Project description:Identification of targets of the protein disulfide reductase thioredoxin using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) and thiol specific differential labeling with isotope-coded affinity tags (ICAT). Reduction of specific target disulfides is quantified by measuring ratios of cysteine residues labeled with the heavy (13C) and light (12C) ICAT reagents in peptides derived from tryptic digests of Trx-treated and non-treated samples. Keywords: protein, LC-MS/MS, ICAT
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
Project description:We observed that transcriptional activity and the number of active genes were significantly correlated with the distribution of 8-oxoG in gene promoter regions, as determined by liquid chromatography/mass spectrometry (LC/MS), and 8-oxoG and RNA sequencing
Project description:We observed that transcriptional activity and the number of active genes were significantly correlated with the distribution of 8-oxoG in gene promoter regions, as determined by liquid chromatography/mass spectrometry (LC/MS), and 8-oxoG and RNA sequencing
Project description:DDA non-targeted LC-MS/MS, PPL-SPE extracted marine organic matter. Positive Mode. Samples taken from during the Scripps Pier Diel Project 2022.
Project description:To better examine the molecular mechanisms behind the virus infection, we conducted a correlation analysis of RNA-Seq and quantitative iTRAQ-LC-MS/MS in TuMV-infected and in healthy Chinese cabbage leaves.
Project description:RNA sequencing of A431 cell line samples before and after gefitinib treatment, at 0, 2, 6 and 24 hours, was performed in order to characterize the cell line's early and late response to this drug, and to compare against proteomics (mass spectrometry) characterization of the cell line using the same setup. These data were used in Branca et al., HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics., Nat Methods. 2014 Jan;11(1):59-62 (doi: 10.1038/nmeth.2732).
Project description:The objective of the present investigation was to consider the level of variation in the protein expression patterns of closely related Salmonella serovars, in order to search for protein factors with levels of expression or posttranslational modifications characteristic for each serovar. For the comparative expression analysis we have utilised classic 2D GE approach which revealed several proteins with serovar specific expression as well as proteins which do not alter their expression levels between serovars and strains. The proteins of interest were identified using LC/MS/MS. Keywords: 2D GE, MS/MS