Project description:Obtaining multiple sample types from the same exhaled breath condensate (EBC) sample can reduce the number of samples needed for diagnostics purposes, allowing for sampling to be completed quicker and making it even easier to collect breath from patients. In this study, we performed analysis for volatile organic compounds (VOCs) and proteins from the same EBC sample. Pooled EBC samples were split into two groups: three samples that utilized immersion thin film-solid phase microextraction (TF-SPME) sampling for VOCs analysis and three samples that did not undergo TF-SPME sampling (non-TF-SPME). All six EBC samples were analyzed using liquid chromatography with tandem mass spectrometry (LC-MS/MS) for proteomics analysis. VOCs were analyzed via two-dimensional gas chromatography-mass spectrometry (GC x GC-MS). One hundred and eighty-four VOCs were found to be more abundant in EBC samples compared to blank or controls. There was no significant difference in the number of proteins detected in the TF-SPME samples compared to the non-TF-SPME samples and 144 of the 206 total unique proteins detected were found in both sample groups. These results indicate that TF-SPME sampling does not negatively affect the number of proteins that can be detected in EBC. This work is a step towards linking VOC and protein data together to obtain multi-omics breath data from a single breath sample.
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 performed a retrospective study on CSF from 20 DMT-naïve MS patients to investigate the correlation between intrathecal immune proteins and clinical MS phenotype.
Project description:A total of 116 patients, 96 with treatment-naïve unresectable hepatocellular carcinoma (uHCC) and 20 chronic liver disease without any cancer, were analysed for 17 cytokines and chemokines from serum and analysed for oncological features.
Project description:To analyze expression of inflammatory cytokines in Exhaled Breath Condensates from pediatric patients with sickle cell disease, asthma, sickle cell disease and asthma, and controls
Project description:Purpose: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Experimental design: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. Results: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. Conclusions and clinical relevance: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
Project description:Purpose: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Experimental design: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. Results: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. Conclusions and clinical relevance: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
Project description:A non-invasive diagnostic test does not exist for acute graft versus host disease (aGVHD). We therefore sought to identify biomarkers for aGVHD using antibody microarrays (Schleicher and Schuell Serum Biomarker Chips, Whatman) that simultaneously assayed 120 plasma proteins. We measured these proteins in a set of 42 patient plasma samples following an allogeneic bone marrow transplant (BMT): 21 patients with a diagnosis of aGVHD grade II-IV (+GVHD) and 21 patients without aGVHD (–GVHD) at similar times after transplant. We excluded data from 2 hybridizations that had very bright dots and appeared as outliers in preliminary principal components analysis, so that we finally compared 20 +GVHD to 20 -GVHD samples. Keywords: disease state analysis, antibody microarray