Revisiting biomarker discovery by plasma proteomics

Genetic determinants of plasma protein levels in the Estonian population

The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.

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.

Comparative profiling by data-independent acquisition mass spectrometry reveals featured plasma proteins in breast cancer: a pilot study

Purpose

Breast cancer is known to be influenced by genetic and environmental factors, and several susceptibility genes have been discovered. Still, the majority of genetic contributors remain unknown. We aimed to analyze the plasma proteome of breast cancer patients in comparison to healthy individuals to identify differences in protein expression profiles and discover novel biomarkers.

Methods

This pilot study was conducted using bioresources from Seoul National University Bundang Hospital's Human Bioresource Center. Serum samples from 10 breast cancer patients and 10 healthy controls were obtained. Liquid chromatography-mass spectrometry analysis was performed to identify differentially expressed proteins.

Results

We identified 891 proteins; 805 were expressed in the breast cancer group and 882 in the control group. Gene set enrichment and differential expression analysis identified 30 upregulated and 100 downregulated proteins in breast cancer. Among these, 10 proteins were selected as potential biomarkers. Three proteins were upregulated in breast cancer patients, including cluster of differentiation 44, eukaryotic translation initiation factor 2-α kinase 3, and fibronectin 1. Seven proteins downregulated in breast cancer patients were also selected: glyceraldehyde-3-phosphate dehydrogenase, α-enolase, heat shock protein member 8, integrin-linked kinase, tissue inhibitor of metalloproteinases-1, vasodilator-stimulated phosphoprotein, and 14-3-3 protein gamma. All proteins had been previously reported to be related to tumor development and progression.

Conclusion

The findings suggest that plasma proteome profiling can reveal potential diagnostic biomarkers for breast cancer and may contribute to early detection and personalized treatment strategies. A further validation study with a larger sample cohort of breast cancer patients is planned.

Cross-sectional network analysis of plasma proteins/metabolites correlated with pathogenesis and therapeutic response in acute promyelocytic leukemia

The treatment of PML/RARA+ acute promyelocytic leukemia (APL) with all-trans-retinoic acid and arsenic trioxide (ATRA/ATO) has been recognized as a model for translational medicine research. Though an altered microenvironment is a general cancer hallmark, how APL blasts shape their plasma composition is poorly understood. Here, we reported a cross-sectional correlation network to interpret multilayered datasets on clinical parameters, proteomes, and metabolomes of paired plasma samples from patients with APL before or after ATRA/ATO induction therapy. Our study revealed the two prominent features of the APL plasma, suggesting a possible involvement of APL blasts in modulating plasma composition. One was characterized by altered secretory protein and metabolite profiles correlating with heightened proliferation and energy consumption in APL blasts, and the other featured APL plasma-enriched proteins or enzymes catalyzing plasma-altered metabolites that were potential trans-regulatory targets of PML/RARA. Furthermore, results indicated heightened interferon-gamma signaling characterizing a tumor-suppressing function of the immune system at the first hematological complete remission stage, which likely resulted from therapy-induced cell death or senescence and ensuing supraphysiological levels of intracellular proteins. Overall, our work sheds new light on the pathophysiology and treatment of APL and provides an information-rich reference data cohort for the exploratory and translational study of leukemia microenvironment.

Mass Spectrometric Proteomics 2.0

This Special Issue, "Mass Spectrometric Proteomics 2 [...].

timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Robust Glycoproteomics Platform Reveals a Tetra-Antennary Site-Specific Glycan Capping with Sialyl-Lewis Antigen for Early Detection of Gastric Cancer

The lack of efficient biomarkers for the early detection of gastric cancer (GC) contributes to its high mortality rate, so it is crucial to discover novel diagnostic targets for GC. Recent studies have implicated the potential of site-specific glycans in cancer diagnosis, yet it is challenging to perform highly reproducible and sensitive glycoproteomics analysis on large cohorts of samples. Here, a highly robust N-glycoproteomics (HRN) platform comprising an automated enrichment method, a stable microflow LC-MS/MS system, and a sensitive glycopeptide-spectra-deciphering tool is developed for large-scale quantitative N-glycoproteome analysis. The HRN platform is applied to analyze serum N-glycoproteomes of 278 subjects from three cohorts to investigate glycosylation changes of GC. It identifies over 20 000 unique site-specific glycans from discovery and validation cohorts, and determines four site-specific glycans as biomarker candidates. One candidate has branched tetra-antennary structure capping with sialyl-Lewis antigen, and it significantly outperforms serum CEA with AUC values > 0.89 compared against < 0.67 for diagnosing early-stage GC. The four-marker panel can provide improved diagnostic performances. Besides, discrimination powers of four candidates are also testified with a verification cohort using PRM strategy. This findings highlight the value of this strong tool in analyzing aberrant site-specific glycans for cancer detection.

Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics

Protein glycosylation, a complex and heterogeneous post-translational modification that is frequently dysregulated in disease, has been difficult to analyse at scale. Here we report a data-independent acquisition technique for the large-scale mass-spectrometric quantification of glycopeptides in plasma samples. The technique, which we named 'OxoScan-MS', identifies oxonium ions as glycopeptide fragments and exploits a sliding-quadrupole dimension to generate comprehensive and untargeted oxonium ion maps of precursor masses assigned to fragment ions from non-enriched plasma samples. By applying OxoScan-MS to quantify 1,002 glycopeptide features in the plasma glycoproteomes from patients with COVID-19 and healthy controls, we found that severe COVID-19 induces differential glycosylation in IgA, haptoglobin, transferrin and other disease-relevant plasma glycoproteins. OxoScan-MS may allow for the quantitative mapping of glycoproteomes at the scale of hundreds to thousands of samples.

Tandem mass tag-based quantitative proteomics analyses of the spermatogenesis-ameliorating effect of Youjing granule on rats

RATIONALE

Male infertility is a common reproductive system disease manifested as aberrant spermatogenesis and identified as "kidney deficiency and dampness" in Chinese traditional medicine. Youjing granule (YG) is a Chinese material medica based on tonifying kidneys and removing dampness. It has proven to be able to regulate semen quality in clinical application, but the underlying mechanism has not been clarified.

