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.

Emerging methods and techniques for cancer biomarker discovery

Modern cancer research depends heavily on the identification and validation of biomarkers because they provide important information about the diagnosis, prognosis, and response to treatment of the cancer. This review will provide a comprehensive overview of cancer biomarkers, including their development phases and recent breakthroughs in transcriptomics and computational techniques for detecting these biomarkers. Blood-based biomarkers have great potential for non-invasive tumor dynamics and treatment response monitoring. These include circulating tumor DNA, exosomes, and microRNAs. Comprehensive molecular profiles are provided by multi-omic technologies, which combine proteomics, metabolomics, and genomes to support the identification of biomarkers and the targeting of therapeutic interventions. Genetic changes are detected by next-generation sequencing, and patterns of protein expression are found by protein arrays and mass spectrometry. Tumor heterogeneity and clonal evolution can be understood using metabolic profiling and single-cell studies. It is projected that the use of several biomarkers-genetic, protein, mRNA, microRNA, and DNA profiles, among others-will rise, enabling multi-biomarker analysis and improving individualised treatment plans. Biomarker identification and patient outcome prediction are further improved by developments in AI algorithms and imaging techniques. Robust biomarker validation and reproducibility require cooperation between industry, academia, and doctors. Biomarkers can provide individualized care, meet unmet clinical needs, and enhance patient outcomes despite some obstacles. Precision medicine will continue to take shape as scientific research advances and the integration of biomarkers with cutting-edge technologies continues to offer a more promising future for personalized cancer care.

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.

Benchmark N-glycoproteomics study of common differential tissue and serum N-glycoproteins of patients with hepatocellular carcinoma

For hepatocellular carcinoma (HCC), N-glycosylation has been proved to be widely involved in various aspects of the disease, including development, metastasis, subtyping, diagnosis and prognosis. The common practice is to discover biomarkers in situ of cancer occurrence (i.e., cancer vs. adjacent tissues) yet to clinically monitor in sera because of non-invasiveness. This study benchmarks N-glycoproteomics characterization of common differential tissue and serum N-glycoproteins of patients with HCC. Differential N-glycosylation in matched tissue and serum samples from the same patients were quantitatively characterized at the intact N-glycopeptide molecular level, and 29 common N-glycoproteins were found. Subcellular localization analysis was carried out to confirm the tissue originality. Secreted N-glycoprotein APOH was up-regulated, and transmembrane and intracellular N-glycoproteins including OSMR, GAT2, CSF-1 and MAGI3 were down-regulated.

Using targeted proteomics-based detection of collagen propeptides to quantify fibrillar collagen biogenesis in vitro

The collagen superfamily, as the major structural component of the extracellular matrix, encompasses 28 distinct subtypes, with type-I and -III forming fibrils crucial for the matrix scaffold. During collagen biogenesis, trimers of type-I and -III procollagen are secreted into the extracellular matrix. The N- and C-terminal propeptides of these trimers are proteolytically cleaved from procollagen during secretion, initiating collagen fibril formation. The propeptides are released into extracellular space and, therefore, have been used to quantify collagen biogenesis. But high-throughput methods for the quantification of these biomarkers are still lacking. This study presents a state-of-the-art multiplexed approach for the simultaneous quantification of PINP, PICP, PIIINP and PIIICP from cell culture supernatants. The ability of targeted proteomics to quantify these propeptides from cell culture samples was assessed in this study. Using tryptic digestion and solid phase extraction, we were able to accurately quantify precollagen propeptides in a range of 3-1000 ng/mL. The assay showed an average inter-assay variance of 6.86 % with an overall recovery ranging from 92 to 98 %. The assay was validated using recombinant protein standards diluted in surrogate matrix and tested using transforming growth factor β1 mediated induction of normal human dermal fibroblasts. In summary, the assay presented in this paper offers a novel, robust, and precise high-throughput method for measuring human collagen propeptides in cell culture supernatants, empowering researchers to assess collagen biogenesis effectively in in vitro experiments.

A Peptide Microarray Platform Approach for Discovery of Immunodominant Antibody Epitopes

Immunodominant epitope discovery platforms play an important role in identifying novel biomarkers for effective immunotherapies and diagnostics. Methods to analyze the B-cell repertoire have been improved both experimentally and computationally. We developed an enhanced peptide microarray platform to discover and subsequently screen immunodominant epitopes. We utilized SARS-Cov-2 IgG positive and negative samples as a proof-of-concept to demonstrate the power of these improved peptide microarrays. The method identified significantly discriminant epitopes that classify positive and negative samples with good performance both as single peptides and in combination. We provide the assay conditions and parameters that justify the use of peptide microarrays in the selection of high-affinity epitopes, and we directly compare peptide performance against proteins. The results suggest that this platform can be used to confidently identify immunodominant antiviral epitopes while also serving as a useful tool for high-volume screening.

