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

IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.

Chemoproteogenomic stratification of the missense variant cysteinome

Cancer genomes are rife with genetic variants; one key outcome of this variation is widespread gain-of-cysteine mutations. These acquired cysteines can be both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain unidentified via chemoproteomics; identification is a critical step to enable functional analysis, including assessment of potential druggability and susceptibility to oxidation. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine genetic variation. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized two-stage false discovery rate (FDR) error controlled proteomic search, which is further enhanced with a user-friendly FragPipe interface. Chemoproteogenomics analysis reveals that cysteine acquisition is a ubiquitous feature of both healthy and cancer genomes that is further elevated in the context of decreased DNA repair. Reference cysteines proximal to missense variants are also found to be pervasive, supporting heretofore untapped opportunities for variant-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and is compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.

A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics

Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.

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.

Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Infrared-Tandem Mass Tags

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS3 approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.

Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing

GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.

Proteomic Discovery of RNA-Protein Molecular Clamps Using a Thermal Shift Assay with ATP and RNA (TSAR)

Uncompetitive inhibition is an effective strategy for suppressing dysregulated enzymes and their substrates, but discovery of suitable ligands depends on often-unavailable structural knowledge and serendipity. Hence, despite surging interest in mass spectrometry-based target identification, proteomic studies of substrate-dependent target engagement remain sparse. Herein, we describe the Thermal Shift Assay with ATP and RNA (TSAR) as a template for proteome-wide discovery of substrate-dependent ligand binding. Using proteomic thermal shift assays, we show that simple biochemical additives can facilitate detection of target engagement in native cell lysates. We apply our approach to rocaglates, a family of molecules that specifically clamp RNA to eukaryotic translation initiation factor 4A (eIF4A), DEAD-box helicase 3X (DDX3X), and potentially other members of the DEAD-box (DDX) family of RNA helicases. To identify unexpected interactions, we optimized a target class-specific thermal denaturation window and evaluated ATP analog and RNA probe dependencies for key rocaglate-DDX interactions. We report novel DDX targets of the rocaglate clamping spectrum, confirm that DDX3X is a common target of several widely studied analogs, and provide structural insights into divergent DDX3X affinities between synthetic rocaglates. We independently validate novel targets of high-profile rocaglates, including the clinical candidate Zotatifin (eFT226), using limited proteolysis-mass spectrometry and fluorescence polarization experiments. Taken together, our study provides a model for screening uncompetitive inhibitors using a systematic chemical-proteomics approach to uncover actionable DDX targets, clearing a path towards characterization of novel molecular clamps and associated RNA helicase targets.

IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.

Accelerating multiplexed profiling of protein-ligand interactions: High-throughput plate-based reactive cysteine profiling with minimal input

Chemoproteomics has made significant progress in investigating small-molecule-protein interactions. However, the proteome-wide profiling of cysteine ligandability remains challenging to adapt for high-throughput applications, primarily due to a lack of platforms capable of achieving the desired depth using low input in 96- or 384-well plates. Here, we introduce a revamped, plate-based platform which enables routine interrogation of either ∼18,000 or ∼24,000 reactive cysteines based on starting amounts of 10 or 20 μg, respectively. This represents a 5-10X reduction in input and 2-3X improved coverage. We applied the platform to screen 192 electrophiles in the native HEK293T proteome, mapping the ligandability of 38,450 reactive cysteines from 8,274 human proteins. We further applied the platform to characterize new cellular targets of established drugs, uncovering that ARS-1620, a KRASG12C inhibitor, binds to and inhibits an off-target adenosine kinase ADK. The platform represents a major step forward to high-throughput proteome-wide evaluation of reactive cysteines.

Inserting Pre-Analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics With Sample Multiplexing

GoDig, a recent platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standard peptides, is a relatively flexible and easy-to-use method that uses tandem mass tags (TMT) to increase sample throughput up to 18-fold relative to label-free targeted proteomics. Though the protein quantification success rate of GoDig is generally high, the peptide-level success rate is more limited, hampering the extension of GoDig to assays of harder-to-quantify proteins and site-specific phenomena. In order to guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates due to improved elution order calibration. In this mode, one or more chromatographic priming runs are automatically performed to determine accurate and precise target elution orders, followed by analytical runs which quantify targets. Using priming runs, peptide-level quantification success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy protein expression profiles across four human cell lines.

High-throughput intact Glycopeptide quantification strategy with targeted-MS (HTiGQs-target) reveals site-specific IgG N-glycopeptides as biomarkers for hepatic disorder diagnosis and staging

Liver disease is one of the leading causes of global mortality, and identifying biomarkers for diagnosing the progression of liver diseases is crucial for improving its outcomes. Targeted mass spectrometry technology is a powerful tool with unique advantages for verifying biomarker candidates and clinical applications. It is particularly useful in validating protein biomarkers with post-translational modifications, eliminating the need for site-specific antibodies. Especially, targeted mass spectrometry technique is particularly critical for translation of glycoproteins into clinical applications as there are no site-specific antibodies for N-glycosylation. Nevertheless, its limitation in analyzing only one sample per run has become apparent when dealing with a large number of clinical samples. Herein, we developed a high-throughput intact N-glycopeptides quantification strategy with targeted-MS (HTiGQs-Target), which allows the validation of 20 samples per run with an average analysis time of only 3 min per sample. We applied HTiGQs-Target in a cohort of 461 serum samples (including 120 healthy controls (HC), 127 chronic hepatitis B (CHB) cases, 106 liver cirrhosis (LC) cases, and 108 hepatocellular carcinomas (HCC) cases) and found that a panel of 10 IgG N-glycopeptides have strong clinical utility in evaluating the severity of the liver disease.

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.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Real-Time Spectral Library Matching for Sample Multiplexed Quantitative Proteomics

Sample multiplexed quantitative proteomics assays have proved to be a highly versatile means to assay molecular phenotypes. Yet, stochastic precursor selection and precursor coisolation can dramatically reduce the efficiency of data acquisition and quantitative accuracy. To address this, intelligent data acquisition (IDA) strategies have recently been developed to improve instrument efficiency and quantitative accuracy for both discovery and targeted methods. Toward this end, we sought to develop and implement a new real-time spectral library searching (RTLS) workflow that could enable intelligent scan triggering and peak selection within milliseconds of scan acquisition. To ensure ease of use and general applicability, we built an application to read in diverse spectral libraries and file types from both empirical and predicted spectral libraries. We demonstrate that RTLS methods enable improved quantitation of multiplexed samples, particularly with consideration for quantitation from chimeric fragment spectra. We used RTLS to profile proteome responses to small molecule perturbations and were able to quantify up to 15% more significantly regulated proteins in half the gradient time compared to traditional methods. Taken together, the development of RTLS expands the IDA toolbox to improve instrument efficiency and quantitative accuracy for sample multiplexed analyses.