Brian K. Erickson

Multiplexed quantitative analyses of complex proteomes enable deep biological insight. While a multitude of workflows have been developed for multiplexed analyses, the most quantitatively accurate method (SPS-MS3) suffers from long acquisition duty cycles. We built a new, real-time database search (RTS) platform, Orbiter, to combat the SPS-MS3 method’s longer duty cycles. RTS with Orbiter eliminates SPS-MS3 scans if no peptide matches to a given spectrum. With Orbiter’s online proteomic analytical pipeline, which includes RTS and false discovery rate analysis, it was possible to process a single spectrum database search in less than 10 milliseconds. The result is a fast, functional means to identify peptide spectral matches using Comet, filter these matches, and more efficiently quantify proteins of interest. Importantly, the use of Comet for peptide spectral matching allowed for a fully featured search, including analysis of post-translational modifications, with well-known and extensively validated scoring. These data could then be used to trigger subsequent scans in an adaptive and flexible manner. In this work we tested the utility of this adaptive data acquisition platform to improve the efficiency and accuracy of multiplexed quantitative experiments. We found that RTS enabled a two-fold increase in mass spectrometric data acquisition efficiency. Orbiter’s RTS quantified more than 8000 proteins across 10 proteomes in half the time of an SPS-MS3 analysis (18 hours for RTS, 36 hours for SPS-MS3).

Gas-phase fractionation enables better quantitative accuracy, improves signal-to-noise, and increases sensitivity in proteomic analyses. However, traditional gas-phase enrichment, which relies upon a large continuous bin, results in sub-optimal enrichment as most chromatographic separations are not 100% orthogonal relative to the first MS dimension (MS1 m/z). As such, ions with similar m/z values tend to elute at the same retention time, which prevents the partitioning of narrow precursor m/z distributions into a few large continuous gas-phase enrichment bins. To overcome this issue, we developed and tested the use of notched isolation waveforms, which simultaneously isolate multiple discreet m/z windows in parallel (e.g. 650-700 m/z and 800-850 m/z). By comparison to a canonical gas-phase fractionation method, notched waveforms do not require bin optimization via in silico digestion or wasteful sample injections to isolate multiple precursor windows. Importantly, collecting all m/z bins simultaneously using the isolation waveform does not suffer from the sensitivity and duty cycle pitfalls inherent to sequential collection of multiple m/z bins. Applying a notched injection waveform provided consistent enrichment of precursor ions, which resulted in improved proteome depth with greater coverage of low abundance proteins. Finally, using a reductive dimethyl labeling approach, we show that notched isolation waveforms increases the number of quantified peptides with improved accuracy and precision across a wider dynamic range.

Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time spectral identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS³ was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Applying the RTS-MS³ method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS³ approach provides dramatic performance improvements for quantitative multiplexed experiments.

Triggered by Offset, Multiplexed, Accurate mass, High resolution, and Absolute Quantitation (TOMAHAQ) is a recently introduced targeted proteomics method that combines peptide and sample multiplexing. TOMAHAQ assays enable sensitive and accurate multiplexed quantification by implementing an intricate data collection scheme that comprises multiple MSⁿ scans, mass inclusion lists, and data-driven filters. Consequently, manual creation of TOMAHAQ methods can be time-consuming and error prone, while the resulting TOMAHAQ data may not be compatible with common mass spectrometry analysis pipelines. To address these concerns we introduce TomahaqCompanion, an open-source desktop application that enables rapid creation of TOMAHAQ methods and analysis of TOMAHAQ data. Starting from a list of peptide sequences, a user can perform each step of TOMAHAQ assay development including (1) generation of priming run target list, (2) analysis of priming run data, (3) generation of TOMAHAQ method file, and (4) analysis and export of quantitative TOMAHAQ data. We demonstrate the flexibility of TomahaqCompanion by creating a variety of methods testing TOMAHAQ parameters (e.g., number of SPS notches, run length, etc.). Lastly, we analyze an interference sample comprising heavy yeast peptides, a standard human peptide mixture, TMT11-plex, and super heavy TMT (shTMT) isobaric labels to demonstrate ∼10–200 attomol limit of quantification within a complex background using TOMAHAQ.

