The human plasma proteome: history, character, and diagnostic prospects

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Deciphering immunodiffusion: In silico optimization for faster protein diagnostics

Immunodiffusion tests offer a simple yet powerful method for detecting protein antigens, but their long assay times hinder clinical utility. We unveil the complex interplay of parameters governing this process using finite element simulations. By meticulously validating our model against real-world data, we elucidate how initial concentrations and diffusivities of antigen and antibody shape the intensity, size, and formation time of the precipitin ring. Our key innovation lies in employing phase diagram analysis to map the combined effects of these parameters on assay performance. This framework enables rapid in silico parameter estimation, paving the way for the design of novel immunodiffusion assays with drastically reduced assay times. The presented approach holds immense potential for optimizing protein diagnostics for fast and reliable diagnostics.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

Defining the Soluble and Extracellular Vesicle Protein Compartments of Plasma Using In-Depth Mass Spectrometry-Based Proteomics

Plasma-derived extracellular vesicles (pEVs) are a potential source of diseased biomarker proteins. However, characterizing the pEV proteome is challenging due to its relatively low abundance and difficulties in enrichment. This study presents a streamlined workflow to identify EV proteins from cancer patient plasma using minimal sample input. Starting with 400 μL of plasma, we generated a comprehensive pEV proteome using size exclusion chromatography (SEC) combined with HiRIEF prefractionation-based mass spectrometry (MS). First, we compared the performance of HiRIEF and long gradient MS workflows using control pEVs, quantifying 2076 proteins with HiRIEF. In a proof-of-concept study, we applied SEC-HiRIEF-MS to a small cohort (12) of metastatic lung adenocarcinoma (LUAD) and malignant melanoma (MM) patients. We also analyzed plasma samples from the same patients to study the relationship between plasma and pEV proteomes. We identified and quantified 1583 proteins in cancer pEVs and 1468 proteins in plasma across all samples. While there was substantial overlap, the pEV proteome included several unique EV markers and cancer-related proteins. Differential analysis revealed 30 DEPs in LUAD vs the MM group, highlighting the potential of pEVs as biomarkers. This work demonstrates the utility of a prefractionation-based MS for comprehensive pEV proteomics and EV biomarker discovery. Data are available via ProteomeXchange with the identifiers PXD039338 and PXD038528.

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 employed, 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.

A Hybrid Orbitrap-Nanoelectromechanical Systems Approach for the Analysis of Individual, Intact Proteins in Real Time

Nanoelectromechanical systems (NEMS)-based mass spectrometry (MS) is an emerging technique that enables determination of the mass of individual adsorbed particles by driving nanomechanical devices at resonance and monitoring the real-time changes in their resonance frequencies induced by each single molecule adsorption event. We incorporate NEMS into an Orbitrap mass spectrometer and report our progress towards leveraging the single-molecule capabilities of the NEMS to enhance the dynamic range of conventional MS instrumentation and to offer new capabilities for performing deep proteomic analysis of clinically relevant samples. We use the hybrid instrument to deliver E. coli GroEL molecules (801 kDa) to the NEMS devices in their native, intact state. Custom ion optics are used to focus the beam down to 40 μm diameter with a maximum flux of 25 molecules/second. The mass spectrum obtained with NEMS-MS shows good agreement with the known mass of GroEL.

Serum proteinogram of gilthead seabream (Sparus aurata) and European sea bass (Dicentrarchus labrax) as a new useful approach for detecting loss of haemostasis

Proteinograms, a semiquantitative analytical method that separates proteins into multiple bands, have not been explored in teleosts for diagnostic or prognostic purposes. This study aimed to establish reference values for proteinograms in the serum of gilthead seabream (Sparus aurata) and European sea bass (Dicentrarchus labrax), two important farmed fish species in the Mediterranean region. Serum proteins were studied using SDS-PAGE, electropherogram, and HPLC-mass spectrometry. SDS-PAGE analysis revealed four major bands of proteins around 11, 25, 70, and 100 kDa in the serum of gilthead seabream and European sea bass. Electropherogram results showed that a protein with a molecular weight of 76.8 kDa was the most abundant protein in the serum of gilthead seabream, while a peak of 75.5 kDa was the most abundant in European sea bass. HPLC-mass spectrometry detected 87 proteins and 119 proteins in the serum of gilthead seabream and European sea bass, respectively, including α1-globulins, α2-globulins, β-globulins, and γ-globulins. Notably, the albumin sequence was not detected in either of the two species. These results help to characterize the serum protein profile and to establish reference proteinograms for these two fish species. They also provide a basis for the development of novel approaches for the rapid detection of loss of haemostasis due to stress, health disorders or disease in farmed fish.

In Situ Measurement of Nanoparticle-Blood Protein Adsorption and Its Heterogeneity with Single-Nanoparticle Resolution via Dual Fluorescence Quantification

The formation of a protein corona gives nanomedicines a distinct biological identity, profoundly influencing their fate in the body. Nonspecific nanoparticle-protein interactions are typically highly heterogeneous, which can lead to unique biological behaviors and in vivo fates for individual nanoparticles that remain underexplored. To address this, we have established an in situ approach that allows quantitative examination of nanoparticle-protein adsorption at the individual nanoparticle level. This method integrates dual fluorescence quantification techniques, wherein the nanoparticles are first individually analyzed via nanoflow cytometry to detect fluorescent signals from adsorbed proteins. The obtained fluorescence intensity is then translated into protein quantities through calibration with microplate reader quantification. Consequently, this approach enables analysis of interparticle heterogeneity of nano-protein interactions, as well as in situ monitoring of protein adsorption kinetics and nanoparticle aggregation status in blood serum, preconditioning for a comprehensive understanding of nano-bio interactions, and predicting in vivo fate of nanomedicines.

Untargeted blood serum proteomics identifies novel proteins related to neurological recovery after human spinal cord injury

BACKGROUND

The discovery of new prognostic biomarkers following spinal cord injury (SCI) is a rapidly growing field that could help uncover the underlying pathological mechanisms of SCI and aid in the development of new therapies. To date, this search has largely focused on the initial days after the lesion. However, during the subacute stage of SCI (weeks to months after the injury), there remains potential for sensorimotor recovery, and numerous secondary events develop in various organs. Additionally, the confounding effects of early interventions after the injury are less likely to interfere with the results.

METHODS

In this study, we conducted an untargeted proteomics analysis to identify biomarkers of recovery in blood serum samples during the subacute phase of SCI patients, comparing those with strong recovery to those with no recovery between 30 and 120 days. We analyzed the fraction of serum that is depleted of the most abundant proteins to unmask proteins that would otherwise go undetected. Linear models were used to identify peptides and proteins related to neurological recovery and we validated changes in some of these proteins using Enzyme-linked Immunosorbent Assay (ELISA).

RESULTS

Our findings reveal that differences in subacute recovery after SCI (from 30 to 120 days) are associated with an enrichment in proteins involved in inflammation, coagulation, and lipid metabolism. Technical validation using commercial ELISAs further confirms that high levels of SERPINE1 and ARHGAP35 are associated with strong neurological recovery, while high levels of CD300a and DEFA1 are associated with a lack of recovery.

