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PubMed2026

Mapping the covalent cysteine interactome of Ebselen reveals high-sensitivity target engagement and redox proteome remodeling.

Ebselen is a covalent organoselenium compound with broad pharmacological activity, yet its cellular cysteine targets and downstream proteomic consequences remain incompletely defined. Here, we integrated competitive gel-based activity-based protein profiling, reactivity-dependent tandem orthogonal proteolysis-activity-based protein profiling, and TMT-based quantitative proteomics to map Ebselen-induced cysteine engagement and proteome remodeling in living cancer cells. Ebselen exhibited dose-dependent cytotoxicity and markedly perturbed intracellular thiol-redox balance, as reflected by glutathione depletion and altered reactive oxygen species-associated fluorescence readouts. Competitive gel-based profiling confirmed concentration-dependent engagement of protein cysteine residues in live cells. Quantitative rdTOP-ABPP further identified hundreds of dose-responsive cysteine sites in HeLa and HepG2 cells and revealed a preference for cysteine microenvironments enriched with basic residues. Cross-cell-line comparison highlighted CDK5 Cys53, SMU1 Cys298, and RPSA2 Cys163 as conserved covalent nodes, among which CDK5 Cys53 showed high sensitivity to Ebselen treatment, a finding validated by competitive labeling and MS-based site assignment. Global TMT proteomics revealed extensive remodeling of redox-related and cell-survival-associated pathways, including compensatory upregulation of selenoproteins such as TXNRD1 and GPX family members. Together, these results define a chemical proteomic atlas of Ebselen-cysteine interactions and provide a framework for understanding and optimizing covalent organoselenium therapeutics.

Humans↗
PubMed2026

Saliva and salivary pellicle composition and proteomic profile in smokers vs. non-smokers and its effect on dental erosion.

OBJECTIVE: To analyse the salivary composition and proteomic profile of saliva and the salivary pellicle in smokers compared to non-smokers, and to examine potential differences in the erosion-protective capacity of the salivary pellicle. METHODS: Twenty-five smokers and 25 non-smokers were included. Unstimulated and stimulated saliva samples were analysed regarding flow rate, pH, buffer capacity, calcium, phosphate, fluoride, and protein content. Saliva and salivary pellicle samples were analysed by data-independent acquisition mass spectrometry (DIA-MS) for proteome profiling. In an in situ experiment, intraoral splints were loaded with bovine enamel and dentine specimens for 120 min. Pellicle-covered specimens were extraorally eroded (HCl, pH 2.3, 60 s). Calcium release was determined photometrically and compared to pellicle-free controls. RESULTS: Except for phosphate in stimulated saliva (padj.=0.003), salivary parameters were not significantly different between smokers and non-smokers. Proteome profiling detected 1759±154 proteins (cumulative 1963) in saliva, and 4262±362 proteins (cumulative 4625) in the salivary pellicle. The relative abundances of 282 (unstimulated saliva), 338 (stimulated saliva), and 4 (salivary pellicle) protein groups differed significantly between smokers and non-smokers. Functional enrichment analysis of differentially abundant human proteins revealed biological processes such as coagulation, immune response, and carcinogenic reactive oxygen species processes to be impacted by smoking. The salivary pellicle had a significant erosion-protective effect in enamel compared to the control (41.4 ± 6.3 nmol/mm2), but no differences between smokers (33.2 ± 10.6 nmol/mm2, padj.=0.001) and non-smokers (32.7 ± 8.6 nmol/mm2, padj.=0.001) were found. CONCLUSION: The proteomic profiles of both unstimulated and stimulated saliva and the salivary pellicle differ between smokers and non-smokers. CLINICAL SIGNIFICANCE: Despite the different proteomic profiles indicating a significant impact of smoking on the oral cavity, the erosion-protective capacity of the salivary pellicle of smokers and non-smokers does not differ.

Dental Pellicle↗
PubMed2026

Plasma proteomic profiling characterizes candidate biomarkers of perimesencephalic non-aneurysmal subarachnoid hemorrhage.

