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The life cycle of a virus involves interacting with the host cell, entry, hijacking host machinery for viral replication, evading the host's immune system, and releasing mature virions. However, viruses, being small in size, can only harbor a genome large enough to code for the minimal number of proteins required for the replication and maturation of the virions. As a result, many viral proteins are multifunctional machines that do not directly obey the classic structure-function paradigm. Often, such multifunctionality is rooted in intrinsic disorder that allows viral proteins to interact with various cellular factors and remain functional in the hostile environment of different cellular compartments.This report covers the classification of flaviviruses, their proteome organization, and the prevalence of intrinsic disorder in the proteomes of different flaviviruses. Further, we have summarized the speculations made about the apparent roles of intrinsic disorder in the observed multifunctionality of flaviviral proteins.Small sizes of viral genomes impose multifunctionality on their proteins, which is dependent on the excessive usage of intrinsic disorder. In fact, intrinsic disorder serves as a universal functional tool, weapon, and armor of viruses and clearly plays an important role in their functionality and evolution.

Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems.Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics.Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.Proteomics uses mass spectrometry to identify as many of the proteins in a system of interest as possible, making it extremely useful in biomedical research and basic biological research. Unlike next-generation DNA/genome sequencing, proteomics directly measures the changes in gene translation in response to a disease state, injury, etc. However, when proteomics data is coupled to and examined together with other forms of ‘omics’ data, such as transcriptomics, genomics, and metabolomics, a full biological picture emerges that can demonstrate the underlying regulatory networks of living systems and how they respond to positive and negative stimuli. This integration is called multi-omics and represents a powerful paradigm shift in systems biology. To be fully compatible with other ‘omics datasets, proteomics must be as complete and accurate as possible; in addition, the task of integrating multiple different kinds of datasets can be daunting to novice researchers. With this in mind, we reviewed in this manuscript the technologies that allow for the generation of the best possible proteomics for multi-omics analysis, in addition to the software tools needed to integrate proteomics data with other ‘omics data. Together, we believe this review will enable other researchers to begin applying multi-omics approaches to answer their research questions.

Mass spectrometry-based single-cell proteomics (scMS) is experiencing rapid evolution due to the increased sensitivity of mass spectrometers as well as advances in multiplexing and sample preparation. To date, researchers have focused on two general approaches to scMS: label-free and isobaric label-based multiplexing. While label-free analysis provides straightforward sample preparation and a clear path to automation, it currently lacks the throughput necessary to practically analyze thousands of single cells. Multiplexed analysis utilizing isobaric labels requires additional sample manipulation but increases throughput such that analyzing thousands of cells is currently achievable. A key feature of multiplexed scMS experiments is a 'carrier proteome' - a sample added at 25x-500x, the single-cell sample that increases the number of proteins that can be identified in an MS analysis.Here, we review early examples of carrier proteomes in quantitative proteomics before summarizing advantages and challenges of using a carrier proteome in scMS experiments.We conclude that the addition of carrier proteomes improves depth of identification for scMS, but high levels of carrier proteomes can have adverse effects on quantitative accuracy and precision.

Introduction While the origin and evolution of proteins remain mysterious, advances in evolutionary genomics and systems biology are facilitating the historical exploration of the structure, function and organization of proteins and proteomes. Molecular chronologies are series of time events describing the history of biological systems and subsystems and the rise of biological innovations. Together with time-varying networks, these chronologies provide a window into the past. Areas covered Here, we review molecular chronologies and networks built with modern methods of phylogeny reconstruction. We discuss how chronologies of structural domain families uncover the explosive emergence of metabolism, the late rise of translation, the co-evolution of ribosomal proteins and rRNA, and the late development of the ribosomal exit tunnel; events that coincided with a tendency to shorten folding time. Evolving networks described the early emergence of domains and a late 'big bang' of domain combinations. Expert opinion Two processes, folding and recruitment appear central to the evolutionary progression. The former increases protein persistence. The later fosters diversity. Chronologically, protein evolution mirrors folding by combining supersecondary structures into domains, developing translation machinery to facilitate folding speed and stability, and enhancing structural complexity by establishing long-distance interactions in novel structural and architectural designs.

