MS-based proteomics of body fluids: The end of the beginning

Early plasma ceramide and sphingomyelin levels reflect APOE genotype but not familial Alzheimer's disease gene mutations in female 5xFAD mice, with brain-region specific sphingolipid alterations

Pathophysiological changes associated with Alzheimer's disease (AD) begin decades before dementia onset, with age and APOE ε4 genotype as major risk factors [1-4]. Primary risk factors for developing AD include aging and number of copies of the apolipoprotein E (APOE) ε4 allele. Altered sphingolipid metabolism is increasingly implicated in early AD. However, the relationship between early plasma and brain sphingolipid changes-particularly in the context of APOE genotype-remains poorly defined. In this study, we analyzed plasma and brain sphingolipid profiles in transgenic AD mice carrying human APOE3 or APOE4 variants, with or without familial AD mutations (E3FAD and E4FAD). Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we assessed 110 sphingolipid species across four major classes (ceramides (Cers), hexosylceramides (HexCers), lactosylceramides (LacCers), and sphingomyelins (SMs)) at 2, 4, and 6 months in plasma and at 6 months in brain tissue in the cortex, hippocampus, striatum, and cerebellum. Our results demonstrate that early plasma sphingolipid alterations are largely driven by APOE genotype rather than AD pathology. Specifically, APOE4 carriers showed significant increases in SM species and reductions in Cer species compared to APOE3 carriers, independent of age or AD genotype. Brain lipid profiles showed minimal changes across genotypes after region correction. However, combined p-value analyses revealed APOE- and EFAD-dependent differences in the composition of primarily cortical sphingolipids. ROC analyses demonstrated high discriminative power of plasma sphingolipids for APOE, but not for AD genotype. These findings suggest that early plasma lipid profiles in female 5xFAD mice are more strongly influenced by APOE genotype than by overt AD pathology, potentially reflecting systemic pathways linked to APOE4-associated AD risk, while early disease-associated changes in the brain appear to be subtle and region-specific. These results underscore the importance of accounting for APOE genotype in early-stage AD lipidomic studies and in the interpretation of peripheral lipid biomarkers.

A compilation of reported alterations in the cerebrospinal fluid proteome in Alzheimer's disease

Alzheimer's disease is a multifaceted neurodegenerative disorder, with diverse underlying pathophysiological processes extending beyond amyloid-β and tau accumulation. The heterogeneity of Alzheimer's disease necessitates the identification of a broad array of biomarkers that capture the diverse mechanisms contributing to disease onset and progression. In this study, we systematically compiled and analysed cerebrospinal fluid proteomics data from omics studies utilizing mass spectrometry, Olink, or SomaScan platforms. Systematic literature searches for each platform revealed a total of 264 studies. From this, a set of 18 studies were selected based on sample size, number of markers analysed, and open data availability. We found a total of 1,448 differentially expressed proteins between Alzheimer's disease and amyloid negative controls across these datasets, with 635 being found in more than one study. A 'top' set of 61 differentially expressed proteins were consistently reported in at least six studies. Clustering and functional enrichment analysis of the top differentially expressed proteins indicated involvement in metabolic regulation, glutathione metabolism and proteins of the 14-3-3 family, reflecting importance of reactive oxygen species (ROS) response. Synaptic signalling processes were found to generally be downregulated. We further integrated the top differentially expressed proteins with results from a study on familial Alzheimer's disease cerebrospinal fluid to assess at which stage of disease progression these proteins change, highlighting markers shared between sporadic and familial Alzheimer's disease datasets. Lastly, we examine the overlap of the top differentially expressed proteins between cerebrospinal fluid and brain tissue using a publicly available database. This analysis provides a comprehensive overview of the Alzheimer's disease cerebrospinal fluid proteomic landscape, indicating changes in key pathways and cellular processes associated with Alzheimer's disease pathology. By integrating data from different platforms, we highlight reproducible protein changes that may serve as promising candidates for further biomarker research aimed at improving patient stratification, tracking disease progression, and assessing therapeutic interventions.

From blood drops to biomarkers: a scoping review of microsampling in mass spectrometry-based proteomics

BACKGROUND

Microsamples are simple blood sampling procedures utilizing small blood draws. Although microsamples are regularly used in some disciplines, proteomic analysis of these samples is an emerging field. Currently, it is unclear whether the quantitative precision and proteome coverage achieved in microsamples is comparable to plasma or serum. As a consequence, microsamples are not used in proteomics to the same degree as more traditional blood samples.

OBJECTIVES

The objective of this scoping review was to report the applications of microsamples within clinical mass spectrometry-based proteomics. This was accomplished by describing both proof-of-concept and clinical proteomics research within this field, with an additional evaluation of the newest advances regarding clinical proteomics.

INCLUSION CRITERIA

Original scientific literature was included where bottom-up mass spectrometry was used to analyze endogenous proteins from human microsamples.

METHODS

Relevant publications were sourced through three scientific databases (MEDLINE, EMBASE and Scopus) in addition to backward and forward citation searches through Scopus. Record screening was performed independently by two separate authors. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines.

RESULTS

A total of 209 records were screened for inclusion from database searches and 3157 records were screened from forward and backward citation searches, resulting in 64 eligible studies. An evaluation of proof-of-concept research within this field revealed that although microsamples are amenable to high-throughput proteomics using a variety of targeted and untargeted acquisition methods, quantification remained a relevant issue. Microsampling practices were heterogeneous, and no standard procedure existed for protein quantification. Clinical studies investigated protein expression in numerous disease or experimental groups, including hemoglobinopathies and immunodeficiency disorders.

CONCLUSION

The use of microsamples is increasing within the proteomics field and these samples are amenable to standard bottom-up workflows. Although microsamples present a clear advantage in terms of sampling procedure, both the sample collection and quantification procedures remain to be standardized. However, there is an incentive to address the remaining issues, since microsampling would greatly reduce the resources necessary to sample large cohorts within clinical proteomics, a field that currently lacks large discovery and validation cohorts.