OBJECT: This study aims to explore the plasma proteomic profiles of angiographically confirmed pmSAH and aSAH, and to identify candidate protein biomarkers for discriminating these subtypes on a biological level. METHODS: The differentially abundant proteins of plasma samples from patients with pmSAH (n = 30) and aSAH (n = 30) were analyzed by data-independent acquisition proteomics, and candidate biomarkers were screened. RESULTS: 291 candidate biomarkers were obtained that could be used to distinguish pmSAH patients from aSAH patients, among which 76 were upregulated and 215 were downregulated in pmSAH. Subsequently, the 10 candidate biomarkers were validated by enzyme-linked immunosorbent assay in a validation cohort of 72 subjects. ORM1, ORM2, HP and NMNAT1 were specifically down-regulated in the pmSAH group, while ANP32A was specifically up-regulated in the pmSAH group. FGL2 was specifically up-regulated in the aSAH group. The combined model of ORM2, HP and ANP32A had the best discriminative power (AUC = 0.880). CONCLUSIONS: This study identified ORM2, HP, and ANP32A as candidate biomarkers reflecting biological differences between pmSAH and aSAH. SIGNIFICANCE: Although some proteomic studies have analyzed aneurysmal subarachnoid hemorrhage, to date, there have been no reports on the circulating proteomic analysis of pmSAH. Comparative analysis of the circulating proteomic differences between pmSAH and aSAH may not only help understand the causes of pmSAH, but also contribute to a deeper understanding of mechanisms showing how pmSAH differs from the formation and rupture mechanisms of intracranial aneurysms.

Humans↗
PubMed2026

Proteome-wide Ubiquitinome Profiling Reveals Substrate-specific Dynamics Within the USP7 Network.

USP7 is a pleiotropic deubiquitylating enzyme that is involved in tumor suppression, (neuro) development, chromatin regulation and the DNA damage response. How USP7 regulates these diverse pathways is still unclear. Here, we report data-independent acquisition and label free quantitation mass spectrometry to profile the proteome-wide impact of USP7 on substrate de-ubiquitylation and overall protein abundance. First, we identified proteins associated with endogenous USP7 by immunopurification followed by data-independent acquisition and label free quantitation mass spectrometry. Integration of our new results with earlier interactomes of epitope-tagged USP7 yielded a consensus set of high-confidence protein targets. Domain mapping analysis revealed that, in addition to the TRAF domain, the ubiquitin-like domains of USP7 play a key role in substrate selection. Using specific enrichment of tryptic K-ε-GG peptides, we mapped proteome-wide changes in ubiquitinome dynamics following inhibition of USP7. Combining unbiased proteome-wide and targeted quantitative mass spectrometry revealed that deubiquitylation by USP7 can have different effects on the stability of distinct substrates, and suggests that USP7's activity profile is substrate-dependent rather than an intrinsic enzymatic property. Thus, in addition to providing a proteome-wide map of USP7 target sites, our multi-angle proteomics approach reveals that the effects of USP7-mediated deubiquitylation on its targets are remarkably variable and substrate-specific. Finally, based on these detailed molecular insights we show how USP7 connects various neurodevelopmental syndromes and tumor suppression pathways.

Ubiquitin-Specific Peptidase 7↗
PubMed2026

Spatial Proteomics of the Normal Breast Collagen Stroma: Links to Density and Body Mass Index.

