Flocculation proteomics TUM 34/70
Ontology highlight
ABSTRACT:
ORGANISM(S): Saccharomyces Pastorianus
SUBMITTER: Florian Weiland
PROVIDER: PXD081760 | JPOST Repository | Mon Aug 03 00:00:00 BST 2026
REPOSITORIES: jPOST
Dataset's files
Items per page: 5 1 - 5 of 11 |
Similar Datasets
Project description:Kidney fibrosis represents an urgent unmet clinical need due to the lack of effective therapies and inadequate understanding of the molecular pathogenesis. We have generated a comprehensive and integrated multi-omics data set (proteomics, mRNA and small RNA transcriptomics) of fibrotic kidneys that is searchable through a user-friendly web application. Two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgically-induced fibrosis model (unilateral ureteral obstruction (UUO)). mRNA and small RNA sequencing as well as 10-plex tandem mass tag (TMT) proteomics were performed with kidney samples from different time points over the course of fibrosis development. The bioinformatics workflow used to process, technically validate, and integrate the single data sets will be described. In summary, we present temporal and integrated multi-omics data from fibrotic mouse kidneys that are accessible through an interrogation tool to provide a searchable transcriptome and proteome for kidney fibrosis researchers.
2019-05-07 | GSE126182 | GEO
Project description:Kidney fibrosis represents an urgent unmet clinical need due to the lack of effective therapies and inadequate understanding of the molecular pathogenesis. We have generated a comprehensive and integrated multi-omics data set (proteomics, mRNA and small RNA transcriptomics) of fibrotic kidneys that is searchable through a user-friendly web application. Two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgically-induced fibrosis model (unilateral ureteral obstruction (UUO)). mRNA and small RNA sequencing as well as 10-plex tandem mass tag (TMT) proteomics were performed with kidney samples from different time points over the course of fibrosis development. The bioinformatics workflow used to process, technically validate, and integrate the single data sets will be described. In summary, we present temporal and integrated multi-omics data from fibrotic mouse kidneys that are accessible through an interrogation tool to provide a searchable transcriptome and proteome for kidney fibrosis researchers.
2019-05-07 | GSE126181 | GEO
Project description:Saccharomyces cerevisiae is unique among yeasts for its ability to grow rapidly in the complete absence of oxygen. S. cerevisiae is therefore an ideal eukaryotic model to study physiological adaptation to anaerobiosis. Recent transcriptome analyses have identified hundreds of genes that are transcriptionally regulated by oxygen availability but the relevance of this cellular response has not been systematically investigated at the key control level of the proteome. Therefore, the proteomic response of the S. cerevisiae to anaerobiosis was investigated using metabolic stable isotope labeling in aerobic and anaerobic glucose-limited chemostat cultures, followed by proteome analysis to relatively quantify protein expression. Using independent replicate cultures and stringent statistical filtering, a robust dataset of 474 quantified proteins was generated, of which 249 showed differential expression levels. While some of these changes were consistent with previous transcriptome studies, many responses of S. cerevisiae to oxygen availability were hitherto unreported. Comparison of transcriptome and proteome from identical cultivations yielded strong evidence for post-transcriptional regulation of key cellular processes, including glycolysis, amino-acyl tRNA synthesis, purine-nucleotide synthesis and amino-acid biosynthesis. The use of chemostat cultures provided well-controlled and reproducible culture conditions, which are essential for generating robust datasets at different cellular information levels. Integration of transcriptome and proteome data led to new insights in the physiology of anaerobically growing yeast that would not have been apparent from differential analyses at either the messenger RNA or protein level alone, thus illustrating the power of multi-level studies in yeast systems biology. Protein levels versus transcript level: Systematic analysis of the control levels at which the yeast response to anaerobiosis takes place was performed using previously published transcript data obtained from yeast cultures grown under strictly identical conditions as described for the current proteome analysis. Affymetrix microarrays from five aerobic and four anaerobic independent culture replicates were used for this analysis. These comparison data are summarized in the table below. These array data are publicly available at the gene expression repository Gene Expression Omnibus under accession number GSE4804. Keywords: proteomic, nanoflow-LC-MS/MS
2007-09-26 | GSE7365 | GEO
Project description:Ctrl samples: TMT-proteomics using Orbitrap FusionTM LumosTM TribridTM Mass Spectrometer, TMT10plex (N=8 sample batch) or TMT16plex (2x N=16 sample batches) Isobaric Label Reagent (Thermo Fisher Scientific, Waltham, MA, USA), experiment type: bottom-up proteomics; data-dependent acquisition.