CONCLUSIONS

Our study identifies new candidates for biomarkers of neurological recovery and for novel therapeutic targets after SCI.

A carrier-based quantitative proteomics method applied to biomarker discovery in pericardial fluid

Data-dependent liquid chromatography tandem mass spectrometry (LC-MS/MS) is challenged by the large concentration range of proteins in plasma and related fluids. We adapted the SCoPE method from single-cell proteomics to pericardial fluid, where a myocardial tissue carrier was used to aid protein quantification. The carrier proteome and patient samples were labeled with distinct isobaric labels, which allowed separate quantification. Undepleted pericardial fluid from patients with type 2 diabetes mellitus and/or heart failure undergoing heart surgery was analyzed with either a traditional LC-MS/MS method or with the carrier proteome. In total, 1398 proteins were quantified with a carrier, compared to 265 without, and a higher proportion of these proteins were of myocardial origin. The number of differentially expressed proteins also increased nearly four-fold. For patients with both heart failure and type 2 diabetes mellitus, pathway analysis of upregulated proteins demonstrated enrichment of immune activation, blood coagulation, and stress pathways. Overall, our work demonstrates the applicability of a carrier for enhanced protein quantification in challenging biological matrices such as pericardial fluid, with potential applications for biomarker discovery. Mass spectrometry data are available via ProteomeXchange with identifier PXD053450.

Plasma proteins and psoriatic arthritis: a proteome-wide Mendelian randomization study

Background

Previous epidemiological studies have identified a correlation between serum protein levels and Psoriatic Arthritis (PsA). However, the precise nature of this relationship remains uncertain. Therefore, our objective was to assess whether circulating levels of 2,923 plasma proteins are associated with the risk of PsA, utilizing the Mendelian randomization (MR) approach.

Methods

Two-sample MR analysis was performed to assess the causal impact of proteins on PsA risk. Exposure data for plasma proteins were sourced from a genome-wide association study (GWAS) conducted within the UK Biobank Pharma Proteomics Project, which encompassed 2,923 unique plasma proteins. The outcome data for PsA were sourced from the FinnGen study, a large-scale genomics initiative, comprising 3,537 cases and 262,844 controls. Additionally, colocalization analysis, Phenome-wide MR analysis, and candidate drug prediction were employed to identify potential causal circulating proteins and novel drug targets.

Results

We thoroughly assessed the association between 1,837 plasma proteins and PsA risk, identifying seven proteins associated with PsA risk. An inverse association of Interleukin-10 (IL-10) with PsA risk was observed [odds ratio (OR)=0.45, 95% confidence interval (CI), 0.28 to 0.70, P FDR=0.072]. Additionally, Apolipoprotein F (APOF) has a positive effect on PsA risk (OR=2.08, 95% CI, 1.51 to 2.86, P FDR=0.005). Subsequently, we found strong evidence indicating that IL-10 and APOF were colocalized with PsA associations (PP.H4 = 0.834 for IL-10 and PP.H4 = 0.900 for APOF). Phenome-wide association analysis suggested that these two proteins may have dual effects on other clinical traits (P FDR<0.1).

Conclusion

This study identified 7 plasma proteins associated with PsA risk, particularly IL-10 and APOF, which offer new insights into its etiology. Further studies are needed to assess the utility and effectiveness of these candidate proteins.

Decreased Serum Apolipoprotein CIII in the Acute Phase of Kawasaki Disease

Plasma exchange is an effective treatment for Kawasaki disease (KD), suggesting that plasma from patients with KD bears its causative agents. The aim of this study was to use mass spectrometry to identify candidate agents in patient sera. Serum samples were obtained from 17 KD patients. In six patients, samples were collected in each of three phases: the acute phase prior to acetylsalicylic acid (ASA) and intravenous immunoglobulin administration (Phase A1), the remission phase with ASA (Phase A2), and the remission phase without any medication (Phase A3). Sera from the remaining 11 patients were collected during Phases A1 and A2. The study also included two age- and gender-matched control groups, one with eight afebrile children and one with eight febrile children diagnosed with infectious disease. Patients in Phase A1 and febrile controls did not differ in body temperature, white blood cell counts, or C-reactive protein levels. Mass spectrometry analysis revealed that the intensity levels of m/z 9416, identified as apolipoprotein CIII (Apo CIII), were lower in Phase A1 samples compared with samples from patients in Phases A2 and A3, and from febrile controls (all comparisons, p < 0.01). Serum Apo CIII levels were also lower in Phase A1 samples compared with samples from Phase A2 patients and afebrile controls (both p < 0.01), but samples from patients in Phase A2 did not differ significantly from those of the afebrile controls (p = 0.55). This study demonstrated that serum Apo CIII level was decreased in the acute phase of KD.

Plasma proteins and inflammatory dermatoses: proteome-wide Mendelian randomization and colocalization analyses

Current genome-wide association studies (GWAS) of plasma proteomes provide additional possibilities for finding new drug targets for inflammatory dermatoses. We performed proteome-wide Mendelian randomization (MR) and colocalization analyses to identify novel potential drug targets for inflammatory dermatoses. We performed MR and colocalization analysis using genetic variation as instrumental variables to determine the causal relationship between circulating plasma proteins and inflammatory dermatoses. 5 plasma proteins were found to be causally associated with dermatitis eczematosa, SLE, urticaria and psoriasis using cis-pQTLs as instrumental variables, but not found in AD and LP. 19 candidate genes with high colocalization evidence were identified. These potential drug targets still require more research and rigorous validation in future trials.

Promises and Challenges of populational Proteomics in Health and Disease

Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.

Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study

BACKGROUND

Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes.

METHODS

We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins.

FINDINGS

Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures.

INTERPRETATION

We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings.

FUNDING

Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Frontiers in plasma proteome profiling platforms: innovations and applications

Biomarkers play a crucial role in advancing precision medicine by enabling more targeted and individualized approaches to diagnosis and treatment. Various biofluids, including serum, plasma, cerebrospinal fluid (CSF), saliva, tears, pancreatic cyst fluids, and urine, have been identified as rich sources of potential for the early detection of disease biomarkers in conditions such as cancer, cardiovascular diseases, and neurodegenerative disorders. The analysis of plasma and serum in proteomics research encounters challenges due to their high complexity and the wide dynamic range of protein abundance. These factors impede the sensitivity, coverage, and precision of protein detection when employing mass spectrometry, a widely utilized technology in discovery proteomics. Conventional approaches such as Neat Plasma workflow are inefficient in accurately quantifying low-abundant proteins, including those associated with tissue leakage, immune response molecules, interleukins, cytokines, and interferons. Moreover, the manual nature of the workflow poses a significant hurdle in conducting large cohort studies. In this study, our focus is on comparing workflows for plasma proteomic profiling to establish a methodology that is not only sensitive and reproducible but also applicable for large cohort studies in biomarker discovery. Our investigation revealed that the Proteograph XT workflow outperforms other workflows in terms of plasma proteome depth, quantitative accuracy, and reproducibility while offering complete automation of sample preparation. Notably, Proteograph XT demonstrates versatility by applying it to various types of biofluids. Additionally, the proteins quantified widely cover secretory proteins in peripheral blood, and the pathway analysis enriched with relevant components such as interleukins, tissue necrosis factors, chemokines, and B and T cell receptors provides valuable insights. These proteins, often challenging to quantify in complex biological samples, hold potential as early detection markers for various diseases, thereby contributing to the improvement of patient care quality.