Collagen breast stroma can become a breast cancer risk factor, yet proteomic regulation of normal breast stroma remains poorly defined. This study evaluates the spatial regulation of the collagen proteome from normal breast tissue. Normal breast tissue sections from the Susan G. Komen tissue bank were used (n = 40), with data including genetic ancestry (n = 20 African ancestry; n = 20 European ancestry), body-mass-index (BMI), age, and mammogram density by the Breast Imaging Reporting and Data System (BI-RADS). 10-plex cell marker staining showed CD44 and COL1A1 markers modulated with BMI. Collagen fiber widths by second harmonic generation microscopy contrasted in BMI categories by genetic ancestry. Targeted extracellular matrix proteomics mass spectrometry imaging showed the collagen alpha-1(I) chain proteome was spatially heterogeneous across the normal breast microenvironment with site-specific post-translational modification of proline hydroxylation. Signatures computationally extracted from stroma-rich regions reported that 47 collagen peptides distinguished BI-RADS categories (area under the receiver operating curve >0.7; p-value >0.05). Multivariate modeling of collagen peptides, fiber metrics, and clinical features supported a strong positive association with BMI as a determinant of collagen alterations in the normal breast. This study provides a foundation for larger studies investigating the clinical value of spatial collagen proteome alterations in human breast.

Humans↗
PubMed2026

ProtPen Combines Sequence- and Structure-based Approaches to Facilitate Protein Function Predictions on a Proteome-wide Scale.

Proteins of unknown function represent a significant gap in our understanding of biological processes, encompassing large portions of the proteomes of many organisms, especially prokaryotes. Addressing this gap is critical to understanding the biology and pathogenicity of such organisms. We introduce ProtPen, an open-source pipeline that facilitates protein function prediction by combining eggNOG-mapper for sequence-based annotation with Foldseek for rapid structural similarity searches using AlphaFold-predicted protein structures. Annotation results from both tools are merged and enriched with UniProt metadata to produce a comprehensive output suitable for downstream analysis. The pipeline requires only a FASTA input file with UniProt identifiers, and is designed to analyze data sets on the scale of whole proteomes. Benchmarking on a curated data set of well-characterized Pseudomonas aeruginosa proteins demonstrated an annotation accuracy of >90%, and highlighted the complementarity of sequence- and structure-based methods. Further evaluation of ProtPen included its application to biologically relevant data sets, comprising proteins of unknown function that exhibited significant differential abundances in a proteomics data set of P. aeruginosa, and uncharacterized glycoproteins from Haloferax volcanii. ProtPen is readily extensible to incorporate additional protein function prediction tools. In summary, this pipeline facilitates the systemwide annotation of proteins of unknown function from proteomic data sets and whole proteomes.

Pseudomonas aeruginosa↗
PubMed2026

A human lysosomal storage disorder toolkit for decoding proteome landscapes in cortical-like and dopaminergic-like induced neurons.

Lysosomes maintain cellular homeostasis by degrading proteins delivered via endocytosis and autophagy and by recycling building blocks for organelle biogenesis. Lysosomal storage disorders (LSDs) comprise a group of diseases affecting diverse lysosomal functions. To facilitate molecular phenotyping across diverse LSD gene classes, we are developing a library of human embryonic stem cells engineered to lack individual LSD genes as a resource for the field. Here, we report our initial stem cell toolkit lacking one of 23 LSD genes, including the majority of genes associated with sphingolipidoses and neuronal ceroid lipofuscinoses, and its use in the generation of a proteomic resource for induced cortical-like and midbrain dopaminergic-like neurons. In-depth abundance and correlation profiling across organelles and suborganelle components revealed potential vulnerabilities that reflect distinct patterns of proteome alterations across both genotypes and neuronal cell types. We characterize alterations in the mitochondrial proteome associated with GBA1 and ASAH1 deficiency and identify synaptic and mitochondrial defects in ASAH1-/- induced neurons that correlate with defects in neuronal firing rates. Moreover, we developed an informatic pipeline for proteome-wide identification of individual protein-protein interactions and protein complexes that may be disrupted as a result of LSD gene deficiency. Finally, we visualized structural alterations of ASAH1-deficient endolysosomes in situ using cryoelectron tomography, revealing swollen organelles that were largely devoid of dense internal membranes characteristic of wild-type cells, but containing numerous intralumenal vesicle compartments. This toolkit and associated proteomic landscapes provide a resource for defining molecular signatures associated with LSD gene dysfunction and organelle vulnerability.

