Advances and Utility of the Human Plasma Proteome

Ion exchange- and enrichment-based technology applied to large-scale plasma proteomic analysis of breast cancer neoadjuvant chemotherapy

Mass spectrometry (MS) based proteomics provides unbiased quantification of all proteins in plasma, which can dynamically reflect individual health states in real time. However, large-scale proteomics studies are constrained by the excessive dynamic range of plasma proteome and low throughput. Herein, two kinds of magnetic metal-organic frameworks (MOFs) modified with ion exchange functional groups (denoted as MHP-UiO-66-SAX and MHP-HKUST-1-SCX) were designed and fabricated to exhibit large protein adsorption capability, which were combined with an automated Liquid-handling System, thus realizing in-depth, high-throughput and automated proteomics studies. The constructed workflow could automatically complete the sample preparation before MS within only six hours and nearly a thousand protein groups per sample could be quantified. In the cohort study of nearly one hundred breast cancer neoadjuvant chemotherapy (NC) plasma samples, two differentially expressed proteins previously reported as biomarkers were related with the pathological complete response (PCR) of the breast cancer, demonstrating the feasibility of the developed technology for preparing large-scale clinical samples and exhibiting the potential application in monitoring the effect of chemotherapy.

Predicted plasma proteomics from genetic scores and treatment outcomes in major depression: a meta-analysis

Proteomics has been scarcely explored for predicting treatment outcomes in major depressive disorder (MDD), due to methodological challenges and costs. Predicting protein levels from genetic scores provides opportunities for exploratory studies and the selection of targeted panels. In this study, we examined the association between genetically predicted plasma proteins and treatment outcomes - including non-response, non-remission, and treatment-resistant depression (TRD) - in 3559 patients with MDD from four clinical samples. Protein levels were predicted from individual-level genotypes using genetic scores from the publicly available OmicsPred database, which estimated genetic scores based on genome-wide genotypes and proteomic measurements from the Olink and SomaScan platforms. Associations between predicted protein levels and treatment outcomes were assessed using logistic regression models, adjusted for potential confounders including population stratification. Results were meta-analysed using a random-effects model. The Bonferroni correction was applied. We analysed 257 proteins for Olink and 1502 for SomaScan; 111 proteins overlapped between the two platforms. Despite no association was significant after multiple-testing correction, many top results were consistent across phenotypes, in particular seven proteins were nominally associated with all the analysed outcomes (CHL1, DUSP13, EVA1C, FCRL2, KITLG, SMAP1, and TIM3/HAVCR2). Additionally, three proteins (CXCL6, IL5RA, and RARRES2) showed consistent nominal associations across both the Olink and SomaScan platforms. The convergence of results across phenotypes is in line with the hypothesis of the involvement of immune-inflammatory mechanisms and neuroplasticity in treatment response. These results can provide hints for guiding the selection of protein panels in future proteomic studies.

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

BACKGROUND

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

OBJECTIVES

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

INCLUSION CRITERIA

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

METHODS

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

RESULTS

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

CONCLUSION

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

Technical Evaluation of Plasma Proteomics Technologies

Plasma proteomics technologies are rapidly evolving and of critical importance to the field of biomedical research. Here, we report a technical evaluation of six notable plasma proteomics technologies─unenriched (Neat), acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, and Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total, we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth (∼4500 proteins detected), while Olink detected ∼2600 proteins. Other MS-based methods ranged from ∼500-2200 proteins detected. In our analysis, Neat, Mag-Net, Seer, and Olink had good reproducibility, while PreOmics and Acid had higher variability (>20% median coefficient of variation). All MS methods showed good linearity with spiked-in C-reactive protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer produced the highest number of quantifiable proteins with a measurable LOD (4407) and LOQ (2696). Olink had the next highest number of quantifiable proteins, with 2002 having an LOD and 1883 having an LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a nonsmall cell lung cancer (NSCLC) cohort. All six methods detected differentially abundant proteins between the cancer and healthy samples but disagreed on which proteins were significant, highlighting the contrast between each method.

Nanopores with an Engineered Selective Entropic Gate Detect Proteins at Nanomolar Concentration in Complex Biological Sample

Biological nanopores enable the electrical detection of biomolecules, making them ideal sensors for use in health-monitoring devices. Proteins are widely recognized as biomarkers for various diseases, but they present a unique challenge due to their vast diversity and concentration range in biological samples. Here, inspired by the nuclear pore complex, we incorporated a layer of disordered polypeptides into the biological nanopore YaxAB. This polypeptide mesh formed an entropic gate, significantly reducing the entry of proteins from a highly concentrated mixture, including blood. The introduction of a specific recognition element within the disordered polypeptides allowed targeted proteins to penetrate through the nanopores, where they were recognized by specific current signatures. This biosensing approach allowed for the recognition of nanomolar proteins directly from blood samples without prior sample preparation. This work paves the way for the next generation of nanopore sensors for the real-time detection of proteins in blood.

From rare to more common: The emerging role of omics in improving understanding and treatment of severe inflammatory and hyperinflammatory conditions

Inflammation is a pathogenic driver of many diseases, including atherosclerosis and rheumatoid arthritis. Hyperinflammation can be seen as any inflammatory response that is deleterious to the host, regardless of cause. In medicine, hyperinflammation is defined as severe, deleterious, and fluctuating systemic or local inflammation with presence of a cytokine storm. It has been associated with rare autoinflammatory disorders. However, advances in omics technologies, including genomics, proteomics, and metabolomics, have revealed it to be more common, occurring in sepsis and severe coronavirus disease 2019. With a focus on proteomics, this review highlights the key role of omics in this shift. Through an exploration of research, we present how omics technologies have contributed to improved diagnostics, prognostics, and targeted therapeutics in the field of hyperinflammation. We also discuss the integration of advanced technologies, multiomics approaches, and artificial intelligence in analyzing complex datasets to develop targeted therapies, and we address their potential for revolutionizing the clinical aspects of hyperinflammation. We emphasize personalized medicine approaches for effective treatments and outline challenges, including the need for standardized methodologies, robust bioinformatics tools, and ethical considerations regarding data privacy. This review aims to provide a comprehensive overview of the molecular mechanisms underpinning hyperinflammation and underscores the potential of omics technologies in enabling successful clinical management.

