Clinical proteomics最新文献

Purpose: Uveitis is an inflammatory ocular disease with diverse etiologies and pathogeneses. It potentially leads to significant visual impairment and socioeconomic burden. Proteomic analysis can provide insights into protein-driven mechanisms that may improve diagnosis, monitor disease progression, and identify therapeutic targets. Here, we summarize the proteomic results from studies investigating the aqueous and vitreous humor in eyes with uveitis versus non-inflammatory controls.

Methods: A comprehensive search of 15 databases was conducted on January 26, 2024. Studies were included if they performed proteomic analyses using mass spectrometry on aqueous or vitreous humor from uveitis patients. The selection, data extraction, and risk of bias assessment were performed independently by multiple reviewers, with a third reviewer consulted in case of disagreement. Six studies met the eligibility criteria, comprising 176 eyes of uveitis patients and 105 control eyes.

Results: Two proteins, complement C1q subcomponent subunit B and C1q subcomponent subunit C, were consistently upregulated in five studies, underscoring the role of complement activation in uveitis pathogenesis. Three additional proteins - alpha-2-HS-glycoprotein, apolipoprotein A-I, and alpha-1-antichymotrypsin - were upregulated in four studies, highlighting the significance of inflammatory modulation. Ceruloplasmin, an acute-phase reactant, was upregulated in four studies. Gelsolin kininogen-1, and alpha-1-antitrypsin were upregulated in three studies, indicating a pro-inflammatory shift towards increased vascular permeability and recruitment of inflammatory cells.

Conclusion: The identified proteome changes highlight central biological processes in uveitis, notably complement activation, acute-phase response, pro-inflammatory shift, and increased vascular permeability. The identified proteins can potentially support future diagnostic and therapeutic advances in uveitis.

Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) are widely used in MS-based proteomics. However, a comprehensive evaluation of their data characteristics-including protein and peptide identification, differential expression analysis, and the performance in revealing biological insights-remains lacking. In this study, we conducted a systematic comparison of DDA and DIA across three model sample types: one disease model, two drug-treated models, and their respective controls. Our analysis extended beyond conventional metrics such as total protein and peptide counts, precision, and accuracy, to include data completeness, detection of positive control markers, reproducibility, functional annotation reliability, and sources of methodological variation. The results demonstrated that DIA outperformed DDA in terms of protein identification (disease group: 7,735 vs. 5,067; drug-treated group 1: 7,987 vs. 4,605), quantitative coverage (average quantifiable protein ratio: DIA 98-99% vs. DDA 95-96%), and reproducibility (intragroup correlation coefficients: DIA > 0.98 vs. DDA 0.93-0.98). We also found DIA exhibited lower variability (intragroup CV < 10% vs. > 15% for DDA) and improved accuracy for low-abundance and housekeeping proteins. Additionally, the functional enrichment analyses further revealed DIA's superior capability in detecting pathway activation. Finally, discrepancies between DIA and DDA were primarily attributed to proteins identified with ≤ 5 peptides, the exclusion of single-peptide proteins enhanced overall data quality. Overall, this study systematically assess the overall capabilities of DDA and DIA approaches in uncovering biologically relevant findings and driving mechanistic insights within authentic pharmacological and disease models, thereby offering practical guidance for methodological choices in future research.

Background: Epilepsy is one of the most common neurological disorders in humans and in dogs. Treatment currently focuses on alleviating symptoms, and a wide range of anti-seizure medications (ASMs) is available. Still, over one-third of patients have an inadequate response to ASM. The proteome of cerebrospinal fluid (CSF)-derived extracellular vesicles (EVs) offers a potential source of biomarkers for drug-resistant epilepsy (DRE).