Proteomic Characterization of Transfusable Blood Components: Fresh Frozen Plasma, Cryoprecipitate, and Derived Extracellular Vesicles via Data-Independent Mass Spectrometry

Extracellular vesicles (EVs) are a heterogeneous collection of particles that play a crucial role in cell-to-cell communication, primarily due to their ability to transport molecules, such as proteins. Thus, profiling EV-associated proteins offers insight into their biological effects. EVs can be isolated from various biological fluids, including donor blood components such as cryoprecipitate and fresh frozen plasma (FFP). In this study, we conducted a proteomic analysis of five single donor units of cryoprecipitate, FFP, and EVs derived from these blood components using a quantitative mass spectrometry approach. EVs were successfully isolated from both cryoprecipitate and FFP based on community guidelines. We identified and quantified approximately 360 proteins across all sample groups. Principal component analysis and heatmaps revealed that both cryoprecipitate and FFP are similar. Similarly, EVs derived from cryoprecipitate and FFP are comparable. However, they differ between the originating fluids and their derived EVs. Using the R-package MS-DAP, differentially expressed proteins (DEPs) were identified. The DEPs for all comparisons, when submitted for gene enrichment analysis, are involved in the complement and coagulation pathways. The protein profile generated from this study will have important clinical implications in increasing our knowledge of the proteins that are associated with EVs derived from blood components.

From proteome to pathogenesis: investigating polycystic ovary syndrome with Mendelian randomization analysis

Background

Polycystic ovary syndrome (PCOS) is defined by oligo/anovulation, hyperandrogenism, and polycystic ovaries with uncertain pathogenesis. The proteome represents a substantial source of therapeutic targets, and their coding genes may elucidate the mechanisms underlying PCOS. However, reports on the profiles of the human plasma protein-coding genes and PCOS are limited. Here, we aimed to investigate novel biomarkers or drug targets for PCOS by integrating genetics and the human plasma proteome.

Methods

Our study acquired the protein quantitative trait loci from DECODE Genetics, offering 4,907 proteins in 35,559 individuals while obtaining PCOS summary statistics by accessing the FinnGen biobank (1,639 cases and 218,970 controls) and the genome-wide association study catalog (797 cases and 140,558 controls). Herein, we sequentially used two-sample Mendelian randomization (MR) analyses and colocalization to verify the causal link between candidate proteins, their coding genes, and PCOS. Further PCOS data download was conducted by accessing the Gene Expression Omnibus and Zenodo platforms. Gene expression level analysis, pathway enrichment analysis, immune cell infiltration, and transcription factor prediction were performed, aiming at detecting specific cell types with enriched expression and exploring potential optimized treatments for PCOS.

Results

MR analysis revealed 243 protein-coding genes with a causal relationship to PCOS risk, of which 12 were prioritized with the most significant evidence. Through colocalization analysis, three key genes, CUB domain-containing protein 1 (CDCP1), glutaredoxin 2 (GLRX2), and kirre-like nephrin family adhesion molecule 2 (KIRREL2), were identified. Subsequently, the three genes were strongly related to immune function and metabolism in terms of biological significance. In single-cell analysis, the expression levels of genes in ovarian theca cells were explored.

Conclusion

Overall, three protein-coding genes (CDCP1, GLRX2, and KIRREL2) may be related to a higher PCOS risk, suggesting that they may be entry points for exploration of PCOS pathogenesis and treatment, warranting further clinical investigations.

Understanding the role of biomolecular coronas in human exposure to nanomaterials

Nanomaterials (NMs) are increasingly used in medical treatments, electronics, and food additives. However, nanosafety-the possible adverse effects of NMs on human health-is an area of active research. This review provides an overview of the influence of biomolecular coronas on NM transformation following various exposure routes. We discuss potential exposure pathways, including inhalation and ingestion, describing the physiology of exposure routes and emphasising the relevance of coronas in these environments. Additionally, we review other routes to NM exposure, such as synovial fluid, blood (translocation and injection), dermal and ocular exposure, as well as the dose and medium impact on NM interactions. We emphasize the need for an in-depth characterisation of coronas in different biological media, highlighting the need and opportunity to study lung and gastric fluids to understand NM behaviour and potential toxicity. Future research aims to predict better in vivo outcomes and address the complexities of NM interactions with biological systems.

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.

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.

