Revisiting biomarker discovery by plasma proteomics

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

High-resolution proteomics and machine-learning identify protein classifiers of honey made by Sicilian black honeybees (Apis mellifera ssp. sicula)

Apis mellifera ssp. sicula, also known as the Sicilian black honeybee, is a Slow Food Presidium that produces honey with outstanding nutraceutical properties, including high antioxidant capacity. In this study, we used high-resolution proteomics to profile the honey produced by sicula and identify protein classifiers that distinguish it from that made by the more common Italian honeybee (Apis mellifera ssp. ligustica). We profiled the honey proteome of genetically pure sicula and ligustica honeybees bred in the same geographical area, so that chemical differences in their honey only reflected the genetic background of the two subspecies, rather than botanical environment. Differentially abundant proteins were validated in sicula and ligustica honeys of different origin, by using the so-called "rectangular strategy", a proteomic approach commonly used for biomarker discovery in clinical proteomics. Then, machine learning was employed to identify which proteins were the most effective in distinguishing sicula and ligustica honeys. This strategy enabled the identification of two proteins, laccase-5 and venome serine protease 34 isoform X2, that were fully effective in predicting whether honey was made by sicula or ligustica honeybees. In conclusion, we profiled the proteome of sicula honey, identified two protein classifiers of sicula honey in respect to ligustica, and proved that the rectangular strategy can be applied to uncover biomarkers to ascertain food authenticity.

Proteomic characterization of the medial prefrontal cortex in chronic restraint stress mice

Depression is a prominent contributor to global disability. A growing body of data suggests that depression is associated with the pathophysiology of the medial prefrontal cortex (mPFC), but the underlying mechanisms remain poorly understood. Mice were subjected to chronic restraint stress (CRS) for 3 weeks to create depression models during this investigation. Protein tandem mass tag (TMT) quantification and LC-MS/MS analysis were conducted to examine proteome patterns. Afterwards, to further explore the enrichment of differential proteins and the signaling pathways involved, we annotated these differentially expressed proteins. We confirmed that CRS mice developed depression-like and anxiety-like behaviors. Among the 8081 measured proteins, a total of 15 proteins were found to be differentially expressed. These proteins exhibited functional enrichment in a variety of biological functions, and among these pathways, alterations in synaptic function and autophagy are noteworthy. In addition, we identified a differentially expressed protein called Wnt2b and found that CRS may disrupt synaptic plasticity by affecting the activation of the Wnt2b/β-catenin pathway. Our findings showed depression-like behaviors in the CRS mouse model and molecular alterations in the mPFC, which may help explain the pathogenesis of depression and identify novel antidepressant medication targets. SIGNIFICANCE: Depression is a prevalent and frequent chronic mental illness and is now a significant contributor to global disability. In this study, we used chronic restraint stress to establish a mouse model of depression, and differentially expressed proteins in the medial prefrontal cortex of depressed model mice were detected by TMT proteomics. Our study verified the presence of altered synaptic function and excessive autophagy in the mPFC of CRS-induced mice from a proteomic perspective. Furthermore, we demonstrated that CRS may disrupt synaptic plasticity by affecting the activation of the Wnt2b/β-catenin pathway, which may be a key link in the pathogenesis of depression and may provide new insights for identifying new antidepressant drug targets.

Derivation and external validation of mass spectrometry-based proteomic model using machine learning algorithms to predict plaque rupture in patients with acute coronary syndrome

BACKGROUND

A poor prognosis is associated with atherosclerotic plaque rupture (PR) despite after conventional therapy for patients with acute coronary syndrome (ACS). Timely identification of PR improves the risk stratification and prognosis of ACS patients.

METHODS

A derivation cohort of 110 patients with ACS who underwent pre-intervention optical coherence tomography (OCT) were matched 1:1 to the PR and intact fibrous cap (IFC) groups according to traditional risk factors. Candidate PR proteins were identified via mass spectrometry (MS)-based proteomics using unbiased machine learning methods and were further validated by enzyme-linked immunosorbent assay (ELISA) in an external validation cohort of 85 patients with ACS. The performance of candidate biomakers was assessed using the receiver operating characteristic curve analysis.

RESULTS

1121 proteins were identified and 535 filtered proteins were used for analysis. Nine candidate proteins were screened by five machine learning algorithms. Three proteins (APOC3, RAB39A, and KNG1) were significantly different between the PR and IFC in validation cohort. The performance of plasm APOC3, RAB39A, and KNG1 for differentiating PR and IFC was superior to that of the conventional biomarkers and risk factors.

CONCLUSION

The proteins (APOC3, RAB39A, and KNG1) serve as a potential novel diagnostic tool to identify PR in ACS patients.

A serum B-lymphocyte activation signature is a key distinguishing feature of the immune response in sarcoidosis compared to tuberculosis

Sarcoidosis and tuberculosis (TB) are two granulomatous diseases that often share overlapping clinical features, including uveitis. We measured 368 inflammation-related proteins in serum in both diseases, with and without uveitis from two distinct geographically separated cohorts: sarcoidosis from the Netherlands and TB from Indonesia. A total of 192 and 102 differentially expressed proteins were found in sarcoidosis and active pulmonary TB compared to their geographical healthy controls, respectively. While substantial overlap exists in the immune-related pathways involved in both diseases, activation of B cell activating factor (BAFF) signaling and proliferation-inducing ligand (APRIL) mediated signaling pathways was specifically associated with sarcoidosis. We identified a B-lymphocyte activation signature consisting of BAFF, TNFRSF13B/TACI, TRAF2, IKBKG, MAPK9, NFATC1, and DAPP1 that was associated with sarcoidosis, regardless of the presence of uveitis. In summary, a difference in B-lymphocyte activation is a key discriminative immunological feature between sarcoidosis/ocular sarcoidosis (OS) and TB/ocular TB (OTB).