Due to its excellent sensitivity, nano-flow liquid chromatography tandem mass spectrometry (LC-MS/MS) is the mainstay in proteome research; however, this comes at the expense of limited throughput and robustness. In contrast, micro-flow LC-MS/MS enables high-throughput, robustness, quantitative reproducibility, and precision while retaining a moderate degree of sensitivity. Such features make it an attractive technology for a wide range of proteomic applications. In particular, large-scale projects involving the analysis of hundreds to thousands of samples.This review summarizes the history of chromatographic separation in discovery proteomics with a focus on micro-flow LC-MS/MS, discusses the current state-of-the-art, highlights advances in column development and instrumentation, and provides guidance on which LC flow best supports different types of proteomic applications.Micro-flow LC-MS/MS will replace nano-flow LC-MS/MS in many proteomic applications, particularly when sample quantities are not limited and sample cohorts are large. Examples include clinical analyses of body fluids, tissues, drug discovery and chemical biology investigations, plus systems biology projects across all kingdoms of life. When combined with rapid and sensitive MS, intelligent data acquisition, and informatics approaches, it will soon become possible to analyze large cohorts of more than 10,000 samples in a comprehensive and fully quantitative fashion.

Introduction Main problems of kidney stone disease are its increasing prevalence and high recurrence rate after calculi removal in almost all areas around the globe. Despite enormous efforts in the past, its pathogenic mechanisms remain unclear and need further elucidations. Proteomics has thus become an essential tool to unravel such sophisticated disease mechanisms at cellular, subcellular, molecular, tissue and whole organism levels. Areas covered This review provides a brief overview of kidney stone disease followed by updates on proteomics for investigating urinary stone modulators, matrix proteins, cellular responses to different types/doses of calcium oxalate (CaOx) crystals, sex hormones and other stimuli, crystal-cell interactions, crystal receptors, secretome, and extracellular vesicles (EVs), all of which lead to better understanding of the disease mechanisms. Finally, the future challenges and translation of these obtained data to the clinic are discussed. Expert opinion/commentary Knowledge from urinary proteomics for exploring the important stone modulators (either inhibitors or promoters) will be helpful for early detection of asymptomatic cases for prompt prevention of symptoms, complications, and new stone formation. Moreover, these modulators may serve as the new therapeutic targets in the future for successful treatment and prevention of kidney stone disease by medications or other means of intervention. Key words Crystal-cell interactions; Crystal receptors; Mass spectrometry; Nephrolithiasis; Proteome; Urinary proteomics; Urolithiasis.

Introduction Fibroblasts maintain tissue and organ homeostasis through output of extracellular matrix that affects nearby cell signaling within the stroma. Altered fibroblast signaling contributes to many disease states and the extracellular matrix secreted by fibroblasts has been used to stratify patient by outcome, recurrence and therapeutic resistance. Recent advances in imaging mass spectrometry allow access to single cell fibroblasts and their ECM niche within clinically relevant tissue samples. Areas covered We review biological and technical challenges as well as new solutions to proteomic access of fibroblast expression within the complex tissue microenvironment. Review topics cover conventional proteomic methods for single fibroblast analysis and current approaches to accessing single fibroblast proteomes by imaging mass spectrometry approaches. Strategies to target and evaluate the single cell stroma proteome on the basis of cell signaling are presented. Expert opinion The promise of defining proteomic signatures from fibroblasts and their extracellular matrix niches is the discovery of new disease markers and the ability to refine therapeutic treatments. Several imaging mass spectrometry approaches exist to define the fibroblast in the setting of pathological changes from clinically acquired samples. Continued technology advances are needed to access and understand the stromal proteome and apply testing to the clinic.

