Journal of Proteome Research最新文献

The aggregation of amyloid-β (Aβ) into fibrils is a molecular hallmark of Alzheimer disease (AD). Cryo-EM results show that Aβ fibrils are polymorphic, with distinct molecular fibril structures depending on the AD subtype. Here, we used a mass spectrometry-based chemical protein footprinting method that is named covalent protein painting (CPP) to obtain conformational information on filamentous Aβ in tissue samples of aged persons without dementia (5), with AD (5) or cerebral cardiovascular angiopathy (CAA, 1) or AD with CAA (3). We measured and quantified the relative abundances of Aβ40 and Aβ42 polymorphs with a newly developed CPP-Aβ-PRM assay. The CPP-Aβ-PRM assay differentiates four conformational states per Aβ peptide, which allows us to deduce the type of Aβ fibril polymorph in the sample. The polymorph composition differed in the brain and meninges. Aβ42 type I polymorphs correlated with sporadic AD in the absence of amyloid angiopathy, whereas Aβ40 type II polymorphs were enriched in the meninges of patients with CAA. The presence of Aβ40 type I polymorphs in brain parenchyma might indicate a type 1 CAA phenotype in patients with AD. We show that the CPP-Aβ-PRM assay might be a novel method to quantify Aβ polymorph information in AD patient samples.

Plasma is an ideal material for proteomics due to its diverse protein content reflecting physiological and pathological states and its compatibility with minimally invasive sampling. Deep proteomic profiling of plasma is limited by high-abundant proteins that mask the detection of low-abundant proteins. To overcome this, we compared five plasma protein enrichment methods, Mag-Net, ENRICHplus, ENRICHiST, EasySep, and EXONET, against neat plasma using LC–MS proteomics. All five methods substantially increased protein identifications, with Mag-Net, ENRICHplus, EasySep, and EXONET yielding up to 4200 proteins per sample, over 7-fold more than neat plasma, using a 44 min gradient on the Evosep One and data-independent acquisition on the timsTOF Pro 2. These methods enriched extracellular vesicle-associated proteins while effectively depleting high-abundant proteins. To further enhance performance and scalability, we optimized the Mag-Net protocol by increasing the plasma-to-bead ratio and automated the workflow, including Evotip loading, on the Biomek i5 liquid handler. The automated Mag-Net, combined with the Orbitrap Astral mass spectrometer, yielded up to 4500 proteins per sample with a throughput of 100 samples per day. The workflow demonstrated high reproducibility and a remarkably low total cost of just a few dollars per sample. Newer enrichment methods (Proteonano, P2-iST Plasma, and P2) showed improved plasma proteome coverage compared with Mag-Net but are likely to incur higher costs. The streamlined Mag-Net enrichment strategy enables affordable, scalable, high-throughput LC–MS plasma proteomics, supporting biomarker discovery across large cohorts.

Plasma serves as a crucial source of circulating protein biomarkers for advancing resuscitation strategies in sudden cardiac arrest (SCA). However, emergency blood collection under the SCA introduces preanalytical variables that may affect proteomic analyses. This study assessed plasma protein stability under blood-handling conditions that simulate real-world first responder scenarios. Blood from four individuals was aliquoted and subjected to different (a) initial storage conditions and (b) delays after collection, simulating first responder blood-handling scenarios. Plasma was analyzed using data-independent acquisition mass spectrometry (DIA-MS) for global proteomics and parallel reaction monitoring (PRM-MS) for 16 SCA biomarker candidates. Global proteomic variation was primarily driven by interindividual differences, with less influence from storage conditions or delays. Notably, 87.5% of the candidate biomarkers (14/16) remained stable under ideal storage (4 °C storage immediately after blood collection), and 62.5% (10/16) retained stability even under the “worst-case” conditions (room temperature (RT) storage for 8 h after blood collection) for up to 24 h. However, several proteins lost stability with delayed processing even under ideal storage. Our study highlights the critical importance of processing blood within 24 h of collection and accounting for protein stability to preserve the reliability of biomarkers for improving resuscitation strategies in SCA.