Humans↗
PubMed2026

Implications of proteome allocation constraints for understanding interbacterial antagonism.

Bacteria live in dense communities where competition influences the composition and, therefore, the function of these communities. Beyond competing for resources, bacteria engage in antagonism by deploying a range of molecular weapon systems to inhibit and kill other bacteria. Investing in antagonism is expected to incur a fitness trade-off, but the nature of this trade-off at the level of molecular physiology remains underexplained. Applying recent advances about the physiological constraints faced by bacterial cells may help us better understand existing studies and design new investigations into interbacterial antagonism. Bacterial cells face two important constraints: a finite amount of protein and a maximum translation speed for ribosomes. As a result, the only way for a cell to grow faster is to allocate more of its finite proteome to synthesizing ribosomes. A cell choosing to attack competitors must therefore allocate some of its limited proteome budget to antagonistic proteins instead of other functions. Conversely, being attacked and resisting the effects of such attacks also require an investment of proteomic resources. The extent to which proteome allocation constraints influence bacterial physiology is not fully understood; consequently, how these constraints influence interbacterial antagonism has not been investigated. Here, I will discuss how proteome allocation constraints can re-contextualize our existing understanding of the costs of both deploying and resisting attacks and how investigation of these constraints may further our understanding of interbacterial antagonism.

Proteome↗
PubMed2026

Proteomics-Driven Strategies for Proximity-Inducing Drug Discovery.

In recent years, proximity-inducing drugs have emerged as a novel therapeutic modality that induces or stabilizes protein-protein interactions, especially by recruiting effector proteins to specific target proteins, thereby achieving functions beyond traditional inhibitors. The potential of proximity-inducing drugs extends beyond targeted protein degradation (TPD), as studies have demonstrated their ability to regulate biological processes such as signal transduction, gene transcription, chromatin regulation, and protein trafficking by modulating protein interaction networks. Rational discovery of proximity-inducing drugs requires clarifying their effects on protein-protein interactions, determining drug selectivity, and developing suitable ligands for drug construction. Proteomics has become a central technology in drug discovery, enabling global identification of the direct drug targets and systematic characterization of proteome-wide downstream responses. This provides a more refined map of drug mechanisms. In parallel, advances in machine learning applied to proteomic data, together with the expansion of proteome-wide ligandability maps, are further accelerating the discovery and optimization of proximity-inducing drugs. This review summarizes recent advances of proximity-inducing drugs, with a particular emphasis on how proteomics facilitates target space expansion, drug efficacy optimization, and ligandability discovery, alongside the emerging contributions of machine learning. Collectively, these insights aim to support the rational development of next-generation proximity-inducing drugs.

Drug Discovery↗
PubMed2026

Quantitative Proteomic Profiling of Pinctada fucata Shell Nacre Defines a Solubility-Based Type Classification of Shell Matrix Proteins.

Shell matrix proteins (SMPs) are key organic components of molluscan biominerals, yet previous nacre proteomics have remained largely qualitative, limiting evaluation of the abundance and fraction association of individual SMPs. Here, we established a quantitative proteomic approach for the nacreous layer of the pearl oyster Pinctada fucata by integrating optimized shell preservation, stepwise fractionation, and data-independent acquisition (DIA) proteomics. SMPs were separated into an ethylenediaminetetraacetic acid (EDTA)-soluble matrix (ESM), an EDTA-insoluble but sodium dodecyl sulfate/dithiothreitol (SDS/DTT)-soluble matrix (SSM), and an SDS/DTT-insoluble matrix (ISM). DIA outperformed data-dependent acquisition in proteome coverage and enabled quantification of 327 SMPs across a broad dynamic range. Fraction-resolved abundance profiling showed that each fraction was characterized by distinct SMP compositions. To summarize these distributions, we introduced a solubility-based type classification that grouped SMPs into four types according to their quantitative partitioning among fractions. Well-known SMPs, including nacrein, Pif 80, and MSI60, were assigned to intuitively consistent types, whereas proteases, protease inhibitors, and tyrosinases also showed biased type distributions. These results support a three-compartment model of the nacreous layer consisting of (i) an insoluble interlamellar membrane core, (ii) a relatively extractable interfacial layer, and (iii) a soluble matrix fraction enriched in proteins potentially involved in ionic regulation and protein maturation. This study provides a quantitative framework for understanding coordinated SMP functions during nacre formation and for comparative analyses of molluscan shell proteomes.