Methods: We utilised a spontaneous canine epilepsy model to study the proteomic content of CSF-derived EVs as a source of biomarkers for DRE. We included 37 drug-naïve dogs with recent onset epilepsy and confirmed diagnosis of idiopathic epilepsy. CSF samples were collected at the onset of epilepsy. After the first visit, ASM treatment was started in all dogs and they were followed up for at least 12 months. After the follow-up period, based on their response to ASM treatment, dogs were grouped as either drug-responsive or drug-resistant. We isolated CSF-derived EVs with ultrafiltration combined with size-exclusion chromatography and then performed proteomic analysis with liquid chromatography-tandem mass spectrometry. A comparison between the drug-responsive and drug-resistant dogs was conducted regarding clinical factors and CSF-derived EV proteomic data.

Results: Younger age at seizure onset and occurrence of cluster seizures were identified as risk factors for drug-resistance. The proteomic analysis of normalised data identified five proteins with differential abundance between the two groups: KRT4, an uncharacterised immunoglobulin-like domain-containing protein (IgDCPa), F2, DSC1b, and LOC607874. A receiver operating characteristic analysis was performed, revealing a predictive value of ≥ 0.90 for two combinations of three proteins (KRT4, IgDCPa, and F2 (area under curve (AUC) = 0.91, confidence interval (CI) = 0.78-1.00); DSC1b, F2, and IgDCPa (AUC = 0.90, CI = 0.78-1.00)).

Conclusions: Proteins with differential abundance studied here are associated with epilepsy due to their potential involvement in critical processes such as neuroprotection, inflammation, cell integrity, and immune response. The observed reduction in the abundance of these proteins in drug-resistant dogs suggests that disruptions in these processes may contribute to the severity of the condition and its resistance to treatment. Results from this pilot study warrant further study in a larger cohort.

Background: The complement system plays a crucial role in immune regulation and inflammation, contributing to intervertebral disc degeneration (IDD) pathogenesis. While tissue-specific complement activation in degenerated discs is well-documented, its systemic expression in degenerative disc disease (DDD) plasma remains unclear.

Methods: This study employed high-throughput mass spectrometry to analyze the plasma and tissue proteomes of 40 DDD patients, comprising of Modic change (MC) and non-Modic change (NMC) patients, alongside 20 healthy volunteers (HV). Only plasma and no plasma-matched tissue samples were collected from HV group.

Results: Proteomic analysis identified 707 proteins in DDD plasma and 655 in HV plasma, with 508 common. Complement and coagulation cascades were enriched, with 46 complement proteins identified. The DDD-plasma group exhibited upregulation of most complement proteins, except for C1q complex (1.38-fold) and Complement Factor D (CFD, 0.64-fold), alongside slight downregulation of vitronectin (VTN), clusterin (CLU), and complement C8G. Elevated C-reactive protein (CRP) levels were observed in DDD-plasma, indicating systemic inflammation. Correlation analysis revealed weak associations between plasma and tissue complement protein levels, suggesting potential regulatory mechanisms.

Conclusion: Our study reveals that systemic complement alterations in DDD, supporting their potential as blood-based biomarkers reflecting localized disc pathology. Further validation in larger cohorts across different disease stages is needed to explore their diagnostic and therapeutic implications. Data are available via ProteomeXchange with identifier PXD063403.

Background: Osteoarthritis (OA) is a prevalent musculoskeletal disorder causing chronic pain and disability, particularly in older adults. It is a multifactorial disease characterized by joint degeneration, with varying pathophysiological mechanisms across different OA subtypes (knee, hip, spine, hand, etc.). This study aimed to explore the genetic mechanisms underlying various OA subtypes using a novel approach combining protein level ratios (rQTLs) with Mendelian Randomization (MR) analysis.

Method: We utilized publicly available Genome-Wide Association Study (GWAS) datasets on rQTLs as exposure variables and OA at various anatomical sites as outcome variables. The study involved conventional multi-related-SNP MR analyses, top-related-SNP MR analyses, advanced Bayesian MR analyses, sensitivity analyses and experiments to validate findings.