METHODS

Using serum containing YG to treat primarily cultured spermatogonial stem cells (SSCs), the apoptotic rate and mitosis phase ratio of SSCs were measured. The liquid chromatography-tandem mass spectrometry with tandem mass tags method was applied for analyzing the serum of rats treated with YG/distilled water, and proteomic analyses were performed to clarify the mechanisms of YG.

RESULTS

Totally, 111 proteins in YG-treated serum samples were differentially expressed compared with control groups, and 43 of them were identified as potential target proteins, which were further annotated based on their enrichment in Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. Proteomic analyses showed that the mechanisms of YG may involve regulation of glycolysis, gluconeogenesis and nucleotide-binding and oligomerization domain-like receptor signaling pathway. In addition, RhoA and Lamp2 were found to be possible responders of YG through reviewing the literature.

CONCLUSIONS

The results demonstrate that our serum proteomics platform is clinically useful in understanding the mechanisms of YG.

The use of biological fluids in microfluidic paper-based analytical devices (μPADs): Recent advances, challenges and future perspectives

The use of microfluidic paper-based analytical devices (μPADs) for aiding medical diagnosis is a growing trend in the literature mainly due to their low cost, easy use, simple manufacturing, and great potential for application in low-resource settings. Many important biomarkers (proteins, ions, lipids, hormones, DNA, RNA, drugs, whole cells, and more) and biofluids are available for precise detection and diagnosis. We have reviewed the advances μPADs in medical diagnostics have achieved in the last few years, focusing on the most common human biofluids (whole blood/plasma, sweat, urine, tears, and saliva). The challenges of detecting specific biomarkers in each sample are discussed, along with innovative techniques that overcome such limitations. Finally, the difficulties of commercializing μPADs are considered, and future trends are presented, including wearable devices and integrating multiple steps in a single platform.

Topographic Distribution of miRNAs (miR-30a, miR-223, miR-let-7a, miR-let-7f, miR-451, and miR-486) in the Plasma Extracellular Vesicles

There are many articles on the quantitative analysis of miRNAs contained in a population of EVs of different sizes under various physiological and pathological conditions. For such analysis, it is important to correctly quantify the miRNA contents of EVs. It should be considered that quantification is skewed depending on the isolation protocol, and different miRNAs are degraded by nucleases with different efficiencies. In addition, it is important to consider the contribution of miRNAs coprecipitating with the EVs population, because the amount of miRNAs in the EVs population under study is skewed without appropriate enzymatic treatment. By studying a population of EVs from the blood plasma of healthy donors, we found that the absolute amount of miRNA inside the vesicles is commensurate with the amount of the same type of miRNA adhered to the outside of the EVs. The inside/outside ratio ranged from 1.02 to 2.64 for different investigated miRNAs. According to our results, we propose the hypothesis that high occupancy of miRNAs on the outer surface of EVs influence on the transporting RNA repertoire no less than the inner cargo received from the host cell.

Serum/Plasma Proteome in Non-Malignant Liver Disease

The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.

New Breakthroughs in the Diagnosis of Leptomeningeal Carcinomatosis: A Review of Liquid Biopsies of Cerebrospinal Fluid

Leptomeningeal carcinomatosis represents a terminal stage and is a devastating complication of cancer. Despite its high incidence, current diagnostic methods fail to accurately detect this condition in a timely manner. This failure to diagnose leads to the refusal of treatment and the absence of clinical trials, hampering the development of new therapy strategies. The use of liquid biopsy is revolutionizing the field of diagnostic oncology. The dynamic and non-invasive detection of tumor markers has enormous potential in cancer diagnostics and treatment. Leptomeningeal carcinomatosis is a condition where invasive tissue biopsy is not part of the routine diagnostic analysis, making liquid biopsy an essential diagnostic tool. Several elements in cerebrospinal fluid (CSF) have been investigated as potential targets of liquid biopsy, including free circulating tumor cells, free circulating nucleic acids, proteins, exosomes, and even non-tumor cells as part of the dynamic tumor microenvironment. This review aims to summarize current breakthroughs in the research on liquid biopsy, including the latest breakthroughs in the identification of tumor cells and nucleic acids, and give an overview of future directions in the diagnosis of leptomeningeal carcinomatosis.

Longitudinal plasma proteome profiling reveals the diversity of biomarkers for diagnosis and cetuximab therapy response of colorectal cancer

Cetuximab therapy is the major treatment for colorectal cancer (CRC), but drug resistance limits its effectiveness. Here, we perform longitudinal and deep proteomic profiling of 641 plasma samples originated from 147 CRC patients (CRCs) undergoing cetuximab therapy with multi-course treatment, and 90 healthy controls (HCs). COL12A1, THBS2, S100A8, and S100A9 are screened as potential proteins to distinguish CRCs from HCs both in plasma and tissue validation cohorts. We identify the potential biomarkers (RRAS2, MMP8, FBLN1, RPTOR, and IMPDH2) for the initial response prediction. In a longitudinal setting, we identify two clusters with distinct fluctuations and construct the model with high accuracy to predict the longitudinal response, further validated in the independent cohort. This study reveals the heterogeneity of different biomarkers for tumor diagnosis, the initial and longitudinal response prediction respectively in the first course and multi-course cetuximab treatment, may ultimately be useful in monitoring and intervention strategies for CRC.

Serum proteomics identifies novel diagnostic biomarkers for asthma in preschool children

Background

Asthma is characterized by airway hyperresponsiveness, reversible airway obstruction, and chronic airway inflammation. It is the most common chronic disease in childhood. However, the diagnosis of childhood asthma remains challenging, and there is an urgent need to develop new diagnostic methods.

Methods

To identify biomarkers of asthma in children, we adopted the Orbitrap-based data-independent acquisition (DIA) mass spectrometry proteomics method to analyze the serum proteomic signatures of children with acute asthma and convalescent children.

Results

We identified 747 proteins in 46 serum samples and 50 differentially expressed proteins (DEPs) that distinguished between asthmatic and healthy children. Next, functional enrichment analysis of the DEPs was conducted, it was indicated that the DEPs were significantly enriched in immune-related and function terms and pathways. Furthermore, we performed statistical analysis and identified MMP14, ABHD12B, PCYOX1, LTBP1, CFHR4, APOA1, IGHG4, ANG and IGFALS proteins as the diagnostic biomarker candidates. Ultimately, a promising asthma diagnostic model for preschool children based on IGFALS was built and evaluated. The area under the curve (AUC) of the IGFALS model was 0.959.

