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

Development of an Adaptive, Economical, and Easy-to-Use SP3-TMT Automated Sample Preparation Workflow for Quantitative Proteomics

Liquid handling robots have been developed to automate various steps of the bottom-up proteomics workflow, however, protocols for the generation of isobarically labeled peptides remain limited. Existing methods often require costly specialty devices and are constrained by fixed workflows. To address this, we developed a cost-effective, flexible, automated sample preparation protocol for TMT-labeled peptides using the Biomek i5 liquid handler (Beckman Coulter). Our approach leverages single-pot solid-phase-enhanced sample preparation with paramagnetic beads to streamline protein cleanup and digestion. The protocol also allows for adjustment of trypsin concentration and peptide-to-TMT ratio to increase throughput and reduce costs, respectively. We compared our automated and manual 18-plex TMT-Pro labeling workflows by monitoring select protein markers of the unfolded protein response in pharmacologically activatable, engineered cell lines. Overall, the automated protocol demonstrated equivalent performance in peptide and protein identifications, digestion and labeling efficiency, and an enhancement in the dynamic range of TMT quantifications. Compared to the manual method, the Biomek protocol significantly reduces hands-on time and minimizes sample handling errors. The 96-well format additionally allows for the number of TMT reactions to be scaled up quickly without a significant increase in user interaction. Our optimized automated workflow enhances throughput, reproducibility, and cost-effectiveness, making it a valuable tool for high-throughput proteomics studies.

Expanding the landscape of aging via orbitrap astral mass spectrometry and tandem mass tag integration

Aging results in a progressive decline in physiological function due to the deterioration of essential biological processes. While proteomics offers insights into aging mechanisms, prior studies are limited in proteome coverage and lifespan range. To address this, we integrate the Orbitrap Astral Mass Spectrometer with the multiplex tandem mass tag (TMT) technology to profile the proteomes of cortex, hippocampus, striatum and kidney in the C57BL/6JN mice, quantifying 8,954 to 9,376 proteins per tissue (12,749 total). Samples spanned both sexes and three age groups (3, 12, and 20 months), representing early to late adulthood. To improve TMT quantitation accuracy, we develop a peptide-spectrum match-based filtering strategy that leverages resolution and signal-to-noise thresholds. Our analysis uncovers distinct tissue-specific patterns of protein abundance, with age and sex differences in the kidney and primarily age-related changes in brain tissues. We also identify both linear and non-linear proteomic trajectories with age, revealing complex protein dynamics over the adult lifespan. Integrating our findings with early developmental proteomic data from brain tissues highlights further divergent age-related trajectories, particularly in synaptic proteins. This study provides a robust data analysis workflow for Orbitrap Astral-based TMT analysis and expands the proteomic understanding of aging across tissues, ages, and sexes.

Temporal Proteomic Profiling of Pheromone-Induced Cell Cycle Re-Entry in Saccharomyces cerevisiae

The regulation of cell cycle progression in response to environmental cues is essential for cellular adaptation. In Saccharomyces cerevisiae, the BAR1 gene modulates sensitivity to the mating pheromone α-factor, which induces cell cycle arrest in G1. Here, we investigated the dynamic proteomic response in the bar1 deletion strain using a 27-plex experimental design with TMTproD isobaric labeling. Asynchronous bar1Δ cells were treated with α-factor and then released from the pheromone-induced cell cycle arrest in G1. Using higher-order TMTpro sample multiplexing, we generated global temporal profiles of protein abundance associated with recovery from this arrest, with triplicate samples collected at eight time points from 0 to 165 min after washing out the pheromone. We identify specific proteins involved in cell cycle re-entry and in the attenuation of the pheromone signal, providing insights into the regulatory mechanisms of mating response in yeast. This study also contributes significantly to dynamic proteomic analysis of cell cycle progression. We present a versatile approach for investigating complex cellular processes and showcase cell cycle progression following release from pheromone-induced arrest in yeast.

Glycogen drives tumour initiation and progression in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is an aggressive cancer defined by oncogenic drivers and metabolic reprogramming. Here we leverage next-generation spatial screens to identify glycogen as a critical and previously underexplored oncogenic metabolite. High-throughput spatial analysis of human LUAD samples revealed that glycogen accumulation correlates with increased tumour grade and poor survival. Furthermore, we assessed the effect of increasing glycogen levels on LUAD via dietary intervention or via a genetic model. Approaches that increased glycogen levels provided compelling evidence that elevated glycogen substantially accelerates tumour progression, driving the formation of higher-grade tumours, while the genetic ablation of glycogen synthase effectively suppressed tumour growth. To further establish the connection between glycogen and cellular metabolism, we developed a multiplexed spatial technique to simultaneously assess glycogen and cellular metabolites, uncovering a direct relationship between glycogen levels and elevated central carbon metabolites essential for tumour growth. Our findings support the conclusion that glycogen accumulation drives LUAD cancer progression and provide a framework for integrating spatial metabolomics with translational models to uncover metabolic drivers of cancer.

Differential aortic aneurysm formation provoked by chemogenetic oxidative stress

Aortic aneurysms are potentially fatal focal enlargements of the aortic lumen; the disease burden is increasing as the human population ages. Pathological oxidative stress is implicated in the development of aortic aneurysms. We pursued a chemogenetic approach to create an animal model of aortic aneurysm formation using a transgenic mouse line, DAAO-TGTie2, that expresses yeast d-amino acid oxidase (DAAO) under control of the endothelial Tie2 promoter. In DAAO-TGTie2 mice, DAAO generated the ROS hydrogen peroxide (H2O2) in endothelial cells only when provided with d-amino acids. When DAAO-TGTie2 mice were chronically fed d-alanine, the animals became hypertensive and developed abdominal, but not thoracic, aortic aneurysms. Generation of H2O2 in the endothelium led to oxidative stress throughout the vascular wall. Proteomics analyses indicated that the oxidant-modulated protein kinase JNK1 was dephosphorylated by the phosphoprotein phosphatase DUSP3 (dual specificity phosphatase 3) in abdominal, but not thoracic, aorta, causing activation of Kruppel-like Factor 4 (KLF4)-dependent transcriptional pathways that triggered phenotypic switching and aneurysm formation. Pharmacological DUSP3 inhibition completely blocked the aneurysm formation caused by chemogenetic oxidative stress. These studies establish that regional differences in oxidant-modulated signaling pathways lead to differential disease progression in discrete vascular beds and identify DUSP3 as a potential pharmacological target for the treatment of aortic aneurysms.

Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE

Despite the significant progress in accuracy and reliability in mass spectrometry technology, as well as the development of strategies based on isotopic labeling or internal standards in recent decades, systematic biases originating from non-biological factors remain a significant challenge in data analysis. In addition, the wide range of available normalization methods renders the choice of a suitable normalization method challenging. We systematically evaluated 17 normalization and 2 batch effect correction methods, originally developed for preprocessing DNA microarray data but widely applied in proteomics, on 6 publicly available spike-in and 3 label-free and tandem mass tag datasets. Opposed to state-of-the-art normalization practice, we found that a reduction in intragroup variation is not directly related to the effectiveness of the normalization methods. Furthermore, our results demonstrated that the methods RobNorm and Normics, specifically developed for proteomics data, in line with LoessF performed consistently well across the spike-in datasets, while EigenMS exhibited a high false-positive rate. Finally, based on experimental data, we show that normalization substantially impacts downstream analyses, and the impact is highly dataset-specific, emphasizing the importance of use-case-specific evaluations for novel proteomics datasets. For this, we developed the PROteomics Normalization Evaluator (PRONE), a unifying R package enabling comparative evaluation of normalization methods, including their impact on downstream analyses, while offering considerable flexibility, acknowledging the lack of universally accepted standards. PRONE is available on Bioconductor with a web application accessible at https://exbio.wzw.tum.de/prone/.

TMT-Based Multiplexed (Chemo)Proteomics on the Orbitrap Astral Mass Spectrometer

Ongoing advancements in instrumentation has established mass spectrometry (MS) as an essential tool in proteomics research and drug discovery. The newly released Asymmetric Track Lossless (Astral) analyzer represents a major step forward in MS instrumentation. Here, we evaluate the Orbitrap Astral mass spectrometer in the context of tandem mass tag (TMT)-based multiplexed proteomics and activity-based proteome profiling, highlighting its sensitivity boost relative to the Orbitrap Tribrid platform-50% at the peptide and 20% at the protein level. We compare TMT data-dependent acquisition and label-free data-independent acquisition on the same instrument, both of which quantify over 10,000 human proteins per sample within 1 h. TMT offers higher quantitative precision and data completeness, while data-independent acquisition is free of ratio compression and is thereby more accurate. Our results suggest that ratio compression is prevalent with the high-resolution MS2-based quantification on the Astral, while real-time search-based MS3 quantification on the Orbitrap Tribrid platform effectively restores accuracy. Additionally, we benchmark TMT-based activity-based proteome profiling by interrogating cysteine ligandability. The Astral measures over 30,000 cysteines in a single-shot experiment, a 54% increase relative to the Orbitrap Eclipse. We further leverage this remarkable sensitivity to profile the target engagement landscape of FDA-approved covalent drugs, including sotorasib and adagrasib. We herein provide a reference for the optimal use of the advanced MS platform.

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.

Hypertrophic Cardiomyopathy-Associated CRYABR123W Activates Calcineurin, Reduces Calcium Sequestration, and Alters the CRYAB Interactome and the Proteomic Response to Pathological Hypertrophy

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular condition in the world, affecting around 1 in 500 people. HCM is characterized by ventricular wall thickening, decreased ventricular chamber volume, and diastolic dysfunction. Inherited HCM is most commonly caused by sarcomere gene mutations; however, approximately 50% of patients do not present with a known mutation, highlighting the need for further research into additional pathological mutations. The alpha-B crystallin (CRYAB) mutation CRYABR123W was previously identified as a novel sarcomere-independent mutation causing HCM associated with pathological NFAT signaling in the setting of pressure overload. We generated stable H9C2 cell lines expressing FLAG-tagged wild-type and mutant CRYAB, which demonstrated that CRYABR123W increases calcineurin activity. Using AlphaFold to predict structural and interaction changes, we generated a model where CRYABR123W uniquely binds to the autoinhibitory domain of calcineurin. Co-immunoprecipitation using the CRYAB FLAG tag followed by mass spectrometry showed novel and distinct changes in the protein interaction patterns of CRYABR123W. Finally, mouse heart extracts from our wild-type CRYAB and CRYABR123W models with and without pressure overload caused by transverse aortic constriction (TAC) were used in global proteomic and phosphoproteomic mass spectrometry analysis, which showed dysregulation in cytoskeletal, metabolomic, cardiac, and immune function. Our data illustrate how CRYABR123W drives calcineurin activation and exhibits distinct changes in protein interaction and cellular pathways during the development of HCM and pathological cardiac hypertrophy.

Development of an Adaptive, Economical, and Easy-to-Use SP3-TMT Automated Sample Preparation Workflow for Quantitative Proteomics

Liquid handling robots have been developed to automate various steps of the bottom-up proteomics workflow, however, protocols for the generation of isobarically labeled peptides remain limited. Existing methods often require costly specialty devices and are constrained by fixed workflows. To address this, we developed a cost-effective, flexible, automated sample preparation protocol for TMT-labeled peptides using the Biomek i5 liquid handler. Our approach leverages Single-Pot Solid-Phase-Enhanced Sample Preparation (SP3) with paramagnetic beads to streamline protein cleanup and digestion. The protocol also allows for adjustment of trypsin concentration and peptide-to-TMT ratio to increase throughput and reduce costs, respectively. We compared our automated and manual 18-plex TMT-Pro labeling workflows by monitoring select protein markers of the Unfolded Protein Response (UPR) in pharmacologically activatable, engineered cell lines. Overall, the automated protocol demonstrated equivalent performance in peptide and protein identifications, digestion and labeling efficiency, and an enhancement in the dynamic range of TMT quantifications. Compared to the manual method, the Biomek protocol significantly reduces hands-on time and minimizes sample handling errors. The 96-well format additionally allows for the number of TMT reactions to be scaled up quickly without a significant increase in user interaction. Our optimized automated workflow enhances throughput, reproducibility, and cost-effectiveness, making it a valuable tool for high-throughput proteomics studies.

