Synthetic Knockout Protein Standard for Evaluating Interference in Tandem Mass Tag-Based Proteomics.

Isobaric labeling is widely used for unbiased, proteome-wide studies, and it provides several advantages, such as fewer missing values among samples and higher quantitative precision. However, ion interference may lead to compressed or distorted observed ratios due to the coelution and coanalysis of peptides. Here, we introduced a synthetic KnockOut standard (sKO) for evaluating interference in tandem mass tags-based proteomics. sKO is made by mixing TMTpro-labeled tryptic peptides derived from four nonhuman proteins and a whole human proteome as background at different proportions. We showcased the utility of the sKO standard by exploring ion interference at different peptide concentrations (up to a 30-fold change in abundance) and using a variety of mass spectrometer data acquisition strategies. We also demonstrated that the sKO standard could provide valuable information for the rational design of acquisition strategies to achieve optimal data quality and discussed its potential applications for high-throughput proteomics workflows development.

Fission yeast CK1 promotes DNA double-strand break repair through both homologous recombination and non-homologous end joining.

The CK1 family are conserved serine/threonine kinases with numerous substrates and cellular functions. The fission yeast CK1 orthologues Hhp1 and Hhp2 were first characterized as regulators of DNA repair, but the mechanism(s) by which CK1 activity promotes DNA repair had not been investigated. Here, we found that deleting Hhp1 and Hhp2 or inhibiting CK1 catalytic activities in yeast or in human cells activated the DNA damage checkpoint due to persistent double-strand breaks (DSBs). The primary pathways to repair DSBs, homologous recombination and non-homologous end joining, were both less efficient in cells lacking Hhp1 and Hhp2 activity. In order to understand how Hhp1 and Hhp2 promote DSB repair, we identified new substrates using quantitative phosphoproteomics. We confirmed that Arp8, a component of the INO80 chromatin remodeling complex, is a bona fide substrate of Hhp1 and Hhp2 that is important for DSB repair. Our data suggest that Hhp1 and Hhp2 facilitate DSB repair by phosphorylating multiple substrates, including Arp8.

Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression.

Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.

Kinase domain autophosphorylation rewires the activity and substrate specificity of CK1 enzymes.

CK1s are acidophilic serine/threonine kinases with multiple critical cellular functions; their misregulation contributes to cancer, neurodegenerative diseases, and sleep phase disorders. Here, we describe an evolutionarily conserved mechanism of CK1 activity: autophosphorylation of a threonine (T220 in human CK1δ) located at the N terminus of helix αG, proximal to the substrate binding cleft. Crystal structures and molecular dynamics simulations uncovered inherent plasticity in αG that increased upon T220 autophosphorylation. The phosphorylation-induced structural changes significantly altered the conformation of the substrate binding cleft, affecting substrate specificity. In T220 phosphorylated yeast and human CK1s, activity toward many substrates was decreased, but we also identified a high-affinity substrate that was phosphorylated more rapidly, and quantitative phosphoproteomics revealed that disrupting T220 autophosphorylation rewired CK1 signaling in Schizosaccharomyces pombe. T220 is present exclusively in the CK1 family, thus its autophosphorylation may have evolved as a unique regulatory mechanism for this important family.

Assessing interference in isobaric tag-based sample multiplexing using an 18-plex interference standard.

Reporter ion interference remains a limitation of isobaric tag-based sample multiplexing. Advances in instrumentation and data acquisition modes, such as the recently developed real-time database search (RTS), can reduce interference. However, interference persists as does the need to benchmark upstream sample preparation and data acquisition strategies. Here, we present an updated Triple yeast KnockOut (TKO) standard as well as corresponding upgrades to the TKO viewing tool (TVT2.5, http://tko.hms.harvard.edu/). Specifically, we expand the TKO standard to incorporate the TMTpro18-plex reagents (TKO18). We also construct a variant thereof which has been digested only with LysC (TKO18L). We compare proteome coverage and interference levels of TKO18 and TKO18L data that are acquired under different data acquisition modes and analyzed using TVT2.5. Our data illustrate that RTS reduces interference while improving proteome coverage and suggest that digesting with LysC alone only modestly reduces interference, albeit at the expense of proteome depth. Collectively, the two new TKO standards coupled with the updated TVT represent a convenient and versatile platform for assessing and developing methods to reduce interference in isobaric tag-based experiments.

Assessing target engagement using proteome-wide solvent shift assays.

