High-throughput Proteomic Profiling Research Articles

Valencene ameliorates ox-LDL induced foam cell formation by suppressing inflammation and modulating key proteins involved in the atherogenesis on THP-1 derived macrophages

Atherosclerosis is a distinct risk factor for cardiovascular and cerebrovascular disorders, which are significant contributors to global mortality. It is defined by macrophage-derived foam cell development followed by persistent inflammation, plaque formation, fibrosis and thrombosis. Studies have shown valencene, a sesquiterpene obtained from Valencia oranges, has several health-promoting properties. However, its protective effect against atherosclerosis and foam cell models remains unexplored. The present investigation revealed that valencene treatment suppresses foam cell generation and accumulation of lipids in THP-1-derived cells macrophage models activated with oxidized low-density lipoprotein (ox-LDL), maintained in vitro. The intracellular lipid content was qualitatively and semi-quantitatively analyzed by Oil Red O staining, and the compound's cytotoxicity was assessed through the MTT assay, considering both time-dependent and dose-dependent factors. The RT-qPCR results showed promising anti-inflammatory and anti-oxidant enzyme status upon valencene treatment. The H2DCFDA staining revealed valencene's ability to reduce the oxidative stress induced by ox-LDL. Further, high-throughput proteomic profiling was carried out to identify the target proteins affected by valencene treatment and thereby explore its mechanism of action on foam cell models. Proteomic studies revealed that valencene treatment regulates the expression of several proteins associated with ox-LDL-induced inflammation, defective cholesterol homeostasis and cholesterol efflux pathways.

Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry.

Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Shared genetics and causal association between plasma levels of SARS-CoV-2 entry receptor ACE2 and Alzheimer's disease.

Alzheimer's disease (AD) is the highest risk of COVID-19 infection, hospitalization, and mortality. However, it remains largely unclear about the link between AD and COVID-19 outcomes. ACE2 is an entry receptor for SARS-CoV-2. Circulating ACE2 is a novel biomarker of death and associated with COVID-19 outcomes. Here, we explored the shared genetics and causal association between AD and plasma ACE2 levels using large-scale genome-wide association study, gene expression, expression quantitative trait loci, and high-throughput plasma proteomic profiling datasets. We found a significant causal effect of genetically increased circulating ACE2 on increased risk of AD. Cross-trait association analysis identified 19 shared genetic variants, and three variants rs3104412, rs2395166, and rs3135344 at chromosome 6p21.32 were associated with COVID-19 infection, hospitalization, and severity. We mapped 19 variants to 117 genes, which were significantly upregulated in lung, spleen, and small intestine, downregulated in brain tissues, and involved in immune system, immune disease, and infectious disease pathways. The plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 were predominantly associated with COVID-19 infection, ventilation, and death. Together, our findings suggest the shared genetics and causal association between AD and plasma ACE2 levels, which may partially explain the link between AD and COVID-19.

Proteomic snapshot of saliva samples predicts new pathways implicated in SARS-CoV-2 pathogenesis.

Information on the microbiome's human pathways and active members that can affect SARS-CoV-2 susceptibility and pathogenesis in the salivary proteome is very scarce. Here, we studied a unique collection of samples harvested from April to June 2020 from unvaccinated patients. We compared 10 infected and hospitalized patients with severe (n = 5) and moderate (n = 5) coronavirus disease (COVID-19) with 10 uninfected individuals, including non-COVID-19 but susceptible individuals (n = 5) and non-COVID-19 and nonsusceptible healthcare workers with repeated high-risk exposures (n = 5). By performing high-throughput proteomic profiling in saliva samples, we detected 226 unique differentially expressed (DE) human proteins between groups (q-value ≤ 0.05) out of 3376 unambiguously identified proteins (false discovery rate ≤ 1%). Major differences were observed between the non-COVID-19 and nonsusceptible groups. Bioinformatics analysis of DE proteins revealed human proteomic signatures related to inflammatory responses, central cellular processes, and antiviral activity associated with the saliva of SARS-CoV-2-infected patients (p-value ≤ 0.0004). Discriminatory biomarker signatures from human saliva include cystatins, protective molecules present in the oral cavity, calprotectins, involved in cell cycle progression, and histones, related to nucleosome functions. The expression levels of two human proteins related to protein transport in the cytoplasm, DYNC1 (p-value, 0.0021) and MAPRE1 (p-value, 0.047), correlated with angiotensin-converting enzyme 2 (ACE2) plasma activity. Finally, the proteomes of microorganisms present in the saliva samples showed 4 main microbial functional features related to ribosome functioning that were overrepresented in the infected group. Our study explores potential candidates involved in pathways implicated in SARS-CoV-2 susceptibility, although further studies in larger cohorts will be necessary.

