OxoScan-MS: Oxonium ion scanning mass spectrometry facilitates plasma glycoproteomics in large scale

Protein glycosylation is a complex and heterogeneous post-translational modification. Specifically, the human plasma proteome is rich in glycoproteins, and as protein glycosylation is frequently dysregulated in disease, glycoproteomics is considered an underexplored resource for biomarker discovery. Here, we present OxoScan-MS, a data-independent mass spectrometric acquisition technology and data analysis software that facilitates sensitive, fast, and cost-effective glycoproteome profiling of plasma and serum samples in large cohort studies. OxoScan-MS quantifies glycosylated peptide features by exploiting a scanning quadrupole to assign precursors to oxonium ions, glycopeptide-specific fragments. OxoScan-MS reaches a high level of sensitivity and selectivity in untargeted glycopeptide profiling, such that it can be efficiently used with fast microflow chromatography without a need for experimental enrichment of glycopeptides from neat plasma. We apply OxoScan-MS to profile the plasma glycoproteomic in an inpatient cohort hospitalised due to severe COVID-19, and obtain precise quantities for 1,002 glycopeptide features. We reveal that severe COVID-19 induces differential glycosylation in disease-relevant plasma glycoproteins, including IgA, fibrinogen and alpha-1-antitrypsin. Thus, with OxoScan-MS we present a strategy for quantitatively mapping glycoproteomes that scales to hundreds and thousands of samples, and report glycoproteomic changes in severe COVID-19.

Functional proteomic profiling links deficient DNA clearance to mortality in patients with severe COVID-19 pneumonia

Hyperinflammation, coagulopathy and immune dysfunction are prominent in patients with severe infections. Extracellular chromatin clearance by plasma DNases suppresses such pathologies in mice but whether severe infection interferes with these pathways is unclear. Here, we show that patients with severe SARS-CoV-2 infection or microbial sepsis exhibit low extracellular DNA clearance capacity associated with the release of the DNase inhibitor actin. Unlike naked DNA degradation (DNase), neutrophil extracellular trap degradation (NETase) was insensitive to G-actin, indicating distinct underlying mechanisms. Activity-based proteomic profiling of severely ill SARS-CoV-2 patient plasma revealed that patients with high NETase and DNase activities exhibited 18-fold higher survival compared to patients with low activity proteomic profiles. Remarkably, low DNA clearance capacity was also prominent in healthy individuals with chronic inflammation, suggesting that pre-existing inflammatory conditions may increase the risk for mortality upon infection. Hence, functional proteomic profiling illustrates that non-redundant DNA clearance activities protect critically ill patients from mortality, uncovering protein combinations that can accurately predict mortality in critically ill patients.

A multiplex protein panel assay determines disease severity and is prognostic about outcome in COVID-19 patients

Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can both help to optimize resource allocation and accelerate the development and evaluation of new therapies. Here, we present a multiplex proteomic panel assay for the assessment of disease severity and outcome prediction in COVID-19. The assay quantifies 50 peptides derived from 30 COVID-19 severity markers in a single measurement using analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM), on equipment that is broadly available in routine and regulated analytical laboratories. We demonstrate accurate classification of COVID-19 severity in patients from two cohorts. Furthermore, the assay outperforms established risk assessments such as SOFA and APACHE II in predicting survival in a longitudinal COVID-19 cohort. The prognostic value implies its use for support of clinical decisions in settings with overstrained healthcare resources e.g. to optimally allocate resources to severely ill individuals with high chance of survival. It can furthermore be helpful for monitoring of novel therapies in clinical trials.

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care. Trial registrationGerman Clinical Trials Register DRKS00021688

Complement activation induces excessive T cell cytotoxicity in severe COVID-19

Severe COVID-19 is linked to both dysfunctional immune response and unrestrained immunopathogenesis, and it remains unclear if T cells also contribute to disease pathology. Here, we combined single-cell transcriptomics and proteomics with mechanistic studies to assess pathogenic T cell functions and inducing signals. We identified highly activated, CD16+ T cells with increased cytotoxic functions in severe COVID-19. CD16 expression enabled immune complex-mediated, T cell receptor-independent degranulation and cytotoxicity not found in other diseases. CD16+ T cells from COVID-19 patients promoted microvascular endothelial cell injury and release of neutrophil and monocyte chemoattractants. CD16+ T cell clones persisted beyond acute disease maintaining their cytotoxic phenotype. Age-dependent generation of C3a in severe COVID-19 induced activated CD16+ cytotoxic T cells. The proportion of activated CD16+ T cells and plasma levels of complement proteins upstream of C3a correlated with clinical outcome of COVID-19, supporting a pathological role of exacerbated cytotoxicity and complement activation in COVID-19.

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.

Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics

The COVID-19 pandemic is an unprecedented global challenge. Highly variable in its presentation, spread and clinical outcome, novel point-of-care diagnostic classifiers are urgently required. Here, we describe a set of COVID-19 clinical classifiers discovered using a newly designed low-cost high-throughput mass spectrometry-based platform. Introducing a new sample preparation pipeline coupled with short-gradient high-flow liquid chromatography and mass spectrometry, our methodology facilitates clinical implementation and increases sample throughput and quantification precision. Providing a rapid assessment of serum or plasma samples at scale, we report 27 biomarkers that distinguish mild and severe forms of COVID-19, of which some may have potential as therapeutic targets. These proteins highlight the role of complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling upstream and downstream of Interleukin 6. Application of novel methodologies hence transforms proteomics from a research tool into a rapid-response, clinically actionable technology adaptable to infectious outbreaks. Highlights- A completely redesigned clinical proteomics platform increases throughput and precision while reducing costs. - 27 biomarkers are differentially expressed between WHO severity grades for COVID-19. - The study highlights potential therapeutic targets that include complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling both upstream and downstream of interleukin 6.