Deep Proteome Research Articles

Deep quantitative proteomics identifies elevated MUC16 in low- compared to high-grade serous ovarian carcinoma

Deep quantitative proteomics identifies elevated MUC16 in low- compared to high-grade serous ovarian carcinoma

Spatial proteomics identifies JAKi as treatment for a lethal skin disease.

Toxic epidermal necrolysis (TEN) is a fatal drug-induced skin reaction triggered by common medications and is an emerging public health issue1-3. Patients with TEN undergo severe and sudden epidermal detachment caused by keratinocyte cell death. Although molecular mechanisms that drive keratinocyte cell death have been proposed, the main drivers remain unknown, and there is no effective therapy for TEN4-6. Here, to systematically map molecular changes that are associated with TEN and identify potential druggable targets, we utilized deep visual proteomics, which provides single-cell-based, cell-type-resolution proteomics7,8. We analysed formalin-fixed, paraffin-embedded archived skin tissue biopsies of three types of cutaneous drug reactions with varying severity and quantified more than 5,000 proteins in keratinocytes and skin-infiltrating immune cells. This revealed a marked enrichment of type I and type II interferon signatures in the immune cell and keratinocyte compartment of patients with TEN, as well as phosphorylated STAT1 activation. Targeted inhibition with the pan-JAK inhibitor tofacitinib in vitro reduced keratinocyte-directed cytotoxicity. In vivo oral administration of tofacitinib, baricitinib or the JAK1-specific inhibitors abrocitinib or upadacitinib ameliorated clinical and histological disease severity in two distinct mouse models of TEN. Crucially, treatment with JAK inhibitors (JAKi) was safe and associated with rapid cutaneous re-epithelialization and recovery in seven patients with TEN. This study uncovers the JAK/STAT and interferon signalling pathways as key pathogenic drivers of TEN and demonstrates the potential of targeted JAKi as a curative therapy.

The Miniaturized Isolation of Neutrophil Granules (MING) method allowed a deep proteome mapping of human neutrophil granules.

Neutrophils are the innate immune system's first line of defense, and their storage organelles are essential to their function. The storage organelles are divided into three different granule types named azurophilic, specific, and gelatinase granules, besides a fourth component called secretory vesicles. The isolation of neutrophil's granules is challenging, and the existing procedures rely on large sample volumes, about 400 mL of peripheral blood, precluding the use of multiple biological and technical replicates. Therefore, the aim of this study was to develop a miniaturized isolation of neutrophil granules (MING) method, using biochemical assays, mass spectrometry-based proteomics and a machine learning approach to investigate the protein content of these organelles. Neutrophils were isolated from 40 mL of blood collected from three apparently healthy volunteers and disrupted using nitrogen cavitation; the organelles were fractionated with a discontinuous 3-layer Percoll density gradient. The isolation was proven successful and allowed for a reasonable separation of neutrophil's storage organelles using a gradient approximately 37 times smaller than the methods described in the literature. Moreover, mass spectrometry-based proteomics identified 368 proteins in at least 3 of the 5 analyzed samples, and using a machine learning strategy aligned with markers from the literature, the localization of 50 proteins was predicted with confidence. When using markers determined within our dataset by a clusterization tool, the localization of 348 proteins was confidently determined. Importantly, this study was the first to investigate the proteome of neutrophil granules using technical and biological replicates, creating a reliable database for further studies.