Motile cilia are complex structures regulated by thousands of genes, essential for various physiological functions like respiration and reproduction. Their dysfunction can result in severe conditions like primary ciliary dyskinesia (PCD), highlighting the need for a deeper molecular understanding of their specific ciliary compartments. Interestingly, ciliated cells harbor multiple proteins with limited evidence on biological function, as defined by Functional Evidence (FE) scores, a grading system developed by the Human Proteome Project (HPP). Building upon the stringent antibody validation pipeline of the Human Protein Atlas (HPA) project, we developed a high-throughput workflow that combines a novel multiplex immunohistochemistry protocol with image analysis to investigate protein expression and subcellular localization in motile ciliated cells across five human tissues: nasopharynx, bronchus, fallopian tube, endometrium, and cervix. We spatially mapped >180 proteins, out of which 73% have FE scores 2–5, suggesting that further evidence is needed to establish these proteins’ biological function. Notably, expression patterns varied between tissues, suggesting that motile cilia proteins are not universally expressed across the different epithelia. Our pipeline constitutes a promising resource for comprehensive mapping of the motile cilia proteome, and a first step toward identifying cilia proteins for functional studies to understand the molecular mechanisms underlying ciliopathies.

Trypanosoma cruzi, the causative agent of Chagas disease, poses a significant health challenge due to limited therapeutic options and an incomplete understanding of its biology. Approximately half of the genome encodes hypothetical proteins with unknown functions, underscoring the need for systematic functional annotation. Protein–protein interactions (PPIs) underpin essential cellular processes, yet no large-scale PPI map has been developed for T. cruzi ─a critical gap that impedes both functional annotation of its proteome and drug discovery. This study presents the first comprehensive PPI network for T. cruzi, constructed using quantitative mass spectrometry-based cofractionation data. The network includes 1319 proteins and more than 16,000 predicted interactions, with 47% of the proteins classified as hypothetical, consistent with the 49% hypothetical annotation rate in the proteome. Their placement within functionally enriched network modules provides unprecedented insights into their potential biological roles. Network analysis revealed densely interconnected cores enriched with essential cellular functions. This PPI network exhibits small-world properties, with conserved proteins showing higher connectivity, reinforcing their central roles in the parasite’s biology. This resource, publicly available at https://2025.trypsnetdb.org/, offers a powerful platform for exploring T. cruzi biology and prioritizing novel therapeutic targets, revealing central hubs of protein organization, resolving ribosomal and proteasomal complexes, and enabling functional predictions for numerous hypothetical proteins through integrative structural modeling.

The identification of drug targets is fundamental to drug development. Traditional affinity-based target screening methods, including chemical biology probes and biotin labeling techniques, rely on the presumption of pre-existing knowledge of targets, thereby limiting their ability to uncover novel mechanisms of action. Recently, limited proteolysis combined with mass spectrometry (LiP-MS) has emerged as a hypothesis-free approach. By detecting drug-induced conformational alterations in proteins and integrating these observations with high-throughput mass spectrometry analysis, LiP-MS enables target identification without prior chemical modification. This article presents a comprehensive review of the underlying principles and workflow of LiP-MS, focusing on recent advancements, existing challenges, and strategies for its integration with complementary technologies. Furthermore, it delineates the advantages of LiP-MS relative to conventional proteomic methods and summarizes drug targets identified through LiP-MS in recent studies.

Pancreatic ductal adenocarcinoma (PDAC) with negative carbohydrate antigen 19-9 (CA19-9) lacks effective diagnostic and monitoring indicators. We aimed to identify metabolic characteristics in such patients through metabolomics and explored potential plasma biomarkers. In this study, untargeted metabolomics analysis was performed on plasma samples from 30 healthy controls and 30 CA19-9 positive and 30 CA19-9 negative PDAC patients by using liquid chromatograph–mass spectrometer (LC–MS) technology. Compared with healthy controls, 36 differential metabolites were upregulated, and 3 differential metabolites were downregulated in the plasma of CA19-9 negative PDAC patients. Four upregulated differential metabolites, including palmitic amide, dolichol phosphate, cholesteryl acetate, and vitamin A2 aldehyde, were potential biomarkers for diagnosing CA19-9 negative PDAC patients. Enrichment analysis of KEGG pathways showed that glycine and threonine metabolism, tyrosine and tryptophan biosynthesis, thiamine metabolism, etc., were disrupted in CA19-9 negative PDAC patients. In summary, we determined the metabolic heterogeneity in PDAC patients with normal CA19-9 levels. The detected metabolic biomarkers may offer new approaches for the clinical early diagnosis of CA19-9 negative PDAC in the future.