Abstract 17139: Angiopoietin 2 Independently Predicts Heart Failure Hospitalization and is Beneficially Modified by GLP1RA: Results From the EXSCEL Trial

Background: Higher plasma concentrations of angiopoietin 2 (ANGPT2) are associated with higher risk for hospitalization for heart failure (HF) and death in univariate analyses. However, the independent association of this ANGPT2 after adjustment for clinical factors or clinically available biomarkers is not known. Methods: High throughput proteomic profiling was performed using the SomaLogic SomaScan platform in 5473 patients with type 2 diabetes enrolled in the EXSCEL clinical trial of the GLP1RA exenatide vs. placebo who had available biospecimens at baseline. The association between ANGPT2 with time-to-HF hospitalization, MACE, and death was assessed using univariable and multivariable Cox modeling, with adjustment for age, sex, HbA1c, NT-proBNP, hs-TnT, left ventricular ejection fraction, HF etiology and study treatment. These analyses were also performed in the subset of participants living with obesity (BMI >30 kg/m 2 ), and those with CKD (CrCl <60ml/min). Results: In 5473 EXSCEL trial participants included in this biomarker sub-study, 215 (3.9%) experienced a hospitalization for heart failure, 813 (14.9%) a MACE event and 528 (9.7%) died during a median follow-up of 3.2 years. Relative ANGPT2 concentration was significantly associated with HF hospitalization (aHR 1.70 [95% CI 1.39 to 2.08]), MACE (1.20 [1.07 to 1.35]), and death (aHR 1.21 [1.05 to 1.39]) in univariable and multivariable analyses, including after adjustment for NT-proBNP and TnT (Figure). Exenatide use resulted in significant changes from baseline in ANGPT2 concentrations (p interaction<0.001) (Figure). Conclusion: ANGPT2 is predictive of hospitalization for HF, MACE, and death independent of clinical factors including traditional biomarkers of NT-proBNP and TnT. ANGPT2 was beneficially modified by exenatide, a GLP1RA, suggesting a potential role for this biomarker in optimizing HF therapy.

Abstract 15726: Large-Scale Plasma Proteomics and Mendelian Randomization of Cardiovascular Performance and Peak Arterio-Venous Oxygen Difference Identify Novel Biology Related to VO 2 max

Introduction: VO 2 max is the product of maximal cardiac output (Q) (maximal stroke volume (SVmax) x heart rate) and peak arterio-venous oxygen content difference (a-vO 2 diff). SVmax and peak a-vO 2 diff have important implications across the spectrum of human health and performance. However, the molecular mechanisms underlying SVmax and peak a-vO 2 diff remain largely unknown. We characterize SVmax and peak a-vO 2 diff using large-scale plasma proteomics and genotyping to identify circulating proteins and putative mediators of these traits. Methods: 763 physically inactive adults without overt cardiovascular disease underwent cardiopulmonary exercise testing with rigorous measurements of Q. High-throughput plasma proteomic profiling was performed to identify proteins associated with SVmax, a-vO 2 diff, and VO 2 max. cis-Mendelian Randomization (MR) analysis was conducted to assess causal relationships between protein markers and cardiovascular performance traits. Results: Proteomic profiling revealed associations between SVmax and several adiposity-related proteins, as well as secretory muscle factors (cathepsin B, α-actinin 2, and troponin I) (Figure). Proteins associated with peak a-vO2diff reflected oxygen-carrying capacity, muscle biology, and glucose metabolism (erythropoietin, myoglobin, fructose-1,6-bisphosphatase). Significant overlap was observed between proteins associated with SVmax, a-vO 2 diff, and VO 2 max. Cis-MR identified myocilin (MYOC) and caspase recruitment domain family member 9 (CARD9) as potential mediators of cardiovascular performance and VO 2 max. Conclusions: This study provides novel insights into the molecular influences of cardiovascular performance and VO 2 max. The identified protein markers associated with SVmax and a-vO 2 diff, along with the possible mediators MYOC and CARD9, merit further investigation into their role in endurance exercise capacity and cardiometabolic health.

