Proteome-wide mapping of short-lived proteins in human cells.

Rapid protein degradation enables cells to quickly modulate protein abundance. Dysregulation of short-lived proteins plays essential roles in disease pathogenesis. A focused map of short-lived proteins remains understudied. Cycloheximide, a translational inhibitor, is widely used in targeted studies to measure degradation kinetics for short-lived proteins. Here, we combined cycloheximide chase assays with advanced quantitative proteomics to map short-lived proteins under translational inhibition in four human cell lines. Among 11,747 quantified proteins, we identified 1,017 short-lived proteins (half-lives ≤ 8 h). These short-lived proteins are less abundant, evolutionarily younger, and less thermally stable than other proteins. We quantified 103 proteins with different stabilities among cell lines. We showed that U2OS and HCT116 cells express truncated forms of ATRX and GMDS, respectively, which have lower stability than their full-length counterparts. This study provides a large-scale resource of human short-lived proteins under translational arrest, leading to untapped avenues of protein regulation for therapeutic interventions.

Spectral library search for improved TMTpro labelled peptide assignment in human plasma proteomics.

Clinical biomarker discovery is often based on the analysis of human plasma samples. However, the high dynamic range and complexity of plasma pose significant challenges to mass spectrometry-based proteomics. Current methods for improving protein identifications require laborious pre-analytical sample preparation. In this study, we developed and evaluated a TMTpro-specific spectral library for improved protein identification in human plasma proteomics. The library was constructed by LC-MS/MS analysis of highly fractionated TMTpro-tagged human plasma, human cell lysates, and relevant arterial tissues. The library was curated using several quality filters to ensure reliable peptide identifications. Our results show that spectral library searching using the TMTpro spectral library improves the identification of proteins in plasma samples compared to conventional sequence database searching. Protein identifications made by the spectral library search engine demonstrated a high degree of complementarity with the sequence database search engine, indicating the feasibility of increasing the number of protein identifications without additional pre-analytical sample preparation. The TMTpro-specific spectral library provides a resource for future plasma proteomics research and optimization of search algorithms for greater accuracy and speed in protein identifications in human plasma proteomics, and is made publicly available to the research community via ProteomeXchange with identifier PXD042546.

Adapting an Isobaric Tag-Labeled Yeast Peptide Standard to Develop Targeted Proteomics Assays.

Targeted proteomics strategies present a streamlined hypothesis-driven approach to analyze specific sets of pathways or disease related proteins. goDig is a quantitative, targeted tandem mass tag (TMT)-based assay that can measure the relative abundance differences for hundreds of proteins directly from unfractionated mixtures. Specific protein groups or entire pathways of up to 200 proteins can be selected for quantitative profiling, while leveraging sample multiplexing permits the simultaneous analysis of up to 18 samples. Despite these benefits, implementing goDig is not without challenges, as it requires access to an instrument application programming interface (iAPI), an elution order and spectral library, a web-based method builder, and dedicated companion software. In addition, the absence of an example test assay may dissuade researchers from testing or implementing goDig. Here, we repurpose the TKO11 standard─which is commercially available but may also be assembled in-lab─and establish it as a de facto test assay for goDig. We build a proteome-wide goDig yeast library, quantify protein expression across several gene ontology (GO) categories, and compare these results to a fully fractionated yeast gold-standard data set. Essentially, we provide a guide detailing the goDig-based quantification of TKO11, which can also be used as a template for user-defined assays in other species.

15N metabolic labeling-TMT multiplexing approach to facilitate the quantitation of glycopeptides derived from cell lines.

The study of glycoproteomics presents a set of unique challenges, primarily due to the low abundance of glycopeptides and their intricate heterogeneity, which is specific to each site. Glycoproteins play a crucial role in numerous biological functions, including cell signaling, adhesion, and intercellular communication, and are increasingly recognized as vital markers in the diagnosis and study of various diseases. Consequently, a quantitative approach to glycopeptide research is essential. One effective strategy to address this need is the use of multiplex glycopeptide labeling. By harnessing the synergies of 15N metabolic labeling via the isotopic detection of amino sugars with glutamine (IDAWG) technique for glycan parts and tandem mass tag (TMT)pro labeling for peptide backbones, we have developed a method that allows for the accurate quantification and comparison of multiple samples simultaneously. The adoption of the liquid chromatography-synchronous precursor selection (LC-SPS-MS3) technique minimizes fragmentation interference, enhancing data reliability, as shown by a 97% TMT labeling efficiency. This method allows for detailed, high-throughput analysis of 32 diverse samples from 231BR cell lines, using both 14N and 15N glycopeptides at a 1:1 ratio. A key component of our methodology was the precise correction for isotope and TMTpro distortions, significantly improving quantification accuracy to less than 5% distortion. This breakthrough enhances the efficiency and accuracy of glycoproteomic studies, increasing our understanding of glycoproteins in health and disease. Its applicability to various cancer cell types sets a new standard in quantitative glycoproteomics, enabling deeper investigation into glycopeptide profiles.