Glycomics of cervicovaginal fluid from women at risk of preterm birth reveals immuno-regulatory epitopes that are hallmarks of cancer and viral glycosylation

During pregnancy the immune system needs to maintain immune tolerance of the foetus while also responding to infection, which can cause premature activation of the inflammatory pathways leading to the onset of labour and preterm birth. The vaginal microbiome is an important modifier of preterm birth risk, with Lactobacillus dominance during pregnancy associated with term delivery while high microbial diversity is associated with an increased risk of preterm birth. Glycans on glycoproteins along the lower female reproductive tract are fundamental to microbiota-host interactions and the mediation of inflammatory responses. However, the specific glycan epitopes involved in these processes are not well understood. To address this, we conducted glycomic analyses of cervicovaginal fluid (CVF) from 36 pregnant women at high risk of preterm birth and 4 non-pregnant women. Our analysis of N- and O-glycans revealed a rich CVF glycome. While O-glycans were shown to be the main carriers of ABO blood group epitopes, the main features of N-glycans were the presence of abundant paucimannose and high mannose glycans, and a remarkable diversity of complex bi-, tri-, and tetra-antennary glycans decorated with fucose and sialic acid. We identified immuno-regulatory epitopes, such as Lewis antigens, and found that fucosylation was negatively correlated to pro-inflammatory factors, such as IL-1β, MMP-8, C3a and C5a, while glycans with only sialylated antennae were mainly positively correlated to those. Similarly, paucimannose glycans showed a positive correlation to pro-inflammatory factors. We revealed a high abundance of glycans which have previously been identified as hallmarks of cancer and viral glycosylation, such as Man8 and Man9 high mannose glycans. Although each pregnant woman had a unique glycomic profile, longitudinal studies showed that the main glycosylation features were consistent throughout pregnancy in women who delivered at term, whereas women who experienced extreme preterm birth exhibited sharp changes in the CVF glycome shortly before delivery. These findings shed light on the processes underlying the role of glycosylation in maintaining a healthy vaginal microbiome and associated host immune responses. In addition, these discoveries facilitate our understanding of the lower female reproductive tract which has broad implications for women's health.

Integrative proteomic and metabolomic elucidation of cardiomyopathy with in vivo and in vitro models and clinical samples

Cardiomyopathy is a prevalent cardiovascular disease that affects individuals of all ages and can lead to life-threatening heart failure. Despite its variety in types, each with distinct characteristics and causes, our understanding of cardiomyopathy at a systematic biology level remains incomplete. Mass spectrometry-based techniques have emerged as powerful tools, providing a comprehensive view of the molecular landscape and aiding in the discovery of biomarkers and elucidation of mechanisms. This review highlights the significant potential of integrating proteomic and metabolomic approaches with specialized databases to identify biomarkers and therapeutic targets across different types of cardiomyopathies. In vivo and in vitro models, such as genetically modified mice, patient-derived or induced pluripotent stem cells, and organ chips, are invaluable in exploring the pathophysiological complexities of this disease. By integrating omics approaches with these sophisticated modeling systems, our comprehension of the molecular underpinnings of cardiomyopathy can be greatly enhanced, facilitating the development of diagnostic markers and therapeutic strategies. Among the promising therapeutic targets are those involved in extracellular matrix remodeling, sarcomere damage, and metabolic remodeling. These targets hold the potential to advance precision therapy in cardiomyopathy, offering hope for more effective treatments tailored to the specific molecular profiles of patients.

Multibiomarker panels in liquid biopsy for early detection of pancreatic cancer - a comprehensive review

Pancreatic ductal adenocarcinoma (PDAC) is frequently detected in late stages, which leads to limited therapeutic options and a dismal overall survival rate. To date, no robust method for the detection of early-stage PDAC that can be used for targeted screening approaches is available. Liquid biopsy allows the minimally invasive collection of body fluids (typically peripheral blood) and the subsequent analysis of circulating tumor cells or tumor-associated molecules such as nucleic acids, proteins, or metabolites that may be useful for the early diagnosis of PDAC. Single biomarkers may lack sensitivity and/or specificity to reliably detect PDAC, while combinations of these circulating biomarkers in multimarker panels may improve the sensitivity and specificity of blood test-based diagnosis. In this narrative review, we present an overview of different liquid biopsy biomarkers for the early diagnosis of PDAC and discuss the validity of multimarker panels.

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.