Proteomics, i.e. the study of the set of proteins produced in a cell, tissue, organism, or biological entity, has made possible analyses and contextual comparisons of proteomes/proteins and biological functions among the most disparate entities, from viruses to the human being. In this way, proteomic scrutiny of tumor-associated proteins, autoantigens, and pathogen antigens offers the tools for fighting cancer, autoimmunity, and infections. Comparative proteomics and immunoproteomics, the new scientific disciplines generated by proteomics, are the main themes of the present review that describes how comparative analyses of pathogen and human proteomes led to re-modulate the molecular mimicry concept of the pre-proteomic era. I.e. before proteomics, molecular mimicry – the sharing of peptide sequences between two biological entities – was considered as intrinsically endowed with immunologic properties and was related to cross-reactivity. Proteomics allowed to redefine such an assumption using physicochemical parameters according to which frequency and hydrophobicity preferentially confer an immunologic potential to shared peptide sequences. Proteomics is outlining peptide platforms to be used for the diagnostics and management of human diseases. A Molecular Medicine targeted to obtain healing without paying the price for adverse events is on the horizon. The next step is to take up the challenge and operate the paradigm shift that the current proteomic era requires.

The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role.In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, andThe knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.

Antifungal agents are essential in the fight against serious fungal disease, however emerging resistance is threatening an already limited collection of therapeutics. Proteomic analyses of effects of antifungal agents can expand our understanding of multifactorial mechanisms of action and have also proven valuable to elucidate proteomic changes associated with antifungal resistance.This review covers the application of proteomic techniques to examine sensitivity and resistance to antifungals including commonly used therapeutics, amphotericin B, echinocandins and the azoles, based predominantly on studies involvingFungal proteomics has evolved in the last decade with increased genome availability and developments in mass spectrometry. Collectively, these have led to the advancement of proteomic techniques, allowing increased coverage of the proteome. Gel-based proteomics laid the foundation for these types of studies, which has now shifted to the more powerful gel-free proteomics. This has resulted in the identification of key mediators and potential biomarkers of antifungal resistance, as well as elucidating the mechanisms of action of novel and established antifungal agents.

Introduction: Proteomic profiling plays an important role in the exploration of cancer from molecular mechanisms to clinical diagnosis and treatment. In recent years, the advent of new technologies has promoted oncoproteomics from the initial global style to a refined single-cell level.Areas Covered: Among them, the development of microfluidic devices, the improvement of liquid mass spectrometry in accuracy and trace sample handling processes, and the emergence of protein sequencing have contributed to the oncoproteomic analysis at the single-cell level.Expert Opinion: The proteomic analysis at the global level and the single-cell level gives different perspectives while combining them can reveal more comprehensive oncoproteomics and help cancer research and treatment strategies.

The N-terminus of a protein can encode several protein features, including its half-live and its localization. As the proteomics field remains dominated by bottom-up approaches and as N-terminal peptides only account for a fraction of all analyzable peptides, there is a need for their enrichment prior to analysis. COFRADIC, TAILS, and the subtiligase method were among the first N-terminomics methods developed, and several variants and novel methods were introduced that often reduce processing time and/or the amount of material required.We present an overview of how the field of N-terminomics developed, including a discussion of the founding methods, several updates made to these and introduce newer methods such as TMPP-labeling, biotin-based methods besides some necessary improvements in data analysis.N-terminomic methods remain being used and improved methods are published however, more efficient use of contemporary mass spectrometers, promising data-independent approaches, and mass spectrometry-free single peptide or protein sequences may threat the N-terminomics field.

Background: Neurocysticercosis (NCC) is an acquired infection of central nervous system associated with epileptic seizures. The parasite ‘Taenia solium’ causes this disease and has a complex life c...

Nonalcoholic fatty disease (NAFLD) affects 3-10% of the pediatric population, making it the most common chronic liver disease among children. The aim of the study is to identify potential biomarkers enabling the diagnosis of NAFLD and monitoring the course of the disease.Proteome analysis was performed in a group of 30 patients (19 boys and 11 girls) in total, of whom 16 children had previously diagnosed NAFLD based on the abdominal ultrasound after excluding other diseases of this organ.A total of 297 proteins have been identified. Thirty-seven proteins (responsible for inflammation, stress response, and regulation of this process) differentiating both experimental groups were identified. Up-regulated proteins included afamin, retinol-binding protein-4, complement components, and hemopexin; while serum protease inhibitors, clusterin, immunoglobulin chains, and vitamin D binding protein were found in the down-regulated group. The correlation between selected proteins and indicators of noninvasive assessment of liver fibrosis (APRI, FIB-4) as well as differences between the serum proteome of patients with normal weight, overweight, and obesity were also assessed.The plasma protein profile is significantly altered in nonalcoholic liver disease in children and may prove to be a valuable source of biomarkers to evaluate the extent of liver disease.