Comprehensive analysis of blood proteome response to necrotic enteritis in broiler chicken

Necrotic enteritis (NE) in broiler chickens is caused by the overgrowth of toxin-producing strains of Clostridium (C.) perfringens. This study aims to analyze the blood proteome of broiler chickens affected by NE, providing insights into the host's response to the infection. Using MS/MS-based proteomics, blood plasma samples from broilers with necrotic lesions of different severity were analyzed and compared to healthy controls. A total of 412 proteins were identified, with 63 showing significant differences; for 25 of those correlation with disease severity was observed. Functional analysis revealed that proteins affected by NE were predominantly associated with the immune and signaling processes and extracellular matrix (ECM) structures. Notably, regulated proteins were significantly involved in bioprocesses related to complement activation, acute phase reaction, proteolysis and humoral immune response. The proteomics findings suggest that the changes in plasma proteins in response to NE are driven by the host's intensified efforts to counteract the infection, demonstrating a.o. activation of ECM-degrading proteases (MMP2, TIMP2), acute phase response (HPS5, CP, EXFABP, TF, VNN) and notable reduction in basement membrane (BM) and ECM-related peptides (PLOD2, POSTN, COL1A1/2, HSPG2, NID2) detected in the blood of NE-affected birds. Moreover, the findings underscore a coordinated effort of the host to mitigate the C. perfringens infection via activating immune (a.o., C3, CFH, MASP2, MBL2) and acute phase (CP, ORM, TF, ExFAB) related proteins. This study provides a deeper understanding of the host-pathogen interactions and identifies potential biomarkers and targets for therapeutic intervention. Data are available via ProteomeXchange with identifier PXD054172.

Extensive modulation of the circulating blood proteome by hormonal contraceptive use across two population studies

BACKGROUND

The study of circulating blood proteins in population cohorts offers new avenues to explore lifestyle-related and genetic influences describing and shaping human health.

METHODS

Utilizing high-throughput mass spectrometry, we quantified 148 highly abundant proteins, functioning in the innate and adaptive immune system, coagulation and nutrient transport in 3632 blood plasma, and 500 serum samples from the CHRIS and BASE-II cross-sectional population studies, respectively. Through multiple regression analyses, we aimed to identify the main factors influencing the circulating proteome at population level.

RESULTS

Many demographic covariates and common medications affect the concentration of high-abundant plasma proteins, but the most significant changes are linked to the use of hormonal contraceptives (HCU). HCU particularly alters amongst others the levels of Angiotensinogen and Transcortin. We robustly replicated these findings in the BASE-II cohort. Furthermore, our results indicate that combined hormonal contraceptives with ethinylestradiol have a stronger effect compared to bioidentical estrogens. Our analysis detects no lasting impact of hormonal contraceptives on the plasma proteome.

CONCLUSIONS

HCU is the dominant factor reshaping the high-abundant circulating blood proteome in two population studies. Given the high prevalence of HCU among young women, it is essential to account for this treatment in human proteome studies to avoid misinterpreting its impact as sex- or age-related effects. Although we did not investigate the influence of HCU-induced proteomic changes on human health, our data suggest that future studies on this topic are warranted.

Plasma proteome analysis of rheumatic patients reveals differences in fingerprints based on cardiovascular history: a pilot study

The risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) is much higher than that in the general population. As its etiology is not fully understood, we performed a pilot study using a shotgun proteomic approach to investigate whether the plasma signature in RA patients with CVD might show an altered profile. Subjects with RA were compared to a group of RA patients with a previous cardiovascular event (CVE). The cohort consisted of an RA control group (n = 10) and a group (n = 10) of RA patients with a history of CVD. Samples were collected at least 6 months before the CVE and 3-6 months after the CVE. All subjects were matched to controls for age, sex, and medication use. Plasma depletion of the 14 most abundant proteins was followed by bottom-up shotgun proteomics analysis (LC‒MS/MS). Relative changes in protein/peptide abundance were investigated using classical statistical analyses with Perseus and XG-Boost machine learning to compare between groups and to determine the relative importance of identified proteins, respectively. Principal component analysis (PCA) revealed no difference in the global protein and peptide signatures between the control and CVE groups. A total of 150, 239 and 74 protein ID's showed in comparison between Post Event vs. controls, Event vs. no Event and Pre event vs. Post Event respectively a statistically difference in relative abundance (p < 0.05). Remarkedly a total of 236 proteins ID's showed a statistical significant difference in relative abundance in the PRE-Event group compared to the control group which could also be confirmed by XGboost machine learning. Here, we demonstrated potential differences in the plasma proteome signature of rheumatic patients with cardiovascular events. Interestingly, this signature may be present prior to CVE's. However the conclusions must be drawn with caution, since this is a pilot study and further investigation with larger cohorts is warranted to identify potential risk markers that may predict the relative risk of CVEs in rheumatic diseases.

Plasma proteomics in pediatric patients with sepsis- hopes and challenges

One of the main causes of morbidity and death in pediatric patients is sepsis. Of the 48.9 million cases of sepsis reported globally, 41.5% involve children under the age of five, with 2.9 million deaths associated with the disease. Clinicians must identify and treat patients at risk of sepsis or septic shock before late-stage organ dysfunction occurs since diagnosing sepsis in young patients is more difficult than in adult patients. As of right now, omics technologies that possess adequate diagnostic sensitivity and specificity can assist in locating biomarkers that indicate how the disease will progress clinically and how the patient will react to treatment. By identifying patients who are at a higher risk of dying or experiencing persistent organ dysfunction, risk stratification based on biomarkers generated from proteomics can enhance prognosis. A potentially helpful method for determining the proteins that serve as biomarkers for sepsis and formulating theories on the pathophysiological mechanisms behind complex sepsis symptoms is plasma proteomics.

