Global proteomics and ubiquitomics in THP-1 derived macrophages stably expressing SspH2 WT/C580A
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Exploris 480
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Olivier Julien Amit Bhavsar
PROVIDER: MSV000100008 | MassIVE | Mon Nov 24 14:24:00 GMT 2025
SECONDARY ACCESSION(S): PXD071147
REPOSITORIES: MassIVE
Similar Datasets
Project description:Single cell proteomics data containing quantitative information of THP1 and U937 monocytes that were treated or not to undergo a macrophage-like differentiation. Dataset also contains "Mix" samples generated by mixing in equal proportions THP1 and U937 peptides in the single-cell range or by processing together 1 THP1 cell and 1 U937 cell. Samples run on the Orbitrap Fusion Lumos Tribrid and the Exploris 240 were acquired by the de Duve institute, UCLouvain. Samples run on the timsTOF SCP were acquired by the GIGA institute, ULiège.
2024-02-02 | PXD046211 | Pride
Project description:These data cover all of the analyses described in the paper "Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics". Specifically, the data consist of - 20 DIA and DDA files from the HEK293T/synthetic phosphopeptides spike-in experiments - 10 DDA files, one for each of ten fractions of an E. coli lysate digest - 14 DIA files for the experiments involving mixtures of synthetic peptides - 11 DDA files for the isolated runs of these synthetic peptides - 84 DIA files for measurements of the phosphoproteome of perturbed PC3 cells - 10 DDA files for spectral library construction for the phosphoproteomics data - 3 DIA and 3 DDA files for analysis of an unfractionated E. coli lysate digest. See the spreadsheet "Specter Datasets Catalog.xlsx" for further descriptions and file metadata.
2018-03-19 | PXD006722 | Pride
Project description:Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n=5) CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) applying to CSF DIA of three replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 minute gradient. Compared to a conventional DDA method, our DIA approach both increased the number of identified protein groups with 1574 compared to DDA with 648 over 50 % with a comprehensive spectral library (generated with DDA measurements from five native CSF and 54 substantia nigra fractions) and decreased the coefficient of variation to 6 %, compared to 11 % with a DDA method. We also could show that a sample specific spectral library generated only from native CSF increased the identification reproducibility from three DIA replicates to 90 % (77 % with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA over 60 brain-originated proteins could be identified compared to only eleven with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF.
2018-09-17 | PXD010708 | Pride
Project description:Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n=5) CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) applying to CSF DIA of three replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 minute gradient. Compared to a conventional DDA method, our DIA approach both increased the number of identified protein groups with 1574 compared to DDA with 648 over 50 % with a comprehensive spectral library (generated with DDA measurements from five native CSF and 54 substantia nigra fractions) and decreased the coefficient of variation to 6 %, compared to 11 % with a DDA method. We also could show that a sample specific spectral library generated only from native CSF increased the identification reproducibility from three DIA replicates to 90 % (77 % with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA over 60 brain-originated proteins could be identified compared to only eleven with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF.
2018-09-17 | PXD010690 | Pride
Project description:Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n=5) CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) applying to CSF DIA of three replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 minute gradient. Compared to a conventional DDA method, our DIA approach both increased the number of identified protein groups with 1574 compared to DDA with 648 over 50 % with a comprehensive spectral library (generated with DDA measurements from five native CSF and 54 substantia nigra fractions) and decreased the coefficient of variation to 6 %, compared to 11 % with a DDA method. We also could show that a sample specific spectral library generated only from native CSF increased the identification reproducibility from three DIA replicates to 90 % (77 % with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA over 60 brain-originated proteins could be identified compared to only eleven with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF.
2018-09-17 | PXD010705 | Pride
Project description:Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n=5) CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) applying to CSF DIA of three replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 minute gradient. Compared to a conventional DDA method, our DIA approach both increased the number of identified protein groups with 1574 compared to DDA with 648 over 50 % with a comprehensive spectral library (generated with DDA measurements from five native CSF and 54 substantia nigra fractions) and decreased the coefficient of variation to 6 %, compared to 11 % with a DDA method. We also could show that a sample specific spectral library generated only from native CSF increased the identification reproducibility from three DIA replicates to 90 % (77 % with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA over 60 brain-originated proteins could be identified compared to only eleven with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF.