Proteome-wide abundance profiling of yeast deletion strains for GET pathway members using sample multiplexing.

The GET pathway is associated with post-translational delivery of tail-anchored (TA) proteins to the endoplasmic reticulum (ER) in yeast, as well as other eukaryotes. Moreover, dysfunction of the GET pathway has been associated with various pathological conditions (i.e., neurodegenerative disorders, cardiovascular ailments, and protein misfolding diseases). In this study, we used yeast deletion strains of Get complex members (specifically, Get1, Get2, Get3, Get4, and Get5) coupled with sample multiplexing-based quantitative mass spectrometry to profile protein abundance on a proteome-wide scale across the five individual deletion strains. Our dataset consists of over 4500 proteins, which corresponds to >75% of the yeast proteome. The data reveal several dozen proteins that are differentially abundant in one or more deletion strains, some of which are membrane-associated, yet the abundance of many TA proteins remained unchanged. This study provides valuable insights into the roles of these Get genes, and the potential for alternative pathways which help maintain cellular function despite the disruption of the GET pathway.

Enhancing Proteome Coverage by Using Strong Anion-Exchange in Tandem with Basic-pH Reversed-Phase Chromatography for Sample Multiplexing-Based Proteomics.

Sample multiplexing-based proteomic strategies rely on fractionation to improve proteome coverage. Tandem mass tag (TMT) experiments, for example, can currently accommodate up to 18 samples with proteins spanning several orders of magnitude, thus necessitating fractionation to achieve reasonable proteome coverage. Here, we present a simple yet effective peptide fractionation strategy that partitions a pooled TMT sample with a two-step elution using a strong anion-exchange (SAX) spin column prior to gradient-based basic pH reversed-phase (BPRP) fractionation. We highlight our strategy with a TMTpro18-plex experiment using nine diverse human cell lines in biological duplicate. We collected three data sets, one using only BPRP fractionation and two others of each SAX-partition followed by BPRP. The three data sets quantified a similar number of proteins and peptides, and the data highlight noticeable differences in the distribution of peptide charge and isoelectric point between the SAX partitions. The combined SAX partition data set contributed 10% more proteins and 20% more unique peptides that were not quantified by BPRP fractionation alone. In addition to this improved fractionation strategy, we provide an online resource of relative abundance profiles for over 11,000 proteins across the nine human cell lines, as well as two additional experiments using ovarian and pancreatic cancer cell lines.

Enriching Cysteine-Containing Peptides Using a Sulfhydryl-Reactive Alkylating Reagent with a Phosphonic Acid Group and Immobilized Metal Affinity Chromatography.

The reduction of disulfide bonds and their subsequent alkylation are commonplace in typical proteomics workflows. Here, we highlight a sulfhydryl-reactive alkylating reagent with a phosphonic acid group (iodoacetamido-LC-phosphonic acid, 6C-CysPAT) that facilitates the enrichment of cysteine-containing peptides for isobaric tag-based proteome abundance profiling. Specifically, we profile the proteome of the SH-SY5Y human cell line following 24 h treatments with two proteasome inhibitors (bortezomib and MG-132) in a tandem mass tag (TMT)pro9-plex experiment. We acquire three datasets─(1) Cys-peptide enriched, (2) the unbound complement, and (3) the non-depleted control─and compare the peptides and proteins quantified in each dataset, with emphasis on Cys-containing peptides. The data show that enrichment using 6C-Cys phosphonate adaptable tag (6C-CysPAT) can quantify over 38,000 Cys-containing peptides in 5 h with >90% specificity. In addition, our combined dataset provides the research community with a resource of over 9900 protein abundance profiles exhibiting the effects of two different proteasome inhibitors. Overall, the seamless incorporation of alkylation by 6C-CysPAT into a current TMT-based workflow permits the enrichment of a Cys-containing peptide subproteome. The acquisition of this "mini-Cys" dataset can be used to preview and assess the quality of a deep, fractionated dataset.

Predicting fragment intensities and retention time of iTRAQ- and TMTPro-labeled peptides with Prosit-TMT.

Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3% - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins.

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.

Quantitative proteome remodeling characterization of two human reference pluripotent stem cell lines during neurogenesis and cardiomyogenesis.

