Expert Review of Proteomics最新文献

Introduction: Ovarian cancer is the most lethal gynecologic malignancy and has seen little progress in early detection and treatment. Mass spectrometry-based proteomics is a powerful technique that can be used to understand tumor biology and identify novel biomarkers that could transform diagnosis, prognosis, and treatment.

Areas covered: This review highlights recent applications of proteomics in ovarian cancer research. Tissue studies have defined histotype-specific pathways and spatial proteomics focuses on intratumoral heterogeneity. Biofluid studies are growing with exciting potential for minimally invasive diagnostics. Post-translational modification profiling has explored signaling alterations and mechanisms of resistance. Proteogenomic integration has improved tumor classification, revealing protein-level alterations and regulatory mechanisms not captured by genomics. Literature was drawn mostly from studies of the past five years, with emphasis on translational applications.

Expert opinion: Proteomics has developed into a tool capable of providing clinically relevant, valuable insight. However, translation will depend on validation and standardization. Continued integration with other omics is critical for moving discoveries from the laboratory to the clinic. Importantly, there is an unmet need for proteomic analysis of less common subtypes, as seen by the bias of this review toward HGSOC.

Introduction: Middle-down proteomics (MDP) bridges bottom-up and top-down proteomics, analyzing 3-10 kDa peptides to enhance sequence coverage and post-translational modification (PTM) localization. This approach is crucial for decoding complex proteoforms and PTM networks, advancing insights into biological and disease processes. However, its application to complex samples like cell lysates or biofluids remains largely underexplored.

Areas covered: This review examines MDP's potential in complex biological samples, focusing on sample preparation, chromatography, mass spectrometry, and bioinformatics. We explore sample lysis, protein precipitation, and alternative proteases (GluC, thermolysin), supported by in-silico analyses revealing peptide length and charge distribution as key limitations for current enzymes. Advanced chromatographic techniques, ion mobility (FAIMS, TIMS), and fragmentation methods (ETD, EThcD) are discussed. Experimental challenges include peptide solubility, ionization efficiency, and bioinformatic complexity from missed cleavages and promiscuous protease specificity.

Expert opinion: MDP offers significant potential to uncover the 'dark' proteome, including PTM-rich regions and proteoforms undetectable by traditional workflows. However, a focused effort on improving high-throughput workflows will require optimizations to enzyme selection, LC-MS parameters, peptide ionization, ion mobility, ion fragmentation, and tailored algorithms are essential to drive MDP's adoption. Only then will deeper proteomic insights and breakthroughs in biological research be obtained.

Background: Kidney transplantation is the preferred therapy for end-stage renal disease (ESRD), yet long-term graft survival remains limited. Effective preservation of transplanted kidneys is essential amid a persistent organ shortage. Post-transplant graft injury arises from both immune and nonimmune mechanisms. Advances in multi-omic technologies have increasingly unraveled pathways underlying graft rejection and tolerance. While most studies have centered on intragraft immune-cell infiltration and circulating biomarkers, the role of antibodies in acute rejection and other graft injuries is not fully studied.

Research design and methods: In this exploratory study, we employed a high-throughput antibody-profiling microarray to quantitatively assess IgG antibodies against Human Leukocyte Antigen (HLA) and non-HLA (nHLA) in urine samples from kidney transplant recipients experiencing graft injury, including acute rejection.

Results: We identified multiple HLA and nHLA antibodies that were selectively enriched in urine at the time of graft injury and acute rejection, indicating antigen-specific humoral responses detectable at the site of injury.

Conclusions: This first-of-its-kind urinary antibody-profiling study reveals promising antibody signatures associated with graft injury. These findings support the potential development of noninvasive, personalized biomarkers for routine monitoring and earlier detection of rejection in kidney transplant recipients.

Introduction: Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations.

Areas covered: In this perspective, we critically examine the application of machine learning for biomarker discovery in clinical proteomics, emphasizing that algorithmic novelty alone cannot compensate for issues such as small sample sizes, batch effects, overfitting, data leakage, and poor model generalization.

Expert opinion: We caution against the uncritical application of complex models, such as deep learning architectures, that often exacerbate these problems, offering limited interpretability and negligible performance gains in typical clinical proteomics datasets. Instead, we advocate for the realistic and responsible use of machine learning, grounded in rigorous study design, appropriate validation strategies, and transparent, reproducible modeling practices. Emphasizing simplicity, interpretability, and domain awareness over hype-driven complexity is essential if machine learning is to fulfill its translational potential in the clinic.

Introduction: Clinical proteomics has become a pivotal component of precision medicine, significantly advancing the understanding of disease mechanisms and informing therapeutic strategies. This review explores how clinical proteomics is transforming diagnostic and therapeutic approaches across multiple fields.

Areas covered: This review highlights recent developments and applications of clinical proteomics in cardiovascular and neurological disorders, as well as its impact on drug development. Technologies such as mass spectrometry and protein microarrays have enhanced diagnostic precision, facilitated the discovery of novel biomarkers, and uncovered new therapeutic targets. In cardiovascular medicine, proteomics supports early disease detection and patient risk stratification, while in neurology, it helps identify disease-specific protein signatures that guide targeted interventions. The integration of proteomics with databases like Universal Protein Resource (UniProt) and the Human Protein Atlas, alongside the use of advanced bioinformatics tools, has streamlined data analysis and accelerated the design of personalized therapies.