Development and Validation of a Capillary Electrophoresis Coupled to Mass Spectrometry Pipeline for Comparable Assessment of the Plasma Peptidome

Although capillary electrophoresis coupled to mass spectrometry (CE-MS) holds promise for urinary peptide profiling, only a limited number of studies have used CE-MS to study plasma peptides. Here we describe the establishment of a workflow, including sample preparation, CE-MS analysis, data processing and normalization optimized for the analysis of plasma peptides. Using 291 plasma samples from 136 patients with end stage kidney failure (including pre- and post-dialysis samples) and 20 patients with chronic kidney disease, we identified and quantified the abundance of 3920 unique plasma peptides. The repeatability and intermediate precision of the analysis were high (with a coefficient of variation of 5% on average for all peptides). Six hundred sixty-one out of 3920 peptides were sequenced by CE-MS/MS. These peptide fragments belonged to 135 parent proteins. Using the pipeline, we identified 169 sequenced plasma peptides with different plasma abundance pre- and post-dialysis. These peptides combined in a support vector machine (SVM) classifier successfully discriminated between pre- and post-dialysis samples in a blinded validation cohort of 45 dialysis patients. Enriched peptides post-dialyses were for the major part associated to inflammation and the coagulation contact systems that may serve as signatures for optimizing dialysis materials. In conclusion, this high-throughput strategy focuses on the plasma peptidome, an understudied component of the plasma, as a promising area for further exploration. Due to their close proximity to the vascular bed, plasma peptides hold significant potential to serve as reliable biomarkers for systemic complications associated with kidney disease.

Comprehensive Proteomic Profiling of Exfoliation Glaucoma Via Mass Spectrometry Reveals SVEP1 as a Potential Biomarker

Purpose

This study investigated the proteomic landscape of exfoliation glaucoma to find potential biomarkers.

Methods

The study enrolled 34 patients diagnosed with either exfoliation syndrome with/without glaucoma or age-related cataract. Plasma proteins were analyzed through mass spectrometry and Mendelian randomization (MR) based on data from deCODE, FinnGen, Atherosclerosis Risk in Communities (ARIC), eQTLGen, and UK Biobank (UKB) cohorts to infer relationships.

Results

Among 2025 plasma proteins analyzed, 130 were differentially expressed in the exfoliation glaucoma group, which exhibited elevated intraocular pressure. Our proteomics data suggested that infection, immune responses including intestinal immune network, endocrine hormones, and complement and coagulation cascades are involved in the development of exfoliation glaucoma. Notably, there was a significant correlation between SVEP1 and exfoliation glaucoma (odds ratio [OR] = 1.20, 95% confidence interval [CI] = 1.10 to 1.31, P = 0.0000428), with findings corroborated in an independent cohort. Further analysis predicted a protective role of LOXL1-AS1 in exfoliation glaucoma through its regulation of SVEP1 expression. In MR phenome-wide association studies, SVEP1 was associated with complications of exfoliation glaucoma. After multiple testing corrections, there was a tendency for SVEP1 to be associated with glaucoma (OR = 1.14, 95% CI = 1.11 to 1.16, P = 0.0000003) and type 2 diabetes (OR = 1.07, 95% CI = 1.05 to 1.08, P = 0.0000067).

Conclusions

Plasma proteomic analysis reveals that high expression of SVEP1 is a risk factor for exfoliation glaucoma, which potentially affects diabetes and is affected by estradiol or LOXL1-AS1. However, further research is needed to establish causality.

Deep plasma and tissue proteome profiling of knockout mice reveals pathways associated with Svep1 deficiency

Despite strong causal associations with cardiovascular and metabolic disorders including coronary artery disease, hypertension, and type 2 diabetes, as well as a range of other diseases, the exact function of the protein SVEP1 remains largely unknown. Animal models have been employed to investigate how SVEP1 contributes to disease, with a focus on murine models exploring its role in development, cardiometabolic disease and platelet biology. In this study, we aimed to comprehensively phenotype the proteome of Svep1 +/- mice compared to wild-type (WT) littermates using liquid chromatography-tandem mass spectrometry (LC-MS/MS) bottom-up proteomics in plasma, heart, aorta, lung, and kidney to identify dysregulated pathways and biological functions associated with Svep1 deficiency. Our findings reveal that Svep1 deficiency leads to significant proteomic alterations across the mouse, with the highest number of dysregulated proteins observed in plasma and kidney. Key dysregulated proteins in plasma include upregulation of ADGRV1, CDH1, and MYH6, and downregulation of MTIF2 and AKAP13 which, alongside other proteins dysregulated across tissues, indicate disruption in cell adhesion, extracellular matrix organisation, platelet degranulation, and Rho GTPase pathways. Novel findings include significant enrichment of complement cascades in plasma, suggesting dysregulation of innate immune responses and hemostasis due to Svep1 deficiency. Pathways related to chylomicron assembly and lipid metabolism were also enriched. Additionally, we developed a high-throughput quantitative targeted LC-MS/MS assay to measure endogenous levels of murine SVEP1. SVEP1 was detectable in lung homogenate and showed a significant reduction in SVEP1 levels in Svep1 +/- vs. WT, but was not identified in plasma, heart, aorta, or kidney, likely due to expression levels below the assay's detection limit. Overall, this deep phenotyping study provides insight into the systemic impact of Svep1 deficiency.

Micro scale chromatography of human plasma proteins for nano LC-ESI-MS/MS

Organic precipitation of proteins with acetonitrile demonstrated complete protein recovery and improved chromatography of human plasma proteins. The separation of 25 μL of human plasma into 22 fractions on a QA SAX resin facilitated more effective protein discovery despite the limited sample size. Micro chromatography of plasma proteins over quaternary amine (QA) strong anion exchange (SAX) resins performed best, followed by diethylaminoethyl (DEAE), heparin (HEP), carboxymethyl cellulose (CMC), and propyl sulfate (PS) resins. Two independent statistical methods, Monte Carlo comparison with random MS/MS spectra and the rigorous X!TANDEM goodness of fit algorithm protein p-values corrected to false discovery rate q-values (q ≤ 0.01) agreed on at least 12,000 plasma proteins, each represented by at least three fully tryptic corrected peptide observations. There was qualitative agreement on 9393 protein/gene symbols between the linear quadrupole versus orbital ion trap but also quantitative agreement with a highly significant linear regression relationship between log observation frequency (F value 4,173, p-value 2.2e-16). The use of a QA resin showed nearly perfect replication of all the proteins that were also found using DEAE-, HEP-, CMC-, and PS-based chromatographic methods combined and together estimated the size of the size of the plasma proteome as ≥12,000 gene symbols.