Animals↗
PubMed2026

Exploring the proteomic landscape of THP-1 monocytes through two-challenge LPS induction.

Proteome remodelling is central to the regulation of innate immune activation, yet the temporal organisation of protein networks engaged during repeated lipopolysaccharide (LPS) stimulation remains incompletely defined. In the present study, label-free quantitative mass spectrometry-based proteomics was used to characterise protein abundance changes in THP-1 monocytes at early (30 min) and later (2 h) time points following a second LPS challenge. This analysis was complemented by an independent co-immunoprecipitation proteomics experiment designed to identify candidate proteins associated with the regulatory pseudo-kinase IRAK3 during early TLR4 signalling. At 30 min, differentially abundant proteins were enriched in pathways associated with pattern-recognition receptor signalling, NF-κB activity, RNA processing, phosphorylation, and ribonucleoprotein complex organisation. By 2 h, the proteomic response broadened to include oxidative phosphorylation, antigen processing and presentation, vesicle-mediated transport, protein folding, and cytokine-regulatory pathways. These findings indicate that repeated LPS stimulation is accompanied by progressive remodelling of inflammatory, metabolic, translational, and proteostatic programmes rather than major changes in protein identity. Co-immunoprecipitation identified established TLR/IRAK3-associated components together with candidate IRAK3-associated proteins linked to RNA regulation, kinase signalling, ubiquitin-mediated processes, redox control, cytoskeletal remodelling, and damage-associated molecular pattern responses. Collectively, these findings define a temporal framework of proteomic adaptation during repeated inflammatory stimulation and expand the range of candidate proteins potentially contributing to IRAK3-centred regulation of innate immune signalling.

Humans↗
PubMed2026

Proteomic responses of the oil palm pest Metisa plana (Psychidae) to farnesyl acetate exposure.

Metisa plana Walker (Lepidoptera: Psychidae) is a major defoliator of oil palm in Malaysia, causing substantial economic losses. Farnesyl acetate (FA), a sesquiterpenoid compound, has been proposed as a potential insecticidal agent against M. plana, yet its molecular impact on larval physiology remains poorly understood. Here, we employed label-free quantitative proteomics, functional enrichment analysis, and targeted transcript assessment to characterize the temporal proteomic response of M. plana larvae at 7 and 14 days after treatment (DAT) with FA. Principal component analysis revealed robust separation between treated and control samples at both time points, indicating sustained treatment-driven proteomic restructuring. Early exposure (7 DAT) elicited a heterogeneous response involving stress-associated proteins, redox enzymes, and cytoskeletal regulators, whereas later exposure (14 DAT) produced a consolidated profile characterized by metabolic reprogramming, downregulation of ribosomal proteins, induction of heat shock proteins, and enrichment of RNA surveillance and mitochondrial pathways. Targeted transcript analysis qualitatively supported proteomic trends for HSP83 and aldehyde dehydrogenase X, although limited amplification precluded quantitative inference. Collectively, these findings demonstrate that FA exposure drives a shift from acute proteomic perturbation toward a maintenance-oriented physiological state, prioritizing proteostasis, energy management, and stress adaptation over growth and development. This integrated molecular perspective provides mechanistic insight into the chronic effects of FA, highlighting its potential to suppress larval performance and informing the development of biorational, physiology-based pest management strategies in non-model insects.

Animals↗
PubMed2026

Beyond ion channel dysfunction: Integration of the transcriptome and proteome from patient-specific re-engineered cardiac cells, and population-level QT genome-wide association study reveals broad cellular dysfunction.