Conclusions

In this study, the DIA proteome strategy was used and the largest number of proteins of asthmatic children serum proteomics was identified. The proteomics results showed that the DEPs play the central role of the inflammation-immune mechanism in asthma pathogenesis, suggesting that these proteins may be used in asthma diagnosis, prognosis, or therapy, and suggested biomarkers for asthma of preschool children. In conclusion, our results provide insight into the pathophysiology of asthma. We believe that the diagnostic model will facilitate clinical decision-making regarding asthma in preschool children.

Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers

Routine measurement of cancer biomarkers is performed for early detection, risk classification, and treatment monitoring, among other applications, and has substantially contributed to better clinical outcomes for patients. However, there remains an unmet need for clinically validated assays of cancer protein biomarkers. Protein tumor markers are of particular interest since proteins carry out the majority of biological processes and thus dynamically reflect changes in cancer pathophysiology. Mass spectrometry-based targeted proteomics is a powerful tool for absolute peptide and protein quantification in biological matrices with numerous advantages that make it attractive for clinical applications in oncology. The use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodologies has allowed laboratories to overcome challenges associated with immunoassays that are more widely used for tumor marker measurements. Yet, clinical implementation of targeted proteomics methodologies has so far been limited to a few cancer markers. This is due to numerous challenges associated with paucity of robust validation studies of new biomarkers and the labor-intensive and operationally complex nature of LC-MS/MS workflows. The purpose of this review is to provide an overview of targeted proteomics applications in cancer, workflows used in targeted proteomics, and requirements for clinical validation and implementation of targeted proteomics assays. We will also discuss advantages and challenges of targeted MS-based proteomics assays for clinical cancer biomarker analysis and highlight some recent developments that will positively contribute to the implementation of this technique into clinical laboratories.

Pain/Stress, Mitochondrial Dysfunction, and Neurodevelopment in Preterm Infants

INTRODUCTION

Preterm infants experience tremendous early life pain/stress during their neonatal intensive care unit (NICU) hospitalization, which impacts their neurodevelopmental outcomes. Mitochondrial function/dysfunction may interface between perinatal stress events and neurodevelopment. Nevertheless, the specific proteins or pathways linking mitochondrial functions to pain-induced neurodevelopmental outcomes in infants remain unidentified. Our study aims to investigate the associations among pain/stress, proteins associated with mitochondrial function/dysfunction, and neurobehavioral responses in preterm infants.

METHODS

We conducted a prospective cohort study, enrolling 33 preterm infants between September 2017 and July 2022 at two affiliated NICUs located in Hartford and Farmington, CT. NICU Network Neurobehavioral Scale (NNNS) datasets were evaluated to explore potential association with neurobehavioral outcomes. The daily pain/stress experienced by infant's during their NICU stay was documented. At 36-38 weeks post-menstrual age (PMA), neurobehavioral outcomes were evaluated using the NNNS and buccal swabs were collected for further analysis. Mass spectrometry-based proteomics was conducted on epithelial cells obtained from buccal swabs to evaluate protein expression level. Lasso statistical methods were conducted to study the association between protein abundance and infants' NNNS summary scores. Multiple linear regression and Gene Ontology (GO) enrichment analyses were performed to examine how clinical characteristics and neurodevelopmental outcomes may be associated with protein levels and underlying molecular pathways.

RESULTS

During NICU hospitalization, preterm premature rupture of membrane (PPROM) was negatively associated with neurobehavioral outcomes. The protein functions including leptin receptor binding activity, glutathione disulfide oxidoreductase activity and response to oxidative stress, lipid metabolism, and phosphate and proton transmembrane transporter activity were negatively associated with neurobehavioral outcomes; in contrast, cytoskeletal regulation, epithelial barrier, and protection function were found to be associated with the optimal neurodevelopmental outcomes. In addition, mitochondrial function-associated proteins including SPRR2A, PAIP1, S100A3, MT-CO2, PiC, GLRX, PHB2, and BNIPL-2 demonstrated positive association with favorable neurodevelopmental outcomes, while proteins of ABLIM1, UNC45A, keratins, MUC1, and CYB5B showed positive association with adverse neurodevelopmental outcomes.

CONCLUSION

Mitochondrial function-related proteins were observed to be associated with early life pain/stress and neurodevelopmental outcomes in infants. Large-scale studies with longitudinal datasets are warranted. Buccal proteins could be used to predict potential neurobehavioral outcomes.

Tandem SERS and MS/MS Profiling of Plasma Extracellular Vesicles for Early Ovarian Cancer Biomarker Discovery

Extracellular vesicles (EVs) are vectors of biomolecular cargo that play essential roles in intercellular communication across a range of cells. Protein, lipid, and nucleic acid cargo harbored within EVs may serve as biomarkers at all stages of disease; however, the choice of methodology may challenge the specificity and reproducibility of discovery. To address these challenges, the integration of rigorous EV purification methods, cutting-edge spectroscopic technologies, and data analysis are critical to uncover diagnostic signatures of disease. Herein, we demonstrate an EV isolation and analysis pipeline using surface-enhanced Raman spectroscopy (SERS) and mass spectrometry (MS) techniques on plasma samples obtained from umbilical cord blood, healthy donor (HD) plasma, and plasma from women with early stage high-grade serous carcinoma (HGSC). Plasma EVs were purified by size exclusion chromatography and analyzed by surface-enhanced Raman spectroscopy (SERS), mass spectrometry (MS), and atomic force microscopy. After determining the fraction of highest EV purity, SERS and MS were used to characterize EVs from HDs, pooled donors with noncancerous gynecological ailments (n = 6), and donors with early stage [FIGO (I/II)] with HGSC. SERS spectra were subjected to different machine learning algorithms such as PCA, logistic regression, support vector machine, naïve Bayes, random forest, neural network, and k nearest neighbors to differentiate healthy, benign, and HGSC EVs. Collectively, we demonstrate a reproducible workflow with the potential to serve as a diagnostic platform for HGSC.

Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins

Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.

