Expert Review of Proteomics最新文献

Introduction: 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.

Area covered: 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.

Expert opinion: 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.

Background: Many of the advanced MS methods applied in proteomics such as nanoflow LC-MS with data-independent acquisition have yet to be verified and/or optimized on metabolomics applications.

Research design and methods: This study evaluates the feasibility of repurposing a proteomics-optimized nanoLC-MS platform for untargeted metabolomics. Using NIST SRM 1950 reference human plasma, we compared the performance of polarity switching and separate polarity modes under DIA conditions, focusing on metabolite coverage, annotation, and response linearity.

Results: We observed, in the separate polarity and switching polarity runs 669 and 353 features in (+) mode and 558 and 446 features in (-) mode, respectively. A total of 233 metabolites were annotated using the (±) separate polarities and 179 using the (±) switching polarity based on MassBank of North America (MoNA) public MS library and filtered with the Human Metabolome Database (HMDB). Both switching and separate polarity methods performed well regarding response linearities which were investigated by spiking some amino acid compounds into plasma matrix.

Conclusions: The polarity switching DIA approach for metabolomics reduced sample consumption and analysis time, but led to fewer detected features and annotations compared to separate polarity runs. These findings support the use of unified nanoLC-MS platforms for integrated multi-omics analysis.

Introduction: The ability of cancer cells to disseminate from the primary tumor and form metastatic lesions frequently leads to fatal outcomes. Recently, however, it has been recognized that this process is driven by complex interactions between the cancer and the neighboring cells, and, overall, made possible by a supportive tumor microenvironment. The emergence of high-throughput technologies is expected to bring much-needed clarity to unraveling the players and intricate communication pathways that promote metastatic progression.

Areas covered: In this report, the impact of mass spectrometry and proteomic technologies on deciphering the cross-talk between cancer and tumor microenvironment cells is discussed. Focus is placed on the role of cell-membrane and secretome proteins as the main enablers of this cross-talk, and on the challenges presented by metastatic tumors that evolve in the brain. Future prospects are assessed in the context of recent biology, technology, and data analysis breakthroughs.

Expert opinion: Advancements in high-throughput proteomic technologies, complemented by the exciting potential of new disease model systems and data processing abilities of artificial intelligence, are expected to bring groundbreaking progress in deciphering the fundamental biological mechanisms that support cancer behavior and metastatic development, revealing novel therapeutic targets, and guiding innovative intervention approaches.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusion: A green sample preparation method for the qualitative analysis of VOCs in the saliva of children was developed.

Introduction: Diabetes mellitus (DM) is a challenging chronic disease worldwide. The incidence is expected to rise dramatically by 2030 due to smoking, obesity, and aging population, increasing the public health burden in coming years. Diabetic retinopathy (DR) is a common microvascular complication and a leading cause of blindness among working-age individuals. Understanding the proteomic mechanisms driving disease progression and visual loss is of paramount importance when laying novel strategies for managing patients in clinical practice.

Areas covered: We summarize proteomic studies addressing molecular biomarkers in human blood for DR. We discuss methodological advantages and challenges. Proteomic technology advancement has reached a level where even low-abundance proteins related to disease can be detected in blood. The pooled results of these studies suggest that the biomarkers are specific to the disease stage and hold promise for personalized risk stratification in DR.

Expert opinion: With the development of advanced mass spectrometry technology for the investigation of biomarkers in blood, this research area is expected to advance in the future. Similarly, it is expected that novel blood-based biomarkers for DR will be validated in international studies and implemented in clinical practice. Therefore, future perspectives hold promises of personalized risk profiling for diagnosis, disease progression, and optimal choice of treatment.

Introduction: Recent failures of therapeutic interventions aimed at elevating high-density lipoprotein cholesterol (HDL-C) have renewed the need to reconceptualize HDL. The HDL proteome constitutes over 50% of the HDL mass and it is the richest among lipoproteins. HDL is also the most heterogeneous lipoprotein, and unraveling the determinants of its pleiotropic functions requires new approaches to characterize HDL subspecies as well as harmonization of isolation and quantification methodologies.

Areas covered: We have reviewed studies focusing on HDL proteomes from the past 5 years, with particular focus on recent developments in mass spectrometry-based proteomics applied to HDL, and the studies of HDL heterogeneity and function. Emphasis is also given on the quantification of the proteome of HDL-specific subspecies and on the studies relating the HDL proteome to its function. We further discuss the need for harmonization in HDL subpopulations and subspecies isolation and in proteome quantification.

Expert opinion: The HDL proteome is undisputedly related to the HDL function. The development of new approaches to isolate HDL subspecies, together with the implementation of consistent quantification strategies, is needed to provide new insights into the structure-function relationship of HDL particles, unravel the role of HDL in the pathology of diseases, and provide new metrics of HDL.

Introduction: The landscape of biomarker development has evolved with advanced analytical technologies, particularly affinity- and mass spectrometry-based techniques. These advancements have deepened our understanding of disease mechanisms, enabling the development of precise diagnostic tools and personalized medicine. Protein biomarkers, which play pivotal roles in biological processes, have become invaluable in diagnosing and monitoring diseases, aided by their presence in various biological samples and the availability of established detection methods.

Areas covered: This review covers the role of protein biomarkers in clinical practice, the development and dimensionality of protein biomarkers, advancements in detection technologies, a comparison of these technologies, and future directions in biomarker discovery and disease mechanism elucidation.

Expert opinion: Advances in biomarker technologies have the potential to transform diagnostics and personalized treatment but face challenges such as high costs and technical complexity. Enhancing reproducibility and integrating multi-omics approaches may offer better insights. The field should evolve toward high-throughput, automated methods, continuously adapting research, and clinical practices.

Introduction: Brain solid tumors are a leading cause of cancer-related mortality at all ages. The updated 2021WHO Central Nervous System tumor classification is based on genomic diagnostics. Nonetheless, extracellular vesicles (EVs) proteomics is a platform for protein biomarker discovery in brain tumor patient management. Tumor metabolic reprogramming, also through epigenetic mechanisms, is central to cancer. EVs transfer proteins and nucleic acids that relay the metabolic and redox state of the parent cells.

Areas covered: This review addresses the biomarker discovery and translation focusing on recent (from 2022 to 2024 PubMed) proteomic studies of EVs in brain tumor patients, selected for their focus on metabolic aspects. The most treatable adult brain tumor is isocitrate dehydrogenase-mutated glioma. Minimally invasive collection of EVs from cerebrospinal fluid or prospectively blood allows proteomic and metabolic analysis of the parent tumor cells.

Expert opinion: The CSF EVs express key CNS tumor biomarkers and therapy targets undetectable in whole CSF and relay the brain tumor metabolic adaptations. Metabolic dependencies can represent potential therapy targets. The clinical implications of biomarker EV proteomic discovery represent a promising platform for diagnostic and prognostic purposes in brain solid tumors, a leading cause of cancer-related mortality at all ages.

Introduction: 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.

Areas covered: 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.

Expert opinion: 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.