Comparative evaluation of peptide vs. protein-based calibration for quantification of cardiac troponin I using ID-LC-MS/MS

OBJECTIVES

An analytical protocol based on isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS), which includes a peptide-based calibration strategy, was developed and validated for the determination of cardiac troponin I (cTnI) levels in clinical samples. Additionally, the developed method was compared with a protein-based calibration strategy, using cTnI serving as a model for low-abundant proteins. The aim is to evaluate new approaches for protein quantification in complex matrices, supporting the metrology community in implementing new methods and developing fit-for-purpose SI- traceable peptide or protein primary calibrators.

METHODS

To establish traceability to SI units, peptide impurity correction amino acid analysis (PICAA) was conducted to determine the absolute content of signature peptides in the primary standards. Immunoaffinity enrichment was used to capture cTnI from human serum, with a comparison between microbeads and nanobeads to improve enrichment efficiency. Parallel reaction monitoring was used to monitor two signature peptides specific to cTnI. Various digestion parameters were optimized to achieve complete digestion.

RESULTS

The analytical method demonstrated selectivity and specificity, allowing the quantification of cTnI within 0.9-22.0 μg/L. The intermediate precision RSD was below 28.9 %, and the repeatability RSD was below 5.8 % at all concentration levels, with recovery rates ranging from 87 % to 121 %. The comparison of calibration strategies showed similar LOQ values, but the peptide-based calibration exhibited significant quantitative bias in recovery rates. The data are available via ProteomeXchange (PXD055104).

CONCLUSIONS

This isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS) method, based on peptide calibration, successfully quantified cTnI in human serum. Comparing this with protein-based calibration highlighted both the strengths and potential limitations of peptide-based strategies.

Electric-Field Molecular Fingerprinting to Probe Cancer

Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for in vitro diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naïve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.

Serum proteomics reveals survival-associated biomarkers in pancreatic cancer patients treated with chemoimmunotherapy

Immunotherapy has transformed the landscape of cancer treatment but remains largely ineffective for patients with pancreatic ductal adenocarcinoma (PDAC). Some patients, however, show improved outcomes when treated with a combination of immunotherapy and chemotherapy. Here, we conducted deep serum proteome analysis to investigate the protein profiles of PDAC patients and changes during this combinatorial treatment. Utilizing an advanced serum workflow, we quantified 1,011 proteins across 211 samples from 62 patients. Glycolytic enzymes were associated with survival in anti-PD-1-treated patients, with their abundances significantly correlating with expression levels in tumor biopsies. Notably, a set of protein biomarkers was found to be highly predictive of survival in anti-PD-1-treated patients (area under the curve [AUC] = 0.91). Overall, our data demonstrate the potential of deep serum proteomics for precision medicine, offering a powerful tool to guide patient selection for treatment through minimally invasive serum protein biomarker measurements.

Blood proteome profiling for biomarker discovery in broilers with necrotic enteritis

Analysis of the blood proteome allows identification of proteins related to changes upon certain physiological conditions. The pathophysiology of necrotic enteritis (NE) has been extensively studied. While intestinal changes have been very well documented, data addressing NE-induced alterations in the blood proteome are scant, although these might have merit in diagnostics. In light of recent technological advancements in proteomics and pressing need for tools to access gut health, the current study employs mass-spectrometry (MS)-based proteomics to identify biomarkers for gastrointestinal health of chickens. Here, we report findings of an untargeted proteomics investigation conducted on blood plasma in chickens under NE challenge. Two MS-strategies were used for analysis: conventional data dependent acquisition coupled to standard nanoflow liquid chromatography (LC) (nano-DDA) and recently-developed data independent acquisition coupled to an Evosep One LC system (Evo-DIA). Despite superior completeness and quantification of the Evo-DIA-acquired data, high degree of agreement in identification and quantification was observed between both approaches. Additionally, we identified 15 differentially expressed proteins (shared by nano-DDA and Evo-DIA) that represent responses of animals to infection and may serve as potential biomarkers. Experimental validation through ELISA immunoassays and targeted MS for selected regulated proteins (CFD, HPS5, and MASP2) confirmed medium-to-high levels of inter-protein correlation. A GSEA analysis revealed enrichment in a number of processes related to adaptive and humoral immunity, immune activation and response in infected animals. Data are available via ProteomeXchange with identifiers PXD050461, PXD050473, and PXD061607.

Unravelling disease complexity: integrative analysis of multi-omic data in clinical research

INTRODUCTION

A holistic view on biological systems is today a reality with the application of multi-omic technologies. These technologies allow the profiling of genome, epigenome, transcriptome, proteome, metabolome as well as newly emerging 'omes.' While the multiple layers of data accumulate, their integration and reconciliation in a single system map is a cumbersome exercise that faces many challenges. Application to human health and disease requires large sample sizes, robust methodologies and high-quality standards.

AREAS COVERED

We review the different methods used to integrate multi-omics, as recent ones including artificial intelligence. With proteomics as an anchor technology, we then present selected applications of its data combination with other omics layers in clinical research, mainly covering literature from the last five years in the Scopus and/or PubMed databases.

EXPERT OPINION

Multi-omics is powerful to comprehensively type molecular layers and link them to phenotype. Yet, technologies and data are very diverse and still strategies and methodologies to properly integrate these modalities are needed.

Omics and rare diseases: challenges, applications, and future perspectives

INTRODUCTION

Rare diseases (RDs) are a heterogeneous group of diseases recognized as a relevant global health priority but posing aspects of complexity, such as geographical scattering of affected individuals, improper/late diagnosis, limited awareness, difficult surveillance and monitoring, limited understanding of natural history, and lack of treatment. Usually, RDs have a pediatric onset and are life-long, multisystemic, and associated with a poor prognosis.

