搜索结果：Expert review of proteomics

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. 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. 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.

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. 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. 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.

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery. We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS. Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Advances in various proteomics technologies, especially high-throughput and reproducibility, have enabled the systematic exploration of the circulating thrombosis proteome. This includes dissecting biological systems and pathways imperative to thrombosis, such as platelet activation, coagulation cascade, complement system, and endothelial cells. These insights strengthen our understanding of the cause and effect of thrombosis and improve precision medicine by identifying better biomarkers and biomarker panels, which may aid clinicians in decision-making in venous thromboembolism (VTE) and other thrombotic patients. This progress has the potential to reduce thrombosis-related morbidity and mortality, ultimately improving patient quality of life. This review highlights recent advances and applications of mass spectrometry and affinity-based proteomics in thrombosis over the past three years (2022-2024), focusing on the thrombotic proteome signature related to VTE. Plasma proteomics, predominantly driven by mass spectrometry and affinity-based proteomics, has shown promise in identifying novel disease biomarkers and pathways. With the recent advances in the field, proteomics holds the potential to revolutionize precision medicine. As thrombosis is an intravascular disease, analysis of the blood proteome can capture environmental, genetic, and epigenetic contributors to risk variation in thrombosis, revealing novel protein biomarkers for diagnosis and risk prediction and new biological pathways.

Early diagnosis is essential for a good prognosis of patients with endometrial cancer; however, there are currently no noninvasive tests available. Despite the good prognosis with early diagnosis, a significant minority of women will recur, and biomarkers are needed to stratify patients according to their risk of recurrence. In recent decades, the discovery of blood biomarkers to facilitate diagnosis and improve risk stratification of EC patients has been actively pursued.  The present review is an update of candidate blood biomarkers for the diagnosis and prognosis of endometrial cancer reported in the past eight years, following an earlier review. The literature search was conducted in the PubMed database for the period between July 2016 and September 2024. This review describes studies investigating tumor markers, proteins, metabolites and miRNAs and their diagnostic and prognostic properties. The quality of the included studies is assessed and the limitations and potential for translation into clinical application are discussed. Individual biomarker candidates do not offer optimal diagnostic and prognostic characteristics. The use of omics for biomarker discovery is promising, but development in this area is lagging behind due to methodological issues and a lack of external validation. If endometrial cancer is detected early, patients have the best chance of a good outcome. But at the moment there is no simple, noninvasive test to help doctors do this.What’s more, in some women the cancer recurs even if it is detected early and does not appear to be aggressive. Therefore, doctors need better tools to identify which patients are more likely to have cancer recurrence so they can get the right treatment from the start.This review looks at the latest research over the last eight years into possible blood tests that could help detect endometrial cancer or predict how it will progress. The researchers searched the PubMed medical database for studies published between July 2016 and September 2024. The review includes different types of molecules in the blood— - such as tumor markers, proteins, small molecules and miRNAs – that could help doctors diagnose the disease or understand a patient’s outlook.We conclude that single biomarkers alone are not sufficient to reliably detect or predict endometrial cancer. New approaches that analyze many molecules simultaneously, known as ‘omics,’ promise better tests, but progress is slow. This is mainly due to the technical challenges and the fact that many early results have not yet been confirmed by other research teams.

Clinical proteomics is an evolving discipline that aims to identify new biomarker candidates of human disease to improve diagnosis, prognosis, treatment monitoring and the discovery of novel therapeutic targets. This article outlines the pathoproteomic characterization of dystrophinopathies, which are classified as muscle wasting diseases due to mutations in the DMD gene. The Special Report focuses on the proteomic profiling of progressive Duchenne muscular dystrophy of early childhood and more benign and later-onset Becker muscular dystrophy. The literature search on proteomics and biomarker discovery in dystrophinopathies was conducted with the standard scientific literature databases PubMed and Google Scholar for the preparation of the general text of this article. This report has outlined the biomedical value of the systematic proteomic characterization of tissue specimens and associated biofluids to establish novel biomarkers for monitoring clinical studies in muscular dystrophy. Both, mass spectrometry-based profiling approaches and high-plex/high-throughput proteomics platforms were shown to be suitable for the systematic study of complex changes versus adaptations in dystrophinopathy. The verification of promising disease indicators, especially minimally invasive biofluid markers of myonecrosis, chronic inflammation, disturbed energy metabolism and myofibrosis, using orthogonal methodology holds great potential for future clinical applications such as therapeutic monitoring.