[Introduction]: Perinatal and pediatric diseases related to neurovascular disorders cause significant problems during life, affecting a population with a long life expectancy. Early diagnosis and assessment of the severity of these diseases are crucial to establish an appropriate neuroprotective treatment. Currently, physical examination, neuroimaging and clinical judgment are the main tools for diagnosis, although these tests have certain limitations. There is growing interest in the potential value of noninvasive biomarkers that can be used to monitor child patients at risk of brain damage, allowing accurate, and reproducible measurements., [Areas covered]: This review describes potential biomarkers for the diagnosis of perinatal neurovascular diseases and discusses the possibilities they open for the classification and treatment of neonatal neurovascular diseases., [Expert opinion]: Although high rates of ischemic and hemorrhagic stroke exist in pediatric populations, most studies have focused on biomarkers of hypoxic-ischemic encephalopathy. Inflammatory and neuronal biomarkers such as S-100B and GFAP, in combination with others yet to be discovered, could be considered as part of multiplex panels to diagnose these diseases and potentially for monitoring response to treatments. Ideally, noninvasive biofluids would be the best source for evaluating these biomarkers in proteomic assays in perinatal patients., This work was supported by grants from Fundación Alicia Koplowitz “Ayudas a proyectos de investigación” 2018 to J.M.; the Andalusian Health Service, Junta de Andalucia (Servicio Andalúz de Salúd, Junta de Andalucía; C-0025-2018 to P.Y.-G.); Instituto de Salud Carlos III (contrato Río Hortega 2018; CM18/00106 to PMV). The Neurovascular Research Lab at IBIS is part of the Spanish StrokeResearch Network (INVICTUS+; RD16/0019/0015).

Proteomic research has been extensively used to identify potential biomarkers or targets for various diseases. Advances in mass spectrometry along with data analytics have led proteomics to become a powerful tool for exploring the critical molecular players associated with diseases, thereby, playing a significant role in the development of proteomic applications for the clinic.This review presents recent advances in the development and clinical applications of proteomics in India toward understanding various diseases including cancer, metabolic diseases, and reproductive diseases. Keywords combined with 'clinical proteomics in India' 'proteomic research in India' and 'mass spectrometry' were used to search PubMed.The past decade has seen a significant increase in research in clinical proteomics in India. This approach has resulted in the development of proteomics-based marker technologies for disease management in the country. The majority of these investigations are still in the discovery phase and efforts have to be made to address the intended clinical use so that the identified potential biomarkers reach the clinic. To move toward this necessity, there is a pressing need to establish some key infrastructure requirements and meaningful collaborations between the clinicians and scientists which will enable more effective solutions to address health issues specific to India.

INTRODUCTION: Cellular senescence is a rapidly growing field with potential relevance for the treatment of multiple human diseases. In the last decade, cellular senescence and the senescence-associated secretory phenotype (SASP) have emerged as central drivers of aging and many chronic diseases, including cancer, neurodegeneration, heart disease and osteoarthritis. Major efforts are underway to develop drugs that selectively eliminate senescent cells (senolytics) or suppress or alter the SASP (senomorphics) to treat age-related diseases in humans. The translation of senescence-targeting therapies into humans is still in early stages. Nonetheless, it is clear that proteomic approaches will facilitate the discovery of important SASP proteins, development of senescence- and SASP-derived biomarkers, and identification of therapeutic targets for senolytic and senomorphic drugs. AREAS COVERED: We review recent proteomic studies of cellular senescence and their translational relevance and, particularly, characterization of the secretory phenotype and preclinical development of biomarkers (from 2008–2020, PubMed). We focus on emerging areas, such as the heterogeneity of senescent cells and the SASP, extracellular vesicles released by senescent cells, and validating biomarkers of aging in vivo. EXPERT OPINION: Proteomic and multi-omic approaches will be important for the development of senescence-based biomarkers to facilitate and monitor future therapeutic interventions that target senescent cells.

