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 provide an atlas of gene and protein expression in Arabidopsis root hair cells, generated by paired-end RNA-seq and LC/MS-MS analysis from protoplasts that carry the root hair-specific pEXP7-GFP reporter construct. In total, transcripts from 23,234 genes were detected in root hairs; those related to cell wall biosynthesis and translation differed most dramatically in abundance when compared to non-GFP root protoplasts.