Preeclampsia and fetal growth restriction: does novel proteomics reveal immunological possible candidate biomarkers?

PURPOSE OF REVIEW

The aim of this review is to explore a possible link between immunological candidate proteins, identified through modern proteomic techniques, and preeclampsia (PE) and fetal growth restriction (FGR).

RECENT FINDINGS

Proteomics has become a promising tool in the search for disease pathways, drug targets, and biomarkers. PE and FGR are adverse pregnancy complications with supposed immunological involvement in their pathogenesis, but no circulating immunological biomarkers are currently established for diagnosis and risk stratification. Several proteomic studies have aimed to identify PE and FGR biomarkers - often with varying results across studies. However, proteomics has revealed altered expression of human leukocyte antigen-I in PE cases, which is supported in Genome-wide association study (GWAS) studies. Proteomic results support the heterogeneous nature of PE by identification of molecular subgroups - including subgroups characterized by immune-related proteins e.g. CXCL10. No specific immunological markers are found on FGR, but differences in overall plasma proteomic signature have been suggested.

SUMMARY

Proteomics certainly holds great potential. The immunological component in PE and FGR are still unclarified, but improvements in proteomic technologies may provide both definition of disease subgroups and subsequent discovery of biomarkers and targeted analysis within each subgroup.

Associations between plasma metabolism-associated proteins and future development of giant cell arteritis: results from a prospective study

OBJECTIVE

The aim of this study was to investigate the relationship between biomarkers associated with metabolism and subsequent development of GCA.

METHOD

Participants in the population-based Malmö Diet Cancer Study (MDCS; N = 30 447) who were subsequently diagnosed with GCA were identified in a structured process. Matched GCA-free controls were selected from the study cohort. Baseline plasma samples were analysed using the antibody-based OLINK proteomics metabolism panel (92 metabolic proteins). Analyses were pre-designated as hypothesis-driven or hypothesis-generating. In the latter, principal component analysis was used to identify groups of proteins that explained the variance in the proteome.

RESULTS

There were 95 cases with a confirmed incident diagnosis of GCA (median 12.0 years after inclusion). Among biomarkers with a priori hypotheses, adhesion G protein-coupled receptor E2 (ADGRE2) was positively associated [odds ratio (OR) per S.D. 1.67; 95% CI 1.08-2.57], and fructose-1,6-bisphosphatase 1 (FBP1) was negatively associated (OR per S.D. 0.59; 95% CI 0.35-0.99) with GCA. In particular, ADGRE2 levels were associated with subsequent GCA in the subset sampled <8.5 years before diagnosis. For meteorin-like protein (Metrnl), the highest impact on the risk of GCA was observed in those patients sampled closest to diagnosis, with a decreasing trend with longer time to GCA (P = 0.03). In the hypothesis-generating analyses, elevated levels of receptor tyrosine-like orphan receptor 1 (ROR1) were associated with subsequent GCA.

CONCLUSION

Biomarkers identified years before clinical diagnosis indicated a protective role of gluconeogenesis (FBP1) and an association with macrophage activation (ADGRE2 and Metrnl) and proinflammatory signals (ROR1) for development of GCA.

Selected Ion Extraction of Peptides with Heavy Isotopes and Hydrogen Loss Reduces the Type II Error in Plasma Proteomics

Naturally occurring peptides display a wide mass distribution after ionization due to the presence of heavy isotopes of C, H, N, O, and S and hydrogen loss. There is a crucial need for sensitive methods that collect as much information as possible about all plasma peptide forms. Statistical analysis of the delta mass distribution of peptide precursors from MS/MS spectra that were matched to 63,077 peptide sequences by X!TANDEM revealed Gaussian peaks representing heavy isotopes and hydrogen loss at integer delta mass values of -3, -2, -1, 0, +1, +2, +3, +4, and +5 Da. Human plasma samples were precipitated in acetonitrile, and the resulting proteins were collected over a quaternary amine resin, eluted with NaCl, digested with trypsin, and analyzed by nano liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) with an orbital ion trap (OIT). Fragment spectra (MS/MS) generated from the OIT data were fit to human fully tryptic peptides by X!TANDEM, which led to the identification of 3,888 protein gene symbols represented by three or more peptides (n ≥ 3). The peptide counts to plasma proteins from experimental MS/MS spectra were corrected against 29 blank LC-ESI-MS/MS spectra and 30 million random MS/MS control spectra to yield 2,784 true positive proteins (n ≥ 3; q ≤ 0.01). Peptides identified by fragmenting ions with Gaussian heavy isotopes and hydrogen loss that were matched to known plasma proteins, such as albumin (ALB), were shown to be true positives and agreed with the peptide sequences identified in the monoisotopic peak. Accepting the ions from the monoisotopic peak alone (±0.1 Da) yielded only 382 plasma proteins (n ≥ 3; type I error q ≤ 0.01; type II error ∼86%). In contrast, accepting all ions within ±0.1 Da around the hydrogen loss, monoisotopic, and heavy isotopic peaks led to the identification of 963 proteins (n ≥ 3; q ≤ 0.01; type II error ∼60%). Using the power of the OIT to resolve the Gaussian peaks from heavy isotopes and hydrogen loss resulted in the identification of three times more proteins with high confidence and a much lower type II error than analyzing peptides from the monoisotopic peak alone. The resolving power of the OIT may be exploited to increase observation frequencies and provide greater proteomic coverage and statistical power in comparative proteomics studies.