Comparative analysis of plasma affinity depletion methods: Impact on protein composition and phosphopeptide abundance in human plasma

Affinity-based protein depletion and TiO2 enrichment methods play a crucial role in detection of low-abundant proteins and phosphopeptides enrichment, respectively. Here, we assessed the effectiveness of HSA/IgG (HU2) and Human 7 (HU7) depletion methods and their impact on phosphopeptides coverage through comparative proteome analysis, utilizing in-solution digestion and nano-LC-Orbitrap mass spectrometry (MS). Our results demonstrated that both HU2 and HU7 affinity depletion significantly decreased high-abundant proteins by 1.5-7.8-fold (p < 0.001). A total of 1491 proteins were identified, with 48 proteins showing significant expression in the depleted groups. Notably, cadherin-13, neutrophil defensin 1, APM1, and desmoplakin variant protein were exclusively detected in the HU2/HU7-depleted groups. Furthermore, study on effect of depletion on phosphopeptides revealed an increase in tandem MS spectral counts with notable decrease (∼50%) in peptide spectrum matching in depleted groups, which was attributed to significant reduction in protein counts. Our post translation modification workflow for phosphoproteomics detected 42 phosphorylated peptides, corresponding to 12 phosphoproteins with unique peptide match ≥2 (high false discovery rates confidence). Among them, 10 phosphorylated proteins are highly expressed in depleted groups. Overall, these findings offer valuable insights in selection of protein depletion methods for comprehensive plasma proteomics analysis.

Mass Spectrometry Proteomics Characterization of Plasma Biomarkers for Colorectal Cancer Associated With Inflammation

Background

Colorectal cancer (CRC) prognosis is determined by the disease stage with low survival rates for advanced stages. Current CRC screening programs are mainly using colonoscopy, limited by its invasiveness and high cost. Therefore, non-invasive, cost-effective, and accurate alternatives are urgently needed.

Objective and design

This retrospective multi-center plasma proteomics study was performed to identify potential blood-based biomarkers in 36 CRC patients and 26 healthy volunteers by high-resolution mass spectrometry proteomics followed by the validation in an independent CRC cohort (60 CRC patients and 44 healthy subjects) of identified selected biomarkers.

Results

Among the 322 identified plasma proteins, 37 were changed between CRC patients and healthy volunteers and were associated with the complement cascade, cholesterol metabolism, and SERPIN family members. Increased levels in CRC patients of the complement proteins C1QB, C4B, and C5 as well as pro-inflammatory proteins, lipopolysaccharide-binding protein (LBP) and serum amyloid A4, constitutive (SAA4) were revealed for first time. Importantly, increased level of C5 was verified in an independent validation CRC cohort. Increased C4B and C8A levels were correlated with cancer-associated inflammation and CRC progression, while cancer-associated inflammation was linked to the acute-phase reactant leucine-rich alpha-2-glycoprotein 1 (LRG1) and ceruloplasmin. Moreover, a 4-protein signature including C4B, C8A, apolipoprotein C2 (APO) C2, and immunoglobulin heavy constant gamma 2 was changed between early and late CRC stages.

Conclusion

Our results suggest that C5 could be a potential biomarker for CRC diagnosis. Further validation studies will aid the application of these new potential biomarkers to improve CRC diagnosis and patient care.

Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer's disease

Background

Blood-based biomarkers are gaining grounds for Alzheimer's disease (AD) detection. However, two key obstacles need to be addressed: the lack of methods for multi-analyte assessments and the need for markers of neuroinflammation, vascular, and synaptic dysfunction. Here, we evaluated a novel multi-analyte biomarker platform, NULISAseq CNS disease panel, a multiplex NUcleic acid-linked Immuno-Sandwich Assay (NULISA) targeting ~120 analytes, including classical AD biomarkers and key proteins defining various disease hallmarks.

Methods

The NULISAseq panel was applied to 176 plasma samples from the MYHAT-NI cohort of cognitively normal participants from an economically underserved region in Western Pennsylvania. Classical AD biomarkers, including p-tau181 p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were also measured using Single Molecule Array (Simoa). Amyloid pathology, tau pathology, and neurodegeneration were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and MRI, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA biomarkers and AD pathologies. Spearman correlations were used to compare NULISA and Simoa.

Results

NULISA concurrently measured 116 plasma biomarkers with good technical performance, and good correlation with Simoa measures. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878-0.983). Fourteen markers were significantly decreased in Aβ-PET+ participants, including TIMP3, which regulates brain Aβ production, the neurotrophic factor BDNF, the energy metabolism marker MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET+ participants. Markers with tau PET-dependent longitudinal changes included the microglial activation marker CHIT1, the reactive astrogliosis marker CHI3L1, the synaptic protein NPTX1, and the cerebrovascular markers PGF, PDGFRB, and VEFGA; all previously linked to AD but only reliably measured in cerebrospinal fluid. SQSTM1, the autophagosome cargo protein, exhibited a significant association with neurodegeneration status after adjusting age, sex, and APOE ε4 genotype.

Conclusions

Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

Top-down proteomics

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

Novel Strategy for Screening Target Proteins by the Common Drugs─Sofosbuvir-Specific Profiling of HCV Patient Serum

It is the scientific basis of precision medicine to study all of the targets of drugs based on the interaction between drugs and proteins. It is worth paying attention to unknown proteins that interact with drugs to find new targets for the design of new drugs. Herein, we developed a protein profiling strategy based on drug-protein interactions and drug-modified magnetic nanoparticles and took hepatitis C virus (HCV) and its corresponding drug sofosbuvir (SOF) as an example. A SOF-modified magnetic separation medium (Fe3O4@POSS@SOF) was prepared, and a gradient elution strategy was employed and optimized to profile specific proteins interacted with SOF. A series of proteomic analyses were performed to profile proteins based on SOF-protein interactions (SPIs) in the serum of HCV patients to evaluate the specificity of the profiling strategy. As a result, five proteins were profiled with strong SPIs and exhibited high relevance with liver tissue, which were potentially new drug targets. Among them, HSP60 was used to confirm the highly specific interactions between the SOF and its binding proteins by Western blotting analysis. Besides, 124 and 29 differential proteins were profiled by SOF material from three HCV patient serum and pooled 20 HCV patient serum, respectively, by comparing with healthy human serum. In comparison with those profiled by the polyhedral oligomeric silsesquioxane (POSS) material, differential proteins profiled by the SOF material were highly associated with liver diseases through GO analysis and pathway analysis. Furthermore, four common differential proteins profiled by SOF material but not by POSS material were found to be identical and expressed consistently in both pooled serum samples and independent serum samples, which might potentially be biomarkers of HCV infection. Taken together, our study proposes a highly specific protein profiling strategy to display distinctive proteomic profiles, providing a novel idea for drug design and development.