The investigation of different proteoforms in clinical samples is a promising approach to elucidate the molecular mechanisms of diseases. Furthermore, proteoform analysis holds great potential for identifying disease-specific biomarkers and targets for personalized medicine. Despite advances in top-down proteomics (TDP) instrumentation, sample preparation and cleanup remain challenging. Work in this area has focused on developing rapid, cost-effective, and less-labor-intensive protocols aimed at minimizing the introduction of artefactual modifications to endogenous proteoforms or bias in proteoform recovery during sample processing. To inform the selection of sample processing approaches in clinical TDP, this review summarizes state-of-the-art targeted (i.e. affinity and non-affinity-based enrichment) and untargeted (i.e. gel-based fractionation) sample preparation protocols. In addition, currently available offline and online sample cleanup procedures (e.g. dialysis, solid-phase extraction, filter-aided sample preparation, precipitation, and solid-phase protein preparation) are reviewed, highlighting their effectiveness for desalting and/or detergent removal. TDP demonstrates great potential in the clinical setting due to its ability to capture disease-specific proteoforms commonly overlooked in traditional diagnostic assays. The establishment of standardized guidelines for reproducible clinical TDP workflows is essential to leverage advances in sample preparation techniques and analytical instrumentation to facilitate wider adoption of TDP for clinical applications.

Progressive supranuclear palsy (PSP) is a rare neurodegenerative disorder. The lack of comprehension about the pathogenesis of the disease, its heterogeneity, and the complex clinical evaluation in early stages, limit the development of effective treatments for PSP patients and highlight the need of further research on the field. In this work, we review the current knowledge of the physio- and neuropathology of PSP, its clinical features, diagnosis markers, and treatment options. We also compare the proteomic-based studies done to date in brain tissues as well as in cerebrospinal fluid and other non-cerebral samples, briefly describing the proteomic approach used and the biological findings obtained in each study. PSP is a complex neurodegenerative disorder marked by tau aggregation, glial dysfunction, and neuroinflammation. Although advances in neuroimaging and biofluid biomarkers have improved PSP diagnostic accuracy, no disease-modifying therapies are currently available. Promising avenues such as tau PET tracers, seed amplification assays, and advanced proteomic-based approaches are enhancing our ability to detect disease-specific tau pathology and hold the potential to provide novel biomarkers for earlier and more precise clinical diagnosis and treatment development that could transform the landscape of PSP.

Neutrophils are central effectors of innate immunity and key contributors to inflammation, host defense, and tissue injury across a wide range of physiological and pathological contexts. Due to their short lifespan, rapid activation, and extensive post-translational regulation, comprehensive molecular characterization of neutrophil function requires approaches that go beyond transcriptomics or marker-based analyses. This review summarizes how proteomic technologies have advanced the understanding of neutrophil biology by enabling unbiased, system-wide profiling of protein abundance, subcellular organization, post-translational modifications, and functional heterogeneity. We discuss global and subcellular proteomics, PTM-centric analyses, and emerging low-input and single-cell proteomic strategies, highlighting recent studies of infection, cancer, metabolic disorders, aging, autoimmune disease, and inflammation. The literature covered includes current large-scale quantitative proteomics, targeted PTMs, and integrative multi-omics studies in both human samples and relevant experimental models. Proteomics has established neutrophils as highly plastic and context-dependent cells whose functions are governed by coordinated remodeling of signaling, metabolism, and effector pathways. Future progress will depend on expanding neutrophil-specific PTM maps, improving low-input workflows, and integrating single-cell and spatial proteomics. Together, these advances are expected to redefine neutrophil functional states and accelerate translation toward clinically meaningful biomarkers and therapeutic strategies.