Expert opinion: Clinical proteomics is rapidly evolving, offering unprecedented opportunities to refine diagnostics, personalize therapies, and improve patient outcomes. Overcoming current challenges in standardization and validation will be essential for its full integration into clinical practice.

Introduction: The human gut microbiome (HGM) profoundly influences human physiology. In recent years, it has become clearer that a healthy HGM is much better defined by its functional profile than by its taxonomic composition. Metaproteomics is the optimal approach to assessing the functional profile of the HGM in a taxon-specific manner, offering a direct view of its biological activity.

Areas covered: First, we summarized the main wet lab and data analysis approaches used in gut metaproteomics. Next, we reviewed metaproteomic studies that have characterized the HGM of healthy adults. Lastly, we examined the functional changes induced in the HGM by specific dietary interventions.

Expert opinion: Current fecal metaproteomics provides an initial understanding of the roles of gut microbes in human health, revealing redundant and taxon-specific functions. Future research should prioritize standardization, large-scale studies, and integration with multi-omics to better understand HGM metabolism. Emerging technologies, advanced mass spectrometry platforms, and AI-driven analytics are expected to increase sensitivity and depth of gut metaproteomics, accelerating discovery and potential clinical applications.

Introduction: Recent advances in multi-omic technologies and computational tools have enabled comprehensive studies of cancer that integrate proteomics, genomics, transcriptomics, and metabolomics to improve disease understanding and outcomes.

Areas covered: 1. Recent improvements in throughput and decreasing sample mass requirements have enabled deep analysis of hundreds of human samples in multi-omic studies, increasing the statistical rigor of these studies and facilitating comparisons across clinical and demographic categories.2. Despite advances in statistical modeling, machine learning, and pathway-aware analysis, the principal outcome from these observational studies remains correlational-strong statistical associations between omic features and clinical characteristics, including clinical outcomes.3. Demonstration of causal relationships requires multi-pronged mechanistic experiments involving techniques in molecular and cellular biology that are distinct from the analytical and computational skills needed to generate these datasets.

Database used: National Library of Medicine PubMed database.

Expert opinion: True clinical utility depends on the demonstration of causal relationships between candidate targets and the biomedical process of interest. Enhanced collaboration with molecular and cellular biologists skilled in the use of modern tools of genetic manipulation and engineered model systems is required to realize the full translational potential of even the most comprehensive multi-omic studies.

Introduction: Targeted quantitative proteomics is vital for accurate protein measurement in biological samples. Techniques like Multiple Reaction Monitoring (MRM or SRM) and Parallel Reaction Monitoring (PRM), often used with isotopically labeled internal standards, provide absolute quantification, and represent the current gold standard. However, developing and validating assays for individual proteins remains labor-intensive. Several repositories, such as CPTAC, SRMAtlas, PanoramaWeb, and PeptideTracker host targeted assay data with varying levels of detail. MRMAssayDB is an integrated platform that hosts and annotates the curated targeted proteomics assays from these resources.

Areas covered: First launched in 2018 and updated in 2021, the latest release of MRMAssayDB includes over 1.1 million assays for 939,000 peptides, enabling quantification of 61,000 proteins from 146 organisms. The database also maps proteins to 19,000 Gene Ontology terms and 4,000 biological pathways. A newly integrated visualization module projects peptide assays onto Alphafold-predicted 3D protein structures, allowing users to examine peptide locations, post-translational modifications, and disease mutations while also supporting mapping to structures in the Protein Data Bank (PDB).

Expert opinion: MRMAssayDB significantly improves access to validated proteotypic peptides and transition data, facilitating efficient assay selection and quantitative panel building for researchers in targeted proteomics. Availability: http://mrmassaydb2.proteomicscentre.com.

Background: Streptococcus pneumoniae meningitis (SPM) is a critical pediatric infection with a high mortality rate. This study aimed to investigate changes in the cerebrospinal fluid (CSF) proteome in SPM patients and identify biomarkers for SPM diagnosis and treatment monitoring.

Research design and methods: Here, we retrospectively collected and evaluated CSF proteomes of 47 SPM and 116 non-CNS-infected patients (Control), including longitudinal samples from 11 SPM patients. Candidate biomarkers were validated by parallel reaction monitoring (PRM) in 146 samples (36 longitudinal/12 SPM, 110 Control) and evaluated via receiver operating characteristic (ROC) analysis.

Results: We identified 648 differentially expressed proteins (DEPs) associated with complement activation and oxidative stress. SPM patients with abnormal CSF white blood cells (SPMAN) exhibited dysregulation in coagulation and fibrinolysis, while those with normal counts (SPMN) displayed redox homeostasis alterations. The ELANE and H4C1 panel achieved a superior diagnostic accuracy (AUC = 0.967), and the combination of IGKV1-17, IGKC, and IGKV4-1 effectively tracked therapy response (AUC = 0.872).

Conclusion: This study establishes CSF proteomic signatures of pediatric SPM, providing dual-purpose biomarker panels for clinical diagnosis and treatment monitoring, with implications for targeted interventions.