| EGAD00010002702 | EGA
Project description:CUD samples: TMT-proteomics using Orbitrap FusionTM LumosTM TribridTM Mass Spectrometer, TMT10plex (N=8 sample batch) or TMT16plex (2x N=16 sample batches) Isobaric Label Reagent (Thermo Fisher Scientific, Waltham, MA, USA), experiment type: bottom-up proteomics; data-dependent acquisition.
| EGAD00010002703 | EGA
Project description:Shotgun Proteomics; Glioblastoma samples from 11 patients were obtained at initial and recurrent tumor stages. Proteins were extracted, identified and quantified via tandem mass spectrometry based on a TMT isobaric labelling approach. Quatitative proteomics reveals 146 differentially abundant proteins using a patient-matched statistical modelling. Analysis of proteolytic processing reveals differential proteolytic patterns in recurrent tumors. Proteogenomics reveals the presense of 30 single-amino acid variants present in glioblastoma tumor and 1 of those as increased in recurrent tumor.
| EGAD00010002350 | EGA
Project description:Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics. Timecourse experiment with six points over 48hr after bortezomib exposure in MM.1S myeloma cells. mRNA-seq and ribosome profiling data at each time point.
2017-05-18 | E-GEOD-69047 | biostudies-arrayexpress
Project description:Current proteomic methods are not well suited to detect protein isoforms. On the one hand, standard shotgun (that is, bottom-up) proteomics involves digestion of proteins into peptides. While this approach identifies many proteins, it results in a loss of isoform information. On the other hand, mass spectrometric analysis of intact proteins (that is, top-down proteomics) distinguishes protein isoforms but only covers a small subset of the proteome. We developed peptide correlation profiling (PepCP) as a method to obtain protein-level information from peptide-centric (that is, bottom-up proteomic) data: First, proteins are fractionated by SDS-PAGE to polypeptides of different length. Second, individual protein fractions are digested into peptides. Third, peptides are identified and quantified in all fractions using quantitative mass spectrometry-based proteomics. Finally, peptide abundance profiles across fractions are analysed to obtain protein-level information.
2026-03-12 | GSE261578 | GEO
Project description:Current proteomic methods are not well suited to detect protein isoforms. On the one hand, standard shotgun (that is, bottom-up) proteomics involves digestion of proteins into peptides. While this approach identifies many proteins, it results in a loss of isoform information. On the other hand, mass spectrometric analysis of intact proteins (that is, top-down proteomics) distinguishes protein isoforms but only covers a small subset of the proteome. We developed peptide correlation profiling (PepCP) as a method to obtain protein-level information from peptide-centric (that is, bottom-up proteomic) data: First, proteins are fractionated by SDS-PAGE to polypeptides of different length. Second, individual protein fractions are digested into peptides. Third, peptides are identified and quantified in all fractions using quantitative mass spectrometry-based proteomics. Finally, peptide abundance profiles across fractions are analysed to obtain protein-level information.
2026-03-12 | GSE261379 | GEO
Project description:Glioblastoma samples from 11 patients were obtained at initial and recurrent tumor stages. Proteins were extracted, identified and quantified via tandem mass spectrometry based on a TMT isobaric labelling approach. Quatitative proteomics reveals 146 differentially abundant proteins using a patient-matched statistical modelling. Analysis of proteolytic processing reveals differential proteolytic patterns in recurrent tumors. Proteogenomics reveals the presense of 30 single-amino acid variants present in glioblastoma tumor and 1 of those as increased in recurrent tumor.
| EGAS00001006395 | EGA