Recent advances in mass spectrometry (MS) have enabled quantitative proteomics to become a powerful tool in the field of drug discovery, especially when applied toward proteome-wide target engagement studies. Similar to temperature gradients, increasing concentrations of organic solvents stimulate unfolding and precipitation of the cellular proteome. This property can be influenced by physical association with ligands and other molecules, making individual proteins more or less susceptible to solvent-induced denaturation. Herein, we report the development of proteome-wide solvent shift assays by combining the principles of solvent-induced precipitation (Zhang et al., 2020) with modern quantitative proteomics. Using this approach, we developed solvent proteome profiling (SPP), which is capable of establishing target engagement through analysis of SPP denaturation curves. We readily identified the specific targets of compounds with known mechanisms of action. As a further efficiency boost, we applied the concept of area under the curve analysis to develop solvent proteome integral solubility alteration (solvent-PISA) and demonstrate that this approach can serve as a reliable surrogate for SPP. We propose that by combining SPP with alternative methods, like thermal proteome profiling, it will be possible to increase the absolute number of high-quality melting curves that are attainable by either approach individually, thereby increasing the fraction of the proteome that can be screened for evidence of ligand binding.

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome.

Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins-half the proteome-in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome whose structure encodes protein function, localization, and complex membership. Comparison across cell lines validates thousands of interactions and reveals extensive customization. Whereas shared interactions reside in core complexes and involve essential proteins, cell-specific interactions link these complexes, "rewiring" subnetworks within each cell's interactome. Interactions covary among proteins of shared function as the proteome remodels to produce each cell's phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.

Iron Deficiency and Recovery in Yeast: A Quantitative Proteomics Approach.

Iron is an essential element for life, as it is critical for oxygen transport, cellular respiration, DNA synthesis, and metabolism. Disruptions in iron metabolism have been associated with several complex diseases like diabetes, cancer, infection susceptibility, neurodegeneration, and others; however, the molecular mechanisms linking iron metabolism with these diseases are not fully understood. A commonly used model to study iron deficiency (ID) is yeast, Saccharomyces cerevisiae. Here, we used quantitative (phospho)proteomics to explore the early (4 and 6 h) and late (12 h) response to ID. We showed that metabolic pathways like the Krebs cycle, amino acid, and ergosterol biosynthesis were affected by ID. In addition, during the late response, several proteins related to the ubiquitin-proteasome system and autophagy were upregulated. We also explored the proteomic changes during a recovery period after 12 h of ID. Several proteins recovered their steady-state levels, but some others, such as cytochromes, did not recover during the time tested. Additionally, we showed that autophagy is active during ID, and some of the degraded proteins during ID can be rescued using KO strains for several key autophagy genes. Our results highlight the complex proteome changes occurring during ID and recovery. This study constitutes a valuable data set for researchers interested in iron biology, offering a temporal proteomic data set for ID, as well as a compendium the proteomic changes associated with episodes of iron recovery.

Temporal proteomic changes induced by nicotine in human cells: A quantitative proteomics approach.

Nicotine is a prominent active compound in tobacco and many smoking cessation products. Some of the biological effects of nicotine are well documented in in vitro and in vivo systems; however, data are scarce concerning the time-dependent changes on protein and phosphorylation events in response to nicotine. Here, we profiled the proteomes of SH-SY5Y and A549 cell lines subjected to acute (15 min, 1 h and 4 h) or chronic (24 h, 48 h) nicotine exposures. We used sample multiplexing (TMTpro16) and quantified more than 9000 proteins and over 7000 phosphorylation events per cell line. Among our findings, we determined a decrease in mitochondrial protein abundance for SH-SY5Y, while we detected alterations in several immune pathways, such as the complement system, for A549 following nicotine treatment. We also explored the proposed association between smoking (specifically nicotine) and SARS-CoV2. Here, we found several host proteins known to interact with viral proteins that were affected by nicotine in a time dependent manner. This dataset can be mined further to investigate the potential role of nicotine in different biological contexts. SIGNIFICANCE: Smoking is a major public health issue that is associated with several serious chronic, yet preventable diseases, including stroke, heart disease, type 2 diabetes, cancer, and susceptibility to infection. Tobacco smoke is a complex mixture of thousands of different compounds, among which nicotine is the main addictive compound. The biological effects of nicotine have been reported in several models, however very little data are available concerning the temporal proteomic and phosphoproteomic changes in response to nicotine. Here, we provide a dataset exploring the potential role of nicotine on different biological processes over time, including implications in the study of SARS-CoV2.

Growth media selection alters the proteome profiles of three model microorganisms.