High-Throughput Plasma Proteomics to Define the Precursor Multiple Myeloma Proteome and Identify Candidate High-Risk Disease Biomarkers of Progression

Introduction Multiple Myeloma (MM) develops from well-defined precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), where patients remain stable or may unknowingly rapidly progress. Bone marrow (BM) biopsies are not routine for precursor disease management, and precursor patients are limited to monitoring few proteins within peripheral blood (PB) for signs of progressive disease. Deep proteome profiling of PB circulating proteins may help track disease; however, the dynamic range of the plasma proteome has limited the depth of detection for MS-based proteomics without first depleting abundant proteins and fractionating the samples after digestion to peptides. Technological advancements in multiplex immunoassays with low cross-reactivity and off-target events have enabled plasma profiling for disease stage classification, defining high-risk disease features, and novel therapeutic target discovery. Here, we perform the first comprehensive plasma proteomic profiling study on patients across the MM disease continuum and longitudinal sequential samples from progressive and stable disease. Methods We carried out high-throughput plasma proteomic profiling for approximately 3000 proteins simultaneously using the Olink® Explore 3072 library and Proximity Extension Assay (PEA) technology. Targeted proteins are recognized by multiplexed, matched pairs of antibodies labelled with unique DNA oligonucleotides that upon binding come into proximity, hybridize, and are extended to generate a unique sequence for protein identification with NextGen DNA sequencing. We profiled 423 PB plasma samples from 348 individuals, including MGUS (n=67), SMM (n=179), MM (n=44), and healthy controls (n=58). Sequential samples from patients with progressive disease (n=27) and patients with stable disease with matched clinical follow-up time were also profiled. Precursor defined samples from progressors ranged 1.03-5.88 yrs prior to diagnosis and were untreated in the precursor setting, while MM disease samples were also collected prior to any active disease therapy. Patients had a median clinical follow-up time of 7.05 years. T-tests, ANOVAs, and a linear mixed effect (LME) model were used to identify proteins that change across disease stages, progression status, and time. Results were adjusted for multiple testing using the Benjamini-Hochberg Method. Results We identified 751 significantly dysregulated proteins with the majority upregulated in progressive disease. We captured circulating levels of proteins highly expressed on the surface of plasma cells, including CD38, SDC1, BCMA and SLAMF7, highlighting the utility of PEA technology to monitor clinically-relevant targets for which monoclonal antibodies, antibody-drug conjugates, CAR-T and BiTE therapies are being developed. We identified proteins that significantly distinguished MGUS, SMM, and NDMM from healthy donors (n=222, 423 and 494), where increasing B-cell maturation antigen (BCMA) was a significant classifier and positive control. Consistent with previous findings, baseline BCMA levels were also significantly elevated in SMM-MM progressors vs. SMM non-progressors, further supporting the potential utility of BCMA measurements during routine blood tests of precursor MM patients. Proinflammatory cytokines were also identified, including IL1, IL5, IL6, IL16, and IL18, known to create a BM environment that promotes malignant cell development by suppressing the microenvironment, promoting cellular adhesion, or increasing angiogenesis. Four novel proteins that are vital for calcium homeostasis and integrin-mediated cell adhesion significantly increased from healthy to MM and were also significantly elevated in SMM progressors vs. SMM non-progressors, nominating these proteins as candidate biomarkers of high-risk disease. Conclusion We performed the most comprehensive plasma proteomics study to date, which characterized disease stage proteomes and identified candidate high-risk disease biomarkers in longitudinal progressor samples. Further advancements are underway to validate the accuracy levels of the novel candidates, test the performance of a classification model that recognizes disease stage-specific proteins, and determine how best to integrate proteins into current MM risk stratification models.