Identified Small Open Reading Frame-Encoded Peptides in Human Serum with Nanoparticle Protein Coronas

The low-molecular-weight proteins (LMWP) in serum and plasma are related to various human diseases and can be valuable biomarkers. A small open reading frame-encoded peptide (SEP) is one kind of LMWP, which has been found to function in many bioprocesses and has also been found in human blood, making it a potential biomarker. The detection of LMWP by a mass spectrometry (MS)-based proteomic assay is often inhibited by the wide dynamic range of serum/plasma protein abundance. Nanoparticle protein coronas are a newly emerging protein enrichment method. To analyze SEPs in human serum, we have developed a protocol integrated with nanoparticle protein coronas and liquid chromatography (LC)/MS/MS. With three nanoparticles, TiO2, Fe3O4@SiO2, and Fe3O4@SiO2@TiO2, we identified 164 new SEPs in the human serum sample. Fe3O4@SiO2 and a nanoparticle mixture obtained the maximum number and the largest proportion of identified SEPs, respectively. Compared with acetonitrile-based extraction, nanoparticle protein coronas can cover more small proteins and SEPs. The magnetic nanoparticle is also fit for high-throughput parallel protein separation before LC/MS. This method is fast, efficient, reproducible, and easy to operate in 96-well plates and centrifuge tubes, which will benefit the research on SEPs and biomarkers.

Proteomic profiling in the progression of psychosis: Analysis of clinical high-risk, first episode psychosis, and healthy controls

BACKGROUND AND HYPOTHESIS

Recent evidence has highlighted the benefits of early detection and treatment for better clinical outcomes in patients with psychosis. Biological markers of the disease have become a focal point of research. This study aimed to identify protein markers detectable in the early stages of psychosis and indicators of progression by comparing them with those of healthy controls (HC) and first episode psychosis (FEP).

STUDY DESIGN

The participants comprised 28 patients in the clinical high-risk (CHR) group, 49 patients with FEP, and 61 HCs aged 15-35 years. Blood samples were collected and analyzed using multiple reaction monitoring-mass spectrometry to measure the expression of 158 peptide targets. Data were adjusted for age, sex, and use of psychotropic drugs.

STUDY RESULTS

A total of 18 peptides (17 proteins) differed significantly among the groups. The protein PRDX2 was higher in the FEP group than in the CHR and HC groups and showed increased expression according to disease progression. The levels of six proteins were significantly higher in the FEP group than in the CHR group. Nine proteins differed significantly in the CHR group compared to the other groups. Sixteen proteins were significantly correlated with symptom severity. These proteins are primarily related to the coagulation cascade, inflammatory response, brain structure, and synaptic plasticity.

CONCLUSIONS

Our findings suggested that peripheral protein markers reflect disease progression in patients with psychosis. Further longitudinal research is needed to confirm these findings and to identify the specific roles of these markers in the pathogenesis of schizophrenia.

Plasma/Serum Proteomics based on Mass Spectrometry

Human blood is a window of physiology and disease. Examination of biomarkers in blood is a common clinical procedure, which can be informative in diagnosis and prognosis of diseases, and in evaluating treatment effectiveness. There is still a huge demand on new blood biomarkers and assays for precision medicine nowadays, therefore plasma/serum proteomics has attracted increasing attention in recent years. How to effectively proceed with the biomarker discovery and clinical diagnostic assay development is a question raised to researchers who are interested in this area. In this review, we comprehensively introduce the background and advancement of technologies for blood proteomics, with a focus on mass spectrometry (MS). Analyzing existing blood biomarkers and newly-built diagnostic assays based on MS can shed light on developing new biomarkers and analytical methods. We summarize various protein analytes in plasma/serum which include total proteome, protein post-translational modifications, and extracellular vesicles, focusing on their corresponding sample preparation methods for MS analysis. We propose screening multiple protein analytes in the same set of blood samples in order to increase success rate for biomarker discovery. We also review the trends of MS techniques for blood tests including sample preparation automation, and further provide our perspectives on their future directions.

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

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

Targeted proteomics in a population-based study identifies serum PECAM-1 and TRIM21 as inflammation markers for periodontitis

OBJECTIVES

Periodontitis (PD) can cause systematic inflammation and is associated with various metabolic processes in the body. However, robust serum markers for these relationships are still lacking. This study aims to identify novel circulating inflammation-related proteins associated with PD using targeted proteomics.

MATERIALS AND METHODS

We used population-based, cross-sectional data from 619 participants of the Polish Longitudinal University Study (Bialystok PLUS). Mean pocket probing depth (mPPD) and proportion of bleeding on probing (pBOP) served as exposure variables. Fifty-two inflammation-related proteins were measured using the Olink Target 96 Cardiovascular III and the Olink Target 96 Immune Response panels. Associations between periodontal measures and proteins were tested using covariate-adjusted linear regression models.

RESULTS

At a false discovery rate of < 0.05, we identified associations of mPPD and pBOP with platelet-endothelial cell adhesion molecule-1 (PECAM-1) and tripartite motif-containing protein 21 (TRIM21).

CONCLUSION

This study revealed novel associations between PD and serum levels of PECAM-1 and TRIM21. Our results suggest that these proteins might be affected by molecular processes that take place in the inflamed periodontium.

CLINICAL RELEVANCE

Novel associations of PECAM-1 and TRIM21 with PD indicate promising serum markers for understanding the disease's pathophysiological processes and call for further biomedical investigations.

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Here, we describe a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 1002 individuals. Machine learning model determined as P4 panel (HABP2, CD163, AFP and PIVKA-II) clearly distinguish HCC from liver cirrhosis (LC, AUC 0.979, sensitivity 0.925, specificity 0.915) and healthy individuals (HC, AUC 0.992, sensitivity 0.975, specificity 1.000) in an independent validation cohort, outperforming existing clinical prediction strategies. Furthermore, the P4 panel can accurately predict LC to HCC conversion (AUC 0.890, sensitivity 0.909, specificity 0.877) with predicting HCC at a median of 11.4 months prior to imaging in prospective external validation cohorts (No.: Keshen 2018_005_02 and NCT03588442). These results suggest that proteomics-driven serum biomarker discovery provides a valuable reference for the liquid biopsy, and has great potential to improve early diagnosis of HCC.

