminophen damage in liver.
In conclusion, Bruderer et al. explain that the success of the HRM workflow in overcoming the problem of missing data experienced with shotgun proteomics lies with its ability to quantify ions at the MS2 level instead of during MS1, resulting in better precision. Although the method surpasses traditional shotgun proteomics, the authors are keen to point out that in their novel workflow the older method is not obsolete; repurposing it to where it is best suited generates the vast spectral libraries on which HRM depends.
1. Bruderer, R., et al. (2015, May) “Extending the limits of quantitative proteome profiling with data‐independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues,” Molecular and Cellular Proteomics, 14 (pp. 1400–10), doi: 10.1074/mcp.M114.044305.
2. Karpievitch, Y.V., et al. (2012) “Normalization and missing value imputation for label-free LC-MS analysis,” BioMed Central Bioinformatics, 13 Suppl. 16 (p. S5), doi: 10.1186/1471-2105-13-S16-S5.
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