The selection of growth media is a very important consideration of any cell-based proteomics experiment. Alterations thereof may result in differences in basal proteomes simply due to disparities in the metabolite composition of the media. We investigate the effect of growth media on the proteomes of three microorganisms, specifically E. coli, S. cerevisiae, and S. pombe, using tandem mass tag (TMT)-based quantitative proteomics. We compared the protein abundance profiles of these microorganisms propagated in two distinct growth media that are commonly used for the respective organism. Our sample preparation strategy included SP3 bead-assisted protein isolation and digestion. In addition, we assembled a replicate set of samples in which we altered the proteolytic digestion from sequential treatment with LysC and trypsin to only LysC. Despite differences in peptides identified and a drop in quantified proteins, the results were similar between the two datasets for all three microorganisms. Approximately 10% of the proteins of each respective microorganism were significantly altered in each dataset. As expected, gene ontology analysis revealed that the majority of differentially expressed proteins are implicated in metabolism. These data emphasize further the importance and the potential consequences of growth media selection. SIGNIFICANCE: Various microorganisms are used as model systems throughout in biological studies, including proteomics-based investigations. The growth conditions of these organisms are of utmost importance, of which one major consideration is the choice of growth media. We hypothesize that growth media selection has a considerable impact on the baseline proteome of a given microorganism. To test this hypothesis, we used tandem mass tag (TMT)-based quantitative multiplexed proteomics to profile the proteomes of E. coli, S. cerevisiae, and S. pombe each grown in two different, yet common, growth media for the respective species. Our data show that approximately 10% of the proteins of each respective microorganism were significantly altered and that many of the differentially expressed proteins are implicated in metabolism. We provide several datasets which are potentially valuable for growth media selection with respect to downstream biochemical analysis.

HYpro16: A Two-Proteome Mixture to Assess Interference in Isobaric Tag-Based Sample Multiplexing Experiments.

Isobaric tagging is a powerful strategy for global proteome profiling. A caveat of isobaric-tag-based quantification is "interference", which may be caused by coeluting peptides that are coisolated, cofragmented, and coanalyzed, thereby confounding quantitative accuracy. Here, we present a two-proteome standard that challenges the mass spectrometer to measure a range of protein abundance ratios in a background of potential interference. The HYpro16 standard consists of tandem mass tag (TMT) pro16-labeled human peptides at a 1:1 ratio across all channels into which is spiked TMTpro16-labeled yeast peptides in triplicate at 20:1, 10:1, 4:1, and 2:1 ratios. We showcase the HYpro16 standard by (1) altering the MS2 isolation window width and (2) examining different data acquisition methods (hrMS2, SPS-MS3, RTS-MS3). Our data illustrate that wider isolation widths moderately increase the TMT signal, the benefits of which are offset by decreased ratio accuracy. We also show that using real-time database searching (RTS)-MS3 resulted in the most accurate ratios. Additionally, the number of quantified yeast proteins using RTS-MS3 approaches that of hrMS2 when using a yeast-specific database for real-time searching. In short, this quality control standard allows for the assessment of multiple quantitative measurements within a single run, which can be compared across instruments to benchmark and track performance.

Sample multiplexing for targeted pathway proteomics in aging mice.

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.

Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics.

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 ms. 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 2-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 h for RTS, 36 h for SPS-MS3).

A Triple Knockout Isobaric-Labeling Quality Control Platform with an Integrated Online Database Search.

Sample multiplexing using isobaric tagging is a powerful strategy for proteome-wide protein quantification. One major caveat of isobaric tagging is ratio compression that results from the isolation, fragmentation, and quantification of coeluting, near-isobaric peptides, a phenomenon typically referred to as "ion interference". A robust platform to ensure quality control, optimize parameters, and enable comparisons across samples is essential as new instrumentation and analytical methods evolve. Here, we introduce TKO-iQC, an integrated platform consisting of the Triple Knockout (TKO) yeast digest standard and an automated web-based database search and protein profile visualization application. We highlight two new TKO standards based on the TMTpro reagent (TKOpro9 and TKOpro16) as well as an updated TKO Viewing Tool, TVT2.0. TKO-iQC greatly facilitates the comparison of instrument performance with a straightforward and streamlined workflow.

Multiplexed proteome profiling of carbon source perturbations in two yeast species with SL-SP3-TMT.