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

Identifying Sex-Specific Serum Patterns of Alzheimer's Mice through Deep TMT Profiling and a Concentration-Dependent Concatenation Strategy

Alzheimer's disease (AD) is the most prevalent form of dementia, disproportionately affecting women in disease prevalence and progression. Comprehensive analysis of the serum proteome in a common AD mouse model offers potential in identifying possible AD pathology- and gender-associated biomarkers. Here, we introduce a multiplexed, nondepleted mouse serum proteome profiling via tandem mass-tag (TMTpro) labeling. The labeled sample was separated into 475 fractions using basic reversed-phase liquid chromatography (RPLC), which were categorized into low-, medium-, and high-concentration fractions for concatenation. This concentration-dependent concatenation strategy resulted in 128 fractions for acidic RPLC-tandem mass spectrometry (MS/MS) analysis, collecting ∼5 million MS/MS scans and identifying 3972 unique proteins (3413 genes) that cover a dynamic range spanning at least 6 orders of magnitude. The differential expression analysis between wild type and the commonly used AD model (5xFAD) mice exhibited minimal significant protein alterations. However, we detected 60 statistically significant (FDR < 0.05), sex-specific proteins, including complement components, serpins, carboxylesterases, major urinary proteins, cysteine-rich secretory protein 1, pregnancy-associated murine protein 1, prolactin, amyloid P component, epidermal growth factor receptor, fibrinogen-like protein 1, and hepcidin. The results suggest that our platform possesses the sensitivity and reproducibility required to detect sex-specific differentially expressed proteins in mouse serum samples.

Plasma protein signatures reflect systemic immunity and allograft function in kidney transplantation

Kidney transplantation causes large perturbations of the immune system. While many studies focus on the allograft, insights into systemic effects are largely missing. Here, we analyzed the systemic immune response in 3 cohorts of kidney transplanted patients. Using serum proteomics, laboratory values, mass cytometry, histological and clinical parameters, inter-patient heterogeneity was leveraged for multi-omic co-variation analysis. We identified circulating immune modules (CIM) that describe extra-renal signatures of co-regulated plasma proteins. CIM are present in nontransplanted controls, in transplant conditions and during rejection. They are enriched in pathways linked to kidney function, extracellular matrix, signaling, and cellular activation. A complex leukocyte response in the blood during allograft quiescence and rejection is associated with CIM activity and CIM-specific cytokines. CIM activity correlates with kidney function including a 2-month prediction. Together, the data suggest a systemic and multi-layered response of transplant immunity that might be insightful for understanding allograft dysfunction and developing translational biomarkers.

Using proteomics for stratification and risk prediction in patients with solid tumors

Proteomics, the study of proteins and their functions, has greatly evolved due to advances in analytical chemistry and computational biology. Unlike genomics or transcriptomics, proteomics captures the dynamic and diverse nature of proteins, which play crucial roles in cellular processes. This is exemplified in cancer, where genomic and transcriptomic information often falls short in reflecting actual protein expression and interactions. Liquid chromatography-mass spectrometry (LC-MS) is pivotal in proteomic data generation, enabling high-throughput analysis of protein samples. The MS-based workflow involves protein digestion, chromatographic separation, ionization, and fragmentation, leading to peptide identification and quantification. Computational biostatistics, particularly using tools in R (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org ), aid in data analysis, revealing protein expression patterns and correlations with clinical variables. Proteomic studies can be explorative, aiming to characterize entire proteomes, or targeted, focusing on specific proteins of interest. The integration of proteomics with genomics addresses database limitations and enhances peptide identification. Case studies in intrahepatic cholangiocarcinoma, glioblastoma multiforme, and pancreatic ductal adenocarcinoma highlight proteomics' clinical applications, from subtyping cancers to identifying diagnostic markers. Moreover, proteomic data augment molecular tumor boards by providing deeper insights into pathway activities and genomic mutations, supporting personalized treatment decisions. Overall, proteomics contributes significantly to advancing our understanding of cellular biology and improving clinical care.

Integrated untargeted and targeted proteomics to unveil plasma prognostic markers for patients with acute paraquat poisoning: A pilot study

Paraquat (PQ) is a widely used but strongly toxic herbicide, which can induce multiple organ failure. The overall survival rate of the poisoned patients was only 54.4% due to lack of specific antidotes. Besides, the definite pathogenic mechanism of PQ is still not fully understood. In this pilot study, untargeted and targeted proteomics were integrated to explore the expression characteristics of plasma protein in PQ poisoned patients, and identify the differentially expressed proteins between survivors and non-survivors. A total of 494 plasma proteins were detected, and of which 47 were upregulated and 44 were downregulated in PQ poisoned patients compared to healthy controls. Among them, five differential plasma proteins (S100A9, S100A8, MB, ACTB and RAB11FIP3) were further validated by multiple reaction monitoring (MRM)-based targeted proteomic approach, and three of them (S100A9, S100A8 and ACTB) were confirmed to be correlated with PQ poisoning. Meanwhile, 84 dysregulated plasma proteins were identified in non-survivors compared with survivors. Moreover, targeted proteomic and ROC analysis suggested that ACTB had a good performance in predicting the prognosis of PQ poisoned patients. These findings highlighted the value of label-free and mass spectrometry-based proteomics in screening prognostic biomarkers of PQ poisoning and studying the mechanism of PQ toxicity.

Research progress of good markers for canine mammary carcinoma

PURPOSE

Mammary gland tumors are the most common neoplastic diseases in elderly female dogs, about 50% of which are considered to be malignant. Canine mammary tumors are similar to human breast cancers in many respects, so canine mammary tumors are frequently studied alongside human breast cancer. This article mentioned KI-67, HER-2, COX-2, BRCA1, BRCA2, P53, CA15-3, MicroRNA, Top2α and so on. All these markers are expected to have an important role in the clinic.

METHODS

Existing markers of canine mammary carcinoma are reviewed, and the expression of each marker and its diagnostic role for this tumor are described in detail.

RESULTS

This article introduced several effective markers of canine mammary tumors, among them, antigen KI-67 (KI-67), human epidermal growth factor receptor 2 (HER-2), cyclooxygenase 2 (COX-2) are promising and can be detected in both serum and tissue samples. Breast cancer caused by mutations in the breast cancer 1 gene (BRCA1) and breast cancer 2 gene (BRCA2) is also a hot topic of research. In addition to the above symbols, tumor protein p53 (p53), cancer antigen15-3 (CA15-3), MicroRNA (miRNA), topoisomerase πα (Top2α), proliferating cell nuclear antigen (PCNA), epidermal growth factor receptor (EGFR) and E-cadherin will also be involved in this paper. We will also mention Mammaglobin, which has been rarely reported so far.