A Technical Evaluation of Plasma Proteomics Technologies

Plasma proteomic technologies are rapidly evolving and of critical importance to the field of biomedical research. Here we report a technical evaluation of six notable plasma proteomic technologies - unenriched (Neat), Acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth (∼4,500), while Olink detected ∼2,600 proteins. Other MS-based methods ranged from ∼500-2,200. Neat, Mag-Net, Seer, and Olink had strong reproducibility, while PreOmics and Acid showed higher variability. All MS methods showed good linearity with spiked-in C-Reactive Protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer had more than double the number of quantifiable proteins (4,800) for both LOD and LOQ than the next best method. Olink was comparable to Neat and Mag-Net for LOD, but worse for LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a non-small cell lung cancer (NSCLC) cohort.

Proteomic patterns associated with ketamine response in major depressive disorders

BACKGROUND

Major depressive disorder (MDD) is characterized by persistent feelings of sadness and loss of interest. Ketamine has been widely used to treat MDD owing to its rapid effect in relieving depressive symptoms. Importantly, not all patients respond to ketamine treatment. Identifying sub-populations who will benefit from ketamine, as well as those who may not, prior to treatment initiation, would significantly advance precision medicine in patients with MDD.

METHODS

Here, we used mass spectrometry-based plasma proteomics to analyze matched pre- and post-ketamine treatment samples from a cohort of 30 MDD patients whose treatment outcomes and demographic and clinical characteristics were considered.

RESULTS

Ketamine responders and non-responders were identified according to their individual outcomes after two weeks of treatment. We analyzed proteomic alterations in post-treatment samples from responders and non-responders and identified a collection of six proteins pivotal to the antidepressive effect of ketamine. Subsequent co-regulation analysis revealed that pathways related to immune response were involved in ketamine response. By comparing the proteomic profiles of samples from the same individuals at the pre- and post-treatment time points, dynamic proteomic rearrangements induced by ketamine revealed that immune-related processes were activated in association with its antidepressive effect. Furthermore, receiver operating characteristic curve analysis of pre-treatment samples revealed three proteins with strong predictive performance in determining the response of patients to ketamine before receiving treatment.

CONCLUSIONS

These findings provide valuable knowledge about ketamine response, which will ultimately lead to more personalized and effective treatments for patients.

TRIAL REGISTRATION

The study was registered in the Chinese Clinical Trials Registry (ChiCTR-OOC-17012239) on May 26, 2017.

Identification of plasma proteins binding oxidized phospholipids using pull-down proteomics and OxLDL masking assay

Oxidized phospholipids (OxPLs) are increasingly recognized as toxic and proinflammatory mediators, which raises interest in the mechanisms of their detoxification. Circulating OxPLs are bound and neutralized by plasma proteins, including both antibodies and non-immunoglobulin proteins. The latter group of proteins is essentially not investigated because only three OxPC-binding plasma proteins are currently known. The goal of this work was to characterize a broad spectrum of plasma proteins selectively binding OxPLs. Using pull-down-proteomic analysis, we found about 150 non-immunoglobulin proteins preferentially binding oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-phosphatidylcholine (OxPAPC) as compared to non-oxidized PAPC. To test if candidate proteins indeed can form a barrier isolating OxPLs from recognition by other proteins, we applied an immune masking assay. Oxidized LDL (OxLDL) immobilized in multiwell plates was used as a carrier of OxPLs, while mAbs recognizing OxPC or OxPE were used as "detectors" showing if OxPLs on the surface of OxLDL are physically accessible to external binding partners. Using an orthogonal combination of pull-down and masking assays we confirmed that previously described OxPL-binding proteins (non-fractionated IgM, CFH, and Apo-M) indeed can bind to and mask OxPC and OxPE on liposomes and OxLDL. Furthermore, we identified additional plasma proteins selectively binding and masking OxPC including Apo-D, Apo-H, pulmonary surfactant-associated protein B, and antithrombin-III. We hypothesize that in addition to circulating antibodies, multiple non-immunoglobulin plasma proteins can also bind OxPLs and modulate their recognition by innate and adaptive immunity.

A Proteomic Examination of Plasma Extracellular Vesicles Across Colorectal Cancer Stages Uncovers Biological Insights That Potentially Improve Prognosis

BACKGROUND

Recent advancements in understanding plasma extracellular vesicles (EVs) and their role in disease biology have provided additional unique insights into the study of Colorectal Cancer (CRC).

METHODS

This study aimed to gain biological insights into disease progression from plasma-derived extracellular vesicle proteomic profiles of 80 patients (20 from each CRC stage I-IV) against 20 healthy age- and sex-matched controls using a high-resolution SWATH-MS proteomics with a reproducible centrifugation method to isolate plasma EVs.

RESULTS

We applied the High-Stringency Human Proteome Project (HPP) guidelines for SWATH-MS analysis, which refined our initial EV protein identification from 1362 proteins (10,993 peptides) to a more reliable and confident subset of 853 proteins (6231 peptides). In early-stage CRC, we identified 11 plasma EV proteins with differential expression between patients and healthy controls (three up-regulated and eight down-regulated), many of which are involved in key cancer hallmarks. Additionally, within the same cohort, we analysed EV proteins associated with tumour recurrence to identify potential prognostic indicators for CRC. A subset of up-regulated proteins associated with extracellular vesicle formation (GDI1, NSF, and TMED9) and the down-regulation of TSG101 suggest that micro-metastasis may have occurred earlier than previously anticipated.

DISCUSSION

By employing stringent proteomic analysis and a robust SWATH-MS approach, we identified dysregulated EV proteins that potentially indicate early-stage CRC and predict recurrence risk, including proteins involved in metabolism, cytoskeletal remodelling, and immune response. While our findings underline discrepancies with other studies due to differing isolation and stringency parameters, they provide valuable insights into the complexity of the EV proteome, emphasising the need for standardised protocols and larger, well-controlled studies to validate potential biomarkers.

The synchronous upregulation of a specific protein cluster in the blood predicts both colorectal cancer risk and patient immune status

BACKGROUND

Early detection and treatment of colorectal cancer (CRC) is crucial for improving patient survival rates. This study aims to identify signature molecules associated with CRC, which can serve as valuable indicators for clinical hematological screening.