BACKGROUND: Congenital long QT syndrome (LQTS) is a cardiac channelopathy with increased risk of cardiac-triggered syncope/seizures, sudden cardiac arrest, and sudden cardiac death. OBJECTIVE: This study aimed to describe the transcriptomic and proteomic profiles in patient-derived inducible pluripotent stem cell-derived cardiomyocyte (iPSC-CM) models of the 3 canonical genotypes of congenital LQTS: LQT1, LQT2, and LQT3 and integrate these omics-level findings with each other and with population/clinical level QT-genome-wide association study (GWAS) data. METHODS: LQT1, LQT2, LQT3 and respective isogenic control iPSC-CMs were cultured, and RNA and protein samples were collected. RNA sequencing and mass spectrometry-enabled proteomic analysis was performed. PrediXcan analysis was performed using QT GWAS summary statistics and transcriptome expression data. Differential gene and protein expression and ingenuity pathway analysis (IPA) was performed comparing each LQT genotype with its respective isogenic control. RESULTS: 1645 differentially expressed genes (DEGs) were identified; 13 were altered in all 3 LQTS genotypes. IPA analysis of DEGs revealed 301 altered pathways; 47 were altered in all LQTS genotypes. Proteomic analysis identified 2561 differentially expressed proteins (DEPs); 30 were altered in all 3 genotypes. IPA analysis of DEPs identified 646 altered pathways. 306 genes/proteins were identified as significantly altered in both the transcriptome and proteome; pathway analysis of these 301 genes identified 201 altered pathways. 7 pathways were altered in all 3 LQTS genotypes in both the transcriptome and proteome. Integration of the population-level PrediXcan results and the cardiomyocyte-derived omics results identified multiple shared pathways. CONCLUSION: Multi-omics analysis of LQTS and integration of omics results with QT GWAS data reveals that primary LQTS-causative ion channel defects precipitate secondary alterations in a wide range of cellular pathways. Our findings suggest more broad molecular level changes throughout the cell. This study lays the foundation for further exploration of broad cellular changes resulting from ion channel disturbances and how they contribute to disease mechanism.

Humans↗
PubMed2026

Proteomic and phosphoproteomic profiles of time-dependent dynamic changes in LPS-induced macrophage polarization.

The temporal proteomic and phosphoproteomic reprogramming during early M1 macrophage polarization (0-6 h) remains poorly understood. We performed time-resolved proteomic and phosphoproteomic analyses of LPS-stimulated RAW264.7 macrophages at seven time points within 6 h. Time-clustering of differentially expressed molecules revealed two patterns: initial change with partial recovery, and sustained dysregulation. Upregulated proteins and phosphorylation sites were enriched in the Rho GTPase signaling pathway, T-cell receptor signaling pathway, NF-κB cascade, osteoclast differentiation pathway, and antiviral immune pathway. Downregulated pathways were associated with cell cycle regulation, chromatin remodeling, RNA metabolism, and mRNA processing, indicating resource reallocation to prioritize acute inflammatory responses. Kinase-substrate network analysis confirmed the mitogen-activated protein kinase (MAPK), cyclin-dependent kinase (CDK), protein kinase B (AKT), and ribosomal S6 kinase (RSK) families as core upstream phosphorylation regulators. Integrated analysis revealed synergistic and antagonistic relationships between proteomic and phosphoproteomic changes. This study provides a temporal molecular atlas of M1 polarization, delineating inflammatory signaling dynamics and offering a basis for therapeutic target discovery in inflammatory diseases. SIGNIFICANCE: Macrophage M1 polarization is a central event in innate immune defense against pathogenic invasion, yet its dysregulation is a pivotal driver of the onset and progression of a broad spectrum of inflammation-associated disorders, spanning autoimmune diseases, infectious conditions and inflammatory bone diseases, making the dissection of its molecular regulatory mechanisms an urgent research priority in immunology and translational medicine. Dynamic molecular events within 0-6 h after LPS stimulation are critical for initiating and shaping M1 inflammatory activation, yet systematic time-resolved proteomic and phosphoproteomic profiling remains insufficient.In this study, we comprehensively characterized temporal proteome and phosphoproteome changes at seven consecutive time points during macrophage polarization, clarified two distinct dynamic molecular patterns, identified core signaling pathways and key kinase regulators involved in inflammatory reprogramming, and uncovered the leading role of post-translational phosphorylation modifications in initiating polarization. This work delineates the time-series molecular atlas of early macrophage activation, provides novel insights into the temporal regulatory mechanism of inflammatory signaling networks, and lays a solid experimental foundation for exploring new intervention targets and regulatory nodes in clinical translational research.

