Project description:16 dietary protein preparations were digested according to a standardized invitro digestion (InfoGest) protocol, simulating gastrointestinal digestion. Resulting oligopeptide mixtures were subsequently analyzed by UPLC-MS using DDA shot-gun detection. Peak intensity data from MS1 spectra were obtained using Progenesis QI. MS1 peak data were matched and filtered based on overlap with the reference digest of Whey protein using a custom made script in R. MSMS fragmentation spectra were converted to .mgf format and de novo interpreted using SearchGUI 3.3.17 and RapidNOVOR algorithm. Raw data, peak intensity tables and de novo interpretaion data are provided.
Project description:This dataset contains label-free DIA proteomics of liver tissue from postnatal day 10 (P10) Taiwanese SMA mice (genotype: Smn−/−; SMN2tg/0), heterozygous carriers (HET, Smn+/−; SMN2tg/0), and wild-type controls (WT, Smn+/+; SMN2 0/0). SMA and HET samples were littermates generated from the same FVB/N × C57BL/6 mixed-background colony, whereas WT controls were age-matched animals on a C57BL/6 background. Whole-liver lysates were processed by in-solution protein digestion and analyzed using data-independent acquisition (DIA) LC–MS/MS on a Thermo Orbitrap Exploris 480 mass spectrometer equipped with a FAIMS Pro differential ion mobility device. A Vanquish Neo nano-HPLC system (Thermo Scientific) was coupled to the mass spectrometer for chromatographic separation. Peptide and protein identification and quantification were performed with DIA-NN v1.8.1using a predicted spectral library generated from the Mus musculus UniProt canonical FASTA file (accession UP000000589, downloaded 2024-01-04). The DIA-NN output was filtered at 1% precursor- and protein-level FDR (global q-value ≤ 0.01), with additional quality filters applied in R (≥ 4 fragment ions per precursor, library q-value ≤ 0.01, unique peptides only). Final LFQ intensities were calculated using the DIA-NN R package, and downstream statistical analysis was performed in Perseus v1.6.15. All RAW files generated during data acquisition are included in this dataset. One SMA biological replicate was identified as an outlier by principal component analysis and excluded from downstream statistical comparisons in the accompanying manuscripts: this corresponds to SMN biological replicate 5 (Raw file: E3_CoIID_656_3632_LI_165). Its RAW file and corresponding DIA-NN output tables are provided here for completeness, transparency, and reproducibility. The dataset contains the original RAW files, DIA-NN search output tables (.tsv), the predicted spectral library used for identification, and the Mus musculus UniProt FASTA database employed for database searching. Experimental factors encoded in the metadata include genotype (WT, HET, SMA) and tissue (liver).
Project description:The advent of on-line multidimensional liquid chromatography-mass spectrometry has significantly impacted proteomic analyses of complex biological fluids such as plasma. However, there is general agreement that additional advances to enhance the peak capacity of such platforms are required to enhance the accuracy and coverage of proteome maps of such fluids. Here, we describe the combination of strong-cation-exchange and reversed-phase liquid chromatographies with ion mobility and mass spectrometry as a means of characterizing the complex mixture of proteins associated with the human plasma proteome. The increase in separation capacity associated with inclusion of the ion mobility separation leads to generation of one of the most extensive proteome maps to date. The map is generated by analyzing plasma samples of five healthy humans; we report a preliminary identification of 9087 proteins from 37,842 unique peptide assignments. An analysis of expected false-positive rates leads to a high-confidence identification of 2928 proteins. The results are catalogued in a fashion that includes positions and intensities of assigned features observed in the datasets as well as pertinent identification information such as protein accession number, mass, and homology score/confidence indicators. Comparisons of the assigned features reported here with other datasets shows substantial agreement with respect to the first several hundred entries; there is far less agreement associated with detection of lower abundance components.
Project description:mgf files, quantification tables and metadata files used in the publication Buedenbender et al., "Bioactive Molecular Networking for Mapping the Antimicrobial Constituents of the Baltic Brown Alga Fucus vesiculosus", Marine Drugs, 2020
Project description:Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.