Clinical characterization and proteomic profiling of lean nonalcoholic fatty liver disease

Introduction

Obesity has been historically associated with nonalcoholic fatty liver disease (NAFLD), but it can also occur in lean individuals. However, limited data is available on this special group. To investigate the clinical and proteomic characteristics of lean subjects with NAFLD, and to identify potential clinical variables and plasma proteins for diagnosing NAFLD in lean individuals, we collected clinical data from a large cohort of 2,236 subjects.

Methods

Diagnosis of NAFLD relied on detecting pronounced hepatic steatosis through abdominal ultrasonography. Participants were categorized into four groups based on body mass index: overweight NAFLD, overweight control, lean NAFLD, and lean control. Plasma proteomic profiling was performed on samples from 20 subjects in each group. The lean NAFLD group was compared to both lean healthy and obese NAFLD groups across all data.

Results and discussion

The results indicated that the lean NAFLD group exhibited intermediate metabolic profiles, falling between those of the lean healthy and overweight NAFLD groups. Proteomic profiling of plasma in lean subjects with or without NAFLD revealed 45 statistically significant changes in proteins, of which 37 showed high diagnostic value (AUC > 0.7) for lean NAFLD. These potential biomarkers primarily involved lipid metabolism, the immune and complement systems, and platelet degranulation. Furthermore, AFM, GSN, CFH, HGFAC, MMP2, and MMP9 have been previously associated with NAFLD or NAFLD-related factors such as liver damage, insulin resistance, metabolic syndromes, and extracellular homeostasis. Overall, lean individuals with NAFLD exhibit distinct clinical profiles compared to overweight individuals with NAFLD. Despite having worse metabolic profiles than their healthy counterparts, lean NAFLD patients generally experience milder systemic metabolic disturbances compared to obese NAFLD patients. Additionally, the plasma proteomic profile is significantly altered in lean NAFLD, highlighting the potential of differentially expressed proteins as valuable biomarkers or therapeutic targets for diagnosing and treating NAFLD in this population.

Tandem Mass Tag-Based Proteomic Profiling Identifies Biomarkers in Drainage Fluid for Early Detection of Anastomotic Leakage after Rectal Cancer Resection

Anastomotic leakage (AL), one of the most severe complications in rectal surgery, is often diagnosed late because of the low specificity of the clinical symptoms and limitations of current clinical investigations. Identification of patients with early AL remains challenging. Here, we explored the protein expression profiles of AL patients to provide potential biomarkers to identify AL in patients who undergo surgery for rectal cancer. We screened differentially expressed proteins (DEPs) in drainage fluid from AL and non-AL patients using a tandem mass tag method. A total of 248 DEPs, including 98 upregulated and 150 downregulated proteins, were identified between AL and non-AL groups. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that DEPs were enriched in neutrophil degranulation, bacterial infection, proteolysis, hemostasis, and complement and coagulation cascades. The results of enzyme-linked immunosorbent assay validated that the expression of the top three upregulated DEPs, AMY2A, RETN, and CELA3A, was significantly increased in the drainage fluid of AL patients, compared with that of non-AL patients (AMY2A, P = 0.001; RETN, P < 0.0001; and CELA3A, P = 0.023). Thus, our findings provide several potential biomarkers for the early diagnosis of AL after rectal cancer resection.

Orbitrap Mass Spectrometry and High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Enable the in-Depth Analysis of Human Serum Proteoforms

Blood serum and plasma are arguably the most commonly analyzed clinical samples, with dozens of proteins serving as validated biomarkers for various human diseases. Top-down proteomics may provide additional insights into disease etiopathogenesis since this approach focuses on protein forms, or proteoforms, originally circulating in blood, potentially providing access to information about relevant post-translational modifications, truncations, single amino acid substitutions, and many other sources of protein variation. However, the vast majority of proteomic studies on serum and plasma are carried out using peptide-centric, bottom-up approaches that cannot recapitulate the original proteoform content of samples. Clinical laboratories have been slow to adopt top-down analysis, also due to higher sample handling requirements. In this study, we describe a straightforward protocol for intact proteoform sample preparation based on the depletion of albumin and immunoglobulins, followed by simplified protein fractionation via polyacrylamide gel electrophoresis. After molecular weight-based fractionation, we supplemented the traditional liquid chromatography-tandem mass spectrometry (LC-MS2) data acquisition with high-field asymmetric waveform ion mobility spectrometry (FAIMS) to further simplify serum proteoform mixtures. This LC-FAIMS-MS2 method led to the identification of over 1000 serum proteoforms < 30 kDa, outperforming traditional LC-MS2 data acquisition and more than doubling the number of proteoforms identified in previous studies.

Serum proteomics identifies S100A11 and MMP9 as novel biomarkers for predicting the early efficacy of sublingual immunotherapy in allergic rhinitis

BACKGROUND

Allergic rhinitis (AR) is a chronic inflammatory disorder, and sublingual immunotherapy (SLIT) is an important therapy. However, SLIT exhibits a wide range of fluctuations and lacks objective monitoring indicators. Therefore, exploring biomarkers for early prediction of the efficacy of SLIT is urgently needed.

METHODS

We recruited two independent cohorts. In the discovery cohort, house dust mite (HDM) -induced AR patients underwent SLIT for at least 1 year, and were categorized into response and no response groups based on early efficacy. Serum proteomics was conducted to detect variations in protein expression levels between the two groups. The candidate proteins were confirmed in the validation cohort with enzyme-linked immunosorbent assay (ELISA), and their predictive values and levels of change before and after treatment were evaluated.