Lipopolysaccharides↗
PubMed2024

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Humans↗
PubMed2026

Limited Impact of Column Chemistry and Length on Proteome Coverage Under High-Speed DIA.

The evolution of mass spectrometry (MS)-based proteomics has been driven by continuous technological advances in sample preparation, liquid-phase separations, instrumentation, and data acquisition. Chromatographic performance has been recognized as a contributing factor to identification depth, particularly on earlier-generation MS platforms. Recent advances in MS sampling speed and sensitivity now raise the question of how strongly chromatographic quality continues to determine overall proteome coverage. We investigate how column chemistry and length influence proteome coverage and chromatographic selectivity under modern data-independent acquisition conditions, and whether traditional optimization priorities still apply. Spanning a matrix of experiments with five distinct stationary phases, including C18 chemistries, C8, and Phenyl-Hexyl, across eight column lengths (40-140 mm), we evaluate protein identification performance using data-independent acquisition on the Orbitrap Astral mass spectrometer. Despite differences in stationary-phase chemistry and column length, we observed remarkably convergent proteome coverage metrics. All C18 and C8 phases consistently achieved over 150,000 precursor- and approximately 9000 protein group identifications, regardless of column length variations. While retention fingerprints persisted across chemistries, these chromatographic differences did not translate into meaningful variations in proteome coverage under high-speed acquisition conditions at 200 Hz. Within the range of modern sub-2 μm reversed-phase materials tested, identification depth showed limited dependence on column chemistry and length, suggesting that for state-of-the-art stationary phases, method development priorities may increasingly favor operational robustness, throughput, and reproducibility over traditional separation optimization.

Proteome↗
PubMed2026

An Instrumental Optimization of a Label-Free Proteomic Method for Trace Protein Input.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics of trace-level samples, such as tens of cells or spatially resolved tissue regions, offers unique biological insights but is often constrained by the requirement for specialized, costly instrumentation. In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.

Proteomics↗
PubMed2026

Reframing Proteomics Measurement: Super Mass Spectrometry Framework and the Role of Delayed Electrospray Ionization Technique.

Dynamic range, repeatability, and reproducibility remain the central limitations of data-independent acquisition (DIA) proteomics. Current workflows emphasize protein group identification counts and throughput, but these metrics mask the fundamental measurement challenge: generating a repeatable, reproducible, high-fidelity, and relatively complete digital representation of complex proteomes. In particular, plasma proteomics spans more than 10 orders of magnitude in protein abundance, far exceeding the capacity and dynamic range of any single mass spectrometer. Incremental advances have not closed this gap. In this Perspectives article, I introduce the Super Mass Spectrometry framework and then highlight the Delayed Electrospray Ionization (Delayed-ESI) technique, as a practical approach to address these limitations. By producing compositionally identical but temporally staggered ion beams, the Delayed-ESI technique enables deterministic remeasurement of the same analyte profile, supporting various novel strategies to improve analytical figures of merit. While recent implementations of the Delayed-ESI technique have emphasized throughput, I argue that the broader value of the Delayed-ESI technique lies in extending dynamic range and improving repeatability and reproducibility─objectives that should take precedence if proteomics is to evolve into a robust measurement science capable of supporting population-scale proteomics studies.

Proteomics↗