Novel Protein Biomarkers and Therapeutic Targets for Type 1 Diabetes and Its Complications: Insights from Summary-Data-Based Mendelian Randomization and Colocalization Analysis

Millions of patients suffer from type 1 diabetes (T1D) and its associated complications. Nevertheless, the pursuit of a cure for T1D has encountered significant challenges, with a crucial impediment being the lack of biomarkers that can accurately predict the progression of T1D and reliable therapeutic targets for T1D. Hence, there is an urgent need to discover novel protein biomarkers and therapeutic targets, which holds promise for targeted therapy for T1D. In this study, we extracted summary-level data on 4907 plasma proteins from 35,559 Icelanders and 2923 plasma proteins from 54,219 UK participants as exposures. The genome-wide association study (GWAS) summary statistics on T1D and T1D with complications were obtained from the R9 release results from the FinnGen consortium. Summary-data-based Mendelian randomization (SMR) analysis was employed to evaluate the causal associations between the genetically predicted levels of plasma proteins and T1D-associated outcomes. Colocalization analysis was utilized to investigate the shared genetic variants between the exposure and outcome. Moreover, transcriptome analysis and a protein-protein interaction (PPI) network further illustrated the expression patterns of the identified protein targets and their interactions with the established targets of T1D. Finally, a Mendelian randomization phenome-wide association study evaluated the potential side effects of the identified core protein targets. In the primary SMR analysis, we identified 72 potential protein targets for T1D and its complications, and nine of them were considered crucial protein targets. Within the group were five risk targets and four protective targets. Backed by evidence from the colocalization analysis, the protein targets were classified into four tiers, with MANSC4, CTRB1, SIGLEC5 and MST1 being categorized as tier 1 targets. Delving into the DrugBank database, we retrieved 11 existing medications for T1D along with their therapeutic targets. The PPI network clarified the interactions among the identified potential protein targets and established ones. Finally, the Mendelian randomization phenome-wide association study corroborated MANSC4 as a reliable target capable of mitigating the risk of various forms of diabetes, and it revealed the absence of adverse effects linked to CTRB1, SIGLEC5 and MST1. This study unveiled many protein biomarkers and therapeutic targets for T1D and its complications. Such advancements hold great promise for the progression of drug development and targeted therapy for T1D.

Integrating plasma protein-centric multi-omics to identify potential therapeutic targets for pancreatic cancer

BACKGROUND

Deciphering the role of plasma proteins in pancreatic cancer (PC) susceptibility can aid in identifying novel targets for diagnosis and treatment.

METHODS

We examined the relationship between genetically determined levels of plasma proteins and PC through a systemic proteome-wide Mendelian randomization (MR) analysis utilizing cis-pQTLs from multiple centers. Rigorous sensitivity analyses, colocalization, reverse MR, replications with varying instrumental variable selections and additional datasets, as well as subsequent meta-analysis, were utilized to confirm the robustness of significant findings. The causative effect of corresponding protein-coding genes' expression and their expression pattern in single-cell types were then investigated. Enrichment analysis, between-protein interaction and causation, knock-out mice models, and mediation analysis with established PC risk factors were applied to indicate the pathogenetic pathways. These candidate targets were ultimately prioritized upon druggability and potential side effects predicted by a phenome-wide MR.

RESULTS

Twenty-one PC-related circulating proteins were identified in the exploratory phase with no evidence for horizontal pleiotropy or reverse causation. Of these, 11 were confirmed in a meta-analysis integrating external validations. The causality at a transcription level was repeated for neutrophil elastase, hydroxyacylglutathione hydrolase, lipase member N, protein disulfide-isomerase A5, xyloside xylosyltransferase 1. The carbohydrate sulfotransferase 11 and histo-blood group ABO system transferase exhibited high-support genetic colocalization evidence and were found to affect PC carcinogenesis partially through modulating body mass index and type 2 diabetes, respectively. Approved drugs have been established for eight candidate targets, which could potentially be repurposed for PC therapies. The phenome-wide investigation revealed 12 proteins associated with 51 non-PC traits, and interference on protein disulfide-isomerase A5 and cystatin-D would increase the risk of other malignancies.

CONCLUSIONS

By employing comprehensive methodologies, this study demonstrated a genetic predisposition linking 21 circulating proteins to PC risk. Our findings shed new light on the PC etiology and highlighted potential targets as priorities for future efforts in early diagnosis and therapeutic strategies of PC.

Enhancing the sequence coverage of nanodiamond-extracted early secretory proteins from the Mycobacterium tuberculosis complex

The unambiguous identification of protein species requires high sequence coverage. In this study, we successfully improved the sequence coverage of early secretory 10 kDa cell filtrate protein (CFP-10) and 6 kDa early secretory antigenic target (ESAT-6) proteins from the Mycobacterium tuberculosis complex (MTC) in broth culture media with the use of the 4-chloro-α-cyanocinnamic acid (Cl-CCA) matrix. Conventional matrices, α-cyano-hydroxy-cinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB), were also used for comparison. After nanodiamond (ND) extraction, the sequence coverage of the CFP-10 protein was 87% when CHCA and DHB matrices were used, and the ESAT-6 protein was not detected. On the other hand, the sequence coverage for ND-extracted CFP-10 and ESAT-6 could reach 94% and 100%, respectively, when the Cl-CCA matrix was used and with the removal of interference from bovine serum albumin (BSA) protein and α-crystallin (ACR) protein. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was also adopted to analyze the protein mass spectra. A total of 6 prominent ion signals were observed, including ESAT-6 protein peaks at mass-to-charge ratios (m/z) of ∼7931, ∼7974, ∼9768, and ∼9813 and CFP-10 protein peaks at m/z of ∼10 100 and ∼10 660. The ESAT-6 ion signals were always detected concurrently with CFP-10 ion signals, but CFP-10 ion signals could be detected alone without the ESAT-6 ion signals. Furthermore, the newly found ESAT-6 peaks were also confirmed using a Mag-Beads-Protein G kit with an ESAT-6 antibody to capture the ESAT-6 protein, which was also consistent with the sequence coverage analysis.

Plasma and Kidney Proteome Profiling Combined with Laser Capture Microdissection Reveal Large Increases in Immunoglobulins with Age

One of the main hallmarks of aging is aging-associated inflammation, also known as inflammaging. In this study, by comparing plasma and kidney proteome profiling of young and old mice using LC-MS profiling, we discovered that immunoglobulins are the proteins that exhibit the highest increase with age. This observation seems to have been disregarded because conventional proteome profiling experiments typically overlook the expression of high-abundance proteins or employ depletion methods to remove them before LC-MS analysis. We show that proteome profiling of immunoglobulins will likely be a useful biomarker of aging. Spatial profiling using immunofluorescence staining of kidney sections indicates that the main increases in immunoglobulins with age are localized in the glomeruli of the kidney. Using laser capture microdissection coupled with LC-MS, we show an increase in multiple immune-related proteins in glomeruli from aged mice. Increased deposition of immunoglobulins, immune complexes, and complement proteins in the kidney glomeruli may be a factor leading to reduced filtering capacity of the kidney with age. Therapeutic strategies to reduce the deposition of immunoglobulins in the kidney may be an attractive strategy for healthy aging.