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

Plasma proteomics in children with new-onset type 1 diabetes identifies new potential biomarkers of partial remission

Partial remission (PR) occurs in only half of people with new-onset type 1 diabetes (T1D) and corresponds to a transient period characterized by low daily insulin needs, low glycemic fluctuations and increased endogenous insulin secretion. While identification of people with newly-onset T1D and significant residual beta-cell function may foster patient-specific interventions, reliable predictive biomarkers of PR occurrence currently lack. We analyzed the plasma of children with new-onset T1D to identify biomarkers present at diagnosis that predicted PR at 3 months post-diagnosis. We first performed an extensive shotgun proteomic analysis using Liquid Chromatography-Tandem-Mass-Spectrometry (LCMS/MS) on the plasma of 16 children with new-onset T1D and quantified 98 proteins significantly correlating with Insulin-Dose Adjusted glycated hemoglobin A1c score (IDAA1C). We next applied a series of both qualitative and statistical filters and selected protein candidates that were associated to pathophysiological mechanisms related to T1D. Finally, we translationally verified several of the candidates using single-shot targeted proteomic (PRM method) on raw plasma. Taken together, we identified plasma biomarkers present at diagnosis that may predict the occurrence of PR in a single mass-spectrometry run. We believe that the identification of new predictive biomarkers of PR and β-cell function is key to stratify people with new-onset T1D for β-cell preservation therapies.

Body fluid multiomics in 3PM-guided ischemic stroke management: health risk assessment, targeted protection against health-to-disease transition, and cost-effective personalized approach are envisaged

Because of its rapid progression and frequently poor prognosis, stroke is the third major cause of death in Europe and the first one in China. Many independent studies demonstrated sufficient space for prevention interventions in the primary care of ischemic stroke defined as the most cost-effective protection of vulnerable subpopulations against health-to-disease transition. Although several studies identified molecular patterns specific for IS in body fluids, none of these approaches has yet been incorporated into IS treatment guidelines. The advantages and disadvantages of individual body fluids are thoroughly analyzed throughout the paper. For example, multiomics based on a minimally invasive approach utilizing blood and its components is recommended for real-time monitoring, due to the particularly high level of dynamics of the blood as a body system. On the other hand, tear fluid as a more stable system is recommended for a non-invasive and patient-friendly holistic approach appropriate for health risk assessment and innovative screening programs in cost-effective IS management. This article details aspects essential to promote the practical implementation of highlighted achievements in 3PM-guided IS management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00376-2.

Enhancing outcome prediction of concurrent chemoradiation treatment in patients with locally advanced cervical cancer through plasma extracellular vesicle proteomics

Most patients with locally advanced cervical cancer (LACC) are primarily treated using concurrent chemoradiation (CCRT); however, LACC lacks reliable predictive biomarkers. Extracellular vesicles (EVs) could define the dynamic biological response to CCRT. However, the relationship between EVs and the therapeutic response to LACC is unestablished. Thus, we aimed to determine the relationship of plasma EVs pre- and post-CCRT in 62 patients with LACC. For proteomic analyses, EVs were isolated using ultracentrifugation (UC) with size exclusion chromatography or UC alone. We found that plasma particle concentration was significantly increased post-treatment in non-responders. After CCRT, there was a decrease in proteins related to serine protease and fibrinogen, which contribute to tumor microenvironment alteration. This reduction also extended to proteins involved in innate immune and viral immune responses, correlating with reduced tumor burden. Sparse partial least squares discriminant analysis revealed 8, 13, and 19 proteins at diagnosis, one month, and three months, respectively, influencing the CCRT response. Among these, FIBG, TFR1, HBA, and FINC are prognostic markers according to The Cancer Genome Atlas tissue gene expression database. Our discriminant model demonstrated excellent specificity and negative predictive value, underscoring the model's reliability in determining responsiveness to CCRT and highlighting the potential clinical applicability of EVs in improving outcomes in LACC.

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.

Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank

Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.

Distinct protein patterns related to postnatal development in small for gestational age preterm infants

BACKGROUND

Preterm infants, especially those born small for gestational age (SGA), are at risk of short-term and long-term health complications. Characterization of changes in circulating proteins postnatally in preterm infants may provide valuable fundamental insights into this population. Here, we investigated postnatal developmental patterns in preterm infants and explored protein signatures that deviate between SGA infants and appropriate for gestational age (AGA) infants using a mass spectrometry (MS)-based proteomics workflow.

METHODS

Longitudinal serum samples obtained at postnatal days 0, 3, 7, 14, and 28 from 67 preterm infants were analyzed using unbiased MS-based proteomics.