24P High-throughput plasma proteomics profiling in early breast cancer

24P High-throughput plasma proteomics profiling in early breast cancer

Serum Proteomic Analysis of Peripartum Cardiomyopathy Reveals Distinctive Dysregulation of Inflammatory and Cholesterol Metabolism Pathways

BackgroundThe pathophysiology of peripartum cardiomyopathy (PPCM) and its distinctive biological features remain incompletely understood. High-throughput serum proteomic profiling, a powerful tool to gain insights into the pathophysiology of diseases at a systems biology level, has never been used to investigate PPCM relative to nonischemic cardiomyopathy. ObjectivesThe aim of this study was to characterize the pathophysiology of PPCM through serum proteomic analysis. MethodsAptamer-based proteomic analysis (SomaScan 7K) was performed on serum samples from women with PPCM (n = 67), women with nonischemic nonperipartum cardiomyopathy (NPCM) (n = 31), and age-matched healthy peripartum and nonperipartum women (n = 10 each). Serum samples were obtained from the IPAC (Investigation of Pregnancy-Associated Cardiomyopathy) and IMAC2 (Intervention in Myocarditis and Acute Cardiomyopathy) studies. ResultsPrincipal component analysis revealed unique clustering of each patient group (P for difference <0.001). Biological pathway analyses of differentially measured proteins in PPCM relative to NPCM, before and after normalization to pertinent healthy controls, highlighted specific dysregulation of inflammatory pathways in PPCM, including the upregulation of the cholesterol metabolism-related anti-inflammatory pathway liver-X receptor/retinoid-X receptor (LXR/RXR) (P < 0.01, Z-score 1.9-2.1). Cardiac recovery by 12 months in PPCM was associated with the downregulation of pro-inflammatory pathways and the upregulation of LXR/RXR, and an additional RXR-dependent pathway involved in the regulation of inflammation and metabolism, peroxisome proliferator-activated receptor α/RXRα signaling. ConclusionsSerum proteomic profiling of PPCM relative to NPCM and healthy controls indicated that PPCM is a distinct disease entity characterized by the unique dysregulation of inflammation-related pathways and cholesterol metabolism-related anti-inflammatory pathways. These findings provide insight into the pathophysiology of PPCM and point to novel potential therapeutic targets.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.

High-Throughput Proteome Profiling of Plasma and Native Plasma Complexes Using Native Chromatography.

We describe a high-throughput method for co-fractionation mass spectrometry (CF-MS) profiling for native plasma protein profiling. CF-MS allows the profiling of endogenous protein complexes between samples. Proteins often interact with other proteins and form macromolecular complexes that are different in disease states as well as cell states and cell types. This protocol describes an example for the sample preparation of 954 individual size exclusion chromatography (SEC) fractions, derived from 18 plasma samples that were separated into 53 fractions. Eighteen plasma samples were chosen based on the TMTpro multiplexing, but this methodology can be adapted for fewer or larger numbers of samples as appropriate. Our automated sample preparation method allows for high-throughput native plasma profiling, and we provide detailed methods for both a label-free and an isobaric labeling approach, discuss the merits of each approach, and detail the advantages of combining these strategies for comprehensive native plasma proteome profiling.

Robust Capillary- and Micro-Flow Liquid Chromatography-Tandem Mass Spectrometry Methods for High-Throughput Proteome Profiling.