Deep Spectral Library of Mice Retina for Myopia Research: Proteomics Dataset generated by SWATH and DIA-NN

The retina plays a crucial role in processing and decoding visual information, both in normal development and during myopia progression. Recent advancements have introduced a library-independent approach for data-independent acquisition (DIA) analyses. This study demonstrates deep proteome identification and quantification in individual mice retinas during myopia development, with an average of 6,263 ± 86 unique protein groups. We anticipate that the use of a predicted retinal-specific spectral library combined with the robust quantification achieved within this dataset will contribute to a better understanding of the proteome complexity. Furthermore, a comprehensive mice retinal-specific spectral library was generated, encompassing a total identification of 9,401 protein groups, 70,041 peptides, 95,339 precursors, and 761,868 transitions acquired using SWATH-MS acquisition on a ZenoTOF 7600 mass spectrometer. This dataset surpasses the spectral library generated through high-pH reversed-phase fractionation by data-dependent acquisition (DDA). The data is available via ProteomeXchange with the identifier PXD046983. It will also serve as an indispensable reference for investigations in myopia research and other retinal or neurological diseases.

Deep Proteome Analysis of Cerebrospinal Fluid from Pediatric Patients with Central Nervous System Cancer.

The cerebrospinal fluid (CSF) is a key matrix for discovery of biomarkers relevant for prognosis and the development of therapeutic targets in pediatric central nervous system malignancies. However, the wide range of protein concentrations and age-related differences in children makes such discoveries challenging. In addition, pediatric CSF samples are often sparse and first prioritized for clinical purposes. The present work focused on optimizing each step of the proteome analysis workflow to extract the most detailed proteome information possible from the limited CSF resources available for research purposes. The strategy included applying sequential ultracentrifugation to enrich for extracellular vesicles (EV) in addition to analysis of a small volume of raw CSF, which allowed quantification of 1351 proteins (+55% relative to raw CSF) from 400 μL CSF. When including a spectral library, a total of 2103 proteins (+240%) could be quantified. The workflow was optimized for CSF input volume, tryptic digestion method, gradient length, mass spectrometry data acquisition method and database search strategy to quantify as many proteins a possible. The fully optimized workflow included protein aggregation capture (PAC) digestion, paired with data-independent acquisition (DIA, 21 min gradient) and allowed 2989 unique proteins to be quantified from only 400 μL CSF, which is a 340% increase in proteins compared to analysis of a tryptic digest of raw CSF.

Characterizing Leishmania infantum-induced resistance to trivalent stibogluconate (SbIII) through deep proteomics

Leishmania infantum belongs to the L. donovani complex, which includes species associated with visceral leishmaniasis. Traditionally, antimonial compounds have served as the primary antiparasitic treatment for all clinical forms of leishmaniasis. However, the global spread of resistance to these compounds has posed a significant challenge in the treatment in some regions. In this study, we aimed to investigate resistance to trivalent sodium stibogluconate in vitro using promastigotes from a wild strain of L. infantum. We compared the growth rates and proteomic profiles of wild-type and resistant line conducting label-free quantitative mass spectrometry-based proteomic analyses. Statistical and bioinformatics analyses were employed to evaluate the significance of protein concentration changes, protein identity annotation, GO term analysis, biosynthetic pathways, and protein-protein interactions. Our findings revealed that the resistant line displayed a notable reduction in growth rate. Proteomic data unveiled similar protein concentrations per cell in both groups but with differing molecule copy numbers. We identified 165 proteins with increased concentration, these were associated with transcription and translation activities, lipid metabolism, energy metabolism, and peroxisome biogenesis. In the decreased protein groups were 56 proteins linked to metal acquisition and metabolism, particularly iron. These results suggest a novel perspective on antimonial resistance, highlighting the importance of post-transcriptional and post-translational regulation, alongside energy expenditure compensation and alterations in organelle membrane lipid composition in antimonial-resistant parasites. Overall, our study provides insights into the proteomic profile of stibogluconate-resistant strain, contributing to our general understanding of the complex landscape of antiparasitic resistance in L. infantum. SignificanceSpecies within the Leishmania donovani complex are implicated in cases of visceral leishmaniasis in the world. Leishmania infantum is a species that predominates in regions spanning the Mediterranean Basin, the Middle East, Central Asia, South and Central America. Antimonials were the first treatment for leishmaniasis, however in the last decades, the resistance has emerged in subregions like India, where it is not a therapeutic option. In contrast, sodium stibogluconate (SbIII) remains the first-line treatment in the Americas. Unfortunately, the emergence of resistance has outpaced the development of new therapeutic options, thereby becoming a critical point in the struggle against the disease. In this study we performed an in-depth proteomic analysis with liquid chromatography mass-mass spectrometry (LC-MS/MS) on L. infantum with Sb-induced resistance in vitro. Results showed a complex proteomic adaptation in the resistant line, involving transcriptional and translational proteins, energy compensation, and homeostasis maintenance. These insights contribute to understanding the molecular adaptation in the parasite and provide information to new investigations related to therapeutics development.

Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states

Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here formalize and comprehensively evaluate a DIA data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We assess the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we uncover co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to-implement strategy for single-cell proteomics.

Sex-specific molecular signature of mouse podocytes in homeostasis and in response to pharmacological challenge with rapamycin

BackgroundSex differences exist in the prevalence and progression of major glomerular diseases. Podocytes are the essential cell-type in the kidney which maintain the physiological blood-urine barrier, and pathological changes in podocyte homeostasis are critical accelerators of impairment of kidney function. However, sex-specific molecular signatures of podocytes under physiological and stress conditions remain unknown. This work aimed at identifying sexual dimorphic molecular signatures of podocytes under physiological condition and pharmacologically challenged homeostasis with mechanistic target of rapamycin (mTOR) inhibition. mTOR is a crucial regulator involved in a variety of physiological and pathological stress responses in the kidney and inhibition of this pathway may therefore serve as a general stress challenger to get fundamental insights into sex differences in podocytes.MethodsThe genomic ROSAmT/mG-NPHS2 Cre mouse model was used which allows obtaining highly pure podocyte fractions for cell-specific molecular analyses, and vehicle or pharmacologic treatment with the mTOR inhibitor rapamycin was performed for 3 weeks. Subsequently, deep RNA sequencing and proteomics were performed of the isolated podocytes to identify intrinsic sex differences. Studies were supplemented with metabolomics from kidney cortex tissues.ResultsAlthough kidney function and morphology remained normal in all experimental groups, RNA sequencing, proteomics and metabolomics revealed strong intrinsic sex differences in the expression levels of mitochondrial, translation and structural transcripts, protein abundances and regulation of metabolic pathways. Interestingly, rapamycin abolished prominent sex-specific clustering of podocyte gene expression and induced major changes only in male transcriptome. Several sex-biased transcription factors could be identified as possible upstream regulators of these sexually dimorphic responses. Concordant to transcriptomics, metabolomic changes were more prominent in males. Remarkably, high number of previously reported kidney disease genes showed intrinsic sexual dimorphism and/or different response patterns towards mTOR inhibition.ConclusionsOur results highlight remarkable intrinsic sex-differences and sex-specific response patterns towards pharmacological challenged podocyte homeostasis which might fundamentally contribute to sex differences in kidney disease susceptibilities and progression. This work provides rationale and an in-depth database for novel targets to be tested in specific kidney disease models to advance with sex-specific treatment strategies.

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules.

The protein corona, a dynamic biomolecular layer that forms on nanoparticle (NP) surfaces upon exposure to biological fluids is emerging as a valuable diagnostic tool for improving plasma proteome coverage analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients (namely, glucose, triglyceride, diglycerol, phosphatidylcholine, phosphatidylethanolamine, L-α-phosphatidylinositol, inosine 5'-monophosphate, and B complex), into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules (n=10) allowed for detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 µg/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depleted the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. By employing an optimized data-independent acquisition (DIA) approach, the inclusion of phosphatidylcholine led to the detection of 1436 proteins in a single plasma sample. Our molecular dynamic results revealed that phosphatidylcholine interacts with albumin via hydrophobic interactions, h-bonds, and water-bridges. Addition of phosphatidylcholine also enabled the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. These significant achievements are made utilizing only a single NP type and one small molecule to analyze a single plasma sample, setting a new standard in plasma proteome profiling. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate widespread adoption of this methodology for identification and clinical translation of proteomic biomarkers into FDA approved diagnostics.

Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

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.

Coupling Microdroplet-Based Sample Preparation, Multiplexed Isobaric Labeling, and Nanoflow Peptide Fractionation for Deep Proteome Profiling of the Tissue Microenvironment.

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 μm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.

Dia-PASEF Proteomics of Tumor and Stroma LMD Enriched from Archived HNSCC Samples.

We employed laser microdissection to selectively harvest tumor cells and stroma from the microenvironment of formalin-fixed, paraffin-embedded head and neck squamous cell carcinoma (HNSCC) tissues. The captured HNSCC tissue fractions were analyzed by quantitative mass spectrometry-based proteomics using a data independent analysis approach. In paired samples, we achieved excellent proteome coverage having quantified 6,668 proteins with a median quantitative coefficient of variation under 10%. We observed significant differences in relevant functional pathways between the spatially resolved tumor and stroma regions. Our results identified extracellular matrix (ECM) as a major component enriched in the stroma, including many cancer associated fibroblast signature proteins in this compartment. We demonstrate the potential for comparative deep proteome analysis from very low starting input in a scalable format that is useful to decipher the alterations in tumor and the stromal microenvironment. Correlating such results with clinical features or disease progression will likely enable identification of novel targets for disease classification and interventions.

Spatial characterization and stratification of colorectal adenomas by deep visual proteomics

Colorectal adenomas (CRAs) are potential precursor lesions to adenocarcinomas, currently classified by morphological features. We aimed to establish a molecular feature-based risk allocation framework toward improved patient stratification. Deep visual proteomics (DVP) is an approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here, we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine male patients, immunohistologically stained for caudal-type homeobox 2 (CDX2), a protein implicated in colorectal cancer, enabling the characterization of cellular heterogeneity within distinct tissue regions and across patients. DVP identified DMBT1, MARCKS, and CD99 as protein markers linked to recurrence, suggesting their potential for risk assessment. It also detected a metabolic shift to anaerobic glycolysis in cells with high CDX2 expression. Our findings underscore the potential of spatial proteomics to refine early stage detection and contribute to personalized patient management strategies and provided novel insights into metabolic reprogramming.

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Boronic Acid-Rich Lanthanide Metal-Organic Frameworks Enable Deep Proteomics with Ultratrace Biological Samples.

Label-free proteomics is widely used to identify disease mechanism and potential therapeutic targets. However, deep proteomics with ultratrace clinical specimen remains a major technical challenge due to extensive contact loss during complex sample pretreatment. Here, a hybrid of four boronic acid-rich lanthanide metal-organic frameworks (MOFs) with high protein affinity is introduced to capture proteins in ultratrace samples jointly by nitrogen-boronate complexation, cation-π and ionic interactions. A MOFs Aided Sample Preparation (MASP) workflow that shrinks sample volume and integrates lysis, protein capture, protein digestion and peptide collection steps into a single PCR tube to minimize sample loss caused by non-specific absorption, is proposed further. MASP is validated to quantify ≈1800 proteins in 10 HEK-293T cells. MASP is applied to profile cerebrospinal fluid (CSF) proteome from cerebral stroke and brain damaged patients, and identified ≈3700 proteins in 1µL CSF. MASP is further demonstrated to detect ≈9600 proteins in as few as 50µg mouse brain tissues. MASP thus enables deep, scalable, and reproducible proteome on precious clinical samples with low abundant proteins.