RESULTS

314 out of 833 quantified serum proteins change postnatally, including previously described age-related changes in immunoglobulins, hemoglobin subunits, and new developmental patterns, e.g. apolipoproteins (APOA4) and terminal complement cascade (C9) proteins. Limited differences between SGA and AGA infants were found at birth while longitudinal monitoring revealed 69 deviating proteins, including insulin-sensitizing hormone adiponectin, platelet proteins, and 24 proteins with an annotated function in the immune response.

CONCLUSIONS

This study shows the potential of MS-based serum profiling in defining circulating protein trajectories in the preterm infant population and its ability to identify longitudinal alterations in protein levels associated with SGA.

IMPACT

Postnatal changes of circulating proteins in preterm infants have not fully been elucidated but may contribute to development of health complications. Mass spectrometry-based analysis is an attractive approach to study circulating proteins in preterm infants with limited material. Longitudinal plasma profiling reveals postnatal developmental-related patterns in preterm infants (314/833 proteins) including previously described changes, but also previously unreported proteins. Longitudinal monitoring revealed an immune response signature between SGA and AGA infants. This study highlights the importance of taking postnatal changes into account for translational studies in preterm infants.

Aptamer-based sensors for fluid biopsies of protein disease markers

Fluid biopsy technology, characterized by its minimally invasive nature, speed, and continuity, has become a rapidly advancing and widely applied real-time diagnostic technique. Among various biomarkers, proteins represent the most abundant class of disease indicators. The sensitive and accurate detection of protein markers in bodily fluids is significantly influenced by the control exerted by recognition ligands. Aptamers, which are structurally dynamic functional oligonucleotides, exhibit high affinity, specific recognition of targets, and notable characteristics of high editability and modularity. These features make aptamer universal "recognition-capture" components, contribute to a significant leap in their applications within the biosensor domain. In this context, we provide a comprehensive review of the extensive application of aptamer-based biosensors in fluid biopsy. We systematically compile the characteristics and construction strategies of aptamer-based biosensors tailored for fluid biopsy, including aptamer sequences, affinity (KD), fluid background, sensing technologies, sensor construction strategies, incubation time, detection performance, and influencing factors. Furthermore, a comparative analysis of their advantages and disadvantages was conducted. In conclusion, we delineate and deliberate on prospective research trajectories and challenges that lie ahead in the realm of aptamer-based biosensors for fluid biopsy.

The Low-Abundance Plasma Proteome Reveals Differentially Abundant Proteins Associated with Breast Implant Capsular Contracture: A Pilot Study

Capsular contracture (CC) is one of the most common postoperative complications associated with breast implant-associated infections. The mechanisms that lead to CC remain poorly understood. Plasma is an ideal biospecimen for early proteomics biomarker discovery. However, as high-abundance proteins mask signals from low-abundance proteins, identifying novel or specific proteins as biomarkers for a particular disease has been hampered. Here, we employed depletion of high-abundance plasma proteins followed by Tandem Mass Tag (TMT)-based quantitative proteomics to compare 10 healthy control patients against 10 breast implant CC patients. A total of 450 proteins were identified from these samples. Among them, 16 proteins were significantly differentially expressed in which 5 proteins were upregulated and 11 downregulated in breast implant CC patients compared to healthy controls. Gene Ontology enrichment analysis revealed that proteins related to cell, cellular processes and catalytic activity were highest in the cellular component, biological process, and molecular function categories, respectively. Further, pathway analysis revealed that inflammatory responses, focal adhesion, platelet activation, and complement and coagulation cascades were enriched pathways. The differentially abundant proteins from TMT-based quantitative proteomics have the potential to provide important information for future mechanistic studies and in the development of breast implant CC biomarkers.

Plasma proteins associated with plant-based diets: Results from the Atherosclerosis Risk in Communities (ARIC) study and Framingham Heart Study (FHS)

BACKGROUND & AIMS

Plant-based diets are associated with a lower risk of chronic diseases. Large-scale proteomics can identify objective biomarkers of plant-based diets, and improve our understanding of the pathways that link plant-based diets to health outcomes. This study investigated the plasma proteome of four different plant-based diets [overall plant-based diet (PDI), provegetarian diet, healthful plant-based diet (hPDI), and unhealthful plant-based diet (uPDI)] in the Atherosclerosis Risk in Communities (ARIC) Study and replicated the findings in the Framingham Heart Study (FHS) Offspring cohort.

METHODS

ARIC Study participants at visit 3 (1993-1995) with completed food frequency questionnaire (FFQ) data and proteomics data were divided into internal discovery (n = 7690) and replication (n = 2543) data sets. Multivariable linear regression was used to examine associations between plant-based diet indices (PDIs) and 4955 individual proteins in the discovery sample. Then, proteins that were internally replicated in the ARIC Study were tested for external replication in FHS (n = 1358). Pathway overrepresentation analysis was conducted for diet-related proteins. C-statistics were used to predict if the proteins improved prediction of plant-based diet indices beyond participant characteristics.