Targeted protein quantification using liquid chromatography-tandem mass spectrometry with stable isotope labelled standards is recognized as the gold standard of practice for protein quantification. Such assays, however, can only cover a limited number of proteins and developing targeted methods for larger numbers of proteins require substantial investment. Alternatively, large-scale global proteomic experiments along with computational methods such as the "total protein approach" (TPA) have the potential to provide extensive protein quantification. In this study, we compared the TPA-based quantitation of seven major hepatic uptake transporters in four human liver tissue samples using global proteomic data obtained from two multiplexed tandem mass tag experiments (performed in two independent laboratories) to the quantitative data from targeted proteomic assays. The TPA-based quantitation of these hepatic transporters [sodium-taurocholate co-transporting polypeptide (NTCP/SLC10A1), organic anion transporter 2 (OAT2/SLC22A7), OAT7/SLC22A9, organic anion-transporting polypeptide 1B1 (OATP1B1/SLCO1B1), OATP1B3/SLCO1B3, OATP2B1/SLCO2B1, and organic cation transporter 1 (OCT1/SLC22A1)] showed good to excellent correlations (Pearson r=0.74-1.00) to the targeted data. In addition, the values were similar to those measured by targeted proteomics with 57% and 93% of the data sets falling within 3-fold of the targeted data. In conclusion, these results demonstrate the capability of multiplexed global proteomic experiments to detect differences in protein expression between samples and provide reasonable estimations of protein expression levels.

Affinity purification–mass spectrometry elucidates protein interaction networks and co-complexes to build, to our knowledge, the largest experimentally derived human protein interaction network so far, termed BioPlex 2.0. The thousands of proteins within a cell function as modules and networks to coordinate their biological activities. Large-scale efforts are underway to build protein interaction maps that reveal cellular proteome architecture. Here, Wade Harper and colleagues use affinity purification mass spectrometry to elucidate protein interaction networks and co-complexes and build the largest experimentally derived human proteome interaction network to date, termed BioPlex 2.0. Containing over 29,000 novel co-associations and 1,300 protein communities representing diverse cellular activities, BioPlex 2.0 is more than double the size of their earlier interaction network BioPlex 1.0 and will be a valuable resource for exploring uncharacterized proteins and candidate disease-linked genes. The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein–protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease1,2,3. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification–mass spectrometry methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering5 of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

Uncoupling protein 1 (UCP1)-dependent thermogenesis in brown adipose tissue is supported by a burst of mitochondrial reactive oxygen species upon cold exposure. Thermogenic respiration in brown adipose tissue (BAT) and beige fat relies on the uncoupling protein 1 (UCP1), a mitochondrial inner membrane protein that produces heat by dissipating the mitochondrial proton gradient generated by the respiratory chain. These authors show that UCP1 activation is supported by a burst of mitochondrial reactive oxygen species (ROS) upon cold exposure. ROS are necessary to sulfenylate a critical cysteine residue in UCP1, which drives its activation — in effect, the 'on/off switch' for UCP1. Brown and beige adipose tissues can dissipate chemical energy as heat through thermogenic respiration, which requires uncoupling protein 1 (UCP1)1,2. Thermogenesis from these adipocytes can combat obesity and diabetes3, encouraging investigation of factors that control UCP1-dependent respiration in vivo. Here we show that acutely activated thermogenesis in brown adipose tissue is defined by a substantial increase in levels of mitochondrial reactive oxygen species (ROS). Remarkably, this process supports in vivo thermogenesis, as pharmacological depletion of mitochondrial ROS results in hypothermia upon cold exposure, and inhibits UCP1-dependent increases in whole-body energy expenditure. We further establish that thermogenic ROS alter the redox status of cysteine thiols in brown adipose tissue to drive increased respiration, and that Cys253 of UCP1 is a key target. UCP1 Cys253 is sulfenylated during thermogenesis, while mutation of this site desensitizes the purine-nucleotide-inhibited state of the carrier to adrenergic activation and uncoupling. These studies identify mitochondrial ROS induction in brown adipose tissue as a mechanism that supports UCP1-dependent thermogenesis and whole-body energy expenditure, which opens the way to improved therapeutic strategies for combating metabolic disorders.