Human pluripotent stem cells (PSCs) have become popular tools within the research community to study developmental and model diseases. While many induced-PSCs (iPSCs) from various genetic background sources are currently available, scientific advancement has been hampered by the considerable phenotypic variations observed between different iPSC lines. A recent collaborative effort selected a novel iPSC line to address this and encourage the adoption of a standardized iPSC line termed KOLF2.1J. Here, leveraging the multiplexing power of isobaric labeling, we systematically investigate, at the 10k proteome level, the relative protein abundance profiles of the KOLF2.1J reference iPSC line upon two distinct cell state differentiation trajectories. In addition, we side-by-side systematically compare this line with the H9 line, an established embryonically derived PSC line that we previously characterized. We noticed differences in the basal proteome of the two cell lines and highlighted the differentially expressed proteins. While the difference between the cell line's proteome subsisted upon differentiation, the global proteome remodeling trajectory was highly similar during the tested differentiation routes. We thus conclude that the KOLF2.1J line performs well at the proteome level upon the neuro and cardiomyogenesis differentiation protocol used. We believe this dataset will serve as a resource of value for the research community.

Profiling Yeast Deletion Strains Using Sample Multiplexing and Network-Based Analyses.

The yeast, Saccharomyces cerevisiae, is a widely used model system for investigating conserved biological functions and pathways. Advancements in sample multiplexing have facilitated the study of the yeast proteome, yet many yeast proteins remain uncharacterized or only partially characterized. Yeast deletion strain collections are powerful resources for yeast proteome studies, uncovering the effects of gene function, genetic interactions, and cellular stresses. As complex biological systems cannot be understood by simply analyzing the individual components, a systems approach is often required in which a protein is represented as a component of large, interacting networks. Here, we evaluate the current state of yeast proteome analysis using isobaric tag-based sample multiplexing (TMTpro16) to profile the proteomes of 75 yeast deletion strains for which we measured the abundance of nearly 5000 proteins. Using statistical approaches, we highlighted covariance and regulation subnetworks and the enrichment of gene ontology classifications for covarying and coregulated proteins. This dataset presents a resource that is amenable to further data mining to study individual deletion strains, pathways, proteins, and/or interactions thereof while serving as a template for future network-based investigations using yeast deletion strain collections.

SimPLIT: Simplified Sample Preparation for Large-Scale Isobaric Tagging Proteomics.

Large scale proteomic profiling of cell lines can reveal molecular signatures attributed to variable genotypes or induced perturbations, enabling proteogenomic associations and elucidation of pharmacological mechanisms of action. Although isobaric labeling has increased the throughput of proteomic analysis, the commonly used sample preparation workflows often require time-consuming steps and costly consumables, limiting their suitability for large scale studies. Here, we present a simplified and cost-effective one-pot reaction workflow in a 96-well plate format (SimPLIT) that minimizes processing steps and demonstrates improved reproducibility compared to alternative approaches. The workflow is based on a sodium deoxycholate lysis buffer and a single detergent cleanup step after peptide labeling, followed by quick off-line fractionation and MS2 analysis. We showcase the applicability of the workflow in a panel of colorectal cancer cell lines and by performing target discovery for a set of molecular glue degraders in different cell lines, in a 96-sample assay. Using this workflow, we report frequently dysregulated proteins in colorectal cancer cells and uncover cell-dependent protein degradation profiles of seven cereblon E3 ligase modulators (CRL4CRBN). Overall, SimPLIT is a robust method that can be easily implemented in any proteomics laboratory for medium-to-large scale TMT-based studies for deep profiling of cell lines.

Retention Time Prediction for TMT-Labeled Peptides in Proteomic LC-MS Experiments.

We present the first detailed study of chromatographic behavior of peptides labeled with tandem mass tags (TMT and TMTpro) in 2D LC for proteomic applications. Carefully designed experimental procedures have permitted generating data sets of over 100,000 nonlabeled and TMT-labeled peptide pairs for the low pH RP in the second separation dimension and data sets of over 10,000 peptide pairs for high-pH RP, HILIC (amide and silica), and SCX separations in the first separation dimension. The average increase in peptide RPLC (0.1% formic acid) retention upon TMT labeling was found to be 3.3% acetonitrile (linear water/acetonitrile gradients), spanning a range of -4 to 10.3%. In addition to the bulk peptide properties such as length, hydrophobicity, and the number of labeled residues, we found several sequence-dependent features mostly associated with differences in N-terminal chemistry. The behavior of TMTpro-labeled peptides was found to be very similar except for a slightly higher hydrophobicity: an average retention shift of 3.7% acetonitrile. The respective versions of the sequence-specific retention calculator (SSRCalc) model have been developed to accommodate both TMT chemistries, showing identical prediction accuracy (R2 ∼ 0.98) for labeled and nonlabeled peptides. Higher retention for TMT-labeled peptides was observed for high-pH RP and HILIC separations, while SCX selectivity remained virtually unchanged.

