Project description:We performed data independent acquisition (DIA)-based proteomics to characterize the proteomes of 67 PDAC resection specimens. Patients received either neoadjuvant chemotherapy or neoadjuvant combined chemo-radiation therapy. We employed DIA, yielding a proteome coverage in excess of 3,500 proteins. The two neoadjuvant therapies yielded highly distinguishable proteome profiles of the residual tumor mass.
Project description:Improved biomarkers of treatment response are needed for patients with High-Grade Serous Ovarian Cancer (HGSC). A challenge is substantial anatomical site-to-site variation in expression. We completed Data Independent Acquisition – Mass Spectrometry (DIA-MS) analysis of over 404 fresh frozen and 78 formalin fixed, paraffin-embedded HGSC tissue samples from ovary (adnexal) and a common secondary site (omentum) in 11 patients. This was compared with mutation testing, gene expression and whole genome copy number profiling. Proteins with relatively stable intra-, and variable inter-individual expression (n=1,651), included a 52-protein module reflecting interferon mediated tissue inflammation indicative of a cGAS-STING pathway cytosolic double-stranded (ds) DNA response. The dsDNA sensing / inflammation (DSI) score was higher in omentum compared with ovary. Ovarian HGSC samples showed marked inter-individual differences in inflammatory and immune responses to DNA damage. Stable discriminative features of the HGSC proteome, a prerequisite for clinical predictive biomarkers, are detectable in ovary (adnexal) tissue samples.
Project description:To characterize potential biomarkers and underlying mechanisms that prompt pathological complete response (pCR) rate of neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients.LARC patients from two hospitals were enrolled with pre-nCRT biopsy tissues examined by pressure cycling technology (PCT) combined with data-independent acquisition (DIA) mass spectrometry. Tumor regression grade (TRG) evaluation was performed with the surgical tissues after nCRT to estimate the efficacy of nCRT. Proteins up regulated in pCR patients were highlighted and immune infiltration analysis was carried out. The candidate biomarker FOSL2 (FOS Like 2, AP-1 Transcription Factor Subunit), was selected and verified its role in vitro and vivo. We then performed gene expression profiling analysis using data obtained from RNA-seq of 2 different cells with or without chemoradiotherapy.
Project description:Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data-independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in E. coli for >2000 proteins over >60 growth conditions, including nutrient limitations, non-metabolic stresses and non-planktonic states. The resulting high-quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low-abundant proteins under various metabolic limitations, the non-specificity of catabolic enzymes upregulated under carbon limitation, the lack of large-scale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain-dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi-omics studies of gene regulation and metabolic control in E. coli.
Project description:In this project we performed data-independent acquisition mass spectrometry (DIA-MS) to study the regulation of phosphosites by protein phosphatases. In in vivo (in cellulo) and in vitro experiments were performed.
Project description:The goal of this project is to compare label free quantification, chemical labeling with tandem mass tags, and data independent acquisition discovery proteomics approaches using lung squamous cell carcinomas and adjacent lung tissues. This additional single sample LC-MS/MS analysis with data dependent acquisition was performed to enable direct comparison to the PRIDE dataset, titled, "Comparison of Lung Cancer Proteome Profiles 3: DIA," where single samples were analyzed with LC-MS/MS using data independent acquisition.
Project description:Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis. </br></br> Serum metabolome of 40 age-related macular degeneration patients and 20 control samples was analyzed using untargeted mass spectrometry. We used data dependent acquisition data to build a MS/MS spectral assay library for more than 1,000 compounds and performed targeted extraction of MS2 ion chromatograms from data independent acquisition analysis.
Project description:To determine the role of autophagy in the maintenance of genome stability and nucleic acid metabolism, the chromatin-bound proteins in autophagy-deficient ATG7-/- HEK293 cells were compared with autophagy-proficient ATG7+/+ HEK293 cells by Data-independent acquisition mass spectrometry (DIA-MS).