Capillary- and micro-flow liquid chromatography-tandem mass spectrometry (capLC-MS/MS and μLC-MS/MS) is becoming a valuable alternative to nano-flow LC-MS/MS due to its high robustness and throughput. The systematic comparison of capLC-MS/MS and μLC-MS/MS systems for global proteome profiling has not been reported yet. Here, the capLC-MS/MS (150 μm i.d. column, 1 μL/min) and μLC-MS/MS (1 mm i.d. column, 50 μL/min) systems were both established based on UltiMate 3000 RSLCnano coupled to an Orbitrap Exploris 240 by integrating with different flowmeters. We evaluated both systems in terms of sensitivity, analysis throughput, separation efficiency, and robustness. capLC-MS/MS was about 10 times more sensitive than μLC-MS/MS at different gradient lengths. Compared with capLC-MS/MS, μLC-MS/MS was able to achieve higher analysis throughput and separation efficiency. During the 7 days' long-term performance test, both systems showed good reproducibility of chromatographic full width (RSD &lt; 3%), retention time (RSD &lt; 0.4%), and protein identification (RSD &lt; 3%). These results demonstrate that capLC-MS/MS is more suitable for high-throughput analysis of clinical samples with a limited starting material. When enough samples are available, μLC-MS/MS is preferred. Together, capLC and μLC coupled to Orbitrap Exploris 240 with moderate sensitivity should well meet the needs of large-cohort clinical proteomic analysis.

Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods.

High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.

Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms.

Limited data exist on the performance of high-throughput proteomics profiling in epidemiological settings, including the impact of specimen collection and within-person variability over time. Thus, the Olink (972 proteins) and SOMAscan7Kv4.1 (7322 proteoforms of 6596 proteins) assays were utilized to measure protein concentrations in archived plasma samples from the Nurses' Health Studies and Health Professionals Follow-Up Study. Spearman's correlation coefficients (r) and intraclass correlation coefficients (ICCs) were used to assess agreement between (1) 42 triplicate samples processed immediately, 24-h or 48-h after blood collection from 14 participants; and (2) 80 plasma samples from 40 participants collected 1-year apart. When comparing samples processed immediately, 24-h, and 48-h later, 55% of assays had an ICC/r≥0.75 and 87% had an ICC/r≥0.40 in Olink compared to 44% with an ICC/r≥0.75 and 72% with an ICC/r≥0.40 in SOMAscan7K. For both platforms, >90% of the assays were stable (ICC/r≥0.40) in samples collected 1-year apart. Among 817 proteins measured with both platforms, Spearman's correlations were high (r>0.75) for 14.7% and poor (r<0.40) for 44.8% of proteins. High-throughput proteomics profiling demonstrated reproducibility in archived plasma samples and stability after delayed processing in epidemiological studies, yet correlations between proteins measured with the Olink and SOMAscan7K platforms were highly variable.