AREAS COVERED

In this work, we review how high-throughput omics technologies such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and other well-established omics, which are increasingly more affordable and efficient, can be applied to the study of RDs promoting diagnosis, understanding of pathological mechanisms, biomarker discovery, and identification of treatments.

EXPERT OPINION

RDs, despite their challenges, offer a niche where collaborative efforts and personalized treatment strategies might be feasible using omics technologies. Specialized consortia fostering multidisciplinary collaboration, data sharing, and the development of biobanks and registries can be built; multi-omics approaches, including so far less exploited omics technologies, along with the implementation of AI tools can be undertaken to deepen our understanding of RDs, driving biomarker discovery and clinical interventions. Nevertheless, technical, ethical, legal, and societal issues must be clearly defined and addressed.

Lanthanide Metal-Organic Framework Flowers for Proteome Profiling and Biomarker Identification in Ultratrace Biofluid Samples

Identifying effective biomarkers has long been a persistent need for early diagnosis and targeted therapy of disease. While mass spectrometry-based label-free proteomics with trace cell has been demonstrated, deep proteomics with ultratrace human biofluid remains challenging due to low protein concentration, extremely limited patient sample volume, and substantial protein contact losses during preprocessing. Herein, we proposed and validated lanthanide metal-organic framework flowers (MOF-flowers), as effective materials, to trap and enrich protein in biofluid jointly through cation-π interaction and O-Ln coordination. We further developed a MOF-flower assisted simplified and single-pot Sample Preparation (Mass-SP) workflow that incorporates protein capture, digest, and peptide elute into one single PCR tube to maximally avoid adsorptive sample loss. We adopted Mass-SP to decipher aqueous humor (AH) proteome signatures from cataract and retinal vein occlusion (RVO) patients and quantified ∼3900 proteins in merely 1 μL of AH. Combined with machine learning, we further identified PFKL as a prioritization biomarker for RVO disease with the areas under the curves of 0.95 ± 0.04. Mass-SP presents a strategy to identify de novo biomarkers and explore potential therapeutic targets with extremely limited clinical human body fluid resources.

ProHap enables human proteomic database generation accounting for population diversity

Amid the advances in genomics, the availability of large reference panels of human haplotypes is key to account for human diversity within and across populations. However, mass spectrometry-based proteomics does not benefit from this information. To address this gap, we introduce ProHap, a Python-based tool that constructs protein sequence databases from phased genotypes of reference panels. ProHap enables researchers to account for haplotype diversity in proteomic searches.

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

Investigating the Use of Novel Blood Processing Methods to Boost the Identification of Biomarkers for Non-Small Cell Lung Cancer: A Proof of Concept

Diagnosis of non-small cell lung cancer (NSCLC) currently relies on imaging; however, these methods are not effective for detecting early stage disease. Investigating blood-based protein biomarkers aims to simplify the diagnostic process and identify disease-associated changes before they can be seen by using imaging techniques. In this study, plasma and frozen whole blood cell pellets from NSCLC patients and healthy controls were processed using both classical and novel techniques to produce a unique set of four sample types from a single blood draw. These samples were analyzed using 12 immunoassays and liquid chromatography-mass spectrometry to collectively screen 3974 proteins. Analysis of all fractions produced a set of 522 differentially expressed proteins, with conventional blood analysis (proteomic analysis of plasma) accounting for only 7 of the total. Boosted regression tree analysis of the differentially expressed proteins produced a panel of 13 proteins that were able to discriminate between controls and NSCLC patients, with an area under the ROC curve (AUC) of 0.864 for the set. Our rapid and reproducible (<10% CV for technical replicates) blood preparation and analysis methods enabled the production of high-quality data from only 30 μL of complex samples that typically require significant fractionation prior to proteomic analysis. With our methods, almost 4000 proteins were identified from a single fraction over a 62.5 min gradient by LC-MS/MS.

Proteomic Analysis of Biological Fluids

Biological fluids are proteinaceous liquids or suspensions released through different body orifices or through penetration of the skin. These fluids are the result of multiple tissues and cell types and contain extensive, highly complex, and dynamic protein populations that reflect both the transcriptional program of the originating cells and a record of the individual's health status. Body fluids are readily accessible to clinicians and researchers, and as such proteomic analyses are an important component of clinical studies, fertility studies, oral health studies, and forensic investigations. Current mass spectrometry (MS) datasets have a dynamic range of up to six orders of magnitude and are as diverse as the originating tissue types. Mass spectrometry has the potential to provide information across a wide range of applications, including basic research into human biology and pathology, biochemical analysis of protein function, biomarker discovery and detection, as well as forensic investigations wherein investigators interpret a protein profile to identify the body site origin of a biological fluid. The method below describes a specimen processing workflow that is flexible in terms of biological fluid type, sample state (e.g., a dried sample extracted from evidence or neat fluid), and level of degradation. The method described here is compatible with both high sensitivity shotgun liquid chromatography-mass spectrometry LC/MS analysis and targeted (qualitative or quantitative) MS-based analysis of biomarker candidates.

Head-to-Head Comparison of Aptamer- and Antibody-Based Proteomic Platforms in Human Cerebrospinal Fluid Samples from a Real-World Memory Clinic Cohort

High-throughput proteomic platforms are crucial to identify novel Alzheimer's disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan® 7k) and antibody-based (Olink® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and inter-platform reproducibility were evaluated through correlations between two independent SomaScan® assays analyzing the same samples, and between SomaScan® and Olink® results. Association analyses were performed between proteomic measures, CSF biological traits, sample demographics, and AD endophenotypes. Our 12-category metric of reproducibility combining correlation analyses identified 2428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.