RESULTS

In ARIC discovery, a total of 837 diet-protein associations (PDI = 233; provegetarian = 182; hPDI = 406; uPDI = 16) were observed at false discovery rate (FDR) < 0.05. Of these, 453 diet-protein associations (PDI = 132; provegetarian = 104; hPDI = 208; uPDI = 9) were internally replicated. In FHS, 167/453 diet-protein associations were available for external replication, of which 8 proteins (PDI = 1; provegetarian = 0; hPDI = 8; uPDI = 0) replicated. Complement and coagulation cascades, cell adhesion molecules, and retinol metabolism were over-represented. C-C motif chemokine 25 for PDI and 8 proteins for hPDI modestly but significantly improved the prediction of these indices individually and collectively (P value for difference in C-statistics<0.05 for all tests).

CONCLUSIONS

Using large-scale proteomics, we identified potential candidate biomarkers of plant-based diets, and pathways that may partially explain the associations between plant-based diets and chronic conditions.

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

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

Falcon Probe: A High-Pressure and Robust Sampling Interface for Coupling Lossless Liquid Chromatography Injection with In Situ Nanoliter-Scale Sample Pretreatment

With the increasing demand for trace sample analysis, injecting trace samples into liquid chromatography-mass spectrometry (LC-MS) systems with minimal loss has become a major challenge. Herein, we describe an in situ LC-MS analytical probe, the Falcon probe, which integrates multiple functions of high-pressure sample injection without sample loss, high-efficiency LC separation, and electrospray. The main body of the Falcon probe is made of stainless steel and fabricated by the computer numerical control (CNC) technique, which has ultrahigh mechanical strength. By coupling a nanoliter-scale droplet reactor made of polyether ether ketone (PEEK) material, the Falcon probe-based LC-MS system was capable of operating at mobile-phase pressures up to 800 bar, which is comparable to those of conventional ultraperformance liquid chromatography (UPLC) systems. Using the probe pressing microamount in situ (PPMI) injection approach, the Falcon probe-based LC-MS system showed high separation efficiency and good repeatability with relative standard deviations (RSDs) of retention time and peak area of 1.8% and 9.9%, respectively, in peptide mixture analysis (n = 6). We applied this system to the analysis of a trace amount of 200 pg of HeLa protein digest and successfully identified an average of 766 protein groups (n = 5). By combining in situ sample pretreatment at the nanoliter range, we further applied the present system in single-cell proteomic analysis, and 241 protein groups were identified in single 293 cells, which preliminarily demonstrated its potential in the analysis of trace amounts of samples with complex compositions.

Recent advances of biocompatible optical nanobiosensors in liquid biopsy: towards early non-invasive diagnosis

Liquid biopsy is a non-invasive diagnostic method that can reduce the risk of complications and offers exceptional benefits in the dynamic monitoring and acquisition of heterogeneous cell population information. Optical nanomaterials with excellent light absorption, luminescence, and photoelectrochemical properties have accelerated the development of liquid biopsy technologies. Owing to the unique size effect of optical nanomaterials, their improved optical properties enable them to exhibit good sensitivity and specificity for mitigating signal interference from various molecules in body fluids. Nanomaterials with biocompatible and optical sensing properties play a crucial role in advancing the maturity and diversification of liquid biopsy technologies. This article offers a comprehensive review of recent advanced liquid biopsy technologies that utilize novel biocompatible optical nanomaterials, including fluorescence, colorimetric, photoelectrochemical, and Raman broad-spectrum-based biosensors. We focused on liquid biopsy for the most significant early biomarkers in clinical medicine, and specifically reviewed reports on the effectiveness of optical nanosensing technology in the detection of real patient samples, which may provide basic evidence for the transition of optical nanosensing technology from engineering design to clinical practice. Furthermore, we introduced the integration of optical nanosensing-based liquid biopsy with modern devices, such as smartphones, to demonstrate the potential of the technology in portable clinical diagnosis.

A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using ProteographTM and Trapped Ion Mobility Mass Spectrometry

There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.

Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-down Proteomics

The dynamic range challenge for detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundant proteins selectively and reproducibly from human plasma and use top-down proteomics to quantify differential enrichment for the 2841 detected proteoforms from 114 proteins. Furthermore, nanoparticle enrichment allowed top-down detection of proteoforms between ∼1 µg/mL and ∼10 pg/mL in absolute abundance, providing up to 10 5 -fold increase in proteome depth over neat plasma in which only proteoforms from abundant proteins (>1 µg/mL) were detected. The ability to monitor medium and some low abundant proteoforms through reproducible enrichment significantly extends the applicability of proteoform research by adding depth beyond albumin, immunoglobins and apolipoproteins to uncover many involved in immunity and cell signaling. As proteoforms carry unique information content relative to peptides, this report opens the door to deeper proteoform sequencing in clinical proteomics of disease or aging cohorts.

Targeted proteomics of plasma extracellular vesicles uncovers MUC1 as combinatorial biomarker for the early detection of high-grade serous ovarian cancer

BACKGROUND

The five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery.

RESULTS

We conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics. Our investigation aimed at uncovering novel biomarkers for the early detection of HGSC by comparing the proteomic signatures of EVs from women with HGSC to those with benign gynecological conditions. The initial cohort, comprising 19 donors, utilized DDA proteomics for spectral library development. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts to identify combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90). Notably, MUC1 emerged as a significant biomarker in both cohorts when used in combination with additional biomarkers. Validation through an ELISA assay on a subset of benign (n = 18), Stage I (n = 9), and stage II (n = 9) plasma samples corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC.

CONCLUSIONS

This study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early ovarian cancer detection.

Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering

BACKGROUND

The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes.

METHODS

For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles. For in-depth analysis, we used immunogenomic sequencing to study the B cell repertoire of a subgroup of patients and an in vitro assay using primary human muscle cells to interrogate serum-induced complement formation.

FINDINGS

This strategy identified four distinct patient phenotypes based on their proteomic patterns in their serum. Notably, one patient phenotype, here named PS3, was characterised by high disease severity and complement activation as defining features. Assessing a subgroup of patients, hyperexpanded antibody clones were present in the B cell repertoire of the PS3 group and effectively activated complement as compared to other patients. In line with their disease phenotype, PS3 patients were more likely to benefit from complement-inhibiting therapies. These findings were validated in a prospective cohort of 18 patients using a cell-based assay.

INTERPRETATION

Collectively, this study suggests proteomics-based clustering as a gateway to assign patients to a biological signature likely to benefit from complement inhibition and provides a stratification strategy for clinical practice.

FUNDING

CN and CBS were supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf. CN was supported by the Else Kröner-Fresenius-Stiftung (EKEA.38). CBS was supported by the Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) with a Walter Benjamin fellowship (project 539363086). The project was supported by the Ministry of Culture and Science of North Rhine-Westphalia (MODS, "Profilbildung 2020" [grant no. PROFILNRW-2020-107-A]).

Selecting potential biomarkers of plasma proteins in mares with endometritis

BACKGROUND

Endometritis is a common condition in mares that causes significant economic loss. Lacking obvious clinical signs, the clinical diagnosis of endometritis in mares relies on case-by-case clinical examinations, which can be particularly inefficient in large-scale farms. Therefore, the identification of potential biomarkers can serve as a non-invasive and efficient screening technique for endometritis in mares.

OBJECTIVES

To compare the blood proteome between fertile mares and mares with endometritis to identify biomarkers potentially associated with the development of endometritis and validate their predictive potential.

STUDY DESIGN

Observational and experimental study.

METHODS

Differentially expressed proteins were identified via Data Independent Acquisition (DIA) proteomic profiling in a screening cohort composed of eight healthy mares and eight mares with endometritis. Subsequently, enzyme-linked immunosorbent assay was employed that included a validation cohort of 40 healthy mares and 40 mares with endometritis to verify the accuracy and sensitivity of the identified proteins, thereby establishing a diagnostic threshold.

RESULTS

In the screening cohort, 12 proteins were significantly differentially expressed between endometritis mares and healthy controls (p < 0.05, outside the 1/1.2 to 1.2-fold). In the validation experiment, all six screened proteins were assessed with area under the curve (AUC) >0.8.

MAIN LIMITATIONS

The samples displayed certain levels of individual heterogeneity, and the number of samples analysed was limited. Additionally, the identified biomarkers were primarily associated with generalised inflammation, which potentially limited their specificity for endometritis.

CONCLUSION

Levels of plasma proteins are sensitive indicators of equine endometritis and potential tools for endometritis screening. In plasma, fetuin B, von Willebrand factor, vitamin K-dependent protein C, insulin-like growth factor binding protein 3, interleukin 1 receptor accessory protein, and type II cell cytoskeleton showed great predictive ability, with fetuin B being the best predictor (AUC = 0.93, 95% CI: 0.89-0.98), which performs better when combined with all six detected proteins (AUC = 1, 95% CI: 0.99-1.00).

Proteomics and its application in the research of acupuncture: An updated review

As a complementary and alternative therapy, acupuncture is widely used in the prevention and treatment of various diseases. However, the understanding of the mechanism of acupuncture effects is still limited due to the lack of systematic biological validation. Notably, proteomics technologies in the field of acupuncture are rapidly evolving, and these advances are greatly contributing to the research of acupuncture. In this study, we review the progress of proteomics research in analyzing the molecular mechanisms of acupuncture for neurological disorders, pain, circulatory disorders, digestive disorders, and other diseases, with an in-depth discussion around acupoint prescription and acupuncture manipulation modalities. The study found that proteomics has great potential in understanding the mechanisms of acupuncture. This study will help explore the mechanisms of acupuncture from a proteomic perspective and provide information to support future clinical decisions.

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.

Evolution of the Umbilical Cord Blood Proteome Across Gestational Development

Neonatal health is dependent on early risk stratification, diagnosis, and timely management of potentially devastating conditions, particularly in the setting of prematurity. Many of these conditions are poorly predicted in real-time by clinical data and current diagnostics. Umbilical cord blood may represent a novel source of molecular signatures that provides a window into the state of the fetus at birth. In this study, we comprehensively characterized the cord blood proteome of infants born between 24 to 42 weeks using untargeted mass spectrometry and functional enrichment analysis. We determined that the cord blood proteome at birth varies significantly across gestational development. Proteins that function in structural development and growth (e.g., extracellular matrix organization, lipid particle remodeling, and blood vessel development) are more abundant earlier in gestation. In later gestations, proteins with increased abundance are in immune response and inflammatory pathways, including complements and calcium-binding proteins. Furthermore, these data contribute to the knowledge of the physiologic state of neonates across gestational age, which is crucial to understand as we strive to best support postnatal development in preterm infants, determine mechanisms of pathology causing adverse health outcomes, and develop cord blood biomarkers to help tailor our diagnosis and therapeutics for critical neonatal conditions.

Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.

A Search for New Biological Pathways in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy by Proteomic Research

Background/Objectives: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is a hereditary small vessel disease leading to significant morbidity and mortality. Despite advances in genetic diagnosis, the underlying pathophysiology remains incompletely understood. Proteomic studies offer insights into disease mechanisms by identifying altered protein expression patterns. Here, we conducted a proteomic analysis to elucidate molecular pathways associated with CADASIL. Methods: We enrolled genetically diagnosed CADASIL patients and healthy, genetically related controls. Plasma samples were subjected to proteomic analysis using the Olink platform, measuring 552 proteins across six panels. The data were analyzed from several approaches by using three different statistical methods: Exploratory Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA), differential expression with moderated t-test, and gene set enrichment analysis (GSEA). In addition, bioinformatics analysis, including volcano plot, heatmap, and Variable Importance on Projection (VIP) scores from the PLS-DA model were drawn. Results: Significant differences in protein expression were observed between CADASIL patients and controls. RSPO1 and FGF-19 exhibited elevated levels (p < 0.05), while PPY showed downregulation (p < 0.05) in CADASIL patients, suggesting their involvement in disease pathogenesis. Furthermore, MIC-A/B expression varied significantly between patients with mutations in exon 4 versus exon 11 of the NOTCH3 gene (p < 0.05), highlighting potential immunological mechanisms underlying CADASIL. We identified altered pathways using GSEA, applied after ranking the study data. Conclusions: Our study provides novel insights into the proteomic profile of CADASIL, identifying dysregulated proteins associated with vascular pathology, metabolic dysregulation, and immune activation. These findings contribute to a deeper understanding of CADASIL pathophysiology and may inform the development of targeted therapeutic strategies. Further research is warranted to validate these biomarkers and elucidate their functional roles in disease progression.

Nanoparticle-Mediated Drug Delivery Systems for Precision Targeting in Oncology

Nanotechnology has emerged as a transformative force in oncology, facilitating advancements in site-specific cancer therapy and personalized oncomedicine. The development of nanomedicines explicitly targeted to cancer cells represents a pivotal breakthrough, allowing the development of precise interventions. These cancer-cell-targeted nanomedicines operate within the intricate milieu of the tumour microenvironment, further enhancing their therapeutic efficacy. This comprehensive review provides a contemporary perspective on precision cancer medicine and underscores the critical role of nanotechnology in advancing site-specific cancer therapy and personalized oncomedicine. It explores the categorization of nanoparticle types, distinguishing between organic and inorganic variants, and examines their significance in the targeted delivery of anticancer drugs. Current insights into the strategies for developing actively targeted nanomedicines across various cancer types are also provided, thus addressing relevant challenges associated with drug delivery barriers. Promising future directions in personalized cancer nanomedicine approaches are delivered, emphasising the imperative for continued optimization of nanocarriers in precision cancer medicine. The discussion underscores translational research's need to enhance cancer patients' outcomes by refining nanocarrier technologies in nanotechnology-driven, site-specific cancer therapy.

Trypsin Encapsulation in the Zeolitic Imidazolate Framework for Low-Molecular Weight Protein Analysis with High Selectivity and Efficiency

Low-molecular weight proteins (LWPs) are important sources of biological information in biomarkers, signaling molecules, and pathology. However, the separation and analysis of LWPs in complex biological samples are challenging, mainly due to their low abundance and the complex sample pretreatment procedure. Herein, trypsin modified by poly(acrylic acid) (PAA) was encapsulated by a zeolitic imidazolate framework (ZIF-L). Mesopores were formed on the ZIF-L with the introduction of PAA. An alternative strategy for separation and pretreatment of LWPs was developed based on the prepared ZIF-L-encapsulated trypsin with adjustable pore size. The mesoporous structure of the prepared materials selectively excluded high-molecular weight proteins from the reaction system, allowing LWPs to enter the pores and react with the internal trypsin, resulting in an improved separation efficiency. The hydrophobicity of the ZIF-L simplified the digestion process by inducing significant structural changes in substrate proteins. In addition, the enzymatic activity was significantly enhanced by the developed encapsulation method that maintained the enzyme conformation, allowed low mass transfer resistance, and possessed a high enzyme-to-substrate ratio. As a result, the ZIF-L-encapsulated trypsin can achieve highly selective separation, valid denaturation, and efficient digestion of LWPs in a short time by simply mixing with substrate proteins, greatly simplifying the separation and pretreatment process of the traditional hydrolysis method. The prepared materials and the developed strategy demonstrated an excellent size-selective assay performance in model protein mixtures, showing great potential in the application of proteomics analysis.

Deciphering and overcoming Anti-PD-1 resistance in Melanoma: A comprehensive review of Mechanisms, biomarker Developments, and therapeutic strategies

Worldwide, tens of thousands of people die from melanoma each year, making it the most frequently fatal form of cutaneous cancer. Immunotherapeutic advancements, particularly with anti-PD-1 medications, have significantly enhanced treatment outcomes over recent decades. With the broad application of anti-PD-1 therapies, insights into the mechanisms of resistance have evolved. Despite the development of combination treatments and early predictive biomarkers, a comprehensive synthesis of these advancements is absent in the current literature. This review underscores the prevailing knowledge of anti-PD-1 resistance mechanisms and underscores the critical role of robust predictive biomarkers in stratifying patients for targeted combinations of anti-PD-1 and other conventional or innovative therapeutic approaches. Additionally, we offer insights that may shape future melanoma treatment strategies.