An Aurora kinase A-BOD1L1-PP2A B56 axis promotes chromosome segregation fidelity

Cancer cells are often aneuploid and frequently display elevated rates of chromosome mis-segregation, called chromosomal instability (CIN). CIN is caused by hyperstable kinetochore-microtubule (K-MT) attachments that reduce the correction efficiency of erroneous K-MT attachments. UMK57, a chemical agonist of the protein MCAK (mitotic centromere-associated kinesin), improves chromosome segregation fidelity in CIN cancer cells by destabilizing K-MT attachments, but cells rapidly develop resistance. To determine the mechanism, we performed unbiased screens, which revealed increased phosphorylation in cells adapted to UMK57 at Aurora kinase A phosphoacceptor sites on BOD1L1 (protein biorientation defective 1-like-1). BOD1L1 depletion or Aurora kinase A inhibition eliminated resistance to UMK57. BOD1L1 localizes to spindles/kinetochores during mitosis, interacts with the PP2A phosphatase, and regulates phosphorylation levels of kinetochore proteins, chromosome alignment, mitotic progression, and fidelity. Moreover, the BOD1L1 gene is mutated in a subset of human cancers, and BOD1L1 depletion reduces cell growth in combination with clinically relevant doses of Taxol or Aurora kinase A inhibitor.

Automated High-Throughput Affinity Capture-Mass Spectrometry Platform with Data-Independent Acquisition

Affinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small-molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process have limited its use in a high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small-molecule target enrichment in the 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis. The streamlined process significantly reduced both the overall and hands-on time needed for sample preparation. Additionally, we developed a data-independent acquisition-mass spectrometry (DIA-MS) method to establish an efficient label-free quantitative chemical proteomic kinome profiling workflow. DIA-MS yielded a coverage of ∼380 kinases, a > 60% increase compared to using a data-dependent acquisition (DDA)-MS method, and provided reproducible target profiling of the kinase inhibitor dasatinib. We further showcased the applicability of this AC-MS workflow for assessing the selectivity of two clinical-stage CDK9 inhibitors against ∼250 probe-enriched kinases. Our study here provides a roadmap for efficient target engagement and selectivity profiling in native cell or tissue lysates using AC-MS.

ROR2 drives right ventricular heart failure via disruption of proteostasis

Background

No therapies exist to reverse right ventricular failure (RVF), and the molecular mechanisms that drive RVF remain poorly studied. We recently reported that the developmentally restricted noncanonical WNT receptor ROR2 is upregulated in human RVF in proportion to severity of disease. Here we test mechanistic role of ROR2 in RVF pathogenesis.

Methods

ROR2 was overexpressed or knocked down in neonatal rat ventricular myocytes (NRVMs). ROR2-modified NRVMs were characterized using confocal microscopy, RNAseq, proteomics, proteostatic functional assays, and contractile properties with pacing. The impact of cardiac ROR2 expression was evaluated in mice by AAV9-mediated overexpression and by AAV9-mediated delivery of shRNA to knockdown ROR2 in a pulmonary artery banded pressure overload RVF model. ROR2-modified mice were evaluated by echocardiography, RV protein synthetic rates and proteasome activity.

Results

In NRVMs, we find that ROR2 profoundly dysregulates the coordination between protein translation and folding. This imbalance leads to excess protein clearance by the ubiquitin proteasome system (UPS) with dramatic impacts on sarcomere and cytoskeletal structure and function. In mice, forced cardiac ROR2 expression is sufficient to disrupt proteostasis and drive RVF, while conversely ROR2 knockdown partially rescues proteostasis and cardiac function in a pressure overload model of RVF.

Conclusions

In sum, ROR2 is a key driver of RVF pathogenesis through proteostatic disruption and, thus, provides a promising target to treat RVF.

High-throughput proteomics-guided biomarker discovery of hepatocellular carcinoma

Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.

Brain phosphoproteomic analysis identifies diabetes-related substrates in Alzheimer's disease pathology in older adults

INTRODUCTION

Type 2 diabetes increases the risk of Alzheimer's disease (AD) dementia. Insulin signaling dysfunction exacerbates tau protein phosphorylation, a hallmark of AD pathology. However, the comprehensive impact of diabetes on patterns of AD-related phosphoprotein in the human brain remains underexplored.

METHODS

We performed tandem mass tag-based phosphoproteome profiling in post mortem human brain prefrontal cortex samples from 191 deceased older adults with and without diabetes and pathologic AD.

RESULTS

Among 7874 quantified phosphosites, microtubule-associated protein tau (MAPT) phosphorylated at T529 and T534 (isoform 8 T212 and T217) were more abundant in AD and showed differential associations with diabetes. Network analysis of co-abundance patterns uncovered synergistic interactions between AD and diabetes, with one module exhibiting higher MAPT phosphorylation (15 MAPT phosphosites) and another displaying lower MAP1B phosphorylation (22 MAP1B phosphosites).

DISCUSSION

This study offers phosphoproteomics insights into AD in diabetes, shedding light on mechanisms that can inform the development of therapeutics for dementia.

HIGHLIGHTS

The risk of Alzheimer's disease (AD) dementia is increased among older adults living with diabetes. The patterns of AD-related phosphoprotein in the human brain in older adults are differential among older adults living with diabetes. Microtubule-associated protein tau phosphorylated at T529 and T534 (isoform 8 T212 and T217) showed differential associations with diabetes. Phosphosite co-abundance networks of synergistic interactions between AD and diabetes were identified.

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

Fasting in combination with the cocktail Sorafenib:Metformin blunts cellular plasticity and promotes liver cancer cell death via poly-metabolic exhaustion

PURPOSE

Dual-Interventions targeting glucose and oxidative metabolism are receiving increasing attention in cancer therapy. Sorafenib (S) and Metformin (M), two gold-standards in liver cancer, are known for their mitochondrial inhibitory capacity. Fasting, a glucose-limiting strategy, is also emerging as chemotherapy adjuvant. Herein, we explore the anti-carcinogenic response of nutrient restriction in combination with sorafenib:metformin (NR-S:M).

RESULTS

Our data demonstrates that, independently of liver cancer aggressiveness, fasting synergistically boosts the anti-proliferative effects of S:M co-treatment. Metabolic and Cellular plasticity was determined by the examination of mitochondrial and glycolytic activity, cell cycle modulation, activation of cellular apoptosis, and regulation of key signaling and metabolic enzymes. Under NR-S:M conditions, early apoptotic events and the pro-apoptotic Bcl-xS/Bcl-xL ratio were found increased. NR-S:M induced the highest retention in cellular SubG1 phase, consistent with the presence of DNA fragments from cellular apoptosis. Mitochondrial functionality, Mitochondrial ATP-linked respiration, Maximal respiration and Spare respiratory capacity, were all found blunted under NR-S:M conditions. Basal Glycolysis, Glycolytic reserve, and glycolytic capacity, together with the expression of glycogenic (PKM), gluconeogenic (PCK1 and G6PC3), and glycogenolytic enzymes (PYGL, PGM1, and G6PC3), were also negatively impacted by NR-S:M. Lastly, a TMT-proteomic approach corroborated the synchronization of liver cancer metabolic reprogramming with the activation of molecular pathways to drive a quiescent-like status of energetic-collapse and cellular death.