Biomarkers of Importance in Monitoring Heart Condition After Acute Myocardial Infarction

Despite notable advancements in cardiovascular medicine, morbidity and mortality rates associated with myocardial infarction (MI) remain high. The unfavourable prognosis and absence of robust post-MI protocols necessitate further intervention. In this comprehensive review, we will focus on well-established and novel biomarkers that can provide insight into the processes that occur after an ischemic event. More precisely, during the follow-up, it is of particular importance to monitor biomarkers that indicate an increase in myocardial stretch and stress, damage and death of cardiomyocytes, remodelling of the extracellular matrix, oxidative stress, and inflammation. This enables the identification of abnormalities in a timely manner, as well as the capacity to respond promptly to any changes. Therefore, we would like to highlight the importance of well-known markers, such as natriuretic peptides, high-sensitivity troponins, soluble suppression of tumorigenicity 2, galactin-3, C-reactive protein, and interleukins in post-MI settings, as well as biomarkers such as adrenomedullin, growth differentiation factor-15, insulin-like growth factor binding protein 7, amyloid beta, vitamin D, trimethylamine N-oxide, and advanced glycation end-products that recently emerged in the cardiovascular filed. The implementation of novel post-MI protocols, which encompass the monitoring of the aforementioned biomarkers deemed pertinent, in conjunction with adherence to established cardiac rehabilitation programmes, along with the already well-established therapeutic strategies and control of cardiovascular risk factors, has the potential to markedly enhance patient outcomes and reduce the elevated level of morbidity and mortality.

Saliva Proteome, Metabolome and Microbiome Signatures for Detection of Alzheimer's Disease

Background: As the burden of Alzheimer's disease (AD) escalates with an ageing population, the demand for early and accessible diagnostic methods becomes increasingly urgent. Saliva, with its non-invasive and cost-effective nature, presents a promising alternative to cerebrospinal fluid and plasma for biomarker discovery. Methods: In this study, we conducted a comprehensive multi-omics analysis of saliva samples (n = 20 mild cognitive impairment (MCI), n = 20 Alzheimer's disease and age- and n = 40 gender-matched cognitively normal individuals), from the South Australian Neurodegenerative Disease (SAND) cohort, integrating proteomics, metabolomics, and microbiome data with plasma measurements, including pTau181. Results: Among the most promising findings, the protein Stratifin emerged as a top candidate, showing a strong negative correlation with plasma pTau181 (r = -0.49, p < 0.001) and achieving an AUC of 0.95 in distinguishing AD and MCI combined from controls. In the metabolomics analysis, 3-chlorotyrosine and L-tyrosine exhibited high correlations with disease severity progression, with AUCs of 0.93 and 0.96, respectively. Pathway analysis revealed significant alterations in vitamin B12 metabolism, with Transcobalamin-1 levels decreasing in saliva as AD progressed despite an increase in serum vitamin B12 levels (p = 0.008). Microbiome analysis identified shifts in bacterial composition, with a microbiome cluster containing species such as Lautropia mirabilis showing a significant decrease in abundance in MCI and AD samples. The overall findings were reinforced by weighted correlation network analysis, which identified key hubs and enriched pathways associated with AD. Conclusions: Collectively, these data highlight the potential of saliva as a powerful medium for early AD diagnosis, offering a practical solution for large-scale screening and monitoring.

Syndecan-1: a key player in health and disease

Syndecan-1 (SDC-1) is a transmembrane protein localized on the basolateral surface of epithelial cells, encompassing a core protein with heparin sulfate and chondroitin sulfate glycosaminoglycan side chains. SDC-1 is involved in a panoply of cellular mechanisms including cell-to-cell adhesion, extracellular matrix interactions, cell cycle modulation, and lipid clearance. Alterations in the expression and function of SDC-1 are implicated in numerous disease entities, making it an attractive diagnostic and therapeutic target. However, despite its broad involvement in several disease processes, the underlying mechanism contributing to its diverse functions, pathogenesis, and therapeutic uses remains underexplored. Therefore, this review examines the role of SDC-1 in health and disease, focusing on liver pathologies, inflammatory diseases, infectious diseases, and cancer, and sheds light on SDC-1-based therapeutic approaches. Moreover, it delves into the mechanisms through which SDC-1 contributes to these diseases, emphasizing cell-type specific mechanisms. By comprehensively summarizing the significance of SDC-1, its association with several diseases, and its underlying mechanisms of action, the findings of this review could inform future research directions toward the development of targeted therapies and early diagnosis for a multitude of disease entities.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

The Perils of Molecular Interpretations from Vibrational Spectra of Complex Samples

Vibrational spectroscopy is a widely used technique for chemical characterizations across various analytical sciences. Its applications are increasingly extending to the analysis of complex samples such as biofluids, providing high-throughput molecular profiling. While powerful, the technique suffers from an inherent limitation: The overlap of absorption information across different spectral domains hinders the capacity to identify individual molecular substances contributing to measured signals. Despite the awareness of this challenge, the difficulty of analyzing multi-molecular spectra is often underestimated, leading to unsubstantiated molecular interpretations. Here, we examine the prevalent overreliance on spectral band assignment and illuminate the pitfalls of correlating spectral signals to discrete molecular entities or physiological states without rigorous validation. Focusing on blood-based infrared spectroscopy, we provide examples illustrating how peak overlap among different substances, relative substance concentrations, and preprocessing steps can lead to erroneous interpretations. We advocate for a viewpoint shift towards a more careful understanding of complex spectra, which shall lead to either accepting their fingerprinting nature and leveraging machine learning analysis - or involving additional measurement modalities for robust molecular interpretations. Aiming to help translate and improve analytical practices within the field, we highlight the limitations of molecular interpretations and feature their viable applications.

Heparin-enriched plasma proteome is significantly altered in Alzheimer's disease

INTRODUCTION

Heparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to β-amyloid (Aβ) and tau pathology in Alzheimer's disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry-based proteomic approaches.

METHODS

We employed heparin-affinity chromatography, followed by off-line fractionation and tandem mass tag mass spectrometry (TMT-MS), to enrich HBPs from plasma obtained from AD (n = 62) and control (n = 47) samples. These profiles were then correlated to Aβ, tau and phosphorylated tau (pTau) CSF biomarkers and plasma pTau181 from the same individuals, as well as a consensus brain proteome network to assess the overlap with AD brain pathophysiology.