MDB-59. INTEGRATIVE PROTEO-GENOMICS TO DECIPHER MOLECULAR MECHANISMS UNDERLYING SUBGROUP-SPECIFIC HETEROGENEITY IN MEDULLOBLASTOMA

Abstract BACKGROUND Medulloblastoma (MB), an aggressive pediatric brain cancer, poses significant treatment challenges due to its complex disease biology, comprising a collection of discrete molecular subtypes that require different treatment. Although the genomics, transcriptomics, and epigenetic landscapes of biologically distinct MB subgroups are well-annotated and have contributed to enhanced diagnostics and risk stratification, the intersection of these molecular layers with the MB proteome remains poorly defined METHODS To overcome this knowledge gap, we sought to create a deep proteomics and phospho-proteomics cohort from a large MB clinical trial cohort (SJMB12), comprising 166 primary patient tumors, complemented with matched DNA methylation, whole-exome sequencing (WES), and RNA sequencing (RNA-seq) datasets. Proteomics data were interrogated using unsupervised computational techniques and evaluated for pathway enrichment and kinase activity. To complement these bulk tumor datasets, we investigated the tumor microenvironment focusing on immune and glial cell populations, using single-nuclei RNA and ATAC sequencing (snRNA-seq and snATAC-seq) for validation RESULTS Distinct proteomics subgroups were identified, correlating with established MB clinical subgroups and unveiling previously unrecognized proteomics subgroups. The integration of proteomics with mutational data revealed downstream effects of genetic alterations not evident in the transcriptome, implicating novel molecular mechanisms contributing to disease pathogenesis. Protein-protein interaction networks discovered novel signaling hubs and pathways, highlighting the power of proteomics to detect subgroup-specific drivers of disease that may represent attractive therapeutic vulnerabilities CONCLUSIONS Our multi-omics approach applied to a large series of primary MB patient tumors revealed novel mechanisms underlying MB subgroup biology that were previously hidden at the genetic, epigenetic, and transcriptional level. These findings offer a comprehensive, systems biology view of MB pathogenesis, fostering provocative insights that serve as a foundation for future discovery and the development of more effective personalized treatment strategies.

A Multi-Omics Approach to Defining Target Organ Injury in Youth with Primary Hypertension.

Primary hypertension in childhood tracks into adulthood and may be associated with increased cardiovascular risk. Studies conducted in children and adolescents provide an opportunity to explore the early cardiovascular target organ injury (CV-TOI) in a population free from many of the comorbid cardiovascular disease risk factors that confound studies in adults. Youths (n=132, mean age 15.8 years) were stratified by blood pressure (BP) as low, elevated, and high-BP and by left ventricular mass index (LVMI) as low- and high-LVMI. Systemic circulating RNA, miRNA, and methylation profiles in peripheral blood mononuclear cells and deep proteome profiles in serum were determined using high-throughput sequencing techniques. VASH1 gene expression was elevated in youths with high-BP with and without high-LVMI. VASH1 expression levels positively correlated with systolic BP (r=0.3143, p=0.0034). The expression of hsa-miR-335-5p, one of the VASH1-predicted miRNAs, was downregulated in high-BP with high-LVMI youths and was inversely correlated with systolic BP (r=-0.1891, p=0.0489). GSE1 hypermethylation, circulating PROZ upregulation (log2FC=0.61, p=0.0049 and log2FC=0.62, p=0.0064), and SOD3 downregulation (log2FC=-0.70, p=0.0042 and log2FC=-0.64, p=0.010) were observed in youths with elevated BP and high-BP with high-LVMI. Comparing the transcriptomic and proteomic profiles revealed elevated HYAL1 levels in youths displaying high-BP and high-LVMI. The findings are compatible with a novel blood pressure-associated mechanism that may occur through impaired angiogenesis and extracellular matrix degradation through dysregulation of Vasohibin-1 and Hyaluronidase1 was identified as a possible mediator of CV-TOI in youth with high-BP and suggests strategies for ameliorating TOI in adult-onset primary hypertension.

Insufficient PD-1 expression during active autoimmune responses: a deep single-cell proteomics analysis in inflammatory arthritis.