CONCLUSION

Altogether, we show that the energy-based polytherapy NR-S:M blunts cellular, metabolic and molecular plasticity of liver cancer. Notwithstanding the in vitro design of this study, it holds a promising therapeutic tool worthy of exploration for this tumor pathology.

Low-input redoxomics facilitates global identification of metabolic regulators of oxidative stress in the gut

Oxidative stress plays a crucial role in organ aging and related diseases, yet the endogenous regulators involved remain largely unknown. This work highlights the importance of metabolic homeostasis in protecting against oxidative stress in the large intestine. By developing a low-input and user-friendly pipeline for the simultaneous profiling of five distinct cysteine (Cys) states, including free SH, total Cys oxidation (Sto), sulfenic acid (SOH), S-nitrosylation (SNO), and S-glutathionylation (SSG), we shed light on Cys redox modification stoichiometries and signaling with regional resolution in the aging gut of monkeys. Notably, the proteins modified by SOH and SSG were associated primarily with cell adhesion. In contrast, SNO-modified proteins were involved in immunity. Interestingly, we observed that the Sto levels ranged from 0.97% to 99.88%, exhibiting two distinct peaks and increasing with age. Crosstalk analysis revealed numerous age-related metabolites potentially involved in modulating oxidative stress and Cys modifications. Notably, we elucidated the role of fumarate in alleviating intestinal oxidative stress in a dextran sulfate sodium (DSS)-induced colitis mouse model. Our findings showed that fumarate treatment promotes the recovery of several cell types, signaling pathways, and genes involved in oxidative stress regulation. Calorie restriction (CR) is a known strategy for alleviating oxidative stress. Two-month CR intervention led to the recovery of many antioxidative metabolites and reshaped the Cys redoxome. This work decodes the complexities of redoxomics during the gut aging of non-human primates and identifies key metabolic regulators of oxidative stress and redox signaling.

Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification

Protein phosphorylation introduces post-genomic diversity to proteins, which plays a crucial role in various cellular activities. Elucidation of system-wide signaling cascades requires high-performance tools for precise identification and quantification of dynamics of site-specific phosphorylation events. Recent advances in phosphoproteomic technologies have enabled the comprehensive mapping of the dynamic phosphoproteomic landscape, which has opened new avenues for exploring cell type-specific functional networks underlying cellular functions and clinical phenotypes. Here, we provide an overview of the basics and challenges of phosphoproteomics, as well as the technological evolution and current state-of-the-art global and quantitative phosphoproteomics methodologies. With a specific focus on highly sensitive platforms, we summarize recent trends and innovations in miniaturized sample preparation strategies for micro-to-nanoscale and single-cell profiling, data-independent acquisition mass spectrometry (DIA-MS) for enhanced coverage, and quantitative phosphoproteomic pipelines for deep mapping of cell and disease biology. Each aspect of phosphoproteomic analysis presents unique challenges and opportunities for improvement and innovation. We specifically highlight evolving phosphoproteomic technologies that enable deep profiling from low-input samples. Finally, we discuss the persistent challenges in phosphoproteomic technologies, including the feasibility of nanoscale and single-cell phosphoproteomics, as well as future outlooks for biomedical applications.

Data acquisition approaches for single cell proteomics

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

Pleiotropic tumor suppressive functions of PTEN missense mutations during gliomagenesis

PTEN plays a crucial role in preventing the development of glioblastoma (GBM), a severe and untreatable brain cancer. In GBM, most PTEN deficiencies are missense mutations that have not been thoroughly examined. Here, we leveraged genetically modified mice and isogenic astrocyte cell cultures to investigate the role of clinically relevant mutations (G36E, L42R, C105F, and R173H) in the development of EGFR-driven GBM. We report that the loss of tumor suppression from these mutants is unrelated to their lipid phosphatase activity and rather relate to elevated localization at the cell membrane. Moreover, expression of these PTEN mutations heightened EGFR activity by sequestering EGFR within endomembranes longer and affected its signaling behavior. Through comprehensive studies on global protein phosphorylation and kinase library analyses in cells with the G36E and L42R PTEN mutations, we identified distinct cancer-promoting pathways activated by EGFR, offering targets for treating GBM with these PTEN alterations.

Tools and techniques for quantitative glycoproteomic analysis

Recent advances in mass spectrometry (MS)-based methods have significantly expanded the capabilities for quantitative glycoproteomics, enabling highly sensitive and accurate quantitation of glycosylation at intact glycopeptide level. These developments have provided valuable insights into the roles of glycoproteins in various biological processes and diseases. In this short review, we summarize pertinent studies on quantitative techniques and tools for site-specific glycoproteomic analysis published over the past decade. We also highlight state-of-the-art MS-based software that facilitate multi-dimension quantification of the glycoproteome, targeted quantification of specific glycopeptides, and the analysis of glycopeptide isomers. Additionally, we discuss the potential applications of these technologies in clinical biomarker discovery and the functional characterization of glycoproteins in health and disease. The review concludes with a discussion of current challenges and future perspectives in the field, emphasizing the need for more precise, high-throughput and efficient methods to further advance quantitative glycoproteomics and its applications.