Proteomic Profiles Of Left Ventricular Assist Device Implantation

Introduction Left ventricular assist devices (LVADs) are widely used to support patients with advanced heart failure, but conversion from normal pulsatile flow is known to lead to vascular remodeling and ultimately may contribute to adverse vascular events. The underlying biology of such changes remains poorly understood. Hypothesis: High-throughput plasma proteomic profiling before and after LVAD implantation will identify novel biomarkers and protein mediators of LVAD cardiovascular effects. Methods Fifteen patients undergoing HeartMate II implantation at University of Colorado Hospital had serial plasma samples collected pre-LVAD (day 0) as well as days 14 and 60 after LVAD implant. Proteomic profiling was performed with 1,196 antibody-based proteomic probes on the Olink® platform. Linear mixed effects models of log2-scaled proteomic measurements, adjusted for renal function, compared protein levels between time points. GO Biological Pathway analysis was performed using both over-representation analysis (ORA) and gene set enrichment analysis (GSEA). A false discovery rate (FDR) of 5% adjusted for multiple comparisons within each analysis. Results The mean age was 57 years; 3 participants (20%) were female, 3 (20%) were Black, and 9 (60%) had a non-ischemic cardiomyopathy. At day 14, 300 proteins were significantly different from day 0 at FDR<5%. At day 60, 161 proteins differed from day 0 at FDR<5% (Figure 1a). Known biomarkers showed expected changes: B-type natriuretic peptide decreased significantly (day 14 vs day 0 β = -0.87 ± 0.26, p = 0.002; day 60 vs day 0 β = -1.19 ± 0.23, p = 1 × 10−5) as did ST2 (day 60 vs day 0 β = -0.72 ± 0.26, p = 0.01). Four proteins had statistically significant (FDR < 5%) differences at 0 vs 14 and 14 vs 60 days, highlighting novel findings (Figure 1b): CCN3, DSG3, THBS4, and COMP are all involved in extracellular matrix biology, and DSG3 and CCN3 are implicated in cell-cell communication in myocardium. Indeed, ORA and GSEA identified enrichment for blood vessel development (p = 4 × 10−6, p = 4 × 10−5, respectively) and cell adhesion (p = 9 × 10−5, p = 8 × 10−5). Conclusions Proteomic profiling identified novel protein changes following LVAD implantation, particularly among proteins involved in blood vessel development and cell adhesion. Further work is needed to understand biological implications and determine if these proteins also associate with clinical outcomes in larger cohorts and in axial compared to centrifugal flow LVADs. Left ventricular assist devices (LVADs) are widely used to support patients with advanced heart failure, but conversion from normal pulsatile flow is known to lead to vascular remodeling and ultimately may contribute to adverse vascular events. The underlying biology of such changes remains poorly understood. Hypothesis: High-throughput plasma proteomic profiling before and after LVAD implantation will identify novel biomarkers and protein mediators of LVAD cardiovascular effects. Fifteen patients undergoing HeartMate II implantation at University of Colorado Hospital had serial plasma samples collected pre-LVAD (day 0) as well as days 14 and 60 after LVAD implant. Proteomic profiling was performed with 1,196 antibody-based proteomic probes on the Olink® platform. Linear mixed effects models of log2-scaled proteomic measurements, adjusted for renal function, compared protein levels between time points. GO Biological Pathway analysis was performed using both over-representation analysis (ORA) and gene set enrichment analysis (GSEA). A false discovery rate (FDR) of 5% adjusted for multiple comparisons within each analysis. The mean age was 57 years; 3 participants (20%) were female, 3 (20%) were Black, and 9 (60%) had a non-ischemic cardiomyopathy. At day 14, 300 proteins were significantly different from day 0 at FDR<5%. At day 60, 161 proteins differed from day 0 at FDR<5% (Figure 1a). Known biomarkers showed expected changes: B-type natriuretic peptide decreased significantly (day 14 vs day 0 β = -0.87 ± 0.26, p = 0.002; day 60 vs day 0 β = -1.19 ± 0.23, p = 1 × 10−5) as did ST2 (day 60 vs day 0 β = -0.72 ± 0.26, p = 0.01). Four proteins had statistically significant (FDR < 5%) differences at 0 vs 14 and 14 vs 60 days, highlighting novel findings (Figure 1b): CCN3, DSG3, THBS4, and COMP are all involved in extracellular matrix biology, and DSG3 and CCN3 are implicated in cell-cell communication in myocardium. Indeed, ORA and GSEA identified enrichment for blood vessel development (p = 4 × 10−6, p = 4 × 10−5, respectively) and cell adhesion (p = 9 × 10−5, p = 8 × 10−5). Proteomic profiling identified novel protein changes following LVAD implantation, particularly among proteins involved in blood vessel development and cell adhesion. Further work is needed to understand biological implications and determine if these proteins also associate with clinical outcomes in larger cohorts and in axial compared to centrifugal flow LVADs.

Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer.

Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.

Genetic Subtyping and Phenotypic Characterization of the Immune Microenvironment and MYC/BCL2 Double Expression Reveal Heterogeneity in Diffuse Large B-cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers. We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors. We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1-high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases. Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.

High-Throughput Plasma Proteomic Profiling.

