SUMMARY Targeted mass spectrometry assays for protein quantitation monitor peptide surrogates, which are easily multiplexed to target many peptides in a single assay. However, these assays have generally not taken advantage of sample multiplexing which allows up to 10 analyses to occur in parallel. We present a two-dimensional multiplexing workflow that utilizes synthetic peptides for each protein to prompt the simultaneous quantification of >100 peptides from up to 10 mixed sample conditions. We demonstrate that targeted analysis of unfractionated lysates (2-hr) accurately reproduces the quantification of fractionated lysates (72-hr analysis), while obviating the need for peptide detection prior to quantification. We targeted 131 peptides corresponding to 69 proteins across all 60 National Cancer Institute cell lines in biological triplicate, analyzing 180 samples in only 48 hours (the equivalent of 16 min/sample). These data further elucidated a correlation between the expression of key proteins and their cellular response to drug treatment.

Pathway proteomics strategies measure protein expression changes in specific cellular processes that carry out related functions. Using targeted tandem mass tags-based sample multiplexing, hundreds of proteins can be quantified across 10 or more samples simultaneously. To facilitate these highly complex experiments, we introduce a strategy that provides complete control over targeted sample multiplexing experiments, termed Tomahto, and present its implementation on the Orbitrap Tribrid mass spectrometer platform. Importantly, this software monitors via the external desktop computer to the data stream and inserts optimized MS2 and MS3 scans in real time based on an application programming interface with the mass spectrometer. Hundreds of proteins of interest from diverse biological samples can be targeted and accurately quantified in a sensitive and high-throughput fashion. It achieves sensitivity comparable to, if not better than, deep fractionation and requires minimal total sample input (∼10 µg). As a proof-of-principle experiment, we selected four pathways important in metabolism- and inflammation-related processes (260 proteins/520 peptides) and measured their abundance across 90 samples (nine tissues from five old and five young mice) to explore effects of aging. Tissue-specific aging is presented here and we highlight the role of inflammation- and metabolism-related processes in white adipose tissue. We validated our approach through comparison with a global proteome survey across the tissues, work that we also provide as a general resource for the community.

Phosphorylation-mediated signaling pathways have major implications in cellular regulation and disease. However, proteins with roles in these pathways are frequently less abundant and phosphorylation is often sub-stoichiometric. As such, the efficient enrichment, and subsequent recovery of phosphorylated peptides, is vital. Mass spectrometry-based proteomics is a well-established approach for quantifying thousands of phosphorylation events in a single experiment. We designed a peptide internal standard-based assay directed toward sample preparation strategies for mass spectrometry analysis to understand better phosphopeptide recovery from enrichment strategies. We coupled mass-differential tandem mass tag (mTMT) reagents (specifically, TMTzero and TMTsuper-heavy), nine mass spectrometry-amenable phosphopeptides (phos9), and peak area measurements from extracted ion chromatograms to determine phosphopeptide recovery. We showcase this mTMT/phos9 recovery assay by evaluating three phosphopeptide enrichment workflows. Our assay provides data on the recovery of phosphopeptides, which complement other metrics, namely the number of identified phosphopeptides and enrichment specificity. Our mTMT/phos9 assay is applicable to any enrichment protocol in a typical experimental workflow irrespective of sample origin or labeling strategy. Graphical Abstract ᅟ.

The combination of chemical cross-linking and mass spectrometry has recently been shown to constitute a powerful tool for studying protein-protein interactions and elucidating the structure of large protein complexes. However, computational methods for interpreting the complex MS/MS spectra from linked peptides are still in their infancy, making the high-throughput application of this approach largely impractical. Because of the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for the identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to (i) efficiently generate large mass spectral reference data for linked peptides at a low cost and (ii) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we were able to identify thousands of MS/MS spectra from disulfide-bridged peptides through comparison with proteome-scale sequence databases and significantly improve the sensitivity of cross-linked peptide identification. This allowed us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on crosslinking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be valuable resources for the future development of new tools for the identification of linked peptides. Molecular & Cellular