RESULTS

Serum proteomics identified a total of 113 differential proteins between the two groups, including 41 proteins upregulated and 72 downregulated in the no response group than the response group. The top 5 up- and down-regulated proteins were selected for further validation, and ELISA results revealed that serum CCL14, LTA4H, S100A11 and MMP9 levels were significantly elevated, and TGFBI and MASP1 were decreased in the response group than those in the no response group(P < 0.05). Moreover, receiver operating characteristic curves revealed that serum S100A11 and MMP9 exhibited greater ability in predicting the early effectiveness of SLIT (AUC > 0.7, P < 0.05). Furthermore, these two biomarkers exhibited significant reductions 1 year after SLIT, particularly in those patients who responded positively to the treatment (P < 0.05).

CONCLUSION

Serum S100A11 and MMP9 have the potential to serve as biomarkers for early prediction of the effectiveness of SLIT and monitoring the therapeutic effects. The circulating proteomic alterations might contribute to guiding treatment and understanding the mechanism of SLIT in AR patients.

Targeted protein quantitation in human body fluids by mass spectrometry

Human body fluids (biofluids) contain various proteins, some of which reflect individuals' physiological conditions or predict diseases. Therefore, the analysis of biofluids can provide substantial information on novel biomarkers for clinical diagnosis and prognosis. In the past decades, mass spectrometry (MS)-based technologies have been developed as proteomic strategies not only for the identification of protein biomarkers but also for biomarker verification/validation in body fluids for clinical applications. The main advantage of targeted MS-based methodologies is the accurate and specific simultaneous quantitation of multiple biomarkers with high sensitivity. Here, we review MS-based methodologies that are currently used for the targeted quantitation of protein components in human body fluids, especially in plasma, urine, cerebrospinal fluid, and saliva. In addition, the currently used MS-based methodologies are summarized with a specific focus on applicable clinical sample types, MS configurations, and acquisition modes.

Foresight in clinical proteomics: current status, ethical considerations, and future perspectives

With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.

Platelet proteo-transcriptomic profiling validates mediators of thrombosis and proteostasis in patients with myeloproliferative neoplasms

Patients with chronic Myeloproliferative Neoplasms (MPN) including polycythemia vera (PV) and essential thrombocythemia (ET) exhibit unique clinical features, such as a tendency toward thrombosis and hemorrhage, and risk of disease progression to secondary bone marrow fibrosis and/or acute leukemia. Although an increase in blood cell lineage counts (quantitative features) contribute to these morbid sequelae, the significant qualitative abnormalities of myeloid cells that contribute to vascular risk are not well understood. Here, we address this critical knowledge gap via a comprehensive and untargeted profiling of the platelet proteome in a large (n= 140) cohort of patients (from two independent sites) with an established diagnosis of PV and ET (and complement prior work on the MPN platelet transcriptome from a third site). We discover distinct MPN platelet protein expression and confirm key molecular impairments associated with proteostasis and thrombosis mechanisms of potential relevance to MPN pathology. Specifically, we validate expression of high-priority candidate markers from the platelet transcriptome at the platelet proteome (e.g., calreticulin (CALR), Fc gamma receptor (FcγRIIA) and galectin-1 (LGALS1) pointing to their likely significance in the proinflammatory, prothrombotic and profibrotic phenotypes in patients with MPN. Together, our proteo-transcriptomic study identifies the peripherally-derived platelet molecular profile as a potential window into MPN pathophysiology and demonstrates the value of integrative multi-omic approaches in gaining a better understanding of the complex molecular dynamics of disease.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Identification of Plasma Inflammatory Markers of Adolescent Depression Using the Olink Proteomics Platform

Purpose

The quality of life of worldwide adolescents has been seriously affected by depression. Notably, the inflammatory response is closely associated with the pathophysiology of depression. The present study applied a novel targeted proteomics technology, Olink proximity extension assay (PEA), to profile circulating immune-related proteins in adolescents with depression.

Methods

In the present study, the expression levels of 92 inflammation-related proteins were compared between adolescents with depression (ADs) (n=15) and healthy controls (HCs) (n=15), using the OLINK PEA inflammation panel. We further validated 5 top proteins that were identified through KEGG and GO analyses between 40 HCs and 50 ADs, including CCL4, CXCL5, CXCL6, CXCL11, and IL-18 using enzyme linked immunosorbent assay (ELISA).

Results

We identified 13 differentially expressed proteins between the two cohorts, including 5 up-regulated and 8 down-regulated proteins. Among them, the TRAIL protein levels were significantly negatively correlated with the HAMA-14 score (r=-0.538, p= 0.038), and the levels of transforming growth factor α (TGF-α) were significantly associated with a change in appetite (r = -0.658, p = 0.008). After validation by ELISA, CCL4, CXCL5, CXCL11, and IL-18 showed significant changes between ADs and HCs (p < 0.05), while CXCL6 showed an up-regulated tendency in ADs (p=0.0673). The pooled diagnostic efficacy (area under the curve [AUC]) of these five inflammation markers in clinical diagnosis for adolescent depression was 0.819 (95% CI: 0.735-0.904).

Conclusion

We report a number of inflammation-related plasma biomarkers, which uncover a potential involvement of chemokines, cytokines, and cytokine receptors in adolescent depression. Their roles in the pathophysiology of depression need to be further elucidated.

Immunoaffinity LC-MS/MS Quantification of the Sepsis Biomarker Procalcitonin Using Magnetic- and Polystyrene-Bead Immobilized Polyclonal Antibodies

Procalcitonin (PCT) is a biomarker for bacterial sepsis, and accurate quantification of PCT is critical for sepsis diagnosis and treatment. Immunological PCT quantification methods are routinely used in clinical laboratories, yet there is a need for harmonization of PCT quantification protocols. An orthogonal method to clinical immunological assays, such as LC-MS/MS, is required. In this study, a highly sensitive and robust immunoaffinity LC-MRM quantitative method for detecting procalcitonin in human serum has been developed. An initial comparison of immunocapture of PCT with a polyclonal anti-PCT antibody immobilized on polystyrene nanoparticles (Latex) and magnetic beads demonstrated superior performance with magnetic beads. Three tryptic PCT peptides from the N- and C-terminal regions of PCT were selected for LC-MS/MS quantification. For PCT quantification, an LLOQ of 0.25 ng/mL of PCT in human serum was achieved using a sample volume of 1 mL. The method's trueness and precision consistently lie within the 15% margin. The parallel measurement of three PCT peptides may allow future differentiation of intact PCT vs other PCT forms originating from potential degradation, processing, or polymorphisms. An established and validated LC-MRM-based quantification of PCT will be relevant as an orthogonal method for harmonization and standardization of clinical assays for PCT.