While plasma and serum are widely used in high-throughput proteomics, the impact of different blood matrix types remains underexplored. Routine diagnostics most commonly use serum or Li-heparin plasma, while the proteomics community primarily focuses on advancing analytical depth in EDTA plasma. Here, we systematically investigated the LC–MS/MS proteomic profiles of pooled blood samples from three healthy, voluntary probands including serum (with/without separation gel) and plasma anticoagulated with EDTA, citrate, or Li-heparin. Sample preparation was conducted with the commercially available iST and ENRICH-iST kits, strong-anion exchange (SAX) beads, the TFA-based approach SPEED, and perchloric acid (perCA) precipitation. Mass-spectrometric measurements were performed on a Q Exactive HF-X and a timsTOF HT in data-independent acquisition mode (DIA). Protein identifications varied considerably across matrix types with EDTA plasma and serum outperforming citrate plasma. Sample preparation methods with SAX beads, ENRICH-iST, and perCA yielded the highest identification numbers but also showed increased variability. Across all samples, 181 protein groups overlapped for timsTOF HT data. Subsets of protein groups were specific for the matrix and preparation. This study shows a systematic approach to determining suitable sample preparation and matrix parameters for the robust identification of individual body fluid marker proteins by mass spectrometry.

TP53 mutation-driven gene expression programs define oncogenic phenotypes. While extensive studies have concentrated on the transcriptome and proteome, post-transcriptional processes, particularly translational variation, remain underexplored. This study presents a comprehensive analysis of the transcriptomics, translatiomics, and proteomics dynamics in the ovarian cancer cell line SKOV3, with a focus on the effects of p53 missense mutations (R175H, R273H, and Y220C) on gene dosage fluctuations. Despite clear transcriptional differences between wild-type and mutant p53, we find that extensive translational and post-translational buffering processes attenuate these discrepancies, yielding comparatively stable protein abundances. Moreover, we delineate that the relative contributions of transcription output, translation engagement, and protein stability collectively shape the final protein abundance in the context of p53 mutations. Clinical proteomic analysis of platinum-resistant ovarian cancer tissues reveals tumor-specific factors and acquired resistance pathways linked to p53 mutations. Our findings elucidate the multilayered regulatory landscape of p53 mutations and identify potential risk factors for platinum resistance associated with these mutations.

Plasma proteomics has been explored extensively for biomarker discovery for both diagnostic and therapeutic purposes. The gold standard for plasma proteomics sample preparation requires immediate plasma isolation following blood collection, which poses logistical challenges in many clinical settings. To evaluate the feasibility of delayed plasma processing, we investigated the impact of prolonged on-ice storage (i.e., 0/4/8 h postcollection) on plasma proteomic profiles using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) workflow. Each sample was analyzed by a label-free quantitative data-independent MS/MS method against a project-specific spectral library in technical replicates without fractionation, identifying 752 to 1,000 (903 ± 58) protein groups within each sample. While 72–86% of protein groups were qualitatively identified in samples of all three preparation time conditions from the same donor, quantitative analysis revealed significant alterations in the plasma proteome with as little as a 4-h delay in plasma preparation. Notably, von Willebrand factor (VWF), a plasma biomarker currently used in several clinical tests, was found to have decreased levels in samples with delayed processing. Peptide-level analysis revealed that several VWF-derived peptides were less susceptible to degradation during storage. These findings suggest that monitoring stable peptide markers, rather than intact proteins, may provide a more accurate reflection of the in vivo proteomic state and enhance biomarker reliability. Advancing the development of robust, disease-specific peptide panels will be key to realizing the full potential of clinical proteomics for precise and predictive diagnostics.