Programmed cell death protein-1 (PD-1) maintains peripheral immune tolerance by preventing T cell continuous activation. Aiming to understand the extent of PD-1 expression in inflammatory arthritis beyond its involvement with T cells, we assess its presence on various circulating single cells. Mass cytometry analysis of patients with active seropositive/seronegative rheumatoid (RA; n=9/8) and psoriatic (PsA; n=9) arthritis versus healthy controls (HC; n=13), re-evaluating patients after 3 months of anti-rheumatic treatment. PD-1 was expressed in all leukocyte subpopulations, with the highest PD-1+ cell frequencies in eosinophils (59-73%) and T cells (50-60%), and the lowest in natural-killer cells (1-3%). PD-1+ cell frequencies and PD-1 median expression were comparable between patient subgroups and HC, in the majority of cell subsets. Exceptions included increases in certain T cell/B cell subsets of seropositive RA and specific monocyte subsets and dendritic cells of PsA; an expanded PD-1+CD4+CD45RA+CD27+CD28+ T subset, denoting exhausted T cells, was common across patient subgroups. Strikingly, significant inverse correlations between individual biomarkers of systemic inflammation (ESR and/or serum CRP) and PD-1+ cell frequencies and/or median expression were evident in several innate and adaptive immunity cell subsets of RA and PsA patients. Furthermore, all inverse correlations noted in individuals with active arthritis were no longer discernible in those who attained remission/low disease activity post-treatment. PD-1 expression may be insufficient, relative to the magnitude of the concomitant systemic inflammatory response on distinct leukocyte subsets, varying between RA and PsA. Our results point to the potential therapeutic benefits of pharmacological PD-1 activation, to rebalance the autoimmune response and reduce inflammation.

Deep proteome profiling of reticulated platelets in patients with chronic coronary syndrome using mass cytometry

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Else Körner Fresenius Stiftung (EKFS) Background Immature or reticulated platelets (RPs), a subgroup of larger and RNA-rich platelets, which are newly released by megakaryocytes from the bone marrow into blood, have gained rising interest because of the further decryption of RPs’ prothrombotic biology and the potential as biomarkers for predicting adverse events in cardiovascular disease. Purpose The biological features of RPs in patients with chronic coronary syndrome (CCS) are largely unknown. With this project, we aimed to perform the first high resolution proteomic characterisation of reticulated platelets using mass cytometry(CyTOF). Method Two samples of thrombocytes from peripheral blood (non-stimulated, TRAP-stimulated) were prepared from each patient with a custom-made CyTOF-antibody panel of 20 antibodies targeting essential transmembrane proteins(anti-CD9, anti-CD29, anti-CD31, anti-CD36-, anti-CD40, anti-CD41, anti-CD42a, anti-CD42b-, anti-CD47, anti-CD61, anti-CD62P-, anti-CD63, anti-CD69, anti-CD107a, anti-CD154, anti-GPVI, anti-GPIIbIIa complex, anti-Par1, anti-PEAR-1 and the negative control anti-CD3 coupled with different metal isotopes). RPs and mature platelets (MPs) were detected based on their RNA content. Earth mover’s distance (EMD) was assessed as a measure of differential expression. Results CyTOF detected an upregulation of important transmembrane receptors in RPs compared to MPs in quiescent platelets (Fig.1A and B). The collagen receptor GPVI (EMD 33.95), the collagen integrin receptor unit ITGB1 (EMD 32.37), the adhesion protein CD9 (EMD 31.78) and the von Willebrand receptor unit CD42b (EMD 30.08) were upregulated in RPs which all serve as crucial regulators of platelet activation and aggregation. RPs show higher median signal intensities of typical activation markers compared to MPs upon TRAP stimulation (Fig.1C). Conclusion We provide the first proteomic characterization of RPs’ pro-thrombotic profile offering the first biomolecular explanation of their hyperactivity and detrimental role in CCS patients.