RESULTS

Heparin enrichment from plasma was highly reproducible, enriched well-known HBPs like APOE and thrombin, and depleted high-abundant proteins such as albumin. A total of 2865 proteins, spanning 10 orders of magnitude in abundance, were measured across 109 samples. Compared to the consensus AD brain protein co-expression network, we observed that specific plasma proteins exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes, highlighting the complex interplay between the two compartments. Elevated proteins in AD plasma, when compared to controls, included members of the matrisome module in brain that accumulate with Aβ deposits, such as SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB, and the APOE4 proteoform. Additionally, heparin-enriched proteins in plasma demonstrated significant correlations with conventional AD CSF biomarkers, including Aβ, total tau, pTau, and plasma pTau181. A panel of five plasma proteins classified AD from control individuals with an area under the curve (AUC) of 0.85. When combined with plasma pTau181, the panel significantly improved the classification performance of pTau181 alone, increasing the AUC from 0.93 to 0.98. This suggests that the heparin-enriched plasma proteome captures additional variance in cognitive dementia beyond what is explained by pTau181.

CONCLUSION

These findings support the utility of a heparin-affinity approach coupled with TMT-MS for enriching amyloid-associated proteins, as well as a wide spectrum of plasma biomarkers that reflect pathological changes in the AD brain.

Ultra-Fast Multi-Organ Proteomics Unveils Tissue-Specific Mechanisms of Drug Efficacy and Toxicity

Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present UFP-MS, an ultra-fast mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively. Our streamlined sample preparation workflow features high-throughput homogenization, adaptive focused acoustics (AFA)-assisted proteolysis, and Evotip-accelerated desalting, allowing for the processing of up to 96 tissue samples in 5 h. As a practical application, we analyzed 507 samples from 13 mouse tissues treated with the enzyme-drug L-asparaginase (ASNase) or its glutaminase-free Q59L mutant, generating a quantitative profile of 11,472 proteins following drug treatment. The MS results confirmed the impact of ASNase on amino acid metabolism in solid tissues. Further analysis revealed broad suppression of anticoagulants and cholesterol metabolism and uncovered numerous tissue-specific dysregulated pathways. In summary, the UFP-MS method greatly accelerates the generation of biological insights and clinically actionable hypotheses into tissue-specific vulnerabilities targeted by ASNase.

Longitudinal proteomics of leptin treatment in humans with acute and chronic energy deficiency-induced hypoleptinemia reveal novel, mainly immune-related, pleiotropic effects

BACKGROUND

Leptin is known for its metabolic, immunomodulatory and neuroendocrine properties, but the full spectrum of molecules downstream of leptin and relevant underlying mechanisms remain to be fully clarified. Our objective was to identify proteins and pathways influenced by leptin through untargeted proteomics in two clinical trials involving leptin administration in lean individuals.

METHODS

We performed untargeted liquid chromatography-tandem mass spectrometry serum proteomics across two studies a) Short-term randomized controlled crossover study of lean male and female humans undergoing a 72-h fast with concurrent administration of either placebo or high-dose leptin; b) Long-term (36-week) randomized controlled trial of leptin replacement therapy in human females with acquired relative energy deficiency and hypoleptinemia. We explored longitudinal proteomic changes and run adjusted mixed models followed by post-hoc tests. We further attempted to identify ontological pathways modulated during each experimental condition and/or comparison, through integrated qualitative pathway and enrichment analyses. We also explored dynamic longitudinal relationships between the circulating proteome with clinical and hormonal outcomes.

RESULTS

289 and 357 unique proteins were identified per each respective study. Short-term leptin administration during fasting markedly upregulated several proinflammatory molecules, notably C-reactive protein (CRP) and cluster of differentiation (CD) 14, and downregulated lecithin cholesterol acyltransferase and several immunoglobulin variable chains, in contrast with placebo, which produced minimal changes. Quantitative pathway enrichment further indicated an upregulation of the acute phase response and downregulation of immunoglobulin- and B cell-mediated immunity by leptin. These changes were independent of participants' biological sex. In the long term study, leptin likewise robustly and persistently upregulated proteins of the acute phase response, and downregulated immunoglobulin-mediated immunity. Leptin also significantly and differentially affected a wide array of proteins related to immune function, defense response, coagulation, and inflammation compared with placebo. These changes were more notable at the 24-week visit, coinciding with the highest measured levels of serum leptin. We further identified distinct co-regulated clusters of proteins and clinical features during leptin administration indicating robust longitudinal correlations between the regulation of immunoglobulins, immune-related molecules, serpins (including cortisol and thyroxine-binding globulins), lipid transport molecules and growth factors, in contrast with placebo, which did not produce similar associations.

CONCLUSIONS

These high-throughput longitudinal results provide unique functional insights into leptin physiology, and pave the way for affinity-based proteomic analyses measuring several thousands of molecules, that will confirm these data and may fully delineate underlying mechanisms.

Soluble Proteoglycans and Proteoglycan Fragments as Biomarkers of Pathological Extracellular Matrix Remodeling

Proteoglycans and their proteolytic fragments diffuse into biological fluids such as plasma, serum, urine, or synovial fluid, where they can be detected by antibodies or mass-spectrometry. Neopeptides generated by the proteolysis of proteoglycans are recognized by specific neoepitope antibodies and can act as a proxy for the activity of certain proteases. Proteoglycan and proteoglycan fragments can be potentially used as prognostic, diagnostic, or theragnostic biomarkers for several diseases characterized by dysregulated extracellular matrix remodeling such as osteoarthritis, rheumatoid arthritis, atherosclerosis, thoracic aortic aneurysms, central nervous system disorders, viral infections, and cancer. Here, we review the main mechanisms accounting for the presence of soluble proteoglycans and their fragments in biological fluids, their potential application as diagnostic, prognostic, or theragnostic biomarkers, and highlight challenges and opportunities ahead of their clinical translation.