Putative Risk Biomarkers of Bipolar Disorder in At-risk Youth

Bipolar disorder is a highly heritable and functionally impairing disease. The recognition and intervention of BD especially that characterized by early onset remains challenging. Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis. We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or (and) clinical risk of BD. Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses. Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores, epigenetic alterations, elevated immune parameters, front-limbic system deficits, and brain circuit dysfunction associated with emotion and reward processing. Future studies need to enhance machine learning integration, make clinical detection methods more objective, and improve the quality of cohort studies.

Proteomics in Psoriasis: Recent Advances

Psoriasis continues to affect a large percentage of patients worldwide and strongly appears to be a systematic disease. Efforts are being made to understand its etiology, which have led to research extended to genomic analysis with a focus on the role of pro-inflammatory cytokines, which play a major role in the pathogenesis of the disease. Plasma proteomic analysis in various diseases has provided promising results for choosing the right treatment for psoriasis, suggesting that it could play a key role in the prevention, prognosis, and treatment of the disease by individualizing treatment choices based on the proteomic profile of each patient. In this review, we focus on existing data in the bibliography on proteomic analysis in psoriasis and relevant approaches to future targeted therapies.

Exploration of potential immune mechanisms in cervical dystonia

BACKGROUND

Although there are many possible causes for cervical dystonia (CD), a specific etiology cannot be identified in most cases. Prior studies have suggested a relationship between autoimmune disease and some cases of CD, pointing to possible immunological mechanisms.

OBJECTIVE

The goal was to explore the potential role of multiple different immunological mechanisms in CD.

METHODS

First, a broad screening test compared neuronal antibodies in controls and CD. Second, unbiased blood plasma proteomics provided a broad screen for potential biologic differences between controls and CD. Third, a multiplex immunoassay compared 37 markers associated with immunological processes in controls and CD. Fourth, relative immune cell frequencies were investigated in blood samples of controls and CD. Finally, sequencing studies investigated the association of HLA DQB1 and DRB1 alleles in controls versus CD.

RESULTS

Screens for anti-neuronal antibodies did not reveal any obvious abnormalities. Plasma proteomics pointed towards certain abnormalities of immune mechanisms, and the multiplex assay pointed more specifically towards abnormalities in T lymphocytes. Abnormal immune cell frequencies were identified for some CD cases, and these cases clustered together as a potential subgroup. Studies of HLA alleles indicated a possible association between CD and DRB1*15:03, which is reported to mediate the penetrance of autoimmune disorders.

CONCLUSIONS

Altogether, the association of CD with multiple different blood-based immune measures point to abnormalities in cell-mediated immunity that may play a pathogenic role for a subgroup of individuals with CD.

Proteomics-Derived Biomarker Panel Facilitates Distinguishing Primary Lung Adenocarcinomas With Intestinal or Mucinous Differentiation From Lung Metastatic Colorectal Cancer

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical (IHC), and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging-based shotgun proteomics to characterize proteomes of formalin-fixed, paraffin-embedded tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest, and the Least Absolute Shrinkage and Selection Operator, were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by IHC to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection. The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of ten candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

Plasma ALS and Gal-3BP differentiate early from advanced liver fibrosis in MASLD patients

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is estimated to affect 30% of the world's population, and its prevalence is increasing in line with obesity. Liver fibrosis is closely related to mortality, making it the most important clinical parameter for MASLD. It is currently assessed by liver biopsy - an invasive procedure that has some limitations. There is thus an urgent need for a reliable non-invasive means to diagnose earlier MASLD stages.

METHODS

A discovery study was performed on 158 plasma samples from histologically-characterised MASLD patients using mass spectrometry (MS)-based quantitative proteomics. Differentially abundant proteins were selected for verification by ELISA in the same cohort. They were subsequently validated in an independent MASLD cohort (n = 200).

RESULTS

From the 72 proteins differentially abundant between patients with early (F0-2) and advanced fibrosis (F3-4), we selected Insulin-like growth factor-binding protein complex acid labile subunit (ALS) and Galectin-3-binding protein (Gal-3BP) for further study. In our validation cohort, AUROCs with 95% CIs of 0.744 [0.673 - 0.816] and 0.735 [0.661 - 0.81] were obtained for ALS and Gal-3BP, respectively. Combining ALS and Gal-3BP improved the assessment of advanced liver fibrosis, giving an AUROC of 0.796 [0.731. 0.862]. The {ALS; Gal-3BP} model surpassed classic fibrosis panels in predicting advanced liver fibrosis.

CONCLUSIONS

Further investigations with complementary cohorts will be needed to confirm the usefulness of ALS and Gal-3BP individually and in combination with other biomarkers for diagnosis of liver fibrosis. With the availability of ELISA assays, these findings could be rapidly clinically translated, providing direct benefits for patients.