Expanding the Landscape of Aging via Orbitrap Astral Mass Spectrometry and Tandem Mass Tag (TMT) Integration

Aging results in a progressive decline in physiological function due to the deterioration of essential biological processes, such as transcription and RNA splicing, ultimately increasing mortality risk. Although proteomics is emerging as a powerful tool for elucidating the molecular mechanisms of aging, existing studies are constrained by limited proteome coverage and only observe a narrow range of lifespan. To overcome these limitations, we integrated the Orbitrap Astral Mass Spectrometer with the multiplex tandem mass tag (TMT) technology to profile the proteomes of three brain tissues (cortex, hippocampus, striatum) and kidney in the C57BL/6JN mouse model, achieving quantification of 8,954 to 9,376 proteins per tissue (cumulatively 12,749 across all tissues). Our sample population represents balanced sampling across both sexes and three age groups (3, 12, and 20 months), comprising young adulthood to early late life (approximately 20-60 years of age for human lifespan). To enhance quantitative accuracy, we developed a peptide filtering strategy based on resolution and signal-to-noise thresholds. Our analysis uncovered distinct tissue-specific patterns of protein abundance, with age and sex differences in the kidney, while brain tissues exhibit notable age changes and limited sex differences. In addition, we identified both proteomic changes that are linear with age (i.e., continuous) and that have a non-linear pattern (i.e., non-continuous), revealing complex protein dynamics over the adult lifespan. Integrating our findings with early developmental proteomic data from brain tissues highlighted further divergent age-related trajectories, particularly in synaptic proteins. This study not only provides a robust data analysis workflow for TMT datasets generated using the Orbitrap Astral mass spectrometer but also expands the proteomic landscape of aging, capturing proteins with age and sex effects with unprecedented depth.

Characterizing protein-protein interactions with thermal proteome profiling

Thermal proteome profiling (TPP) is an innovative technique that uses the principle of protein thermal stability to identify potential protein interaction partners. Employing quantitative mass spectrometry, TPP measures protein stability across the proteome, offering a comprehensive snapshot of protein interactions in a single experiment. When studying protein-protein interactions (PPI), TPP leverages changes in apparent protein melting temperatures to identify transient and weak interactions that most traditional PPI detection methodologies struggle to measure. This review discusses current TPP methodologies, the challenges of interpreting the resulting complex datasets, and opportunities to deepen and improve PPI networks. By advancing our grasp of intricate protein interactions, TPP promises to illuminate the molecular basis of diseases and drive the discovery of novel therapeutic targets.

Advancing Nanomaterial Toxicology Screening Through Efficient and Cost-Effective Quantitative Proteomics

Proteomic investigations yield high-dimensional datasets, yet their application to large-scale toxicological assessments is hindered by reproducibility challenges due to fluctuating measurement conditions. To address these limitations, this study introduces an advanced tandem mass tag (TMT) labeling protocol. Although labeling approaches shorten data acquisition time by multiplexing samples compared to traditional label-free quantification (LFQ) methods in general, the associated costs may surge significantly with large sample sets, for example, in toxicological screenings. However, the introduced advanced protocol offers an efficient, cost-effective alternative, reducing TMT reagent usage (by a factor of ten) and requiring minimal biological material (1 µg), while demonstrating increased reproducibility compared to LFQ. To demonstrate its effectiveness, the advanced protocol is employed to assess the toxicity of nine benchmark nanomaterials (NMs) on A549 lung epithelial cells. While LFQ measurements identify 3300 proteins, they proved inadequate to reveal NM toxicity. Conversely, despite detecting 2600 proteins, the TMT protocol demonstrates superior sensitivity by uncovering alterations induced by NM treatment. In contrast to previous studies, the introduced advanced protocol allows simultaneous and straightforward assessment of multiple test substances, enabling prioritization, ranking, and grouping for hazard evaluation. Additionally, it fosters the development of New Approach Methodologies (NAMs), contributing to innovative methodologies in toxicological research.

An optimised method to isotopically label pure synthetic peptides 'in-house' for absolute quantification in bottom-up proteomics

RATIONALE

Heavy-labelled internal standards increasingly represent the gold standard for absolute quantitation in mass spectrometry (MS)-based bottom-up proteomics. The biggest drawbacks of using these standards are that they have high costs and lengthy lead times.

METHODS

We describe an efficient, low-cost optimised method to enable 'in-house' heavy labelling of synthetic tryptic peptides for absolute quantification using tandem LC-MS/MS mass spectrometry. Our methodology uses 18O water in a trypsin-catalysed oxygen exchange reaction at the carboxyl terminus with the overall aim of reducing the costs and lead time associated with sourcing heavy standards from commercial vendors.

RESULTS

Step-by-step instructions are provided on how to execute this protocol with high-throughput adaptations utilising a 96-well plate and a liquid-handling robot. Detailed notes on experimental setup, tips for troubleshooting and suggested improvements to maximise labelling efficiencies are highlighted to achieve the best results. Under optimum conditions, labelling efficiencies of peptides can reach from 95% to 100%.

CONCLUSIONS

The application of the 'in-house' labelled standards in generating calibration curves to quantify endogenous peptide concentrations is just as effective as using the synthetically sourced standards while also having great cost reduction implications as well as saving time spent waiting for peptides to arrive. The protocol is highly adaptable and can be customized to fit the specific setup of any laboratory, maximizing achievable labelling efficiencies.

Comparative Proteomics Highlights that GenX Exposure Leads to Metabolic Defects and Inflammation in Astrocytes

Exposure to PFAS such as GenX (HFPO dimer acid) has become increasingly common due to the replacement of older generation PFAS in manufacturing processes. While neurodegenerative and developmental effects of legacy PFAS exposure have been studied in depth, there is a limited understanding specific to the effects of GenX exposure. To investigate the effects of GenX exposure, we exposed Drosophila melanogaster to GenX and assessed the motor behavior and performed quantitative proteomics of fly brains to identify molecular changes in the brain. Additionally, metabolic network-based analysis using the iDrosophila1 model unveiled a potential link between GenX exposure and neurodegeneration. Since legacy PFAS exposure has been linked to Parkinson's disease (PD), we compared the proteome data sets between GenX-exposed flies and a fly model of PD expressing human α-synuclein. Considering the proteomic data- and network-based analyses that revealed GenX may be regulating GABA-associated pathways and the immune system, we next explored the effects of GenX on astrocytes, as astrocytes in the brain can regulate GABA. An array of assays demonstrated GenX exposure may lead to mitochondrial dysfunction and neuroinflammatory response in astrocytes, possibly linking non-cell autonomous neurodegeneration to the motor deficits associated with GenX exposure.

Discovery and significance of protein-protein interactions in health and disease

The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis of PPIs increased to a genome-wide scale with the introduction of intracellular tagging methods, affinity purification (AP) followed by mass spectrometry (MS), and co-fractionation MS (CF-MS). Now, combining the resulting catalogs of interactions with complementary methods, including crosslinking MS (XL-MS) and cryogenic electron microscopy (cryo-EM), helps distinguish direct interactions from indirect ones within the same or between different protein complexes. These powerful approaches and the promise of artificial intelligence applications like AlphaFold herald a future where PPIs and protein complexes, including energy-driven protein machines, will be understood in exquisite detail, unlocking new insights in the contexts of both basic biology and disease.