Proteomics of human platelet lysates and insight from animal studies on platelet protein diffusion to hippocampus upon intranasal administration

Human platelet lysates (HPLs) from allogeneic platelet concentrates (PCs) are biomaterials, which are rich in various trophic factors, increasingly used in regenerative medicine and biotherapy. Understanding how preparation methods influence the HPL protein profile, biological function, and clinical outcomes is crucial. Our study sheds light on the proteomes and functionality of different HPLs, with the aim of advancing their scientifically grounded clinical applications. To achieve this, PCs suspended in plasma underwent three distinct processing methods, resulting in seven HPL types. We used three characterization techniques: label-free proteomics and tandem mass tag (TMT)-based quantitative proteomics, both before and after the immunodepletion of abundant plasma proteins. Bioinformatic tools assessed the proteome, and western blotting validated our quantitative proteomics data. Subsequent pre-clinical studies with fluorescent labeling and label-free proteomics were used as a proof of concept for brain diffusion. Our findings revealed 1441 proteins detected using the label-free method, 952 proteins from the TMT experiment before and after depletion, and 1114 proteins from the subsequent TMT experiment on depleted HPLs. Most detected proteins were cytoplasmic, playing key roles in catalysis, hemostasis, and immune responses. Notably, the processing methodologies significantly influenced HPL compositions, their canonical pathways, and, consequently, their functionality. Each HPL exhibited specific abundant proteins, providing valuable insight for tailored clinical applications. Immunoblotting results for selected proteins corroborated our quantitative proteomics data. The diffusion and differential effects to the hippocampus of a neuroprotective HPL administered intranasally to mice were demonstrated. This proteomics study advances our understanding of HPLs, suggesting ways to standardize and customize their production for better clinical efficacy in regenerative medicine and biotherapy. Proteomic analyses also offered objective evidence that HPPL, upon intranasal delivery, not only effectively diffuses to the hippocampus but also alters protein expression in mice, bolstering its potential as a treatment for memory impairments.

Translational toxicology of metal(loid) species: linking their bioinorganic chemistry in the bloodstream to organ damage onset

The quantification of arsenic, mercury, cadmium and lead in the human bloodstream is routinely used today to assess exposure to these toxic metal(loid)s, but the interpretation of the obtained data in terms of their cumulative health relevance remains problematic. Seemingly unrelated to this, epidemiological studies strongly suggest that the simultaneous chronic exposure to these environmental pollutants is associated with the etiology of autism, type 2 diabetes, irritable bowel disease and other diseases. This from a public health point of view undesirable situation urgently requires research initiatives to establish functional connections between human exposure to multiple toxic metal(loid) species and adverse health effects. One way to establish causal exposure-response relationships is a molecular toxicology approach, which requires one to unravel the biomolecular mechanisms that unfold after individual toxic metal(loid)s enter the bloodstream/organ nexus as these interactions ultimately determine which metabolites impinge on target organs and thus provide mechanistic links to diseases of unknown etiology. In an attempt to underscore the importance of the toxicological chemistry of metal(loid)s in the bloodstream, this review summarizes recent progress into relevant bioinorganic processes that are implicated in the etiology of adverse organ-based health effects and possibly diseases. A better understanding of these bioinorganic processes will not only help to improve the regulatory framework to better protect humans from the adverse effects of toxic metal(loid) species, but also represents an important starting point for the development of treatments to ameliorate pollution-induced adverse health effects on human populations, including pregnant women, the fetus and children.

In-Depth Endogenous Phosphopeptidomics of Serum with Zirconium(IV)-Grafted Mesoporous Silica Enrichment

Detection of endogenous peptides, especially those with modifications (such as phosphorylation) in biofluids, can serve as an indicator of intracellular pathophysiology. Although great progress has been made in phosphoproteomics in recent years, endogenous phosphopeptidomics has largely lagged behind. One main hurdle in endogenous phosphopeptidomics analysis is the coexistence of proteins and highly abundant nonmodified peptides in complex matrices. In this study, we developed an approach using zirconium(IV)-grafted mesoporous beads to enrich phosphopeptides, followed by analysis with a high resolution nanoRPLC-MS/MS system. The bifunctional material was first tested with digests of standard phosphoproteins and HeLa cell lysates, with excellent enrichment performance achieved. Given the size exclusion nature, the beads were directly applied for endogenous phosphopeptidomic analysis of serum samples from pancreatic ductal adenocarcinoma (PDAC) patients and controls. In total, 329 endogenous phosphopeptides (containing 113 high confidence sites) were identified across samples, by far the largest endogenous phosphopeptide data set cataloged to date. In addition, the method was readily applied for phosphoproteomics of the same set of samples, with 172 phosphopeptides identified and significant changes in dozens of phosphopeptides observed. Given the simplicity and robustness of the proposed method, we envision that it can be readily used for comprehensive phosphorylation studies of serum and other biofluid samples.

Proteomic analysis of diabetic retinopathy identifies potential plasma-protein biomarkers for diagnosis and prognosis

OBJECTIVES

To identify molecular pathways and prognostic- and diagnostic plasma-protein biomarkers for diabetic retinopathy at various stages.

METHODS

This exploratory, cross-sectional proteomics study involved plasma from 68 adults, including 15 healthy controls and 53 diabetes patients for various stages of diabetic retinopathy: non-diabetic retinopathy, non-proliferative diabetic retinopathy, proliferative diabetic retinopathy and diabetic macular edema. Plasma was incubated with peptide library beads and eluted proteins were tryptic digested, analyzed by liquid chromatography-tandem mass-spectrometry followed by bioinformatics.

RESULTS

In the 68 samples, 248 of the 731 identified plasma-proteins were present in all samples. Analysis of variance showed differential expression of 58 proteins across the five disease subgroups. Protein-Protein Interaction network (STRING) showed enrichment of various pathways during the diabetic stages. In addition, stage-specific driver proteins were detected for early and advanced diabetic retinopathy. Hierarchical clustering showed distinct protein profiles according to disease severity and disease type.

CONCLUSIONS

Molecular pathways in the cholesterol metabolism, complement system, and coagulation cascade were enriched in patients at various stages of diabetic retinopathy. The peroxisome proliferator-activated receptor signaling pathway and systemic lupus erythematosus pathways were enriched in early diabetic retinopathy. Stage-specific proteins for early - and advanced diabetic retinopathy as determined herein could be 'key' players in driving disease development and potential 'target' proteins for future therapies. For type 1 and 2 diabetes mellitus, the proteomic profiles were especially distinct during the early disease stage. Validation studies should aim to clarify the role of the detected molecular pathways, potential biomarkers, and potential 'target' proteins for future therapies in diabetic retinopathy.