Introduction: After the genomic era, the analysis of the proteome has gained increasing importance. Peptides and/or proteins present in tissue or body fluids can depict health and are prone to change during disease, not only in configuration but also in abundance. Early on, high throughput proteome analysis was implemented in the diagnostic of therapy-linked or induced complications arising after allogeneic hematopoietic stem cell transplantation (HSCT). Several proteomic approaches are currently used in the prediction or diagnosis of acute and/or chronic graft-versus-host disease (GvHD).Areas covered: This review will report on two high throughput proteomics technologies used in the clinical setting to date, namely enzyme-linked-immunosorbent assays (ELISA) for key proteins involved in the pathogenesis of acute GvHD and on capillary electrophoresis coupled on-line to mass spectrometry (CE-MS). Here, we summarize the current data and discuss the strength as well as the limitations of each method and compare the usefulness and practicability in the post-HSCT setting for prediction and diagnosis of acute GvHD.Expert commentary: Both technologies are applied in the clinic and have been tested on several hundred patients after HSCT. The data from both technologies may complement each other in diagnosis of GvHD.

Introduction: Mitochondria (mt) are protein-protein interaction (PPI) hubs in the cell where mt-localized and associated proteins interact in a fashion critical for cell fitness. Altered mtPPIs are linked to neurodegenerative disorders (NDs) and drivers of pathological associations to mediate ND progression. Mapping altered mtPPIs will reveal how mt dysfunction is linked to NDs. Areas covered: This review discusses how database sources reflect on the number of mt protein or interaction predictions, and serves as an update on mtPPIs in mt dynamics and homeostasis. Emphasis is given to mRNA expression profiles for mt proteins in human tissues, cellular models relevant to NDs, and altered mtPPIs in NDs such as Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS) and Alzheimer’s disease (AD). Expert opinion: We highlight the scarcity of biomarkers to improve diagnostic accuracy and tracking of ND progression, obstacles in recapitulating NDs using human cellular models to underpin the pathophysiological mechanisms of disease, and the shortage of mt protein interactome reference database(s) of neuronal cells. These bottlenecks are addressed by improvements in induced pluripotent stem cell creation and culturing, patient-derived 3D brain organoids to recapitulate structural arrangements of the brain, and cell sorting to elucidate mt proteome disparities between cell types.

Around 20,000 protein-coding genes have been predicted from the analysis of the human genome. Many thousands of alternatively spliced isoforms have been predicted from transcriptomic analyses, but ...

Introduction: Metaproteomics is an established method to obtain a comprehensive taxonomic and functional view of microbial communities. After more than a decade, we are now able to describe the promise, reality, and perspectives of metaproteomics and provide useful information about the choice of method, applications, and potential improvement strategies.Areas covered: In this article, we will discuss current challenges of species and proteome coverage, and also highlight functional aspects of metaproteomics analysis of microbial communities with different levels of complexity. To do this, we re-analyzed data from microbial communities with low to high complexity (8, 72, 200 and >300 species). High species diversity leads to a reduced number of protein group identifications in a complex community, and thus the number of species resolved is underestimated. Ultimately, low abundance species remain undiscovered in complex communities. However, we observed that the main functional categories were better represented within complex microbiomes when compared to species coverage.Expert opinion: Our findings showed that even with low species coverage, metaproteomics has the potential to reveal habitat-specific functional features. Finally, we exploit this information to highlight future research avenues that are urgently needed to enhance our understanding of taxonomic composition and functions of complex microbiomes.

Introduction: The biological heterogeneity of acute myeloid leukemia (AML) complicates personalized medicine. Individual prognosis is typically based on the presence of chromosomal and genetic lesions. Nevertheless, these classifications often lack a priori information about response to therapy. Since the protein expression landscape reflects the functional activity state of cells, we hypothesize that analyzing this can be used for the identification of protein activity markers to provide better risk stratification as well as may provide targeted therapeutic guidance in AML. Areas covered: Herein, we review recently new adopted drugs in the treatment for AML and discuss how quantitative proteomic techniques may contribute to better therapeutic selection in AML. Expert commentary: The net functional state of the cell is defined by the activity of protein within all the pathways that are active in the cell. Recognition of the proteomic profile of the leukemic blast could, therefore, complement current classification systems by providing a better a priori description of what pathways are important within a cell as a guide to the selection of therapy for the patient.