Results: Key findings include significant associations between specific rQTLs and hip OA, such as DNMBP/FKBP5 and MME-related ratios, indicating their potential role in disease pathogenesis. For knee OA, rQTLs like INPP1/MPI were associated with increased risk, while FABP5/PPCDC and LYN/TACC3 were associated with reduced risk. In contrast, most rQTLs showed minimal influence on spine OA, hand OA, finger OA, and thumb OA. Advanced Bayesian MR analyses, sensitivity analyses and experiments confirmed a significant causal effect of the DNMBP/FKBP5 ratio on hip OA risk.

Conclusions: This study provides new insights into the genetic and molecular mechanisms of OA subtypes, highlighting potential therapeutic targets. The integration of protein ratio GWAS with network MR offers a comprehensive approach to understanding the complex pathogenesis of OA and emphasizes the need for subtype-specific therapeutic strategies.

Background: Formalin-fixed paraffin-embedded (FFPE) tissue proteomics has emerged as a promising approach for precision medicine, offering access to vast clinical archives. Despite technological advances enabling identification of thousands of proteins from FFPE samples, no proteomic diagnostic tests based on FFPE tissues have achieved regulatory approval for clinical diagnostics, raising fundamental questions about the translational viability of this approach.

Main body: This review critically evaluates the realistic barriers preventing clinical translation of FFPE proteomics and identifies targeted applications with genuine promise for near-term implementation. We demonstrate that while comprehensive discovery-based proteomics faces insurmountable challenges including validation failure rates exceeding 90%, targeted proteomic strategies focused on specific clinical questions show substantially greater potential. Current implementation barriers extend beyond technical limitations to encompass economic constraints (5-10-fold higher costs than immunohistochemistry), regulatory uncertainties, and fundamental incompatibilities with clinical laboratory workflows. The persistent emphasis on increasingly complex analytical platforms may represent misallocated resources given unresolved standardization and validation challenges.

Conclusion: Strategic redirection toward targeted proteomic applications addressing specific diagnostic needs, rather than comprehensive molecular profiling, offers the most viable pathway for clinical translation. Success will require prioritizing applications where FFPE proteomics provides unique, actionable information that justifies its complexity and cost relative to established methodologies. We propose specific criteria for identifying high-impact applications and outline a pragmatic roadmap for achieving clinical implementation within realistic timeframes.

Cerebrospinal fluid (CSF) provides a unique window into brain pathology, yet challenges in unbiased mass-spectrometric (MS) discovery persist due to sample complexity and the need for optimized analytical workflows. Multiple laboratory workflows have been developed for CSF proteomics, each with distinct advantages for specific applications. To interrogate which laboratory workflow is most suitable for this biological matrix, we benchmarked five orthogonal sample-preparation strategies- MStern, Proteograph™ nanoparticle enrichment (Seer), N-glycopeptide capture (N-Gp), and two extracellular-vesicle (EV) fractions isolated by differential ultracentrifugation (P20- and P150-EV)- in CSF from 19 patients with central nervous system lymphoma. The protocols span a practical spectrum of input volume (6000-50 µL), hands-on time, and reagent cost, enabling informed method selection for translational applications. In total we performed 82 LC-MS/MS experiments and detected over 38,000 unique peptides and more than 3000 proteins across all modalities. Seer achieved the best proteomic depth (~ 17,000 unique peptides) across samples, followed by P20-EV (~ 9,000), MStern (~ 5,500), P150-EV (~ 5,000), and N-Gp (~ 1,000). None of the methods introduced systematic bias in peptide or protein isoelectric point or hydrophobicity, yet each selectively highlighted distinct biological niches: P20-EVs favoured mitochondrial signatures, N-Gp capture lysosomal and plasma membrane signatures and Seer enhanced nuclear representation. These findings demonstrate that no single protocol suffices for every research question; instead, workflow selection should align with sample-volume constraints, budget and biological question. Our comparative framework empowers investigators to match CSF proteomics strategies to specific neuro-oncological objectives, thereby accelerating the translation of CSF biomarkers into clinically actionable assays.