METHOD

We have systematically searched the Human Protein Atlas database and the relevant literature for blood protein-coding genes. The CRC dataset from TCGA was used to compare the acquired genes and identify differentially expressed molecules (DEMs). Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify modules of co-expressed molecules and key molecules within the DEMs. Signature molecules of CRC were then identified from the key molecules using machine learning. These findings were further validated in clinical samples. Finally, Logistic regression was used to create a predictive model that calculated the likelihood of CRC in both healthy individuals and CRC patients. We evaluated the model's sensitivity and specificity using the ROC curve.

RESULT

By utilizing the CRC dataset, WGCNA analysis, and machine learning, we successfully identified seven signature molecules associated with CRC from 1478 blood protein-coding genes. These markers include S100A11, INHBA, QSOX2, MET, TGFBI, VEGFA and CD44. Analyzing the CRC dataset showed its potential to effectively discriminate between CRC and normal individuals. The up-regulated expression of these markers suggests the existence of an immune evasion mechanism in CRC patients and is strongly correlated with poor prognosis.

CONCLUSION

The combined detection of the seven signature molecules in CRC can significantly enhance diagnostic efficiency and serve as a novel index for hematological screening of CRC.

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

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

Dynamics of the blood plasma proteome during hyperacute HIV-1 infection

The complex dynamics of protein expression in plasma during hyperacute HIV-1 infection and its relation to acute retroviral syndrome, viral control, and disease progression are largely unknown. Here, we quantify 1293 blood plasma proteins from 157 longitudinally linked plasma samples collected before, during, and after hyperacute HIV-1 infection of 54 participants from four sub-Saharan African countries. Six distinct longitudinal expression profiles are identified, of which four demonstrate a consistent decrease in protein levels following HIV-1 infection. Proteins involved in inflammatory responses, immune regulation, and cell motility are significantly altered during the transition from pre-infection to one month post-infection. Specifically, decreased ZYX and SCGB1A1 levels, and increased LILRA3 levels are associated with increased risk of acute retroviral syndrome; increased NAPA and RAN levels, and decreased ITIH4 levels with viral control; and increased HPN, PRKCB, and ITGB3 levels with increased risk of disease progression. Overall, this study provides insight into early host responses in hyperacute HIV-1 infection, and present potential biomarkers and mechanisms linked to HIV-1 disease progression and viral load.

Identification of fibrinogen as a plasma protein binding partner for lecanemab biosimilar IgG

OBJECTIVE

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

METHODS

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

RESULTS

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

INTERPRETATION

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

π-HuB: the proteomic navigator of the human body

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.

Advancements in mass spectrometry-based proteomics: a new era in pathology research and diagnostics

BACKGROUND

Mass spectrometry (MS)-based proteomics is rapidly transforming pathology research and diagnostics by enabling comprehensive studies of protein expression and post-translational modifications (PTMs).

OBJECTIVE

This article discusses recent advancements in MS-based proteomics, focusing on emerging technologies in sample preparation, MS instrumentation, and data analysis. These developments are scrutinized for their applications in clinical cohort studies and molecular pathology diagnostics.

MATERIALS AND METHODS

The article reviews innovations in automated sample preparation, chromatography systems, advanced MS technologies, and proteomic data analysis in the context of pathology. Specific applications such as liquid biopsy, spike-in heavy peptide panels, immunopeptidomics, and PTM screening are highlighted alongside opportunities for data integration.

RESULTS

Recent technological improvements have significantly increased the throughput, precision, and scope of proteomic studies, enabling the analysis of large clinical cohorts and small specimens with unprecedented sensitivity. Advanced MS techniques have broadened applications, opening new avenues for discovery and diagnosis of marker proteins and therapeutic targets.

CONCLUSION

Advancements in MS-based proteomics have created new opportunities in clinical research and diagnostics. By facilitating more comprehensive and integrated analyses of proteomes, these technologies are set to play a pivotal role in the future of personalized medicine and pathology research.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SIGNIFICANCE: Liver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Trypsin Digestion Conditions of Human Plasma for Observation of Peptides and Proteins from Tandem Mass Spectrometry

Previous meta-analysis indicated that plasma or serum proteome groups using various experimental conditions detected different peptides from the same plasma proteins, which is strong evidence for the veracity of blood fluid LC-ESI-MS/MS but also evidences that the trypsin digestion step is a key source of variation in plasma proteomics. Agreement between different digestion conditions and MS/MS algorithms may serve as an independent confirmation of the validity of the LC-ESI-MS/MS analysis of plasma peptides. Plasma contains a high percentage of albumin held together by multiple disulfide bonds; hence, reduction and/or alkylation may greatly enhance the digestion efficiency of albumin. Plasma proteins were precipitated in 90% acetonitrile, collected over quaternary amine resin, and eluted in NaCl prior to digestion treatments. To determine the effect of trypsin digestion methods, the plasma proteins were digested in 600 mM urea and 5% acetonitrile with trypsin alone, or reduced with 2 mM DTT followed by trypsin, or DTT followed by 15 mM iodoacetamide and then trypsin. The resulting peptides were analyzed by LC-ESI-MS/MS with a linear quadrupole ion trap (LIT). The MS/MS spectra were directly fit to peptides by the X!TANDEM and SEQUEST algorithms. Blank noise injections served as the analytical control, and 30 million random MS/MS served as the statistical control. Digesting human plasma with DTT reduction, or reduction and alkylation, resulted in a dramatic increase in the number and observation frequency of albumin peptides. In contrast, digestion with trypsin alone suppressed the observation of albumin, and instead, many low abundance plasma and cellular proteins showed higher observation frequency. Digestion with trypsin alone increased the observation frequency of APOC1, ACAN, ATRN, CPB2, GP2, GPX3, HBA1, PAPD5, PKD1, and many cellular proteins. After correction against noise and random controls, SEQUEST showed good agreement with the true positive plasma proteins identified by X!TANDEM and resulted in an R-squared of 0.5238 with an F-statistic of 10,930 on 9,935 protein gene symbols with a p-value < 2.2e-16. Digestion of plasma with trypsin alone avoids the complete digestion of albumin and permits the enhanced detection of some other cellular proteins from plasma. Different digestion approaches were complimentary and together resulted in a more comprehensive plasma proteome. The protein FDR q-values, the modest effect of background and Monte Carlo correction, and the significant STRING analysis were all consistent with the high fidelity of the rigorous X!TANDEM algorithm. In contrast, SEQUEST required significant correction against noise and statistical controls and selection of high cross correlation (XCorr) scores to show good agreement with X!TANDEM. There was qualitative and quantitative agreement between plasma proteins digested without alkylation from the orbital ion trap (OIT) versus the LIT instrument that showed highly significant regression against the X!TANDEM OIT monoisotopic results, those from heavy isotopes and other masses from X!TANDEM, and with those from MaxQuant. There was significant qualitative and quantitative agreement between the complementary digestion conditions consistent with the good fidelity of plasma analysis by LC-ESI-MS/MS with a sensitive linear ion trap.