From Proteomics to the Analysis of Single Protein Molecules

Limit of detection (LoD) is a term that is used to characterize the sensitivity of an analytical method. The existing limitation of the sensitivity of analysis using modern mass spectrometry methods has been experimentally shown to be a limiting factor in the application of proteomic technologies in medicine. This article proposes a concept of a new technology that will set a new vector of development in the development of systems for solving problems of medical diagnostics and deals with theoretical and practical aspects of creating a new technology for the detection of single biomacromolecules (in particular, proteins) in biological samples. Such technology should be based on the principle of signal registration similar to that used in a Geiger counter (also known as a Geiger-Müller counter or G-M counter), a device that automatically counts the number of ionizing particles that hit it. This counter is free from probabilistic components; it registers a signal if there is at least one target molecule in the analysis chamber. Predictive medical diagnostics require technology based on methods where sensitivity allows for the detection of single marker molecules in a biological sample volume of 1-10 µL, the smallest volume of biomaterial used in laboratory diagnostics. Creation of a detector with a sensitivity of 10-18 M would allow for the detection of one molecule in 1 µL of the sample, which fundamentally makes this approach analogous to a G-M counter for solutions. To date, bioanalytical methods are limited to a sensitivity of 10-12 M (which is approximately 1 million molecules per 1 μL), which is insufficient to capture the early stages of pathological processes.

Plasma proteomes of acute myeloid leukemia patients treated with transfusions reveal signatures of inflammation and hemostatic dysregulation

Background

Bone marrow aplasia is a common feature in acute myeloid leukemia (AML) patients during their remission induction treatment, and is associated with potential complications such as bleeding, infection and anemia. Frequent platelet and red cell transfusions are administered to prevent and treat these complications. However, platelet counts are poorly associated with bleeding events in this population. Therefore, plasma protein levels could add valuable insights to improve our understanding of the patient's health state. In this study, we aimed to delineate the plasma proteome, including inflammatory pathways, hemostatic and immune components, of AML patients during treatment with intensive transfusion support.

Methods

We employed unbiased mass spectrometry (MS)-based proteomics on longitudinal plasma samples from 10 AML patients during intensive-transfusion treatment phase with healthy individuals as baseline control.

Results

A total of 450 proteins were quantified in plasma samples from AML patients and healthy controls. Alteration in proteins levels were mainly observed for proteins involved in inflammation (e.g. SAA1 and CRP), and complement (e.g. C9 and MASP2) when comparing AML versus healthy individuals. Correlation analysis revealed additional affected protein dynamics, including proteins associated with coagulation cascade, endopeptidase inhibitors activity and lipoprotein remodeling.

Conclusion

The plasma proteome from AML patients during intensive treatment shows a disbalance in inflammation, endopeptidase inhibitors activity, lipoprotein remodeling, coagulation and complement. These effects and potential associations with bleeding risk will be further studied in a bigger cohort.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41231-024-00189-5.

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Profiling cells with DELs: Small molecule fingerprinting of cell surfaces

DNA-encoded small molecule library technology has recently emerged as a new paradigm for identifying ligands against drug targets. To date, it has been used to identify ligands against targets that are soluble or overexpressed on cell surfaces. Here, we report applying cell-based selection methods to profile surfaces of mouse C2C12 myoblasts and myotube cells in an unbiased, target agnostic manner. A panel of on-DNA compounds were identified and confirmed for cell binding selectivity. We optimized the cell selection protocol and employed a novel data analysis method to identify cell selective ligands against a panel of human B and T lymphocytes. We discuss the generality of using this workflow for DNA encoded small molecule library selection and data analysis against different cell types, and the feasibility of applying this method to profile cell surfaces for biomarker and target identification.

Plasma proteomics identify biomarkers predicting Parkinson's disease up to 7 years before symptom onset

Parkinson's disease is increasingly prevalent. It progresses from the pre-motor stage (characterised by non-motor symptoms like REM sleep behaviour disorder), to the disabling motor stage. We need objective biomarkers for early/pre-motor disease stages to be able to intervene and slow the underlying neurodegenerative process. Here, we validate a targeted multiplexed mass spectrometry assay for blood samples from recently diagnosed motor Parkinson's patients (n = 99), pre-motor individuals with isolated REM sleep behaviour disorder (two cohorts: n = 18 and n = 54 longitudinally), and healthy controls (n = 36). Our machine-learning model accurately identifies all Parkinson patients and classifies 79% of the pre-motor individuals up to 7 years before motor onset by analysing the expression of eight proteins-Granulin precursor, Mannan-binding-lectin-serine-peptidase-2, Endoplasmatic-reticulum-chaperone-BiP, Prostaglaindin-H2-D-isomaerase, Interceullular-adhesion-molecule-1, Complement C3, Dickkopf-WNT-signalling pathway-inhibitor-3, and Plasma-protease-C1-inhibitor. Many of these biomarkers correlate with symptom severity. This specific blood panel indicates molecular events in early stages and could help identify at-risk participants for clinical trials aimed at slowing/preventing motor Parkinson's disease.

Serum proteomic analysis uncovers novel serum biomarkers for depression

Objective

The identification of depression primarily relies on the clinical symptoms and psychiatric evaluation of the patient, in the absence of objective and quantifiable biomarkers within clinical settings. This study aimed to explore potential serum biomarkers associated with depression.

Methods

Serum samples from a training group comprising 48 depression patients and 48 healthy controls underwent proteomic analysis. Magnetic bead-based weak cation exchange (MB-WCX) and MALDI-TOF-MS were used in combination. To screen the differential peaks, ClinProTools software was employed. The proteins were identified using LC-MS/MS. ELISA was employed to confirm the expression of entire protein in the serum of the verification cohort, which encompassed 48 individuals who had been diagnosed with Depression and 48 healthy controls who were collected prospectively. Subsequently, logistic regression analysis was conducted to determine the diagnostic efficacy of the aforementioned predictors.

