Demichev Lab/Ralser Lab
COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
A time-resolved proteomic and prognostic map of COVID-19
Demichev Lab/Ralser Lab
Self-sustaining interleukin-8 loops drive a prothrombotic neutrophil phenotype in severe COVID-19
Neutrophils provide a critical line of defense in immune responses to various pathogens, but also inflict self-damage upon transition to a hyperactivated, procoagulant state. Recent work has highlighted proinflammatory neutrophil phenotypes contributing to lung injury and acute respiratory distress syndrome (ARDS) in patients suffering from COVID-19. Here, we utilize state-of-the art mass spectrometry-based proteomics, transcriptomic and correlative analyses as well as functional in vitro and in vivo studies to dissect how neutrophils contribute to the progression to severe COVID-19. We identify a reinforcing loop of both systemic and neutrophil intrinsic interleukin-8 (CXCL8/IL-8) dysregulation, which initiates and perpetuates neutrophil-driven immunopathology. This positive feedback loop of systemic and neutrophil autocrine IL-8 production leads to an activated, prothrombotic neutrophil phenotype characterized by degranulation and neutrophil extracellular trap (NET) formation. In severe COVID-19, neutrophils directly initiate the coagulation and complement cascade, highlighting a link to the immunothrombotic state observed in these patients. Targeting the IL-8-CXCR-1/-2 axis interferes with this vicious cycle and attenuates neutrophil activation, degranulation, NETosis, and IL-8 release. Finally, we show that blocking IL-8-like signaling reduces SARS-CoV-2 spike protein-induced, hACE2-dependent pulmonary microthrombosis in mice. In summary, our data provide comprehensive insights into the activation mechanisms of neutrophils in COVID-19 and uncover a self-sustaining neutrophil-IL-8-axis as promising therapeutic target in severe SARS-CoV-2 infection.
A universal peptide matrix interactomics approach to disclose motif dependent protein binding
Protein-protein interactions (PPIs) mediated by intrinsically disordered regions (IDRs) are often based on short linear motifs (SLiM). SLiMs are implicated in signal transduction and gene regulation, yet remain technically laborious and notoriously challenging to study. Here, we present an optimized method for a PRotein Interaction Screen on a peptide MAtrix (PRISMA) in combination with quantitative mass spectrometry. The protocol was benchmarked with previously described SLiM based PPIs using peptides derived from EGFR, SOS1, GLUT1 and CEBPB and extended to map binding partners of kinase activation loops. The detailed protocol provides practical considerations for setting up a PRISMA screen and subsequently implementing PRISMA on a liquid handling robotic platform as a cost effective high-throughput method. Optimized PRISMA can be universally applied to systematically study SLiM based interactions and associated post translational modifications (PTMs) or mutations to advance our understanding of the largely uncharacterized interactomes of intrinsically disordered protein regions.
Comprehensive micro-scaled proteome and phosphoproteome characterization of archived retrospective cancer repositories
Klauschen Lab/Mertins Lab
Formalin-fixed paraffin-embedded (FFPE) tissues are a valuable resource for retrospective clinical studies. Here, we evaluate the feasibility of (phospho-)proteomics on FFPE lung tissue regarding protein extraction, quantification, pre-analytics, and sample size. After comparing protein extraction protocols, we use the best-performing protocol for the acquisition of deep (phospho-)proteomes from lung squamous cell and adenocarcinoma with >8,000 quantified proteins and >14,000 phosphosites with a tandem mass tag (TMT) approach. With a microscaled approach, we quantify 7,000 phosphosites, enabling the analysis of FFPE biopsies with limited tissue amounts. We also investigate the influence of pre-analytical variables including fixation time and heat-assisted de-crosslinking on protein extraction efficiency and proteome coverage. Our improved workflows provide quantitative information on protein abundance and phosphosite regulation for the most relevant oncogenes, tumor suppressors, and signaling pathways in lung cancer. Finally, we present general guidelines to which methods are best suited for different applications, highlighting TMT methods for comprehensive (phospho-)proteome profiling for focused clinical studies and label-free methods for large cohorts.
Ultra-fast proteomics with Scanning SWATH
Ralser Lab/Demichev Lab
Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.
Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging
Klein Lab/Eggert Lab
Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity.
Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging
Klein Lab/Blüthgen Lab/Sehouli Lab/Braicu Lab
Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.
Salt Transiently Inhibits Mitochondrial Energetics in Mononuclear Phagocytes
Dietary high salt (HS) is a leading risk factor for mortality and morbidity. Serum sodium transiently increases postprandially but can also accumulate at sites of inflammation affecting differentiation and function of innate and adaptive immune cells. Here, we focus on how changes in extracellular sodium, mimicking alterations in the circulation and tissues, affect the early metabolic, transcriptional, and functional adaption of human and murine mononuclear phagocytes.
Extracellular sodium was taken up into the intracellular compartment, followed by the inhibition of mitochondrial respiration in murine and human macrophages. Mechanistically, HS reduces mitochondrial membrane potential, electron transport chain complex II activity, oxygen consumption, and ATP production independently of the polarization status of macrophages. Subsequently, cell activation is altered with improved bactericidal function in HS-treated M1-like macrophages and diminished CD4+ T cell migration in HS-treated M2-like macrophages. Pharmacological uncoupling of the electron transport chain under normal salt phenocopies HS-induced transcriptional changes and bactericidal function of human and murine mononuclear phagocytes. Clinically, also in vivo, rise in plasma sodium concentration within the physiological range reversibly reduces mitochondrial function in human monocytes. In both a 14-day and single meal HS challenge, healthy volunteers displayed a plasma sodium increase of and respectively, that correlated with decreased monocytic mitochondrial oxygen consumption.
SODAR Core: a Django-based framework for scientific data management and analysis web apps
Modern life science is generating large data sets at a unprecedented speed. A major source of data are so-called omics (e.g., genomics, metabolomics, or proteomics) experiments. Consequently, management and analysis of scientific data has become a major challenge. Further, the heterogeneity of projects makes “one size fits all” data analysis systems infeasible and calls for specialized data analysis platforms. The authors are actively developing applications for the FAIR (findable, accessible, interoperable, and reuseable, cf. Wilkinson et al. (2016) data management of omics data and their analysis. In order to prevent duplication of work, we have extracted the commonly useful components into SODAR Core, a Python framework on top of Django. It is used in our actively developed applications and also proved useful in internal web app prototypes. Examples for using SODAR Core in the development of scientific data management web apps is Digestiflow (Holtgrewe et al., 2019) and the Filesfolders module shipping with SODAR Core. An example for using SODAR Core in the development of scientific data analysis web apps is VarFish (Holtgrewe et al., 2020).
Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection
Ralser Lab/Demichev Lab
The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
Ralser Lab/Demichev Lab
We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.