Examination of the Complex Molecular Landscape in Obesity and Type 2 Diabetes

The escalating prevalence of metabolic disorders, notably type 2 diabetes (T2D) and obesity, presents a critical global health challenge, necessitating deeper insights into their molecular underpinnings. Our study integrates proteomics and metabolomics analyses to delineate the complex molecular landscapes associated with T2D and obesity. Leveraging data from 130 subjects, including individuals with T2D and obesity as well as healthy controls, we elucidate distinct molecular signatures and identify novel biomarkers indicative of disease progression. Our comprehensive characterization of cardiometabolic proteins and serum metabolites unveils intricate networks of biomolecular interactions and highlights differential protein expression patterns between T2D and obesity cohorts. Pathway enrichment analyses reveal unique mechanisms underlying disease development and progression, while correlation analyses elucidate the interplay between proteomics, metabolomics, and clinical parameters. Furthermore, network analyses underscore the interconnectedness of cardiometabolic proteins and provide insights into their roles in disease pathogenesis. Our findings may help to refine diagnostic strategies and inform the development of personalized interventions, heralding a new era in precision medicine and healthcare innovation. Through the integration of multi-omics approaches and advanced analytics, our study offers a crucial framework for deciphering the intricate molecular underpinnings of metabolic disorders and paving the way for transformative therapeutic strategies.

Multiplex proteomics identifies inflammation-related plasma biomarkers for aging and cardio-metabolic disorders

BACKGROUND

Cardio-metabolic disorders (CMDs) are common in aging people and are pivotal risk factors for cardiovascular diseases (CVDs). Inflammation is involved in the pathogenesis of CVDs and aging, but the underlying inflammatory molecular phenotypes in CMDs and aging are still unknown.

METHOD

We utilized multiple proteomics to detect 368 inflammatory proteins in the plasma of 30 subjects, including healthy young individuals, healthy elderly individuals, and elderly individuals with CMDs, by Proximity Extension Assay technology (PEA, O-link). Protein-protein interaction (PPI) network and functional modules were constructed to explore hub proteins in differentially expressed proteins (DEPs). The correlation between proteins and clinical traits of CMDs was analyzed and diagnostic value for CMDs of proteins was evaluated by ROC curve analysis.

RESULT

Our results revealed that there were 161 DEPs (adjusted p < 0.05) in normal aging and EGF was the most differentially expressed hub protein in normal aging. Twenty-eight DEPs were found in elderly individuals with CMDs and MMP1 was the most differentially expressed hub protein in CMDs. After the intersection of DEPs in aging and CMDs, there were 10 overlapping proteins: SHMT1, MVK, EGLN1, SLC39A5, NCF2, CXCL6, IRAK4, REG4, PTPN6, and PRDX5. These proteins were significantly correlated with the level of HDL-C, TG, or FPG in plasma. They were verified to have good diagnostic value for CMDs in aging with an AUC > 0.7. Among these, EGLN1, NCF2, REG4, and SLC39A2 were prominently increased both in normal aging and aging with CMDs.

CONCLUSION

Our results could reveal molecular markers for normal aging and CMDs, which need to be further expanded the sample size and to be further investigated to predict their significance for CVDs.

Serum proteomic profiling of patients with compensated advanced chronic liver disease with and without clinically significant portal hypertension

INTRODUCTION

Portal hypertension (PH) drives the progression of liver cirrhosis to decompensation and death. Hepatic venous pressure gradient (HVPG) measurement is the standard of PH quantification, and HVPG≥10 mmHg defines clinically significant PH (CSPH). We performed proteomics-based serum profiling to search for a proteomic signature of CSPH in patients with compensated advanced chronic liver disease (cACLD).

MATERIALS AND METHODS

Consecutive patients with histologically confirmed cACLD and results of HVPG measurements were prospectively included. Serum samples were pooled according to the presence/absence of CSPH and analysed by liquid chromatography-mass spectrometry. Gene set enrichment analysis was performed, followed by comprehensive literature review for proteins identified with the most striking difference between the groups.

RESULTS

We included 48 patients (30 with, and 18 without CSPH). Protein CD44, involved in the inflammatory response, vascular endothelial growth factor C (VEGF-C) and lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1), both involved in lymphangiogenesis were found solely in the CSPH group. Although identified in both groups, proteins involved in neutrophil extracellular traps (NET) formation, as well as tenascin C, autotaxin and nephronectin which mediate vascular contractility and lymphangiogenesis were more abundant in CSPH.

DISCUSSION AND CONCLUSION

We propose that altered inflammatory response, including NET formation, vascular contractility and formation of new lymph vessels are key steps in PH development. Proteins such as CD44, VEGF-C, LYVE-1, tenascin C, Plasminogen activator inhibitor 1, Nephronectin, Bactericidal permeability-increasing protein, Autotaxin, Myeloperoxidase and a disintegrin and metalloproteinase with thrombospondin motifs-like protein 4 might be considered for further validation as potential therapeutic targets and candidate biomarkers of CSPH in cACLD.

The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.

Plasma proteomic characterization of colorectal cancer patients with FOLFOX chemotherapy by integrated proteomics technology

BACKGROUND

Colorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization.

METHODS

Using a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients.

RESULTS

We achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods.

CONCLUSIONS

Our study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.

Characterization of the plasma proteome from healthy adult dogs

Introduction

Bloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine.

Methods

In this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting high-abundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose.

Results and discussion

Among all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies.

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.