Background: Helminth infections cause widespread morbidity and are a significant global disease burden. One among them is Neurocysticercosis, a central nervous system infection caused by the larvae Taenia solium, leading to epilepsy. Helminths are strong immune modulators and can survive for a long time in adverse host environments. Kinases are molecular switches and are essential to initiate/propagate signaling cascades and are detrimental to the regulation of homeostasis. They have been implicated in the progression of many diseases and are potentially lucrative drug targets.Objective: To identify kinases in T. solium proteome and prioritize them as drug targets.Methodology: A Hidden Markov Model (HMM) was used to curate and classify kinases into families based on sequence homology to model organisms followed by phylogenetic analysis of each family. To predict potential drug targets, kinases were identified based on a homologically lethal relationship to C. elegans but non-lethal to humans. Kinases thus selected were searched for matching ligands in SARFkinase and DrugBank databases.Result and conclusion: T. solium kinases make up 1.8% of its proteome, CMGC is the largest kinase family and RGC is the smallest and catalytically inactive family. We predict 23-potential kinases to be drug targets for T. solium.[Figure: see text].

Introduction: Autophagy is one of the most conserved clearance systems through which eukaryotes manage to handle dysfunctional and excess organelles and macromolecules. This catabolic process has not only a role in the maintenance of basal turnover of cellular components, but it is also essential in cells adaptation to stress conditions. In the last decades, defects in autophagic machinery have been identified as a feature in neurodegenerative diseases. In this context, mass spectrometry-based proteomics has become an important tool in the comprehensive analysis of proteins involved in the autophagic flux.Area covered: In this review, we discuss recent contributions of proteomic techniques in the study of defective autophagy related to neurodegenerative illness. Particular emphasis is given to the identification of i) shared autophagic markers between different disorders, which support common pathological mechanisms; ii) unique autophagic signature, which could aid to discriminate among diseases.Expert opinion: Proteomic approaches are valuable in the identification of alterations of components to the autophagic process at different steps of the process. The investigation of autophagic defects associated with neurological disorders is crucial in order to unravel all the potential mechanism leading to neurodegeneration and propose effective therapeutic strategies targeting autophagy.

Introduction: The understanding of novel antibiotic resistance mechanisms is essential to develop strategies against antibiotic-resistant pathogens, which has become an urgent task due to the worldwide emergence of antibiotic resistance. Areas covered: In this review, the authors summarize the recent progress on antibiotic resistance caused by lab-evolved bacteria and clinical multidrug-resistant bacterial pathogens from the proteomics perspective. Expert opinion: Proteomics provides a new platform for a comprehensive understanding of change in protein pathways that are engaged in antibiotics resistance, which is different from a genetic view that focuses on the role of an individual gene or protein. Further work is required to understand why and how the involved pathways are integrated for surviving antibiotic-mediated killing, to use other OMICs for better comprehension of antibiotic resistance mechanisms, and to develop reprogramming proteomics, which reverts an 'antibiotic resistance proteome' to an 'antibiotic sensitive or antibiotic sensitive-like' proteome, for the control of antibiotic-resistant pathogens.

Introduction Lipid droplets (LDs) are dynamic and evolutionary conserved lipid-enriched organelles composed of a core of neutral lipids surrounded by a monolayer of phospholipids associated with a diverse array of proteins that are cell- and stimulus-regulated. Far beyond being simply a deposit of neutral lipids, accumulating evidence demonstrate that LDs act as spatial and temporal local for lipid and protein compartmentalization and signaling organization. Areas covered This review focuses on the progress in our understanding of LD protein diversity and LD functions in the context of cell signaling and immune responses, highlighting the relationship between LD composition with the multiple roles of this organelle in immunometabolism, inflammation and host-response to infection. Expert opinion LDs are essential platforms for various cellular processes, including metabolic regulation, cell signaling, and immune responses. The functions of LD in infection and inflammatory disease are associated with the dynamic and complexity of their proteome. Our contemporary view place LDs as critical regulators of different inflammatory and infectious diseases and key markers of leukocyte activation.