Proteome of plasma extracellular vesicles as a source of colorectal cancer biomarkers

The search for minimally invasive methods for diagnostics of colorectal cancer (CRC) is the most important task for early diagnostics of the disease and subsequent successful treatment. Human plasma represents the main type of biological material used in the clinical practice; however, the complex dynamic range of substances circulating in it complicates determination of CRC protein markers by the mass spectrometric (MS) method. Studying the proteome of extracellular vesicles (EVs) isolated from human plasma represents an attractive approach for the discovery of tissue-secreted CRC markers. We performed shotgun mass spectrometry analysis of EV samples obtained from plasma of CRC patients and healthy volunteers. This MS analysis resulted in identification of 370 proteins (which were registered by at least two peptides). Stable isotope-free relative quantitation identified 55 proteins with altered abundance in EV samples obtained from plasma samples of CRC patients as compared to healthy controls. Among the EV proteins isolated from blood plasma we found components involved in cell adhesion and the VEGFA-VEGFR2 signaling pathway (TLN1, HSPA8, VCL, MYH9, and others), as well as proteins expressed predominantly by gastrointestinal tissues (polymeric immunoglobulin receptor, PIGR). The data obtained using the shotgun proteomic profiling may be added to the panel for targeted MS analysis of EV-associated protein markers, previously developed using CRC cell models.

Automated multiplexed affinity-based enrichment of peptides for LC-MS/MS plasma proteomics

Plasma proteomics offers high potential for biomarker discovery, as plasma is collected through a minimally invasive procedure and constitutes the most complex human-derived proteome. However, the wide dynamic range poses a significant challenge. Here, we propose a semi-automated method based on the use of multiple single chain variable fragment antibodies, each enriching for peptides found in up to a few hundred proteins. This approach allows for the analysis of a complementary fraction compared to full proteome analysis. Proteins from pooled plasma were extracted and digested before testing the performance of 29 different antibodies with the aim of reproducibly maximizing peptide enrichment. Our results demonstrate the enrichment of 3662 peptides not detected in neat plasma or negative controls. Moreover, most antibodies were able to enrich for at least 155 peptides across different levels of abundance in plasma. To further reduce analysis time, a combination of antibodies was used in a multiplexed setting. Repeated sample analyses showed low coefficients of variation, and the method is flexible in terms of affinity binders. It does not impose drastic increases in instrument time, thus showing excellent potential for usage in large scale discovery projects.

Non-human peptides revealed in blood reflect the composition of intestinal microbiota

BACKGROUND

The previously underestimated effects of commensal gut microbiota on the human body are increasingly being investigated using omics. The discovery of active molecules of interaction between the microbiota and the host may be an important step towards elucidating the mechanisms of symbiosis.

RESULTS

Here, we show that in the bloodstream of healthy people, there are over 900 peptides that are fragments of proteins from microorganisms which naturally inhabit human biotopes, including the intestinal microbiota. Absolute quantitation by multiple reaction monitoring has confirmed the presence of bacterial peptides in the blood plasma and serum in the range of approximately 0.1 nM to 1 μM. The abundance of microbiota peptides reaches its maximum about 5 h after a meal. Most of the peptides correlate with the bacterial composition of the small intestine and are likely obtained by hydrolysis of membrane proteins with trypsin, chymotrypsin and pepsin - the main proteases of the gastrointestinal tract. The peptides have physicochemical properties that likely allow them to selectively pass the intestinal mucosal barrier and resist fibrinolysis.

CONCLUSIONS

The proposed approach to the identification of microbiota peptides in the blood, after additional validation, may be useful for determining the microbiota composition of hard-to-reach intestinal areas and monitoring the permeability of the intestinal mucosal barrier.

Comparison of biuret and refractometery method for serum total protein measurements in cattle and goat

OBJECTIVES

The biuret method is frequently used to determine serum total protein. On the other hand refractometer, a quicker and less expensive option, is available to determine serum total protein. However, there is no study conducted in Ethiopia to compare serum total protein measurement in veterinary settings. Therefore, this study was conducted to compare the serum total protein concentration measurement in cattle and goats obtained by the biuret method and refractometer.

RESULTS

Serum samples from 60 cattle and 60 goats were assayed by both methods and data were analyzed with a paired t-test, Pearson's correlation, and Bland-Altman plots. There was a strong positive correlation between the total protein values determined with the refractometer and the biuret method in cattle (r = 0.93) and goats (r = 0.97). There were no significant differences (p > 0.05) in the protein values measured with the refractometer and those evaluated with the biuret method in both species. Bland-Altman plots showed that biases indicating the analytic and user error were 8.33% in both species which is below the acceptable total error (< 10%). Thus, refractometer can be used in place of biuret method since it is valid enough to measure serum total protein in cattle and goats.

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.

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.

A Carrier-Based Quantitative Proteomics Method Applied to Biomarker Discovery in Pericardial Fluid

Data-dependent liquid chromatography tandem mass spectrometry 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 liquid chromatography tandem mass spectrometry 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 the 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.

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.

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.

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 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.

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.

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.