Protein tyrosine sulfation is of growing scientific interest due to its biological and clinical significance, yet it remains an underexplored post-translational modification (PTM). Catalyzed by Golgi-localized TPST1 and TPST2, tyrosine sulfation modulates protein-protein interactions and receptor-ligand binding in inflammation, hemostasis, immunity, and viral entry. Despite functional relevance, this modification is underrepresented in databases such as UniProt (accessed July 2025), in large part due to a lack of robust analytical strategies. Advances in mass spectrometry (MS)-based analyses have recently improved sensitivity of detection, expanding the known tyrosine 'sulfome.' Systematic profiling of sulfated residues can now be undertaken, expanding knowledge of their regulatory roles in both health and disease, and for pioneering new sulfation-targeted therapeutics. We review known biological roles of protein sulfation by TPSTs and approaches for characterization of sulfation of tyrosine and other residues such as cysteine. More broadly, we consider how these strategies might be useful in a clinical context. High throughput MS-based proteomics has proven invaluable for the discovery of PTMs, advancing understanding of their roles in human health and disease. With recent advances in strategies for the characterization of protein sulfation, the field is now ready for exploration in a clinical context.

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. 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. 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.

The origin and evolution of the genetic code is a central problem in molecular biology. Classical models have emphasized stereochemistry, frozen accidents, or adaptive optimization, often treating proteins as passive products of preexisting codes. More recent views instead portray the code as a dynamic, coevolving system shaped by reciprocal interactions among amino acids, RNA, and early catalysts. Here, I review efforts of phylogeny reconstruction of the history of tRNA, protein structural domains, and dipeptide sequences in proteomes. These complementary approaches allow exploration of the entry of amino acids and codons into the code, and the transition from an operational RNA code in the tRNA acceptor arm to the canonical code in the anticodon loop. Evidence for ancestral synthetase enzymes with dual functions in aminoacylation and peptide-bond formation, as well as early bidirectional (sense-antisense) coding reflected in dipeptide-antidipeptide emergence is also discussed. The genetic code is best viewed as a proteome-driven, evolvable system in which early peptides actively shaped coding rules by stabilizing structure, expanding chemical diversity, and enhancing catalysis. This perspective connects origin-of-life studies with modern efforts of code expansion, translational engineering, and peptide-based therapeutics, highlighting the impact of the code's proteomic origin.

Recent work identified members of the evolutionarily conserved coronin protein family as key regulators of cell population size. This work originated ~25 years ago through the identification, by two-dimensional gel electrophoresis, of coronin 1 as a host protein involved in the virulence of Mycobacterium tuberculosis. We here describe the journey from a spot on a 2D gel to the recent realization that coronin proteins represent key controllers of eukaryotic cell population sizes, using ever more sophisticated proteomic techniques. We discuss the value of 'old school' proteomics using relatively simple and cost-effective technologies that allowed to gain insights into subcellular proteomes and describe how label-free quantitative (phospho)proteomics using mass spectrometry allowed to disentangle the role for coronin 1 in eukaryotic cell population size control. Finally, we mention potential implications of coronin-mediated cell population size control for health and disease. Proteome analysis has been revolutionized by the advent of modern-day mass spectrometers and is indispensable for a better understanding of biology. Here, we discuss how careful dissection of physio-pathological processes by a combination of proteomics, genomics, biochemistry and cell biology may allow to zoom in on the unexplored, thereby possibly tackling hitherto unasked questions and defining novel mechanisms.

Blood-based tau proteoforms have emerged as specific, scalable biomarkers of Alzheimer's pathology, addressing the limitations of symptom-based diagnosis, neuroimaging, and invasive cerebrospinal fluid (CSF) testing. This review synthesizes advances in tau phosphorylation and truncation biology, evaluates translation from CSF to plasma with state-of-the-art proteomics, and outlines the analytical standards and cross-matrix calibration needed for clinical adoption. We conducted a literature search in PubMed and Google Scholar. We reviewed studies published between January 2005 and September 2025 investigating tau proteoforms in Alzheimer's disease. Blood-based tau proteoforms are poised to move Alzheimer's diagnostics from specialized imaging to accessible frontline testing, with plasma p-tau217 approaching positron emission tomography (PET) and CSF performance and multi-analyte panels with glial fibrillary acidic protein (GFAP) or neurofilament light (NfL) improving differential diagnosis while reducing invasiveness and cost. Building on the first FDA-cleared plasma assay (Lumipulse G p-tau217/Aβ1-42 Ratio) in May 2025, we anticipate a dual pathway over the next decade in which referral centers use high-plex mass spectrometry (MS) panels for phosphoforms and truncations, while primary care adopts automated high-throughput immunoassays (e.g. chemiluminescent enzyme immunoassay (CLEIA)) for triage, supported by harmonized standard operating procedures (SOPs), cross-matrix calibration, and robust reference materials.