AI-empowered perturbation proteomics for complex biological systems

The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems.

End-to-End Throughput Chemical Proteomics for Photoaffinity Labeling Target Engagement and Deconvolution

Photoaffinity labeling (PAL) methodologies have proven to be instrumental for the unbiased deconvolution of protein-ligand binding events in physiologically relevant systems. However, like other chemical proteomic workflows, they are limited in many ways by time-intensive sample manipulations and data acquisition techniques. Here, we describe an approach to address this challenge through the innovation of a carboxylate bead-based protein cleanup procedure to remove excess small-molecule contaminants and couple it to plate-based, proteomic sample processing as a semiautomated solution. The analysis of samples via label-free, data-independent acquisition (DIA) techniques led to significant improvements on a workflow time per sample basis over current standard practices. Experiments utilizing three established PAL ligands with known targets, (+)-JQ-1, lenalidomide, and dasatinib, demonstrated the utility of having the flexibility to design experiments with a myriad of variables. Data revealed that this workflow can enable the confident identification and rank ordering of known and putative targets with outstanding protein signal-to-background enrichment sensitivity. This unified end-to-end throughput strategy for processing and analyzing these complex samples could greatly facilitate efficient drug discovery efforts and open up new opportunities in the chemical proteomics field.

Achieving a 35-Plex Tandem Mass Tag Reagent Set through Deuterium Incorporation

Mass spectrometry-based sample multiplexing with isobaric tags permits the development of high-throughput and precise quantitative biological assays with proteome-wide coverage and minimal missing values. Here, we nearly doubled the multiplexing capability of the TMTpro reagent set to a 35-plex through the incorporation of one deuterium isotope into the reporter group. Substituting deuterium frequently results in suboptimal peak coelution, which can compromise the accuracy of reporter ion-based quantification. To counteract the deuterium effect on quantitation, we implemented a strategy that necessitated the segregation of nondeuterium and deuterium-containing channels into distinct subplexes during normalization procedures, with reassembly through a common bridge channel. This multiplexing strategy of "design independent sub-plexes but acquire together" (DISAT) was used to compare protein expression differences between human cell lines and in a cysteine-profiling (i.e., chemoproteomics) experiment to identify compounds binding to cysteine-113 of Pin1.

Carrier-Guided Proteome Analysis in a High Protein Background: An Improved Approach to Host Cell Protein Identification

Many shotgun proteomics experiments are negatively influenced by highly abundant proteins, such as those measuring residual host cell proteins (HCP) amidst highly abundant recombinant biotherapeutic or plasma proteins amidst albumin and immunoglobulins. While western blotting and ELISAs can reveal the presence of specific low abundance proteins from highly abundant background proteins, mass spectrometry approaches are required to define the low abundance protein composition in these scenarios. The challenge in detecting low abundance proteins in a high protein background by standard shotgun approaches is that spectra are often not triggered on their peptides in data dependent acquisition methods but rather on the highly abundant background peptides. Here, we use tandem mass tags (TMT) to introduce a carrier proteome approach to enhance the detection of proteins, such as from residual host cell proteomes amidst a highly abundant background. Using a mixture of bovine serum albumin (BSA) and E. coli as a mock high background/low abundance target protein formulation, we demonstrate proof-of-principle experiments allowing the improved detection of target proteins amidst a high protein background. While we observed significant coisolation interference, we mitigated it by using a spike-in interference detection TMT channel. Finally, we use the approach to identify 300 residual E. coli proteins from a protein A pulldown of a human IgG antibody, demonstrating that it may be applicable to analysis of HCPs in biotherapeutic protein formulations.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Chemical proteomic mapping of reversible small molecule binding sites in native systems

The impact of small molecules in human biology are manifold; not only are they critical regulators of physiological processes, but they also serve as probes to investigate biological pathways and leads for therapeutic development. Identifying the protein targets of small molecules, and where they bind, is critical to understanding their functional consequences and potential for pharmacological use. Over the past two decades, chemical proteomics has emerged as a go-to strategy for the comprehensive mapping of small molecule-protein interactions. Recent advancements in this field, particularly innovations of photoaffinity labeling (PAL)-based methods, have enabled the robust identification of small molecule binding sites on protein targets, often in live cells. In this opinion article, we examine these advancements as well as reflect on how their strategic integration with other emerging tools can advance therapeutic development.

Streamlined analysis of drug targets by proteome integral solubility alteration indicates organ-specific engagement

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions.

Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition

Targeted proteomics has been playing an increasingly important role in hypothesis-driven protein research and clinical biomarker discovery. We previously created a workflow, Tomahto, to enable real-time targeted pathway proteomics assays using two-dimensional multiplexing technology. Coupled with the TMT 11-plex reagent, hundreds of proteins of interest from up to 11 samples can be targeted and accurately quantified in a single-shot experiment with remarkable sensitivity. However, room remains to further improve the sensitivity, accuracy, and throughput, especially for targeted studies demanding a high peptide-level success rate. Here, bearing in mind the goal to improve peptide-level targeting, we introduce several new functionalities in Tomahto, featuring the integration of gas-phase fractionation using the FAIMS device, an accompanying software program (TomahtoPrimer) to customize fragmentation for each peptide target, and support for higher multiplexing capacity with the latest TMTpro reagent. We demonstrate that adding these features to the Tomahto platform significantly improves overall success rate from 89% to 98% in a single 60 min targeted assay of 290 peptides across human cell lines, while boosting quantitative accuracy via reducing TMT reporter ion interference.

Multifaceted Proteome Analysis at Solubility, Redox, and Expression Dimensions for Target Identification

Multifaceted interrogation of the proteome deepens the system-wide understanding of biological systems; however, mapping the redox changes in the proteome has so far been significantly more challenging than expression and solubility/stability analyses. Here, the first high-throughput redox proteomics approach integrated with expression analysis (REX) is devised and combined with the Proteome Integral Solubility Alteration (PISA) assay. The whole PISA-REX experiment with up to four biological replicates can be multiplexed into a single tandem mass tag TMTpro set. For benchmarking this compact tool, HCT116 cells treated with auranofin are analyzed, showing great improvement compared with previous studies. PISA-REX is then applied to study proteome remodeling upon stimulation of human monocytes by interferon α (IFN-α). Applying this tool to study the proteome changes in plasmacytoid dendritic cells (pDCs) isolated from wild-type versus Ncf1-mutant mice treated with interferon α, shows that NCF1 deficiency enhances the STAT1 pathway and modulates the expression, solubility, and redox state of interferon-induced proteins. Providing comprehensive multifaceted information on the proteome, the compact PISA-REX has the potential to become an industry standard in proteomics and to open new windows into the biology of health and disease.