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

Identification of Fibrinogen as a Plasma Protein Binding Partner for Lecanemab Biosimilar IgG: Implications for Alzheimer's Disease Therapy

OBJECTIVE

Recombinant monoclonal therapeutic antibodies like lecanemab, which target amyloid beta in Alzheimer's disease, offer a promising approach for modifying the disease progression. Due to its relatively short half-life, Lecanemab, administered as a bi-monthly infusion (typically 10mg/kg) has a relatively brief half-life. Interaction with abundant plasma proteins binder in the bloodstream can affect pharmacokinetics of drugs, including their half-life. In this study we investigated potential plasma protein binding interaction to lecanemab using lecanemab biosimilar.

METHODS

Lecanemab biosimilar used in this study was based on publicly available sequences. ELISA and Western blotting were used to assess lecanemab biosimilar immunoreactivity in the fractions human plasma sample obtained through size exclusion chromatography. The binding of lecanemab biosimilar to candidate binders was confirmed by Western blotting, ELISA, and surface plasmon resonance analysis.

RESULTS

Using a combination of equilibrium dialysis, ELISA, and Western blotting in human plasma, we first describe the presence of likely plasma protein binding partner to lecanemab biosimilar, and then identify fibrinogen as one of them. Utilizing surface plasmon resonance, we confirmed that lecanemab biosimilar does bind to fibrinogen, although with lower affinity than to monomeric amyloid beta.

CONCLUSION

In the context of lecanemab therapy, these results imply that fibrinogen levels could impact the levels of free antibodies in the bloodstream and that fibrinogen might serve as a reservoir for lecanemab. More broadly, these results indicate that plasma protein binding may be an important consideration when clinically utilizing therapeutic antibodies in neurodegenerative disease.

Protopine and Allocryptopine Interactions with Plasma Proteins

A comprehensive study of the interactions of human serum albumin (HSA) and α-1-acid glycoprotein (AAG) with two isoquinoline alkaloids, i.e., allocryptopine (ACP) and protopine (PP), was performed. The UV-Vis spectroscopy, molecular docking, competitive binding assays, and circular dichroism (CD) spectroscopy were used for the investigations. The results showed that ACP and PP form spontaneous and stable complexes with HSA and AAG, with ACP displaying a stronger affinity towards both proteins. Molecular docking studies revealed the preferential binding of ACP and PP to specific sites within HSA, with site 2 (IIIA) being identified as the favored location for both alkaloids. This was supported by competitive binding assays using markers specific to HSA's drug binding sites. Similarly, for AAG, a decrease in fluorescence intensity upon addition of the alkaloids to AAG/quinaldine red (QR) complexes indicated the replacement of the marker by the alkaloids, with ACP showing a greater extent of replacement than PP. CD spectroscopy showed that the proteins' structures remained largely unchanged, suggesting that the formation of complexes did not significantly perturb the overall spatial configuration of these macromolecules. These findings are crucial for advancing the knowledge on the natural product-protein interactions and the future design of isoquinoline alkaloid-based therapeutics.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

METHODS

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

RESULTS

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

CONCLUSIONS

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

Nutritional, molecular, and functional properties of a novel enzymatically hydrolyzed porcine plasma product

In the present study, an enzymatically hydrolyzed porcine plasma (EHPP) was nutritionally and molecularly characterized. EHPP molecular characterization showed, in contrast to spray-dried plasma (SDP), many peptides with relative molecular masses (Mr) below 8,000, constituting 73% of the protein relative abundance. IIAPPER, a well-known bioactive peptide with anti-inflammatory and antioxidant properties, was identified. In vivo functionality of EHPP was tested in C. elegans and two different mouse models of intestinal inflammation. In C. elegans subjected to lipopolysaccharide exposure, EHPP displayed a substantial anti-inflammatory effect, enhancing survival and motility by 40% and 21.5%, respectively. Similarly, in mice challenged with Staphylococcus aureus enterotoxin B or Escherichia coli O42, EHPP and SDP supplementation (8%) increased body weight and average daily gain while reducing the percentage of regulatory Th lymphocytes. Furthermore, both products mitigated the increase of pro-inflammatory cytokines expression associated with these challenged mouse models. In contrast, some significant differences were observed in markers such as Il-6 and Tnf-α, suggesting that the products may present different action mechanisms. In conclusion, EHPP demonstrated similar beneficial health effects to SDP, potentially attributable to the immunomodulatory and antioxidant activity of its characteristic low Mr bioactive peptides.

Proteome-Wide Mendelian Randomisation Identifies Causal Links of Plasma Proteins With Periodontitis

OBJECTIVE

Periodontitis is a complex and multifactorial disease and it is challenging to decipher its underlying causes and mechanisms. This study attempted to explore potential circulating proteins in connection to periodontitis through proteome-wide Mendelian randomisation (MR).

METHODS

We analysed 1722 circulating proteins to identify prospective drug targets for tackling periodontitis, using the genomic dataset from the FinnGen study. Two-sample MR was conducted to evaluate the bidirectional relationship between circulating proteins and periodontitis risk. A dataset from the UK Biobank was used to validate the findings. Single-cell analysis was performed to assess the cellular expression of the identified proteins within gingival tissues.

RESULTS

MR analyses found that genetically predicted circulating levels of von Willebrand factor A domain-containing 1 (von Willebrand factor A domain containing 1 [VWA1], odds ratios: 0.94, 95% CI 0.92-0.97, P = 1.28 × 10-5) were inversely associated with periodontitis. In contrast, the level of growth differentiation factor 15 (growth differentiation factor 15 [GDF15], odds ratios: 1.05, 95% CI 1.02-1.07, P = 2.12 × 10-5) might be associated with an increased risk of periodontitis. Single-cell analysis indicated that VWA1 was primarily expressed in endothelial cells of healthy gingival tissues, while the main source of GDF15 was not derived from periodontal cells.

CONCLUSIONS

The present study suggests that certain plasma proteins like VWA1 and GDF15 may be potentially indicative of the risk and susceptibility to periodontitis. These proteins could possibly be the potential therapeutic targets for treating periodontitis, and further investigation is highly warranted.

Establishing Personalized Blood Protein Reference Ranges Using Noninvasive Microsampling and Targeted Proteomics: Implications for Antidoping Strategies

To prevent doping practices in sports, the World Anti-Doping Agency implemented the Athlete Biological Passport (ABP) program, monitoring biological variables over time to indirectly reveal the effects of doping rather than detect the doping substance or the method itself. In the context of this program, a highly multiplexed mass spectrometry-based proteomics assay for 319 peptides corresponding to 250 proteins was developed, including proteins associated with blood-doping practices. "Baseline" expression profiles of these potential biomarkers in capillary blood (dried blood spots (DBS)) were established using multiple reaction monitoring (MRM). Combining DBS microsampling with highly multiplexed MRM assays is the best-suited technology to enhance the effectiveness of the ABP program, as it represents a cost-effective and robust alternative analytical method with high specificity and selectivity of targets in the attomole range. DBS data were collected from 10 healthy athlete volunteers over a period of 140 days (28 time points per participant). These comprehensive findings provide a personalized targeted blood proteome "fingerprint" showcasing that the targeted proteome is unique to an individual and likely comparable to a DNA fingerprint. The results can serve as a baseline for future studies investigating doping-related perturbations.

