Development and Evaluation of a Parallel Reaction Monitoring Strategy for Large-Scale Targeted Metabolomics Quantification

Recent advances in mass spectrometers which have yielded higher resolution and faster scanning speeds have expanded their application in metabolomics of diverse diseases. Using a quadrupole-Orbitrap LC–MS system, we developed an efficient large-scale quantitative method targeting 237 metabolites involved in various metabolic pathways using scheduled, parallel reaction monitoring (PRM). We assessed the dynamic range, linearity, reproducibility, and system suitability of the PRM assay by measuring concentration curves, biological samples, and clinical serum samples. The quantification performances of PRM and MS1-based assays in Q-Exactive were compared, and the MRM assay in QTRAP 6500 was also compared. The PRM assay monitoring 237 polar metabolites showed greater reproducibility and quantitative accuracy than MS1-based quantification and also showed greater flexibility in postacquisition assay refinement than the MRM assay in QTRAP 6500. We present a workflow for convenient PRM data processing using Skyline software which is free of charge. In this study we have established a reliable PRM methodology on a quadrupole-Orbitrap platform for evaluation of large-scale targeted metabolomics, which provides a new choice for basic and clinical metabolomics study

Additional file 5 of Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

Additional file 5: Figure S5. Metabolomics data analysis on the weighted gene co-expression network analysis (WGCNA) in COPD compared to healthy controls. A, Positive metabolites; B, Negative metabolites

Additional file 1 of Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

Additional file 1: Figure S1. Quality control analysis of proteomic data. A. The lengths of peptides. B. The mass errors of peptides. C. Protein mass. D. Coverage and sequence distribution of the proteins. E. Number of proteins in comparable groups. F. Protein subcellular distribution

Table_2_Proteomic Analysis of Preoperative CSF Reveals Risk Biomarkers of Postoperative Delirium.DOCX

Objective: To analyze the proteome of preoperative cerebrospinal fluid (CSF) in older orthopedic patients with or without postoperative delirium (POD) using untargeted proteomics.Methods: A prospective cohort study was conducted. Eighty hip fracture patients aged ≥65 years were recruited. After successful spinal anesthesia, CSF was collected. The patients were divided into POD and No-POD groups based on the Confusion Assessment Method, and patients with POD were graded using the Memorial Delirium Assessment Scale (MDAS). Thirty No-POD patients were matched to 10 POD patients by age (±2 years) and Mini–Mental State Examination score (±2 scores). Label-free proteomic analysis was performed using a liquid chromatography coupled to mass spectrometry (LC-MS) workflow. Validation was performed using mass-spectrometry-based parallel reaction monitoring (PRM) for the 30 No-POD and 10 POD patients, as well as for an additional 5 POD patients. Bioinformatics were used to investigate possible relevant pathological mechanisms.Results: The incidence of POD in older orthopedic patients was 18.8% in our cohort of 80 patients. Proteomics results revealed 63 dysregulated CSF proteins, and PRM analysis validated these results. The preoperative CSF levels of both V-set and transmembrane domain-containing protein 2B (VSTM2B) and coagulation factor V (FA5) were positively correlated with MDAS scores on postoperative day 1 (r > 0.8, p Conclusion: We identified and validated several novel CSF proteins that are dysregulated in POD, and revealed several pathways that are relevant to POD development. Our results not only provide risk biomarkers for POD, but also give clues for further investigations into the pathological mechanisms of delirium.Clinical trial registration: This study was registered in the Chinese Clinical Trial Registry (ChiCTR1900021533).</p

Additional file 7 of Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

Additional file 7: Table S2. Predictive efficacy of single biomarker

Additional file 4 of Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

Additional file 4: Figure S4. Correlation distributions for total and selected-metabolites. Correlation distributions for total and selected-metabolites grouped by COPD and healthy controls

Additional file 9 of Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

Additional file 9: Table S4. Predictive efficacy of the combined biomarkers for the best serum biomarkers

Additional file 6 of Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

Additional file 6: Table S1. Proteins was generated for the targeted proteomic survey

Additional file 8 of Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

Additional file 8: Table S3. Predictive efficacy of the combined biomarkers

Additional file 3 of Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

Additional file 3: Figure S3. Quality control analysis of metabolomic data. Correlation distributions for positive and negative metabolites, respectively, and EBAM plots, normalization, PLS-DA, and t test generated