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:Multicellular organisms such as plants contain different types of cells with specialized functions. Analyzing the characteristics of each type of cells will reveal specific cell functions and enhance understanding of how an organism organizes and works. Here we report a high-sensitive and efficient cell-type-specific multi-omics pipeline, combining simplified flow cytometry-based fluorescent cell-sorting for fluorescent protoplasts and optimized nano-scale proteomics and metabolomics methods, which allow us to in-depth analyze the proteomics and metabolomics of a particular type of cells. By this method, we quantitatively compared the proteomics and metabolomics between guard cells and mesophyll cells and revealed that the enrichment of signal transduction-related proteins enables guard cells to respond to various environmental stimuli quickly. We uncovered a guard-cell-specific kinase cascade consisting of RAF15 and OST1 mediates the ABA-induced stomatal closure in guard cells. This pipeline is applicable to various types of cells in plant or non-plant systems to acquire systemic knowledge of how cells work specifically and in highly organized multiple cell organisms.
Project description:Multicellular organisms such as plants contain different types of cells with specialized functions. Analyzing the characteristics of each type of cells will reveal specific cell functions and enhance understanding of how an organism organizes and works. Here we report a high-sensitive and efficient cell-type-specific multi-omics pipeline, combining simplified flow cytometry-based fluorescent cell-sorting for fluorescent protoplasts and optimized nano-scale proteomics and metabolomics methods, which allow us to in-depth analyze the proteomics and metabolomics of a particular type of cells. By this method, we quantitatively compared the proteomics and metabolomics between guard cells and mesophyll cells and revealed that the enrichment of signal transduction-related proteins enables guard cells to respond to various environmental stimuli quickly. We uncovered a guard-cell-specific kinase cascade consisting of RAF15 and OST1 mediates the ABA-induced stomatal closure in guard cells. This pipeline is applicable to various types of cells in plant or non-plant systems to acquire systemic knowledge of how cells work specifically and in highly organized multiple cell organisms.
Project description:Neurons and glia are distinct in their morphology, development, and function. They have unique transcriptomes and proteomes, but little is known about their metabolomes. The challenge of brain cell metabolic profiling is to obtain a large number of pure cells for reliable analysis. Here, we purify microglia, astrocytes, and neurons from the genetically labeled-brain. We identified >70 metabolites in them with targeted metabolomics and 9,854 metabolite features with untargeted metabolomics. We systematically characterized cell type–enriched metabolites and metabolic pathways. The enrichment of glutathione (GSH) metabolism in microglia was further validated in vivo. A significant decrease in GSH levels and GSH metabolism observed in microglia in aging and Alzheimer's disease (AD) models. Disrupting GSH metabolism in microglia results in aberrant morphogenesis, upregulation of mitophagy-related genes, and the deposition of β-Amyloid. Our results provide a valuable resource for metabolic studies related to aging, AD and other neurological diseases.
Project description:DC3000 cultures were grown under highly controlled conditions and after the addition of iron citrate or sodium citrate to the media. In the cultures supplemented with iron, we found that cell-associated iron increased rapidly while culture densities were not significantly different over 4 hours when compared to cultures with sodium citrate added. Microarray analysis of samples taken from before and after the addition of either sodium citrate or iron citrate identified 386 differentially regulated genes with high statistical confidence. Differentially regulated genes were clustered based on expression patterns observed between comparison of samples taken at different time points and with different supplements. This analysis grouped genes associated with the same regulatory motifs and/or had similar putative or known function. Keywords: iron response, environmental signal, time course
Project description:The aim of the study is to identify a pattern of chemoresistive sensors able to recognise the presence of a tumoral pathology from a health state through the analysis of Volatile Organic Compounds inside the specimen.
The chemoresistive nanostructured sensors are into an innovative patented device SCENT B1 which can analyse different specimens: blood samples, tissue biopsies, cell cultures.
In this study SCENT B1 wil be used to compare the measures of:
* tumoral and health tissues taken from different neoplasms after their surgical resection
* blood samples from healthy and tumor affected people
* pre and post- operative blood samples of tumor affected people