Project description:The sample condition is an important factor in urine proteomics with stability and accuracy. However, a general protocol of urine protein preparation in mass spectrometry analysis has not yet been established. Here, we proposed a workflow for optimized sample preparation based on methanol/chloroform (M/C) precipitation and in-solution trypsin digestion in LC-MS/MS-based urine proteomics. The urine proteins prepared by M/C precipitation showed around 80% of the protein recovery rate. The samples showed the largest number of identified proteins, which were over 1000 on average compared with other precipitation methods in LC-MS/MS-based urine proteomics. For further improvement of the workflow, the essences were arranged in protein dissolving and trypsin digestion step for the extraction of urine proteins. Addition of Ethylene diamine tetraacetic acid (EDTA) dramatically enhanced the dissolution of protein and promoted the trypsin activity in the digestion step because the treatment increased the number of identified proteins with less missed cleavage sites. Eventually, an optimized workflow was established by a well-organized strategy for daily use in the LC-MS/MS-based urine proteomics. The workflow will be of great help for several aims based on urine proteomics approaches, such as diagnosis and biomarker discovery.
Project description:Trihydroxyoctadecenoic acids (TriHOMEs) are linoleic acid-derived oxylipins with potential physiological relevance in inflammatory processes as well as in maintaining an intact skin barrier. Due to the high number of possible TriHOME isomers with only subtle differences in their physicochemical properties, the stereochemical analysis is challenging and usually involves a series of laborious analytical procedures. We herein report a straightforward analytical workflow that includes reversed-phase ultra-HPLC-MS/MS for rapid quantification of 9,10,13- and 9,12,13-TriHOME diastereomers and a chiral LC-MS method capable of resolving all sixteen 9,10,13-TriHOME and 9,12,13-TriHOME regio- and stereoisomers. We characterized the workflow (accuracy, 98-120%; precision, coefficient of variation ≤6.1%; limit of detection, 90-98 fg on column; linearity, R2 = 0.998) and used it for stereochemical profiling of TriHOMEs in bronchoalveolar lavage fluid (BALF) of individuals with chronic obstructive pulmonary disease (COPD). All TriHOME isomers were increased in the BALF of COPD patients relative to that of smokers (P ≤ 0.06). In both COPD patients and smokers with normal lung function, TriHOMEs with the 13(S) configuration were enantiomerically enriched relative to the corresponding 13(R) isomers, suggesting at least partial enzymatic control of TriHOME synthesis. This method will be useful for understanding the synthetic sources of these compounds and for elucidating disease mechanisms.
Project description:In recent years, several rational designed therapies have been developed for treatment of mucopolysaccharidoses (MPS), a group of inherited metabolic disorders in which glycosaminoglycans (GAGs) are accumulated in various tissues and organs. Thus, improved disease-specific biomarkers for diagnosis and monitoring treatment efficacy are of paramount importance. Specific non-reducing end GAG structures (GAG-NREs) have become promising biomarkers for MPS, as the compositions of the GAG-NREs depend on the nature of the lysosomal enzyme deficiency, thereby creating a specific pattern for each subgroup. However, there is yet no straightforward clinical laboratory platform which can assay all MPS-related GAG-NREs in one single analysis. Here, we developed and applied a GAG domain mapping approach for analyses of urine samples of ten MPS patients with various MPS diagnoses and corresponding aged-matched controls. We describe a nano-LC-MS/MS method of GAG-NRE profiling, utilizing 2-aminobenzamide reductive amination labeling to improve the sensitivity and the chromatographic resolution. Diagnostic urinary GAG-NREs were identified for MPS types IH/IS, II, IIIc, IVa and VI, corroborating GAG-NRE as biomarkers for these known enzyme deficiencies. Furthermore, a significant reduction of diagnostic urinary GAG-NREs in MPS IH (n = 2) and MPS VI (n = 1) patients under treatment was demonstrated. We argue that this straightforward glycomic workflow, designed for the clinical analysis of MPS-related GAG-NREs in one single analysis, will be of value for expanding the use of GAG-NREs as biomarkers for MPS diagnosis and treatment monitoring.
Project description:Clinical proteomics has substantially advanced in identifying and quantifying proteins from biofluids, such as blood, contributing to the discovery of biomarkers. The throughput and reproducibility of serum proteomics for large-scale clinical sample analyses require improvements. High-throughput analysis typically relies on automated equipment, which can be costly and has limited accessibility. In this study, we present a rapid, high-throughput workflow low-microflow LC-MS/MS method without automation. This workflow was optimized to minimize the preparation time and costs by omitting the depletion and desalting steps. The developed method was applied to data-independent acquisition (DIA) analysis of 235 samples, and it consistently yielded approximately 6000 peptides and 600 protein groups, including 33 FDA-approved biomarkers. Our results demonstrate that an 18-min DIA high-throughput workflow, assessed through intermittently collected quality control samples, ensures reproducibility and stability even with 2 µL of serum. It was successfully used to analyze serum samples from patients with diabetes having chronic kidney disease (CKD), and could identify five dysregulated proteins across various CKD stages.