Introduction Lung cancer remains the most prevalent cause of cancer mortality worldwide mainly due to insufficient availability of early screening methods for wide-scale application. Exhaled breath condensate (EBC) is currently considered as one of the promising targets for early screening and is particularly attractive due to its absolutely noninvasive collection and possibility for long-term frozen storage. EBC proteome analysis can provide valuable information about the (patho)physiological changes in the respiratory system and may help to identify in time a high risk of lung cancer. Mass spectrometry (MS) profiling of EBC proteome seems to have no alternative in obtaining the most extensive data and characteristic marker panels for screening. Areas covered This special report summarizes the data of several proteomic studies of EBC in normal and lung cancer (from 2012 to 2021, PubMed), focuses on the possible reasons for the significant discrepancy in the results, and discusses some aspects for special attention in further studies. Expert opinion The significant discrepancy in the results of various studies primarily highlights the need to create standardized protocols for the collection and preparation of EBC for proteomic analysis. The application of quantitative and targeted LC-MS/MS based approaches seems to be the most promising in further EBC proteomic studies.

In clinical research, biomarkers can be defined as biomolecules that identify a specific disease. The number of studies that use mass spectrometry (MS)-based proteomics for protein biomarker discov...

Introduction: Abdominal aortic aneurysm (AAA) is a common, complex, and life-threatening disease. Currently, the pathogenesis of AAA is not well understood. No biomarkers or specific drugs are available for AAA in clinical applications. Proteomics is a powerful tool in biomarker discovery, exploration of pathogenesis, and drug target identification.Areas covered: We review the application of mass spectrometry-based proteome analysis in AAA patients within the last ten years. Differentially expressed proteins associated with AAA were identified in multiple sample sources, including vascular tissue, intraluminal thrombus, tissue secretome, blood, and cells. Some potential disease biomarkers, pathogenic mechanisms, or therapeutic targets for AAA were discovered using proteome analysis. The challenges and prospects of proteomics applied to AAA are also discussed.Expert opinion: Since most of the previous proteomic studies used relatively small sample sizes, some promising biomarkers need to be validated in multicenter cohorts to accelerate their clinical application. With the rapid development of mass spectrometry technology, modification-specific proteomics and multi-omics research in the future will enhance our understanding of the pathogenesis of AAA and promote biomarker discovery and drug development for clinical translation.

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray. Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology. Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.

Introduction: High-density lipoprotein (HDL) particles are heterogeneous and their proteome is complex and distinct from HDL cholesterol. However, it is largely unknown whether HDL proteins are associated with cardiovascular protection. Areas covered: HDL isolation techniques and proteomic analyses are reviewed. A list of HDL proteins reported in 37 different studies was compiled and the effects of different isolation techniques on proteins attributed to HDL are discussed. Mass spectrometric techniques used for HDL analysis and the need for precise and robust methods for quantification of HDL proteins are discussed. Expert opinion: Proteins associated with HDL have the potential to be used as biomarkers and/or help to understand HDL functionality. To achieve this, large cohorts must be studied using precise quantification methods. Key factors in HDL proteome quantification are the isolation methodology and the mass spectrometry technique employed. Isolation methodology affects what proteins are identified in HDL and the specificity of association with HDL particles needs to be addressed. Shotgun proteomics yields imprecise quantification, but the majority of HDL studies relied on this approach. Few recent studies used targeted tandem mass spectrometry to quantify HDL proteins, and it is imperative that future studies focus on the application of these precise techniques.