Saccharomyces cerevisiae and Schizosaccharomyces pombe are the most commonly studied yeast model systems, yet comparisons of global proteome remodeling between these yeast species are scarce. Here, we profile the proteomes of S. cerevisiae and S. pombe cultured with either glucose or pyruvate as the sole carbon source to define common and distinctive alterations in the protein landscape across species. In addition, we develop an updated streamlined-tandem mass tag (SL-TMT) strategy that substitutes chemical-based precipitation with more versatile bead-based protein aggregation method (SP3) prior to enzymatic digestion and TMT labeling. Our new workflow, SL-SP3-TMT, allow for near-complete proteome profiles in a single experiment for each species. The data reveal expected alterations in protein abundance and differences between species, highlighted complete canonical biochemical pathways, and provided insight into previously uncharacterized proteins. The techniques used herein, namely SL-SP3-TMT, can be applied to virtually any experiment aiming to study remodeling of the proteome using a high-throughput, comprehensive, yet streamlined mass spectrometry-based strategy. SIGNIFICANCE: Saccharomyces cerevisiae and Schizosaccharomyces pombe are single-celled eukaryotes that diverged from a common ancestor over a period of 100 million years, such that evolution has driven fundamental differences between the two species. Cellular metabolism and the regulation thereof are vital for living organisms. Here, we hypothesize that large scale proteomic alterations are prevalent upon the substitution of glucose with another carbon source, in this case pyruvate. To efficiently process our samples, we developed an updated streamlined-tandem mass tag (SL-TMT) strategy with more versatile bead-based protein aggregation. The data revealed expected alterations in protein abundance and illustrated differences between species. We highlighted complete canonical biochemical pathways and provided insight into previously uncharacterized proteins.

PCIF1 Catalyzes m6Am mRNA Methylation to Regulate Gene Expression.

mRNA modifications play important roles in regulating gene expression. One of the most abundant mRNA modifications is N6,2-O-dimethyladenosine (m6Am). Here, we demonstrate that m6Am is an evolutionarily conserved mRNA modification mediated by the Phosphorylated CTD Interacting Factor 1 (PCIF1), which catalyzes m6A methylation on 2-O-methylated adenine located at the 5' ends of mRNAs. Furthermore, PCIF1 catalyzes only 5' m6Am methylation of capped mRNAs but not internal m6A methylation in vitro and in vivo. To study the biological role of m6Am, we developed a robust methodology (m6Am-Exo-Seq) to map its transcriptome-wide distribution, which revealed no global crosstalk between m6Am and m6A under assayed conditions, suggesting that m6Am is functionally distinct from m6A. Importantly, we find that m6Am does not alter mRNA transcription or stability but negatively impacts cap-dependent translation of methylated mRNAs. Together, we identify the only human mRNA m6Am methyltransferase and demonstrate a mechanism of gene expression regulation through PCIF1-mediated m6Am mRNA methylation.

Physical and Molecular Landscapes of Mouse Glioma Extracellular Vesicles Define Heterogeneity.

Cancer extracellular vesicles (EVs) are highly heterogeneous, which impedes our understanding of their function as intercellular communication agents and biomarkers. To deconstruct this heterogeneity, we analyzed extracellular RNAs (exRNAs) and extracellular proteins (exPTNs) from size fractionation of large, medium, and small EVs and ribonucleoprotein complexes (RNPs) from mouse glioblastoma cells by RNA sequencing and quantitative proteomics. mRNA from medium-sized EVs most closely reflects the cellular transcriptome, whereas small EV exRNA is enriched in small non-coding RNAs and RNPs contain precisely processed tRNA fragments. The exPTN composition of EVs and RNPs reveals that they are closely related by vesicle type, independent of their cellular origin, and single EV analysis reveals that small EVs are less heterogeneous in their protein content than larger ones. We provide a foundation for better understanding of segregation of macromolecules in glioma EVs through a catalog of diverse exRNAs and exPTNs.

Active Instrument Engagement Combined with a Real-Time Database Search for Improved Performance of Sample Multiplexing Workflows.

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.

Characterization and Optimization of Multiplexed Quantitative Analyses Using High-Field Asymmetric-Waveform Ion Mobility Mass Spectrometry.

Multiplexed, isobaric tagging methods are powerful techniques to increase throughput, precision, and accuracy in quantitative proteomics. The dynamic range and accuracy of quantitation, however, can be limited by coisolation of tag-containing peptides that release reporter ions and conflate quantitative measurements across precursors. Methods to alleviate these effects often lead to the loss of protein and peptide identifications through online or offline filtering of interference containing spectra. To alleviate this effect, high-Field Asymmetric-waveform Ion Mobility Spectroscopy (FAIMS) has been proposed as a method to reduce precursor coisolation and improve the accuracy and dynamic range of multiplex quantitation. Here we tested the use of FAIMS to improve quantitative accuracy using previously established TMT-based interference standards (triple-knockout [TKO] and Human-Yeast Proteomics Resource [HYPER]). We observed that FAIMS robustly improved the quantitative accuracy of both high-resolution MS2 (HRMS2) and synchronous precursor selection MS3 (SPS-MS3)-based methods without sacrificing protein identifications. We further optimized and characterized the main factors that enable robust use of FAIMS for multiplexed quantitation. We highlight these factors and provide method recommendations to take advantage of FAIMS technology to improve isobaric-tag-quantification moving forward.