Plasma and serum are rich sources of proteins that are commonly used for clinical proteome profiling and biomarkers discovery. However, high-throughput plasma proteome profiling and quantitative analysis using mass spectrometry are challenging because of the large dynamic range of protein abundance and complexity. To overcome these challenges, we developed a convenient high-throughput workflow of depleted plasma using the 4D-Proteomics feature of the Bruker timsTOF Pro mass spectrometer with data-dependent (PASEF) and data-independent acquisition (diaPASEF) method that can potentially be used in a clinical proteome profiling and biomarker discoveries. This workflow is robust, optimal for high throughput, high proteome depth, and is reproducible. In our sample preparation steps, we used immuno-depletion steps to remove high-abundance plasma proteins, and without any further cleanup steps, we can use depleted plasma samples directly for enzymatic digestion. Immuno-depletion steps and 4D-Proteomics features of timsTOF Pro increase the plasma proteome depth, and accuracy with the identification of >800 protein groups.

High-throughput Proteomic Profiling of Male Breast Cancer Tissue.

Until now, little emphasis has been placed on the protein expression profile of male breast cancer (MBC) tumors, due to the rarity of the disease. The present study aimed to identify a proteomic pattern that is characteristic for malignant male breast tissue epithelium. The protein content of four male breast tumors and corresponding adjacent healthy (control) tissues was analyzed by high-throughput nano-liquid chromatography-MS/MS technology. A total of 2,352 proteins were identified, that correspond to 1,249 single gene products, with diverse biological roles. Of those, a panel of 119 differentially expressed tissue proteins was identified in MBC samples compared to controls; 90 were found to be over-expressed in MBC tissues, while 29 were down-regulated. Concurrently, 844 proteins were detected only in MBC tumors and 197 were expressed exclusively in control mammary samples. Differential proteomic expression was found in MBC tissue, leading to improved understanding of MBC pathology and highlighting the need for personalized management of male patients.

The proteomic architecture of schizophrenia iPSC-derived cerebral organoids reveals alterations in GWAS and neuronal development factors

Schizophrenia (Scz) is a brain disorder that has a typical onset in early adulthood but otherwise maintains unknown disease origins. Unfortunately, little progress has been made in understanding the molecular mechanisms underlying neurodevelopment of Scz due to ethical and technical limitations in accessing developing human brain tissue. To overcome this challenge, we have previously utilized patient-derived Induced Pluripotent Stem Cells (iPSCs) to generate self-developing, self-maturating, and self-organizing 3D brain-like tissue known as cerebral organoids. As a continuation of this prior work, here we provide an architectural map of the developing Scz organoid proteome. Utilizing iPSCs from n = 25 human donors (n = 8 healthy Ctrl donors, and n = 17 Scz patients), we generated 3D cerebral organoids, employed 16-plex isobaric sample-barcoding chemistry, and simultaneously subjected samples to comprehensive high-throughput liquid-chromatography/mass-spectrometry (LC/MS) quantitative proteomics. Of 3,705 proteins identified by high-throughput proteomic profiling, we identified that just ~2.62% of the organoid global proteomic landscape was differentially regulated in Scz organoids. In sum, just 43 proteins were up-regulated and 54 were down-regulated in Scz patient-derived organoids. Notably, a range of neuronal factors were depleted in Scz organoids (e.g., MAP2, TUBB3, SV2A, GAP43, CRABP1, NCAM1 etc.). Based on global enrichment analysis, alterations in key pathways that regulate nervous system development (e.g., axonogenesis, axon development, axon guidance, morphogenesis pathways regulating neuronal differentiation, as well as substantia nigra development) were perturbed in Scz patient-derived organoids. We also identified prominent alterations in two novel GWAS factors, Pleiotrophin (PTN) and Podocalyxin (PODXL), in Scz organoids. In sum, this work serves as both a report and a resource that researchers can leverage to compare, contrast, or orthogonally validate Scz factors and pathways identified in observational clinical studies and other model systems.