Incorporating Ceragenins into Coatings Protects Peripherally Inserted Central Catheter Lines against Pathogen Colonization for Multiple Weeks

Healthcare-acquired infections and multi-drug resistance in pathogens pose a major crisis for the healthcare industry. Novel antibiotics which are effective against resistant strains and unlikely to elicit strong resistance are sought after in these settings. We have previously developed synthetic mimics of ubiquitous antimicrobial peptides and have worked to apply a lead compound, CSA-131, to the crisis. We aimed to generate a system of CSA-131-containing coatings for medical devices that can be adjusted to match elution and compound load for various environments and establish their efficacy in preventing the growth of common pathogens in and around these devices. Peripherally inserted central catheter (PICC) lines were selected for our substrate in this work, and a polyurethane-based system was used to establish coatings for evaluation. Microbial challenges by methicillin-resistant Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Candida albicans were performed and SEM was used to evaluate coating structure and colonization. The results indicate that selected coatings show activity against selected planktonic pathogens that extend between 16 and 33 days, with similar periods of biofilm prevention.

Healthy lifestyle linked to innate immunity and lipoprotein metabolism: a cross-sectional comparison using untargeted proteomics

This study used untargeted proteomics to compare blood proteomic profiles in two groups of adults that differed widely in lifestyle habits. A total of 52 subjects in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females) participated in this cross-sectional study. Age, education level, marital status, and height did not differ significantly between LIFE and CON groups. The LIFE and CON groups differed markedly in body composition, physical activity patterns, dietary intake patterns, disease risk factor prevalence, blood measures of inflammation, triglycerides, HDL-cholesterol, glucose, and insulin, weight-adjusted leg/back and handgrip strength, and mood states. The proteomics analysis showed strong group differences for 39 of 725 proteins identified in dried blood spot samples. Of these, 18 were downregulated in the LIFE group and collectively indicated a lower innate immune activation signature. A total of 21 proteins were upregulated in the LIFE group and supported greater lipoprotein metabolism and HDL remodeling. Lifestyle-related habits and biomarkers were probed and the variance (> 50%) in proteomic profiles was best explained by group contrasts in indicators of adiposity. This cross-sectional study established that a relatively small number of proteins are associated with good lifestyle habits.

Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies

Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.

Exploring the potential of paper-based electrokinetic phenomena in PoC biosensing

In order to decentralize health care, the development of point-of-care (PoC) assays has gained significant attention in recent decades. The lateral flow immunoassay (LFIA) has emerged as a promising bioanalytical method due to its low cost and single-step detection process. However, its limited sensitivity and inability to detect disease biomarkers at clinically relevant levels have hindered its application for early diagnosis. This review explores the potential of merging different electrokinetic phenomena into paper-based assays to enhance their analytical performance, offering a versatile and affordable approach for PoC testing. The review exposes the challenges faced in integrating electrokinetic phenomena with paper-based biosensing and concludes by discussing the issues that need to be improved to maximize the potential of this technology for early diagnosis.

Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias

Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.

Establishment and clinical application evaluations of a deep mining strategy of plasma proteomics based on nanomaterial protein coronas

Research on plasma proteomics has received extensive attention, because human plasma is an important sample for disease biomarker research due to its easy clinical accessibility and richness in biological information. Plasma samples contain a large number of leaked proteins from different tissues in the body, immune proteins and communication signal proteins. However, MS signal suppression from high-abundance proteins results in a large number of proteins that are present in low abundance in plasma not being detected by the LC-MS method. This situation makes it more difficult to study neurological diseases, where tissue sampling is difficult and body fluid samples such as plasma or cerebrospinal fluid are both affected by signal suppression. A large number of methods have been developed to deeply mine plasma proteomics information; however, their application limitations remain to some extent. Traditional immuno- or affinity-based depletion, fractionation and subproteome enrichment methods cannot meet the challenges of large clinical cohort applications due to limited time efficiency. In this study, a deep mining strategy of plasma proteomics was established by combing the protein corona formed by deep mining beads (DMB beads, hereafter referred to as magnetic covalent organic frameworks Fe3O4@TpPa-1), DIA-MS detection and the DIA-NN library searching method. By optimizing the enrichment step, mass spectrometry acquisition and data processing, the evaluation results of the deep mining strategy showed the following: depth, the strategy identified and quantified results of 2000+ proteins per plasma sample; stability, more than 87% of the enriched low-abundance proteins had CV < 20%; accuracy, good agreement between measured and theoretical values (1.81/2, 8.68/10, 38.36/50) for the gradient addition of E. coli proteins to a plasma sample; time efficiency, the processing time was reduced from >12h in the traditional method to <5h (incubation 30 min, washing 15 min, reductive/alkylation/digestion/desalting 4 h), and more importantly, 96 samples can be processed simultaneously in combination with the magnetic module of the automated device. The optimal strategy enables greater enrichment of neurological disease-related proteins, including SNCA and BDNF. Finally, the deep mining strategy was applied in a pilot study of multiple system atrophy (MSA) for biomarker discovery. The results showed that a total of 215 proteins were upregulated and 184 proteins were downregulated (p < 0.05) in the MSA group compared with the healthy control group. Eighteen proteins of these differentially expressed proteins were reported to be associated with neurological diseases or expressed specifically in brain tissue, 8 and 4 of which have reference concentrations of μg/L and ng/L, respectively. The alterations of ENPP2 and SLC2A1/Glut1 were reanalyzed by ELISA, further supporting the results of mass spectrometry. In conclusion, the results of the evaluation and application of the deep mining strategy showed promise for clinical research applications.

Assessing the Role of Trypsin in Quantitative Plasma and Single-Cell Proteomics toward Clinical Application

Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large-scale biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa cell lysate, human plasma, and single HEK293 cells, which were analyzed over a range of selected reaction monitoring (SRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) instrument methods on three LC-MS instruments. The quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive data set, we report variations between commercial trypsins, their source, and their concentration. Furthermore, we provide suggestions on the handling of trypsin in large-scale studies.

Resolving the Temporal Splenic Proteome during Fungal Infection for Discovery of Putative Dual Perspective Biomarker Signatures

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass-spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.