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

The dynamic range challenge for the detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundance 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 a 105-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.

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

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

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

Background

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

Methods

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

Results

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

Conclusion

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

Supplementary Information

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

Vago-splenic signal transduction of cardioprotection in humans

BACKGROUND AND AIMS

The spleen serves as an important relay organ that releases cardioprotective factor(s) upon vagal activation during remote ischaemic conditioning (RIC) in rats and pigs. The translation of these findings to humans was attempted.

METHODS

Remote ischaemic conditioning or electrical auricular tragus stimulation (ATS) were performed in 10 healthy young volunteers, 10 volunteers with splenectomy, and 20 matched controls. Venous blood samples were taken before and after RIC/ATS or placebo, and a plasma dialysate was infused into isolated perfused rat hearts subjected to global ischaemia/reperfusion.

RESULTS

Neither left nor right RIC or ATS altered heart rate and heart rate variability in the study cohorts. With the plasma dialysate prepared before RIC or ATS, respectively, infarct size (% ventricular mass) in the recipient rat heart was 36 ± 6% (left RIC), 34 ± 3% (right RIC) or 31 ± 5% (left ATS), 35 ± 5% (right ATS), and decreased with the plasma dialysate from healthy volunteers after RIC or ATS to 20 ± 4% (left RIC), 23 ± 6% (right RIC) or to 19 ± 4% (left ATS), 26 ± 9% (right ATS); infarct size was still reduced with plasma dialysate 4 days after ATS and 9 days after RIC. In a subgroup of six healthy volunteers, such infarct size reduction was abrogated by intravenous atropine. Infarct size reduction by RIC or ATS was also abrogated in 10 volunteers with splenectomy, but not in their 20 matched controls.

CONCLUSIONS

In humans, vagal innervation and the spleen as a relay organ are decisive for the cardioprotective signal transduction of RIC and ATS.

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

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

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

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

Integration of proteomics with prospective birth cohort to elucidate early life origins of cardiometabolic diseases: rationale, study design, lab assay, and quality control

There is growing evidence that the plasma proteome provides insights into personal health status at different stages of life. However, limited data are available on high-throughput proteomic studies in pediatric populations, especially, using prospective birth cohorts. We launched a proteomics study in 990 children from a US predominantly urban, low-income, multi-ethnic prospective Boston Birth Cohort (BBC, referred as "BBC proteomics study"), which aimed to leverage proteomics to investigate the biological pathways underlying the link between preterm birth and child long-term cardiometabolic health. The objective of this paper is to describe the rationale, study design, proteomic assay and quality control steps for the BBC proteomics study in a subset of children with available proteomic profiling. Using the OLINK® Explore 3072 platform, proteomic profiling was performed in cord plasma at birth and in postnatal plasma collected during early childhood. Quality control (QC) steps were performed, including calculation of coefficient of variation (CV), missingness rates per sample or per protein, principal component analyses to identify clustering and outliers, and correlation analyses among the duplicates to indicate reproducibility. A total of 2,941 proteins from eight OLINK panels were successfully measured at both time points. Almost 100% of samples passed lab-prespecified QC. Approximately 89% of proteins were detected in > 50% samples; 79.6% had intra-CV < 15% and 79.9% of had inter-CV < 30%. Four samples were identified as outliers due to high missingness rates. Our data also demonstrated that this assay had a good reproducibility with correlation coefficient (r) > 0.65 in most of the duplicates, although we also identified potential batch effects. In conclusion, our data suggests that this high-throughput proteomic profiling is feasible and reproducible in archived plasma samples, including cord blood. We anticipated that successful completion of this proteomics study will help identify novel predictive biomarkers and therapeutic targets so that high-risk newborns can be identified, and effective interventions can be initiated during the earliest developmental window when they may have the greatest life-long benefit.

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

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

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.

Optimized workflow of EV enrichment from human plasma samples for downstream mass spectrometry analysis

To improve the prognosis of bladder and prostate cancer, highly specific and sensitive biomarkers are needed for early detection, prognosis prediction, and therapeutic stratification. Extracellular vesicles (EV) from plasma could fill this gap due to their potential to serve as cancer biomarkers. However, the enrichment of EV is a major challenge, because the highly abundant plasma proteins are interfering with analytical downstream applications like mass spectrometry (MS). Therefore, the purity requirements of the EV samples must be carefully considered when selecting or developing a suitable EV enrichment method. The aim of this study was to compare a self-designed EV enrichment method based on density cushion centrifugation (DCC) combined with size exclusion chromatography (SEC) and concentration (method 1) with the exoRNeasy midi kit from Qiagen (method 2) and with unprocessed plasma. Furthermore, the single steps of method 1 were evaluated for their effectiveness to enrich EV from plasma. The results showed that the EV samples enriched with method 1 contained the highest levels of EV and exosome markers with simultaneously low levels of highly abundant plasma proteins. In summary, the combination of DCC, SEC and concentration proved to be a promising approach to discover EV-based biomarkers from plasma of cancer patients.