Glycosaminoglycans (GAGs) are linear polysaccharide chains that are usually linked to proteins to create proteoglycans and play an essential role in the extracellular matrix (ECM). Mucopolysaccharidoses (MPS) are a group of rare disorders that arise due to impairment in the breakdown of glycosaminoglycans (GAGs). Key technological advances in mass spectrometry (MS) have had a significant impact on the study and diagnosis of MPS, as well as its clinical management. This review summarizes the current applications of mass spectrometry-based approaches in MPS, emphasizing its role in the understanding of pathophysiological disease mechanisms, and towards improved patient care. Mass spectrometry-based proteomics and metabolomics have identified novel biomarkers and metabolic perturbations related to the pathophysiology of MPS. In addition, mass spectrometry-based glycomics analyses have been employed for the structural characterization of GAGs to reveal their heterogeneity. The sensitivity and specificity of liquid chromatography tandem mass spectrometry (LC-MS/MS) as compared to conventional methods for the quantitation of GAGs have revolutionized diagnostics. High-resolution mass spectrometers such as Orbitrap and Fourier transform ion cyclotron resonance, permit more accurate GAG characterization. Mass spectrometry has also proven valuable in monitoring patients undergoing treatment, thereby allowing the sensitive monitoring of the therapeutic efficacy of both enzyme replacement and gene therapies. Mass spectrometry has enabled improved newborn screening and multiplex assays for screening multiple MPS types. Despite the important contributions of mass spectrometry to enhance MPS research and clinical management, there still remain challenges related to long and complex sample preparation processes, lack of standardization and lack of accessibility in routine clinical settings. We envision that future initiatives will incorporate multiple omics technologies to obtain a more holistic view of the pathophysiology of MPS. Fortunately, mass spectrometry technologies and methods continue to evolve rapidly, promising further advancements in MPS diagnosis, monitoring of patients on therapy and research that should ultimately lead to improved patient outcomes and quality of life.

Background: Premature coronary artery disease (PCAD) is characterized by early onset, rapid progression, and poor prognosis, which seriously affects patients' health and quality of life. In this study, we analyzed the proteomic network and biological pathways of PCAD patients by bioinformatics methods, and mined out the key differential proteins, which provided a theoretical basis for clinical intervention.

Methods: Patients who attended the heart center of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to December 2024 and completed coronary angiography were selected. According to the relevant inclusion and exclusion criteria, a total of 129 patients were included, including 69 in the PCAD group and 60 in the control group. The clinical baseline data of the patients were systematically analyzed. Plasma protein extraction, trypsin digestion and mass spectrometry were completed. The mass spectrometry data were initially separated with the help of proteomics software, and the differential proteins were functionally enriched by RStudio software. Protein interaction networks were constructed by STRING platform and core differential proteins screened were visualized using Cytoscape software (MCODE plug-in).

Results: Differences in gender, smoking, alcohol consumption, hypertension, diabetes, HDL-C, Glu, FIB, LPa, NT-pro-BNP, PCT, and IL-6 were statistically significant (P < 0.05). Sex (P = 0.009, OR = 6.782,95% CI: 1.600-28.746), FIB (P = 0.001, OR = 2.662,95% CI: 1.471-4.818), and LPa (P = 0.041, OR = 1.002,95% CI: 1.000-1.004) were independent risk factors for PCAD. A total of 348 up-regulated proteins and 92 down-regulated proteins were screened by bioinformatics analysis. The occurrence of PCAD is associated with protein synthesis, intercellular communication, molecular interactions, ribosomal metabolism, glyoxylate and dicarboxylic acid metabolic pathways. Ribosomal and translational proteins influence the development of PCAD.

Conclusion: In this study, we found that gender, FIB, and LPa are risk factors for PCAD. The analysis identified 348 up-regulated and 92 down-regulated proteins. Among them, the differentially expressed proteins DHX9, F7, APCS, and PROC were closely related to the biological process of PCAD. The screened ribosomal and translational proteins showed high-frequency associations in protein-protein interaction networks, providing potential differentially expressed proteins for a deeper understanding of the disease.