Project description:TARGETED PROTEOMICS FOR THE DETECTION OF SARS-COV-2 PROTEINS: SARS-CoV-2, COVID-19, proteomics, targeted mass spectrometry, LC-MS, parallel reaction monitoring (PRM), limit of detection
Project description:The binding patterns of some transcription factors have been shown to diverge substantially between closely related species. Here, we show that the binding pattern of the developmental transcription factor Twist is highly conserved across six Drosophila species, revealing strong functional constraints at developmental enhancers. Conserved binding correlates with sequence motifs for Twist and its partners, permitting the de novo discovery of their cooperative binding. It also includes over 10,000 low-occupancy sites near the detection limit, which tend to mark enhancers of later developmental stages. We predict that conservation, dynamic occupancy, and combinatorial regulation will be generally true for developmental enhancers.
Project description:TARGETED PROTEOMICS FOR THE DETECTION OF SARS-COV-2 PROTEINS: SARS-CoV-2, COVID-19, proteomics, targeted mass spectrometry, LC-MS, parallel reaction monitoring (PRM), limit of detection
Project description:Explore 1536 dataset from serum of patients enrolled in the COVACTA trial. This dataset includes limit of detection values provided by Olink.
Project description:The rapid, sensitive and specific detection of SARS-CoV-2 is critical in responding to the current COVID-19 outbreak. Here, we explore the potential of targeted mass spectrometry based proteomics for the detection of SARS-CoV-2 proteins in both research and clinical samples. First, we assessed the limit of detection for several SARS-CoV-2 proteins by parallel reaction monitoring (PRM) mass spectrometry. For Nucleocapsid the limit of detection was found to be in the mid-attomole range (0.9 x 10-12 g). Next, this PRM assay is applied to the detection of viral proteins in in vitro mucus substitutes, as well as in various clinical specimens such as nasopharyngeal swabs and sputum. In this proof-of-concept study SARS-CoV-2 proteins could unambiguously be detected in various clinical samples, suggesting that the sensitivity of this technology may be sufficiently high to further explore its potential role in diagnostics.
Project description:The early detection of tissue and organ damage associated with autoimmune diseases (AID) has been identified as key to improve long-term survival, but non-invasive biomarkers are lacking. Elevated cell-free DNA (cfDNA) levels have been observed in AID and inflammatory bowel disease (IBD), prompting interest to use cfDNA as a potential non-invasive diagnostic and prognostic biomarker. Despite these known disease-related changes in concentration, it remains impossible to identify AID and IBD patients through cfDNA analysis alone. By using unsupervised clustering on large sets of shallow whole-genome sequencing (sWGS) cfDNA data, we uncover AID- and IBD-specific genome-wide patterns in plasma cfDNA in both the obstetric and general AID and IBD populations. Supervised learning of the genome-wide patterns allows AID prediction with 50% sensitivity at 95% specificity. Importantly, the method can identify pregnant women with AID during routine non-invasive prenatal screening. Since AID pregnancies have an increased risk of severe complications, early recognition or detection of new onset AID can redirect pregnancy management and limit potential adverse events. This method opens up new avenues for screening, diagnosis and monitoring of AID and IBD.
Project description:Native metabolomics method validation for chymotrypsin.
1. Limit of detection Mollasamide- Chymotrypsin
2. Flowinjection of Mollasamide over UHPLC gradients (with make-up).
3. Binding tests with chymotrypsin and different standards.
Project description:Native metabolomics method validation for chymotrypsin.
1. Limit of detection Mollasamide- Chymotrypsin
2. Flowinjection of Mollasamide over UHPLC gradients (with make-up).
3. Binding tests with chymotrypsin and different standards.
Project description:A key requirement of liquid chromatography-mass spectrometry (LC-MS)-based allergenic food protein analysis methods is to use protein marker peptides with good analytical performances in LC-MS analysis of commercial processed foods. In this study, we developed a multistage walnut protein marker peptide selection strategy involving marker peptide discovery and verification and LC-MS validation of chemically equivalent stable isotope-labeled peptides. This strategy proposed three walnut protein marker peptides, including two new marker peptides. Our LC-MS-based walnut protein analysis method using the three stable isotope-labeled peptides showed acceptable linearity (R2 >0.99), matrix effects (coefficient of variation <±15%), sensitivity (limit of detection >0.3 pg/μL, limit of quantification >0.8 pg/μL), recovery (85.1–103.4%), accuracy, and precision (coefficient of variation <10%). In conclusion, our multistage marker peptide selection strategy effectively selects specific protein marker peptides for sensitive detection and absolute quantification of walnut proteins in LC-MS analysis of commercial processed foods.