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.

Sample-to-answer lateral flow assay with integrated plasma separation and NT-proBNP detection

Through enabling whole blood detection in point-of-care testing (POCT), sedimentation-based plasma separation promises to enhance the functionality and extend the application range of lateral flow assays (LFAs). To streamline the entire process from the introduction of the blood sample to the generation of quantitative immune-fluorescence results, we combined a simple plasma separation technique, an immunoreaction, and a micropump-driven external suction control system in a polymer channel-based LFA. Our primary objective was to eliminate the reliance on sample-absorbing separation membranes, the use of active separation forces commonly found in POCT, and ultimately allowing finger prick testing. Combining the principle of agglutination of red blood cells with an on-device sedimentation-based separation, our device allows for the efficient and fast separation of plasma from a 25-µL blood volume within a mere 10 min and overcomes limitations such as clogging, analyte adsorption, and blood pre-dilution. To simplify this process, we stored the agglutination agent in a dried state on the test and incorporated a filter trench to initiate sedimentation-based separation. The separated plasma was then moved to the integrated mixing area, initiating the immunoreaction by rehydration of probe-specific fluorophore-conjugated antibodies. The biotinylated immune complex was subsequently trapped in the streptavidin-rich detection zone and quantitatively analyzed using a fluorescence microscope. Normalized to the centrifugation-based separation, our device demonstrated high separation efficiency of 96% and a yield of 7.23 µL (= 72%). Furthermore, we elaborate on its user-friendly nature and demonstrate its proof-of-concept through an all-dried ready-to-go NT-proBNP lateral flow immunoassay with clinical blood samples.

Full-length single-molecule protein fingerprinting

Proteins are the primary functional actors of the cell. While proteoform diversity is known to be highly biologically relevant, current protein analysis methods are of limited use for distinguishing proteoforms. Mass spectrometric methods, in particular, often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate fluorescence resonance energy transfer (FRET)-based single-molecule protein fingerprinting to map the location of individual amino acids and post-translational modifications within single full-length protein molecules. Our data show that both intrinsically disordered proteins and folded globular proteins can be fingerprinted with a subnanometer resolution, achieved by probing the amino acids one by one using single-molecule FRET via DNA exchange. This capability was demonstrated through the analysis of alpha-synuclein, an intrinsically disordered protein, by accurately quantifying isoforms in mixtures using a machine learning classifier, and by determining the locations of two O-GlcNAc moieties. Furthermore, we demonstrate fingerprinting of the globular proteins Bcl-2-like protein 1, procalcitonin and S100A9. We anticipate that our ability to perform proteoform identification with the ultimate sensitivity may unlock exciting new venues in proteomics research and biomarker-based diagnosis.

Naïve Inflammatory Proteome Profiles of Glucocorticoid Responsive Polymyalgia Rheumatica and Rheumatic Arthritis Patients-Links to Triggers and Proteomic Manifestations

Polymyalgia rheumatica (PMR) is an inflammatory disorder of unknown etiology, sharing symptoms with giant cell arthritis (GCA) and rheumatoid arthritis (RA). The pathogenic inflammatory roots are still not well understood, and there is a lack of extensive biomarker studies to explain the disease debut and post-acute phase. This study aimed to deeply analyze the serum proteome and inflammatory response of PMR patients before and after glucocorticoid treatment. We included treatment-naïve PMR patients, collecting samples before and after 3 months of treatment. For comparison, disease-modifying antirheumatic drug (DMARD)-naïve RA patients were included and matched to healthy controls (CTL). The serum proteome was examined using label-free quantitative mass spectrometry, while inflammation levels were assessed using multiplex inflammatory cytokine and cell-free DNA assays. The serum proteomes of the four groups comprised acute phase reactants, coagulation factors, complement proteins, immunoglobulins, and apolipoproteins. Serum amyloid A (SAA1) was significantly reduced by active PMR treatment. Cell-free DNA levels in PMR and RA groups were significantly higher than in healthy controls due to acute inflammation. Complement factors had minimal changes post-treatment. The individual serum proteome in PMR patients showed over 100 abundantly variable proteins, emphasizing the systemic impact of PMR disease debut and the effect of treatment. Interleukin (IL)-6 and interferon-gamma (IFN-γ) were significantly impacted by glucocorticoid treatment. Our study defines the PMR serum proteome during glucocorticoid treatment and highlights the role of SAA1, IL-6, and IFN-γ in treatment responses. An involvement of PGLYRP2 in acute PMR could indicate a response to bacterial infection, highlighting its role in the acute phase of the immune response. The results suggest that PMR may be an aberrant response to a bacterial infection with an exacerbated IL-6 and acute phase inflammatory response and molecular attempts to limit the inflammation.

Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

HIV-2 mediated effects on target and bystander cells induce plasma proteome remodeling

Despite low or undetectable plasma viral load, people living with HIV-2 (PLWH2) typically progress toward AIDS. The driving forces behind HIV-2 disease progression and the role of viremia are still not known, but low-level replication in tissues is believed to play a role. To investigate the impact of viremic and aviremic HIV-2 infection on target and bystander cell pathology, we used data-independent acquisition mass spectrometry to determine plasma signatures of tissue and cell type engagement. Proteins derived from target and bystander cells in multiple tissues, such as the gastrointestinal tract and brain, were detected at elevated levels in plasma of PLWH2, compared with HIV negative controls. Moreover, viremic HIV-2 infection appeared to induce enhanced release of proteins from a broader range of tissues compared to aviremic HIV-2 infection. This study expands the knowledge on the link between plasma proteome remodeling and the pathological cell engagement in tissues during HIV-2 infection.

Biomarkers of chemotherapy-induced cardiotoxicity: toward precision prevention using extracellular vesicles

Detrimental side effects of drugs like doxorubicin, which can cause cardiotoxicity, pose barriers for preventing cancer progression, or treating cancer early through molecular interception. Extracellular vesicles (EVs) are valued for their potential as biomarkers of human health, chemical and molecular carcinogenesis, and therapeutics to treat disease at the cellular level. EVs are released both during normal growth and in response to toxicity and cellular death, playing key roles in cellular communication. Consequently, EVs may hold promise as precision biomarkers and therapeutics to prevent or offset damaging off-target effects of chemotherapeutics. EVs have promise as biomarkers of impending cardiotoxicity induced by chemotherapies and as cardioprotective therapeutic agents. However, EVs can also mediate cardiotoxic cues, depending on the identity and past events of their parent cells. Understanding how EVs mediate signaling is critical toward implementing EVs as therapeutic agents to mitigate cardiotoxic effects of chemotherapies. For example, it remains unclear how mixtures of EV populations from cells exposed to toxins or undergoing different stages of cell death contribute to signaling across cardiac tissues. Here, we present our perspective on the outlook of EVs as future clinical tools to mitigate chemotherapy-induced cardiotoxicity, both as biomarkers of impending cardiotoxicity and as cardioprotective agents. Also, we discuss how heterogeneous mixtures of EVs and transient exposures to toxicants may add complexity to predicting outcomes of exogenously applied EVs. Elucidating how EV cargo and signaling properties change during dynamic cellular events may aid precision prevention of cardiotoxicity in anticancer treatments and development of safer chemotherapeutics.