Therapeutic modulation of ROCK overcomes metabolic adaptation of cancer cells to OXPHOS inhibition and drives synergistic anti-tumor activity

Genomic studies have identified frequent mutations in subunits of the SWI/SNF chromatin remodeling complex including SMARCA4 and ARID1A in non-small cell lung cancer. Previously, we and others have identified that SMARCA4-mutant lung cancers are highly dependent on oxidative phosphorylation (OXPHOS). Despite initial excitements, therapeutics targeting metabolic pathways such as OXPHOS have largely been disappointing due to rapid adaptation of cancer cells to inhibition of single metabolic enzymes or pathways, suggesting novel combination strategies to overcome adaptive responses are urgently needed. Here, we performed a functional genomics screen using CRISPR-Cas9 library targeting genes with available FDA approved therapeutics and identified ROCK1/2 as a top hit that sensitizes cancer cells to OXPHOS inhibition. We validate these results by orthogonal genetic and pharmacologic approaches by demonstrating that KD025 (Belumosudil), an FDA approved ROCK inhibitor, has highly synergistic anti-cancer activity in vitro and in vivo in combination with OXPHOS inhibition. Mechanistically, we showed that this combination induced a rapid, profound energetic stress and cell cycle arrest that was in part due to ROCK inhibition-mediated suppression of the adaptive increase in glycolysis normally seen by OXPHOS inhibition. Furthermore, we applied global phosphoproteomics and kinase-motif enrichment analysis to uncover a dynamic regulatory kinome upon combination of OXPHOS and ROCK inhibition. Importantly, we found converging phosphorylation-dependent regulatory cross-talk by AMPK and ROCK kinases on key RHO GTPase signaling/ROCK-dependent substrates such as PPP1R12A, NUMA1 and PKMYT1 that are known regulators of cell cycle progression. Taken together, our study identified ROCK kinases as critical mediators of metabolic adaptation of cancer cells to OXPHOS inhibition and provides a strong rationale for pursuing ROCK inhibitors as novel combination partners to OXPHOS inhibitors in cancer treatment.

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.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

High-Throughput Metabolomics using 96-plex Isotope Tagging

Liquid chromatography-mass spectrometry (LC-MS) based metabolomics suffers from extended duty cycles and matrix-dependent quantitation. Chemical tags with 96 unique masses are reported, which alleviate the metabolomic workflow bottleneck and allow for absolute quantitation. A metabolic screen for carboxylic acids was performed on mammalian cells deprived of various nutrients and showed 24% RSD and analysis of 288 samples in 2 h.

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition

Intelligent data acquisition (IDA) strategies, such as a real-time database search (RTS), have improved the depth of proteome coverage for experiments that utilize isobaric labels and gas phase purification techniques (i.e., SPS-MS3). In this work, we introduce inSeqAPI, an instrument application programing interface (iAPI) program that enables construction of novel data acquisition algorithms. First, we analyze biotinylated cysteine peptides from ABPP experiments to demonstrate that a real-time search method within inSeqAPI performs similarly to an equivalent vendor method. Then, we describe PairQuant, a method within inSeqAPI designed for the hyperplexing approach that utilizes protein-level isotopic labeling and peptide-level TMT labeling. PairQuant allows for TMT analysis of 36 conditions in a single sample and achieves ∼98% coverage of both peptide pair partners in a hyperplexed experiment as well as a 40% improvement in the number of quantified cysteine sites compared with non-RTS acquisition. We applied this method in the ABPP study of ligandable cysteine sites in the nucleus leading to an identification of additional druggable sites on protein- and DNA-interaction domains of transcription regulators and on nuclear ubiquitin ligases.

Mass Spectrometry Acquisition and Fractionation Recommendations for TMT11 and TMT16 Labeled Samples

Proteome coverage and accurate protein quantification are both important for evaluating biological systems; however, compromises between quantification, coverage, and mass spectrometry (MS) resources are often necessary. Consequently, experimental parameters that impact coverage and quantification must be adjusted, depending on experimental goals. Among these parameters is offline prefractionation, which is utilized in MS-based proteomics to decrease sample complexity resulting in higher overall proteome coverage upon MS analysis. Prefractionation leads to increases in required MS analysis time, although this is often mitigated by isobaric labeling using tandem-mass tags (TMT), which allow samples to be multiplexed. Here we evaluate common prefractionation schemes, TMT variants, and MS acquisition methods and their impact on protein quantification and coverage. Furthermore, we provide recommendations for experimental design depending on the experimental goals.

Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release spatially to amplify liver fibrosis

Liver fibrosis is characterized by the activation of perivascular hepatic stellate cells (HSCs), the release of fibrogenic nanosized extracellular vesicles (EVs), and increased HSC glycolysis. Nevertheless, how glycolysis in HSCs coordinates fibrosis amplification through tissue zone-specific pathways remains elusive. Here, we demonstrate that HSC-specific genetic inhibition of glycolysis reduced liver fibrosis. Moreover, spatial transcriptomics revealed a fibrosis-mediated up-regulation of EV-related pathways in the liver pericentral zone, which was abrogated by glycolysis genetic inhibition. Mechanistically, glycolysis in HSCs up-regulated the expression of EV-related genes such as Ras-related protein Rab-31 (RAB31) by enhancing histone 3 lysine 9 acetylation on the promoter region, which increased EV release. Functionally, these glycolysis-dependent EVs increased fibrotic gene expression in recipient HSC. Furthermore, EVs derived from glycolysis-deficient mice abrogated liver fibrosis amplification in contrast to glycolysis-competent mouse EVs. In summary, glycolysis in HSCs amplifies liver fibrosis by promoting fibrogenic EV release in the hepatic pericentral zone, which represents a potential therapeutic target.