Isobaric Tag-Based Protein Profiling across Eight Human Cell Lines Using High-Field Asymmetric Ion Mobility Spectrometry and Real-Time Database Searching.

A vast number of human cell lines are available for cell culture model-based studies, and as such the potential exists for discrepancies in findings due to cell line selection. To investigate this concept, the authors determine the relative protein abundance profiles of a panel of eight diverse, but commonly studied human cell lines. This panel includes HAP1, HEK293T, HeLa, HepG2, Jurkat, Panc1, SH-SY5Y, and SVGp12. A mass spectrometry-based proteomics workflow designed to enhance quantitative accuracy while maintaining analytical depth is used. To this end, this strategy leverages TMTpro16-based sample multiplexing, high-field asymmetric ion mobility spectrometry, and real-time database searching. The data show that the differences in the relative protein abundance profiles reflect cell line diversity. The authors also determine several hundred proteins to be highly enriched for a given cell line, and perform gene ontology and pathway analysis on these cell line-enriched proteins. An R Shiny application is designed to query protein abundance profiles and retrieve proteins with similar patterns. The workflows used herein can be applied to additional cell lines to aid cell line selection for addressing a given scientific inquiry or for improving an experimental design.

Temporal Proteomic Profiling of SH-SY5Y Differentiation with Retinoic Acid Using FAIMS and Real-Time Searching.

The SH-SY5Y cell line is often used as a surrogate for neurons in cell-based studies. This cell line is frequently differentiated with all-trans retinoic acid (ATRA) over a 7-day period, which confers neuron-like properties to the cells. However, no analysis of proteome remodeling has followed the progress of this transition. Here, we quantitatively profiled over 9400 proteins across a 7-day treatment with retinoic acid using state-of-the-art mass spectrometry-based proteomics technologies, including FAIMS, real-time database searching, and TMTpro16 sample multiplexing. Gene ontology analysis revealed that categories with the highest increases in protein abundance were related to the plasma membrane/extracellular space. To showcase our data set, we surveyed the protein abundance profiles linked to neurofilament bundle assembly, neuron projections, and neuronal cell body formation. These proteins exhibited increases in abundance level, yet we observed multiple patterns among the queried proteins. The data presented represent a rich resource for investigating temporal protein abundance changes in SH-SY5Y cells differentiated with retinoic acid. Moreover, the sample preparation and data acquisition strategies used here can be readily applied to any analogous cell line differentiation analysis.

A Compendium of Murine (Phospho)Peptides Encompassing Different Isobaric Labeling and Data Acquisition Strategies.

Targeted mass spectrometry-based assays typically rely on previously acquired large data sets for peptide target selection. Such repositories are widely available for unlabeled peptides. However, they are less common for isobaric tagged peptides. Here we have assembled two series of six data sets originating from a mouse embryonic fibroblast cell line (NIH/3T3). One series is of peptides derived from a tryptic digest of a whole cell proteome and a second from enriched phosphopeptides. These data sets encompass three labeling approaches (unlabeled, TMT11-labeled, and TMTpro16-labeled) and two data acquisition strategies (ion trap MS2 with and without FAIMS-based gas phase separation). We identified a total of 1 509 526 peptide-spectrum matches which covered 11 482 proteins from the whole cell proteome tryptic digest, and 188 849 phosphopeptides from the phosphopeptide enrichment. The data sets were of similar depth, and while overlap across data sets was modest, protein overlap was high, thus reinforcing the comprehensiveness of these data sets. The data also supported FAIMS as a means to increase data set depth. These data sets provide a rich resource of peptides that may be used as starting points for targeted assays. Future data sets may be compiled for any genome-sequenced organism using the technologies and strategies highlighted herein. The data have been deposited in the ProteomeXchange Consortium with data set identifier PXD024298.

Improved Proteomics-Based Drug Mechanism-of-Action Studies Using 16-Plex Isobaric Mass Tags.