Immobilized metal ion affinity chromatography (IMAC) is an effective method developed in the 1980s for the separation and purification of proteins. The system consists of a solid-phase matrix, a linking ligand, and a metal ion. The method is based on the ability of metal ions to bind specifically to certain specific amino acid residues of proteins, thereby selectively enriching and purifying proteins. This review aims to describe current knowledge of fundamental principle of IMAC and summarize the supports, chelating ligands, and metal ions of IMAC. In addition, how IMAC technology is used in proteomics and nucleic acids research are highlighted. Over the past decades, IMAC has been extensively utilized as a predominant technique for protein enrichment in a variety of biological and medical research, such as disease diagnosis, tumor biomarker identification, protein purification, and nucleic acids research. In the future, IMAC should be integrated with other emerging proteomics technologies to promote the applications of metalloproteomes in disease diagnosis, metallodrug development, and clinical translation.

Saliva, an easily accessible biofluid, has emerged as a promising source of biomarkers for noninvasive disease diagnostics. Human saliva comprises a complex mixture of volatile organic compounds (VOCs) originating from various sources, including exhaled breath, diet, environmental factors, and the body's metabolic activity. Their presence in saliva can be influenced by several factors, including age, gender, diet, lifestyle, health status, and the oral microbiome. Qualitative analysis of salivary VOCs as potential indicators of specific lifestyles and early environmental exposure in children was performed. Saliva samples from 40 children were analyzed using a headspace high-capacity sorptive extraction (HS-HiSorb) technique coupled with thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). The method was developed and conditions such as the choice of sorbent material and sample preparation steps were optimized. A range of VOCs in the saliva samples, including alcohols, aldehydes, ketones, nitrogen and sulfur containing compounds, organic acids, esters and hydrocarbons were identified and a selection of them were quantified. The influence of the HiSorb coating composition and the extraction parameters on the observed chromatographic areas of each analyte were also evaluated. A green sample preparation method for the qualitative analysis of VOCs in the saliva of children was developed.

Ovarian clear cell carcinoma (OCCC) is a rare gynecologic malignancy with a high mortality rate and a lack of response to standard chemotherapy. Despite the functional association between the loss of ARID1A and mitochondrial dependency, the clinical translation of mitochondria-targeted therapies in OCCC has been hindered by a substantial disconnect between biological insight and therapeutic application. There is an urgent, unmet need to identify novel, more specific and effective therapies targeting the mitochondria-related molecular vulnerabilities of ARID1A-mutant OCCC. This critical perspective is informed by results from PubMed literature searches and recent webinars and presentations providing insight into opportunities for mass spectrometry (MS)-based proteomic approaches to enhance and accelerate the clinical translation of mitochondria-targeted therapies in OCCC. The MS-based proteomic analysis of clinically-relevant experimental models of OCCC will provide a unique opportunity to progress beyond simplified preclinical models and incorporate the full spectrum of patient-specific systemic and microenvironmental factors that may influence therapeutic response, including the adipocyte-related metabolic dependencies of OCCC. Targeted MS is a precise and robust approach that can be applied to verify these novel, mechanistic insights into how mitochondria-targeted therapies intersect with tumor metabolism in OCCC.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry. We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture. Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

Mass spectrometry (MS)-based proteomics, especially the targeted applications, hold great potential as Laboratory Developed Tests (LDTs) for clinical applications. They are suitable for widespread clinical use due to their impressive sample/protein multiplexing capabilities, analytical sensitivity and replicability, adaptability to diverse clinical samples, and highly evolved sample processing protocols. Although multiple LDTs have been developed and approved by regulatory agencies, various areas still need improvement. This article focuses on introducing MS-based LDT as a potential clinical technology, its superiority over low-throughput or antibody-based methods, existing hurdles in the adoption of such LDTs in clinics, what they can adopt to, and regulatory and analytical considerations that need to be addressed to develop a robust MS-based LDT. Recent efforts to optimize instrumentation and sample preparation for MS-based applications have made these LDTs promising contenders for clinical utilization. With focused research to answer quality assessment requirements, data interpretability, method scalability, and ease of use, MS-based LDTs can revolutionize clinical diagnostics. Drawing parallels to other omics technologies, these LDTs can address the long-standing multiplexing hinge and further establish multi-protein diagnostics as next-generation diagnostics of low-throughput methods.