Results

Five potential biomarker peaks indicating depression were identified in serum samples (peak 1, m/z: 1868.21; peak 2, m/z: 1062.35; peak 3, m/z: 1452.12; peak 4, m/z: 1208.72; peak 5, m/z: 1619.58). All of these peaks had higher expression in the pre-therapy group and were confirmed to be Tubulin beta chain (TUBB), Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), Complement component 3 (C3), and Complement C4A precursor (C4A) by ELISA validation. Multivariate logistic regression analysis revealed that serum levels of TUBB, ITIH4, C3, and C4A were significant independent risk factors for the development of depression.

Conclusion

Depression is a prevalent psychiatric condition. Timely detection is challenging, resulting in poor prognoses for patients. Our study on plasma proteomics for depression demonstrated that TUBB, ITIH4, C3, and C4A differentiate between depression patients and healthy controls. The proteins that were identified could potentially function as biomarkers for the diagnosis of depression. Pinpointing these biomarkers could enable early identification of depression, which would advance precise treatment.

Mass Spectrometry-Based Proteomics of Poly(methylmethacrylate)-Embedded Bone

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely employed technique in proteomics research for studying the proteome biology of various clinical samples. Hard tissues, such as bone and teeth, are routinely preserved using synthetic poly(methyl methacrylate) (PMMA) embedding resins that enable histological, immunohistochemical, and morphological examination. However, the suitability of PMMA-embedded hard tissues for large-scale proteomic analysis remained unexplored. This study is the first to report on the feasibility of PMMA-embedded bone samples for LC-MS/MS analysis. Conventional workflows yielded merely limited coverage of the bone proteome. Using advanced strategies of prefractionation by high-pH reversed-phase liquid chromatography in combination with isobaric tandem mass tag labeling resulted in proteome coverage exceeding 1000 protein identifications. The quantitative comparison with cryopreserved samples revealed that each sample preparation workflow had a distinct impact on the proteomic profile. However, workflow replicates exhibited a high reproducibility for PMMA-embedded samples. Our findings further demonstrate that decalcification prior to protein extraction, along with the analysis of solubilization fractions, is not preferred for PMMA-embedded bone. The biological applicability of the proposed workflow was demonstrated using samples of human PMMA-embedded alveolar bone and the iliac crest, which revealed anatomical site-specific proteomic profiles. Overall, these results establish a crucial foundation for large-scale proteomics studies contributing to our knowledge of bone biology.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Proteomics of prostate cancer serum and plasma using low and high throughput approaches

Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.

Unlocking the promise of liquid biopsies in precision oncology

Liquid biopsies have emerged as a promising and minimally invasive alternative to traditional tissue biopsies for detecting and monitoring cancer. Liquid biopsies offer a comprehensive analysis of cancer genetics and tumor burden by examining circulating cells and cell-derived analytes using a variety of assays, including conventional PCR methods and cutting-edge tools like long-read sequencing and nanotechnology. However, there are still some limitations and challenges that need to be overcome for their implementation in clinical routine, including the need for further research on their sensitivity and specificity, cost-effectiveness, standardization, and regulatory approval. Despite these challenges, liquid biopsies have the potential to become widely used tools in oncology. Here we provide an overview of the current state of liquid biopsies, highlighting recent advancements in the field and their potential benefits in clinical settings for cancer patients. The article further discusses the challenges that need to be addressed in order to facilitate their application worldwide. Prompt resolution of these challenges can be achieved by fostering international research collaborations and establishing standardized guidelines for liquid biopsy sample management and studies.

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Unlocking protein-based biomarker potential for graft-versus-host disease following allogenic hematopoietic stem cell transplants

Despite the numerous advantages of allogeneic hematopoietic stem cell transplants (allo-HSCT), there exists a notable association with risks, particularly during the preconditioning period and predominantly post-intervention, exemplified by the occurrence of graft-versus-host disease (GVHD). Risk stratification prior to symptom manifestation, along with precise diagnosis and prognosis, relies heavily on clinical features. A critical imperative is the development of tools capable of early identification and effective management of patients undergoing allo-HSCT. A promising avenue in this pursuit is the utilization of proteomics-based biomarkers obtained from non-invasive biospecimens. This review comprehensively outlines the application of proteomics and proteomics-based biomarkers in GVHD patients. It delves into both single protein markers and protein panels, offering insights into their relevance in acute and chronic GVHD. Furthermore, the review provides a detailed examination of the site-specific involvement of GVHD. In summary, this article explores the potential of proteomics as a tool for timely and accurate intervention in the context of GVHD following allo-HSCT.

Serum proteomic biomarker investigation of vascular depression using data-independent acquisition: a pilot study

Background

Vascular depression (VaD) is a depressive disorder closely associated with cerebrovascular disease and vascular risk factors. It remains underestimated owing to challenging diagnostics and limited information regarding the pathophysiological mechanisms of VaD. The purpose of this study was to analyze the proteomic signatures and identify the potential biomarkers with diagnostic significance in VaD.

Methods

Deep profiling of the serum proteome of 35 patients with VaD and 36 controls was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Functional enrichment analysis of the quantified proteins was based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Reactome databases. Machine learning algorithms were used to screen candidate proteins and develop a protein-based model to effectively distinguish patients with VaD.

Results

There were 29 up-regulated and 31 down-regulated proteins in the VaD group compared to the controls (|log2FC| ≥ 0.26, p ≤ 0.05). Enrichment pathways analyses showed that neurobiological processes related to synaptic vesicle cycle and axon guidance may be dysregulated in VaD. Extrinsic component of synaptic vesicle membrane was the most enriched term in the cellular components (CC) terms. 19 candidate proteins were filtered for further modeling. A nomogram was developed with the combination of HECT domain E3 ubiquitin protein ligase 3 (HECTD3), Nidogen-2 (NID2), FTO alpha-ketoglutarate-dependent dioxygenase (FTO), Golgi membrane protein 1 (GOLM1), and N-acetylneuraminate lyase (NPL), which could be used to predict VaD risk with favorable efficacy.