Introduction: The last decade has yielded significant developments in the field of proteomics, especially in mass spectrometry (MS) and data analysis tools. In particular, a shift from gel-based to MS-based proteomics has been observed, thereby providing a platform with which to construct proteome atlases for all life forms. Nevertheless, the analysis of plant proteomes, especially those of samples that contain high-abundance proteins (HAPs), such as soybean seeds, remains challenging. Areas covered: Here, we review recent progress in soybean seed proteomics and highlight advances in HAPs depletion methods and peptide pre-fractionation, identification, and quantification methods. We also suggest a pipeline for future proteomic analysis, in order to increase the dynamic coverage of the soybean seed proteome. Expert opinion: Because HAPs limit the dynamic resolution of the soybean seed proteome, the depletion of HAPs is a prerequisite of high-throughput proteome analysis, and owing to the use of two-dimensional gel electrophoresis-based proteomic approaches, few soybean seed proteins have been identified or characterized. Recent advances in proteomic technologies, which have significantly increased the proteome coverage of other plants, could be used to overcome the current complexity and limitation of soybean seed proteomics.

INTRODUCTION Plasma proteomics has been extensively utilized for studies that investigate various disease settings (e.g. cardiovascular disease), as well as to monitor the effect of pharmaceuticals on the plasma proteome (e.g. chemotherapy). However, plasma proteomic studies focusing on children represent a very small proportion of the plasma proteomic studies completed to date. Early disease detection and prevention is critical in pediatrics, as children must live with the disease outcomes for many years and often carry negative outcomes into adulthood. Pediatrics represents an area of plasma proteomics that is about to undergo a significant expansion. Areas covered: This review is based on a PubMed search focusing on five keywords that are plasma, biomarkers, pediatric, proteomics, and children. It is a comprehensive summary of plasma proteomic studies specific to the pediatric patient and discusses aspects such as the clinical setting, sample size, methodological approaches and outlines the significance of the findings. Expert commentary: Plasma proteomics is expanding significantly as a result of major advancements in proteomic technology. This is in synergy with the growing focus on true early disease detection and prevention in early life. We are about to see a new era of advanced medical science built from pediatric proteomics.

INTRODUCTION Pluripotency emerges transiently during embryogenesis in two main forms with different developmental potential, termed naive and primed states. Importantly, these pluripotent states can be recapitulated in vitro under specific culture conditions, representing a unique model to study the regulatory principles of development and cellular plasticity. Areas covered: A complex network of signaling pathways that senses intrinsic and extrinsic cues controls the fine balance between self-renewal and differentiation. Much of our knowledge on this tight regulation originates from epigenetic and transcriptomic approaches. However, the presence of post-transcriptional and post-translational mechanisms demands a direct assessment of the proteome in its multiple facets. Mass spectrometry-based proteomics is now a mature technique and has started to deliver new insights in the stem cell field. Expert opinion: Here, we review our current understanding on the mechanisms that dictate the spectrum of pluripotency levels. We put special emphasis on the emerging proteomic studies that focused on the molecular properties behind the naive and primed states. In addition, we hypothesize on the impact that future developments in proteomic technologies can have to improve our view of pluripotency.

INTRODUCTION Synovial fluid (SF) is in close proximity to tissues which are primarily altered during articular disease and has significant potential to better understand the underlying disease pathogeneses of articular pathologies and biomarker discovery. Although development of mass spectrometry-based methods has allowed faster and higher sensitivity techniques, interrogation of the SF proteome has been hindered by its large protein concentration dynamic range, impeding quantification of lower abundant proteins. Areas covered: Recent advances have developed methodologies to reduce the large protein concentration dynamic range of SF and subsequently allow deeper exploration of the SF proteome. This review concentrates on methods to overcome biofluid complexity, mass spectrometry proteomics methodologies, extracellular vesicles proteomics and the application of advances within the field in clinical disease, including osteoarthritis, rheumatoid arthritis, spondyloarthritis and juvenile arthritis. A narrative review was conducted with articles searched using PubMed, 1991-2018. Expert opinion: The SF proteomics field faces various challenges, including the requirement for rigorous and standardised methods of sample collection/storage, the sensitivity and specificity of proteomic assays, techniques to combat the large protein concentration dynamic range and comprehensive data analysis to reduce falsely identified markers. Additionally, there are challenges in developing multi 'omic' integration techniques, with computational integration enhancing analysis.