Targeting Tyro3, Axl, and MerTK Receptor Tyrosine Kinases Significantly Sensitizes Triple-Negative Breast Cancer to CDK4/6 Inhibition

Triple-negative breast cancer (TNBC) is the most aggressive subtype with high metastasis and mortality rates. Given the lack of actionable targets such as ER and HER2, TNBC still remains an unmet therapeutic challenge. Despite harboring high CDK4/6 expression levels, the efficacy of CDK4/6 inhibition in TNBC has been limited due to the emergence of resistance. The resistance to CDK4/6 inhibition is mainly mediated by RB1 inactivation. Since our aim is to overcome resistance to CDK4/6 inhibition, in this study, we primarily used the cell lines that do not express RB1. Following a screening for activated receptor tyrosine kinases (RTKs) upon CDK4/6 inhibition, we identified the TAM (Tyro3, Axl, and MerTK) RTKs as a crucial therapeutic vulnerability in TNBC. We show that targeting the TAM receptors with a novel inhibitor, sitravatinib, significantly sensitizes TNBC to CDK4/6 inhibitors. Upon prolonged HER2 inhibitor treatment, HER2+ breast cancers suppress HER2 expression, physiologically transforming into TNBC-like cells. We further show that the combined treatment is highly effective against drug-resistant HER2+ breast cancer as well. Following quantitative proteomics and RNA-seq data analysis, we extended our study into the immunophenotyping of TNBC. Given the roles of the TAM receptors in promoting the creation of an immunosuppressive tumor microenvironment (TME), we further demonstrate that the combination of CDK4/6 inhibitor abemaciclib and sitravatinib modifies the immune landscape of TNBC to favor immune checkpoint blockade. Overall, our study offers a novel and highly effective combination therapy against TNBC and potentially treatment-resistant HER2+ breast cancer that can be rapidly moved to the clinic.

Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.

Ion channel inhibition by targeted recruitment of NEDD4-2 with divalent nanobodies

Targeted recruitment of E3 ubiquitin ligases to degrade traditionally undruggable proteins is a disruptive paradigm for developing new therapeutics. Two salient limitations are that <2% of the ~600 E3 ligases in the human genome have been exploited to produce proteolysis targeting chimeras (PROTACs), and the efficacy of the approach has not been demonstrated for a vital class of complex multi-subunit membrane proteins- ion channels. NEDD4-1 and NEDD4-2 are physiological regulators of myriad ion channels, and belong to the 28-member HECT (homologous to E6AP C-terminus) family of E3 ligases with widespread roles in cell/developmental biology and diverse diseases including various cancers, immunological and neurological disorders, and chronic pain. The potential efficacy of HECT E3 ligases for targeted protein degradation is unexplored, constrained by a lack of appropriate binders, and uncertain due to their complex regulation by layered intra-molecular and posttranslational mechanisms. Here, we identified a nanobody that binds with high affinity and specificity to a unique site on the N-lobe of the NEDD4-2 HECT domain at a location physically separate from sites critical for catalysis- the E2 binding site, the catalytic cysteine, and the ubiquitin exosite- as revealed by a 3.1 Å cryo-electron microscopy reconstruction. Recruiting endogenous NEDD4-2 to diverse ion channel proteins (KCNQ1, ENaC, and CaV2.2) using a divalent (DiVa) nanobody format strongly reduced their functional expression with minimal off-target effects as assessed by global proteomics, compared to simple NEDD4-2 overexpression. The results establish utility of a HECT E3 ligase for targeted protein downregulation, validate a class of complex multi-subunit membrane proteins as susceptible to this modality, and introduce endogenous E3 ligase recruitment with DiVa nanobodies as a general method to generate novel genetically-encoded ion channel inhibitors.

Overcoming the detrimental O-acylation in TMTpro labeling improves the proteome depth and quantification precision

BACKGROUND

With the advent of proline-based reporter isobaric Tandem Mass Tag (TMTpro) reagents, the sample multiplexing capacity of tandem mass tags (TMTs) has been expanded, and up to 18 samples can be quantified in a multiplexed manner. Like classic TMT reagents, TMTpro reagents contain a tertiary amine group, which markedly enhances their reactivity toward hydroxyl groups and results in O-acylation of serine, threonine and tyrosine residues. This overlabeling significantly compromises proteome analysis in terms of depth and precision. In particular, the reactivity of hydroxyl-containing residues can be dramatically enhanced when coexisting with a histidine in the same peptides, leading to a severe systematic bias against the analysis of these peptides. Although some protocols using a reduced molar excess of TMT under alkaline conditions can alleviate overlabeling of histidine-free peptides to some extent, they have a limited effect on histidyl- and hydroxyl-containing peptides.

RESULTS

Here, we report a novel TMTpro labeling method that overcomes detrimental overlabeling while providing high labeling efficiency of amines. Additionally, our method is cost-effective, as it requires only half the amount of TMTpro reagents recommended by the reagent manufacturer. In a deep-scale analysis of a yeast/human two-proteome model sample, we compared our method with a typical alkaline labeling method using a reduced molar excess of TMTpro. Even at a depth of over 10,000 proteins, our method detected 23.7% more unique peptides and 8.7% more protein groups compared to the alkaline labeling method. Moreover, our method significantly improved the quantitative precision due to the reduced variability in labeling and increased protein sequence coverage. This substantially enhanced the statistical power of our method for detecting differentially abundant proteins, providing an average of 13% more yeast proteins that reached statistical significance.

SIGNIFCANCE

We presented a novel TMTpro labeling method that overcomes the detrimental O-acylation and thus significantly improves the depth and quantitative precision for proteome analysis.

The One Hour Human Proteome

We describe deep analysis of the human proteome in less than 1 h. We achieve this expedited proteome characterization by leveraging state-of-the-art sample preparation, chromatographic separations, and data analysis tools, and by using the new Orbitrap Astral mass spectrometer equipped with a quadrupole mass filter, a high-field Orbitrap mass analyzer, and an asymmetric track lossless (Astral) mass analyzer. The system offers high tandem mass spectrometry acquisition speed of 200 Hz and detects hundreds of peptide sequences per second within data-independent acquisition or data-dependent acquisition modes of operation. The fast-switching capabilities of the new quadrupole complement the sensitivity and fast ion scanning of the Astral analyzer to enable narrow-bin data-independent analysis methods. Over a 30-min active chromatographic method consuming a total analysis time of 56 min, the Q-Orbitrap-Astral hybrid MS collects an average of 4319 MS1 scans and 438,062 tandem mass spectrometry scans per run, producing 235,916 peptide sequences (1% false discovery rate). On average, each 30-min analysis achieved detection of 10,411 protein groups (1% false discovery rate). We conclude, with these results and alongside other recent reports, that the 1-h human proteome is within reach.