Exploring the cross-cancer effect of circulating proteins and discovering potential intervention targets for 13 site-specific cancers

BACKGROUND

The proteome is an important reservoir of potential therapeutic targets for cancer. This study aimed to examine the causal associations between plasma proteins and cancer risk and to identify proteins with cross-cancer effects.

METHODS

Genetic instruments for 3991 plasma proteins were extracted from a large-scale proteomic study. Summary-level data of 13 site-specific cancers were derived from publicly available datasets. Proteome-wide Mendelian randomization and colocalization analyses were used to investigate the causal effect of circulating proteins on cancers. Protein-protein interactions and druggability assessment were conducted to prioritize potential therapeutic targets. Finally, systematical Mendelian randomization analysis between healthy lifestyle factors and cancer-related proteins was conducted to identify which proteins could act as interventional targets by lifestyle changes.

RESULTS

Genetically determined circulating levels of 58 proteins were statistically significantly associated with 7 site-specific cancers. A total of 39 proteins were prioritized by colocalization, of them, 11 proteins (ADPGK, CD86, CLSTN3, CSF2RA, CXCL10, GZMM, IL6R, NCR3, SIGLEC5, SIGLEC14, and TAPBP) were observed to have cross-cancer effects. Notably, 5 of these identified proteins (CD86, CSF2RA, CXCL10, IL6R, and TAPBP) have been targeted for drug development in cancer therapy; 8 proteins (ADPGK, CD86, CXCL10, GZMM, IL6R, SIGLEC5, SIGLEC14, TAPBP) could be modulated by healthy lifestyles.

CONCLUSION

Our study identified 39 circulating protein biomarkers with convincing causal evidence for 7 site-specific cancers, with 11 proteins demonstrating cross-cancer effects, and prioritized the proteins as potential intervention targets by either drugs or lifestyle changes, which provided new insights into the etiology, prevention, and treatment of cancers.

Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome

Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ~1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a "liquid biopsy," while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson's disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06.

A study of size-selective renal elimination using a novel human model

The recently discovered selective glomerular hypofiltration syndromes have increased interest in the actual elimination of molecules in the human kidney. In the present study, a novel human model was introduced to directly measure the single-pass renal elimination of molecules of increasing size. Plasma concentrations of urea, creatinine, C-peptide, insulin, pro-BNP, β2-microglobulin, cystatin C, troponin-T, orosomucoid, albumin, and IgG were analysed in arterial and renal venous blood from 45 patients undergoing Transcatheter Aortic Valve Implantation (TAVI). The renal elimination ratio (RER) was calculated as the arteriovenous concentration difference divided by the arterial concentration. Estimated glomerular filtration rate (eGFR) was calculated by the CKD-EPI equations for both creatinine and cystatin C. Creatinine (0.11 kDa) showed the highest RER (21.0 ± 6.3%). With increasing molecular size, the RER gradually decreased, where the RER of cystatin C (13 kDa) was 14.4 ± 5.3% and troponin-T (36 kDa) was 11.3 ± 4.6%. The renal elimination threshold was found between 36 and 44 kDa as the RER of orosomucoid (44 kDa) was -0.2 ± 4.7%. The RER of creatinine and cystatin C showed a significant and moderate positive linear relationship with eGFR (r = 0.48 and 0.40). In conclusion, a novel human model was employed to demonstrate a decline in renal elimination with increasing molecular size. Moreover, RERs of creatinine and cystatin C were found to correlate with eGFR, suggesting the potential of this model to study selective glomerular hypofiltration syndromes.

Cerebrospinal Fluid Liquid Biopsies in the Evaluation of Adult Gliomas

PURPOSE OF REVIEW

This review aims to discuss recent research regarding the biomolecules explored in liquid biopsies and their potential clinical uses for adult-type diffuse gliomas.

RECENT FINDINGS

Evaluation of tumor biomolecules via cerebrospinal fluid (CSF) is an emerging technology in neuro-oncology. Studies to date have already identified various circulating tumor DNA, extracellular vesicle, micro-messenger RNA and protein biomarkers of interest. These biomarkers show potential to assist in multiple avenues of central nervous system (CNS) tumor evaluation, including tumor differentiation and diagnosis, treatment selection, response assessment, detection of tumor progression, and prognosis. In addition, CSF liquid biopsies have the potential to better characterize tumor heterogeneity compared to conventional tissue collection and CNS imaging. Current imaging modalities are not sufficient to establish a definitive glioma diagnosis and repeated tissue sampling via conventional biopsy is risky, therefore, there is a great need to improve non-invasive and minimally invasive sampling methods. CSF liquid biopsies represent a promising, minimally invasive adjunct to current approaches which can provide diagnostic and prognostic information as well as aid in response assessment.

Enrichment methods of N-linked glycopeptides from human serum or plasma: A mini-review

Human diseases often correlate with changes in protein glycosylation, which can be observed in serum or plasma samples. N-glycosylation, the most common form, can provide potential biomarkers for disease prognosis and diagnosis. However, glycoproteins constitute a relatively small proportion of the total proteins in human serum and plasma compared to the non-glycosylated protein albumin, which constitutes the majority. The detection of microheterogeneity and low glycan abundance presents a challenge. Mass spectrometry facilitates glycoproteomics research, yet it faces challenges due to interference from abundant plasma proteins. Therefore, methods have emerged to enrich N-glycans and N-linked glycopeptides using glycan affinity, chemical properties, stationary phase chemical coupling, bioorthogonal techniques, and other alternatives. This review focuses on N-glycans and N-glycopeptides enrichment in human serum or plasma, emphasizing methods and applications. Although not exhaustive, it aims to elucidate principles and showcase the utility and limitations of glycoproteome characterization.

Biomolecular Corona of Gold Nanoparticles: The Urgent Need for Strong Roots to Grow Strong Branches

Gold nanoparticles (GNPs) are largely employed in diagnostics/biosensors and are among the most investigated nanomaterials in biology/medicine. However, few GNP-based nanoformulations have received FDA approval to date, and promising in vitro studies have failed to translate to in vivo efficacy. One key factor is that biological fluids contain high concentrations of proteins, lipids, sugars, and metabolites, which can adsorb/interact with the GNP's surface, forming a layer called biomolecular corona (BMC). The BMC can mask prepared functionalities and target moieties, creating new surface chemistry and determining GNPs' biological fate. Here, the current knowledge is summarized on GNP-BMCs, analyzing the factors driving these interactions and the biological consequences. A partial fingerprint of GNP-BMC analyzing common patterns of composition in the literature is extrapolated. However, a red flag is also risen concerning the current lack of data availability and regulated form of knowledge on BMC. Nanomedicine is still in its infancy, and relying on recently developed analytical and informatic tools offers an unprecedented opportunity to make a leap forward. However, a restart through robust shared protocols and data sharing is necessary to obtain "stronger roots". This will create a path to exploiting BMC for human benefit, promoting the clinical translation of biomedical nanotools.