Multiomics: Functional Molecular Biomarkers of Micronutrients for Public Health Application

Adequate micronutrient intake and status are global public health goals. Vitamin and mineral deficiencies are widespread and known to impair health and survival across the life stages. However, knowledge of molecular effects, metabolic pathways, biological responses to variation in micronutrient nutriture, and abilities to assess populations for micronutrient deficiencies and their pathology remain lacking. Rapidly evolving methodological capabilities in genomics, epigenomics, proteomics, and metabolomics offer unparalleled opportunities for the nutrition research community to link micronutrient exposure to cellular health; discover new, arguably essential micronutrients of microbial origin; and integrate methods of molecular biology, epidemiology, and intervention trials to develop novel approaches to assess and prevent micronutrient deficiencies in populations. In this review article, we offer new terminology to specify nutritional application of multiomic approaches and encourage collaboration across the basic to public health sciences to advance micronutrient deficiency prevention.

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

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

Characterization of effective, simple, and low-cost precipitation methods for depleting abundant plasma proteins to enhance the depth and breadth of plasma proteomics

Plasma is an abundant source of proteins and potential biomarkers to aid in the detection, diagnosis, and prognosis of human diseases. These proteins are often present at low levels in the blood and difficult to identify and measure due to the large dynamic range of proteins. The goal of this work was to characterize and compare various protein precipitation methods related to how they affect the depth and breadth of plasma proteomic studies. Abundant protein precipitation with perchloric acid (PerCA) can increase protein identifications and depth of plasma proteomic studies. Three acid- and four solvent-based precipitation methods were evaluated. All methods tested provided excellent plasma proteomic coverage (>600 identified protein groups) and detected protein in the low pg/mL range. Functional enrichment analysis revealed subtle differences within and larger changes between the precipitant groups. Methanol-based precipitation outperformed the other methods based on identifications and reproducibility. The methods' performance was verified using eight lung cancer patient samples, where >700 protein groups were measured and proteins with an estimated plasma concentration of ∼10 pg/mL were detected. Various protein precipitation agents are amenable to extending the depth and breadth of plasma proteomes. These data can guide investigators to implement inexpensive, high-throughput methods for their plasma proteomic workflows.

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.

Longitudinal Fluctuations in Protein Concentrations and Higher-Order Structures in the Plasma Proteome of Kidney Failure Patients Subjected to a Kidney Transplant

Using proteomics and complexome profiling, we evaluated in a year-long study longitudinal variations in the plasma proteome of kidney failure patients, prior to and after a kidney transplantation. The post-transplant period was complicated by bacterial infections, resulting in dramatic changes in the proteome, attributed to an acute phase response (APR). As positive acute phase proteins (APPs), being elevated upon inflammation, we observed the well-described C-reactive protein and Serum Amyloid A (SAA), but also Fibrinogen, Haptoglobin, Leucine-rich alpha-2-glycoprotein, Lipopolysaccharide-binding protein, Alpha-1-antitrypsin, Alpha-1-antichymotrypsin, S100, and CD14. As negative APPs, being downregulated upon inflammation, we identified the well-documented Serotransferrin and Transthyretin, but added Kallistatin, Heparin cofactor 2, and interalpha-trypsin inhibitor heavy chain H1 and H2 (ITIH1, ITIH2). For the patient with the most severe APR, we performed plasma complexome profiling by SEC-LC-MS on all longitudinal samples. We observed that several plasma proteins displaying alike concentration patterns coelute and form macromolecular complexes. By complexome profiling, we expose how SAA1 and SAA2 become incorporated into high-density lipid particles, replacing largely Apolipoprotein (APO)A1 and APOA4. Overall, our data highlight that the combination of in-depth longitudinal plasma proteome and complexome profiling can shed further light on correlated variations in the abundance of several plasma proteins upon inflammatory events.

Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow

Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.

Extraction of naturally occurring peptides versus the tryptic digestion of proteins from fetal versus adult bovine serum for LC-ESI-MS/MS

The naturally occurring peptides and digested proteins of fetal versus adult bovine serum were compared by LC-ESI-MS/MS after correction against noise from blank injections and random MS/MS spectra as statistical controls. Serum peptides were extracted by differential precipitation with mixtures of acetonitrile and water. Serum proteins were separated by partition chromatography over quaternary amine resin followed by tryptic digestion. The rigorous X!TANDEM goodness of fit algorithm that has a low error rate as demonstrated by low FDR q-values (q ≤ 0.01) showed qualitative and quantitative agreement with the SEQUEST cross correlation algorithm on 12,052 protein gene symbols. Tryptic digestion provided a quantitative identification of the serum proteins where observation frequency reflected known high abundance. In contrast, the naturally occurring peptides reflected the cleavage of common serum proteins such as C4A, C3, FGB, HPX, A2M but also proteins in lower concentration such as F13A1, IK, collagens and protocadherins. Proteins associated with cellular growth and development such as actins (ACT), ribosomal proteins like Ribosomal protein S6 (RPS6), synthetic enzymes and extracellular matrix factors were enriched in fetal calf serum. In contrast to the large literature from cord blood, IgG light chains were absent from fetal serum as observed by LC-ESI-MS/MS and confirmed by ELISA.

Network-based approach for drug repurposing against mpox

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis

Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of sera allowed for the discovery of markers of disease presence, severity, and cirrhosis and the exploration of the involvement of CCL24, a chemokine with fibro-inflammatory activity. Sera from 30 healthy controls and 45 PSC patients were profiled with proximity extension assay, quantifying the expression of 2870 proteins, and used to train an elastic net model. Proteins that contributed most to the model were tested for correlation to enhanced liver fibrosis (ELF) score and used to perform pathway analysis. Statistical modeling for the presence of cirrhosis was performed with principal component analysis (PCA), and receiver operating characteristics (ROC) curves were used to assess the useability of potential biomarkers. The model successfully predicted the presence of PSC, where the top-ranked proteins were associated with cell adhesion, immune response, and inflammation, and each had an area under receiver operator characteristic (AUROC) curve greater than 0.9 for disease presence and greater than 0.8 for ELF score. Pathway analysis showed enrichment for functions associated with PSC, overlapping with pathways enriched in patients with high levels of CCL24. Patients with cirrhosis showed higher levels of CCL24. This data-driven approach to characterize PSC and its severity highlights potential serum protein biomarkers and the importance of CCL24 in the disease, implying its therapeutic potential in PSC.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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