Multiplexed quantitative proteomics enabled complex workflows to study the mechanisms by which small molecule drugs interact with the proteome such as thermal proteome profiling (TPP) or multiplexed proteome dynamics profiling (mPDP). TPP measures changes in protein thermal stability in response to drug treatment and thus informs on direct targets and downstream regulation events, while the mPDP approach enables the discovery of regulated protein synthesis and degradation events caused by small molecules and other perturbations. The isobaric mass tags available for multiplexed proteomics have thus far limited the efficiency and sensitivity by which such experiments could be performed. Here we evaluate a recent generation of 16-plex isobaric mass tags and demonstrate the sensitive and time efficient identification of Staurosporine targets in HepG2 cell extracts by recording full thermal denaturation/aggregation profiles of vehicle and compound treated samples in a single mass spectrometry experiment. In 2D-TPP experiments, isothermal titration over seven concentrations per temperature enabled comprehensive selectivity profiling of Staurosporine with EC50 values for kinase targets tightly matching to the kinobeads gold standard assay. Finally, we demonstrate time and condition-based multiplexing of dynamic SILAC labeling experiments to delineate proteome-wide effects of the molecular glue Indisulam on synthesis and degradation rates.

TMTpro Complementary Ion Quantification Increases Plexing and Sensitivity for Accurate Multiplexed Proteomics at the MS2 Level.

Multiplexed proteomics is a powerful tool to assay cell states in health and disease, but accurate quantification of relative protein changes is impaired by interference from co-isolated peptides. Interference can be reduced by using MS3-based quantification, but this reduces sensitivity and requires specialized instrumentation. An alternative approach is quantification by complementary ions, the balancer group-peptide conjugates, which allows accurate and precise multiplexed quantification at the MS2 level and is compatible with most proteomics instruments. However, complementary ions of the popular TMT-tag form inefficiently and multiplexing is limited to five channels. Here, we evaluate and optimize complementary ion quantification for the recently released TMTpro-tag, which increases complementary ion plexing capacity to eight channels (TMTproC). Furthermore, the beneficial fragmentation properties of TMTpro increase sensitivity for TMTproC, resulting in ∼65% more proteins quantified compared to TMTpro-MS3 and ∼18% more when compared to real-time-search TMTpro-MS3 (RTS-SPS-MS3). TMTproC quantification is more accurate than TMTpro-MS2 and even superior to RTS-SPS-MS3. We provide the software for quantifying TMTproC data as an executable that is compatible with the MaxQuant analysis pipeline. Thus, TMTproC advances multiplexed proteomics data quality and widens access to accurate multiplexed proteomics beyond laboratories with MS3-capable instrumentation.

TMTpro-18plex: The Expanded and Complete Set of TMTpro Reagents for Sample Multiplexing.

The development of the TMTpro-16plex series expanded the breadth of commercial isobaric tagging reagents by nearly 50% over classic TMT-11plex. In addition to the described 16plex reagents, the proline-based TMTpro molecule can accommodate two additional combinations of heavy carbon and nitrogen isotopes. Here, we introduce the final two labeling reagents, TMTpro-134C and TMTpro-135N, which permit the simultaneous global protein profiling of 18 samples with essentially no missing values. For example, six conditions with three biological replicates can now be perfectly accommodated. We showcase the 18plex reagent set by profiling the proteome and phosphoproteome of a pair of isogenic mammary epithelial cell lines under three conditions in triplicate. We compare the depth and quantitative performance of this data set with a TMTpro-16plex experiment in which two samples were omitted. Our analysis revealed similar numbers of quantified peptides and proteins, with high quantitative correlation. We interrogated further the TMTpro-18plex data set by highlighting changes in protein abundance profiles under different conditions in the isogenic cell lines. We conclude that TMTpro-18plex further expands the sample multiplexing landscape, allowing for complex and innovative experimental designs.

Automated 16-Plex Plasma Proteomics with Real-Time Search and Ion Mobility Mass Spectrometry Enables Large-Scale Profiling in Naked Mole-Rats and Mice.

Performing large-scale plasma proteome profiling is challenging due to limitations imposed by lengthy preparation and instrument time. We present a fully automated multiplexed proteome profiling platform (AutoMP3) using the Hamilton Vantage liquid handling robot capable of preparing hundreds to thousands of samples. To maximize protein depth in single-shot runs, we combined 16-plex Tandem Mass Tags (TMTpro) with high-field asymmetric waveform ion mobility spectrometry (FAIMS Pro) and real-time search (RTS). We quantified over 40 proteins/min/sample, doubling the previously published rates. We applied AutoMP3 to investigate the naked mole-rat plasma proteome both as a function of the circadian cycle and in response to ultraviolet (UV) treatment. In keeping with the lack of synchronized circadian rhythms in naked mole-rats, we find few circadian patterns in plasma proteins over the course of 48 h. Furthermore, we quantify many disparate changes between mice and naked mole-rats at both 48 h and one week after UV exposure. These species differences in plasma protein temporal responses could contribute to the pronounced cancer resistance observed in naked mole-rats. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD022891.