Conclusion

This study offers a comprehensive and integrated view of serum proteomics and contributes to a valuable proteomics-based diagnostic model for VaD.

Special Issue "Deployment of Proteomics Approaches in Biomedical Research"

Many angles of personalized medicine, such as diagnostic improvements, systems biology [...].

Mass spectrometry-based proteomics data from thousands of HeLa control samples

Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

Quantification of putative ovarian cancer serum protein biomarkers using a multiplexed targeted mass spectrometry assay

BACKGROUND

Ovarian cancer is the most lethal gynecologic malignancy in women, and high-grade serous ovarian cancer (HGSOC) is the most common subtype. Currently, no clinical test has been approved by the FDA to screen the general population for ovarian cancer. This underscores the critical need for the development of a robust methodology combined with novel technology to detect diagnostic biomarkers for HGSOC in the sera of women. Targeted mass spectrometry (MS) can be used to identify and quantify specific peptides/proteins in complex biological samples with high accuracy, sensitivity, and reproducibility. In this study, we sought to develop and conduct analytical validation of a multiplexed Tier 2 targeted MS parallel reaction monitoring (PRM) assay for the relative quantification of 23 putative ovarian cancer protein biomarkers in sera.

METHODS

To develop a PRM method for our target peptides in sera, we followed nationally recognized consensus guidelines for validating fit-for-purpose Tier 2 targeted MS assays. The endogenous target peptide concentrations were calculated using the calibration curves in serum for each target peptide. Receiver operating characteristic (ROC) curves were analyzed to evaluate the diagnostic performance of the biomarker candidates.

RESULTS

We describe an effort to develop and analytically validate a multiplexed Tier 2 targeted PRM MS assay to quantify candidate ovarian cancer protein biomarkers in sera. Among the 64 peptides corresponding to 23 proteins in our PRM assay, 24 peptides corresponding to 16 proteins passed the assay validation acceptability criteria. A total of 6 of these peptides from insulin-like growth factor-binding protein 2 (IBP2), sex hormone-binding globulin (SHBG), and TIMP metalloproteinase inhibitor 1 (TIMP1) were quantified in sera from a cohort of 69 patients with early-stage HGSOC, late-stage HGSOC, benign ovarian conditions, and healthy (non-cancer) controls. Confirming the results from previously published studies using orthogonal analytical approaches, IBP2 was identified as a diagnostic biomarker candidate based on its significantly increased abundance in the late-stage HGSOC patient sera compared to the healthy controls and patients with benign ovarian conditions.

CONCLUSIONS

A multiplexed targeted PRM MS assay was applied to detect candidate diagnostic biomarkers in HGSOC sera. To evaluate the clinical utility of the IBP2 PRM assay for HGSOC detection, further studies need to be performed using a larger patient cohort.

Proteomics and Metabolomics in Congenital Zika Syndrome: A Review of Molecular Insights and Biomarker Discovery

Zika virus (ZIKV) infection can be transmitted vertically, leading to the development of congenital Zika syndrome (CZS) in infected fetuses. During the early stages of gestation, the fetuses face an elevated risk of developing CZS. However, it is important to note that late-stage infections can also result in adverse outcomes. The differences between CZS and non-CZS phenotypes remain poorly understood. In this review, we provide a summary of the molecular mechanisms underlying ZIKV infection and placental and blood-brain barriers trespassing. Also, we have included molecular alterations that elucidate the progression of CZS by proteomics and metabolomics studies. Lastly, this review comprises investigations into body fluid samples, which have aided to identify potential biomarkers associated with CZS.

Virus-like Iron-Gold Heterogeneous Nanoparticles for Drug Target Screening

Drug-target recognition has great impacts on revealing mechanisms of pharmacological activities, especially drug resistance and off-target effects. In recent years, chemoproteomics has been widely used for drug target screening and discovery due to its high-throughput, high accuracy, and sensitivity. However, there still remain challenges on how to efficiently and unambiguously track target proteins from complex biological matrices. Herein, we report a drug target screening method based on virus-like iron-gold heterogeneous nanoparticles (Au@Fe3O4 NPs). The unique structure of Au@Fe3O4 NPs not only maintains the magnetism of Fe3O4 NPs to facilitate protein enrichment and purification, but also increases drug modification by introducing more active sites on the surface of Au NPs. After coincubating the drug modified NPs with the cell lysate, the high loading of drug on the surface of Au@Fe3O4 NPs was beneficial for capturing target proteins with low abundance. This well-designed heterogeneous nanomaterial provides a novel strategy for improving the efficiency and accuracy of affinity-based proteomics.

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.

Analysis of Structural Changes in the Protein near the Phosphorylation Site

Modification of the protein after synthesis (PTM) often affects protein function as supported by numerous studies. However, there is no consensus about the degree of structural protein changes after modification. For phosphorylation of serine, threonine, and tyrosine, which is a common PTM in the biology of living organisms, we consider topical issues related to changes in the geometric parameters of a protein (Rg, RMSD, Cα displacement, SASA). The effect of phosphorylation on protein geometry was studied both for the whole protein and at the local level (i.e., in different neighborhoods of the modification site). Heterogeneity in the degree of protein structural changes after phosphorylation was revealed, which allowed for us to isolate a group of proteins having pronounced local structural changes in the neighborhoods of up to 15 amino acid residues from the modification site. This is a comparative study of protein structural changes in neighborhoods of 3-15 amino acid residues from the modified site. Amino acid phosphorylation in proteins with pronounced local changes caused switching from the inactive functional state to the active one.

Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.