2018-09-17 | PXD010698 | Pride
Project description:Full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA) are the three common data acquisition modes in high resolution mass spectrometry-based untargeted metabolomics. It is an important yet underrated research topic on which acquisition mode is more suitable for a given untargeted metabolomics application. In this work, we compared the three data acquisition techniques using a standard mixture of 134 endogenous metabolites and a human urine sample. Both hydrophilic interaction and reversed-phase liquid chromatographic separation along with positive and negative ionization modes were tested. Both the standard mixture and urine samples generated consistent results. Full-scan mode is able to capture the largest number of metabolic features, followed by DIA and DDA (53.7% and 64.8% respective features fewer on average in urine than full-scan). Comparing the MS2 spectra in DIA and DDA, spectra quality is higher in DDA with average dot product score 83.1% higher than DIA in Urine(H), and the number of MS2 spectra (spectra quantity) is larger in DIA (on average 97.8% more than DDA in urine). Moreover, a comparison of relative standard deviation distribution between modes shows consistency in the quantitative precision, with the exception of DDA showing a minor disadvantage (on average 19.8% and 26.8% fewer features in urine with RSD < 5% than full-scan and DIA). In terms of data preprocessing convenience, full-scan and DDA data can be processed by well-established software. In contrast, several bioinformatic issues remain to be addressed in processing DIA data and the development of more effective computational programs is highly demanded.
2020-05-21 | MTBLS1572 | MetaboLights
Project description:Mycoplasma gallisepticum (MG) is one of the smallest free-living and self-replicating organisms, it is characterized by lack of cell wall and reduced genome size. As a result of genome reduction, MG has a limited variety of DNA-binding proteins (DBP) and transcription factors. To investigate the dynamic changes of the proteomic profile of MG nucleoid, that may assist in revealing its mechanisms of functioning, regulation of chromosome organization and stress adaptation, a quantitative proteomic study was performed on MG nucleoids obtained from the cell culture in logarithmic and stationary phases of synchronous growth. MG cells were grown in a liquid medium with a 9h starvation period. Nucleoids were obtained from the cell culture at the 26th and the 50th hour (logarithmic and stationary phases respectively) by sucrose density gradient centrifugation. LC-MS analysis was carried out on an Ultimate 3000 RSLCnano HPLC system connected to a Fusion Lumos mass spectrometer, controlled by XCalibur software (ThermoFisher Scientific) via a nanoelectrospray source (Thermo Fisher Scientific). For comprehensive peptide library generation one sample from each biological replicate was run in DDA mode. Then, all the sample were run in a single LC-MS DIA run. Identification of DDA files and DIA quantitation was performed with MaxQuant and Skyline software.
2020-05-12 | PXD019077 | Pride
Project description:Generation of a new library of targeted mass spectrometry assays for accurate protein quantification in triple negative breast cancer (TNBC) tissues. Primary tumor tissue lysates from 105 TNBC patients treated at Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic, were used to generate the spectral library. This project covers raw files from data-dependent acquisition (DDA) – parallel accumulation-serial fragmentation (PASEF) measurements of 12 hydrophilic chromatography (HILIC) fractions of aliquot pool from complete set of 105 samples measured on timsTOF Pro; raw files of 16 individual samples measured in data-independent acquisition (DIA) – PASEF mode and used for hybrid library generation and for demonstrative quantitative DIA data extraction; Pulsar archive generated in Spectronaut 16.0 from 12 DDA-PASEF measurements of HILIC fractions and from 16 data-independent acquisition DIA-PASEF measurements of individual samples. The 16 DIA-PASEF runs of individual samples used for library generation were analyzed using newest versions of Spectronaut (version 18.5) and DIA-NN (version 1.8.1) software tools in library-based setting using the newly generated library as well as in library-free setting showing library-based method to outperform the use of predicted libraries in the terms of identification numbers.
2024-05-31 | PXD047793 | Pride
Project description:These mouse gut lysates contained three sets, with Set A including 21 DDA experiments and 21 DIA experiments, Set C including 24 DDA and 24 DIA experiments, and Set G including 27 DDA RAWs and 26 DIA RAWs (one of the Set G DDA experiments was run twice). Experiment Set A was intended to compare lysosomal compositions among ad libitum feeding, 24h fasted, and 24h fasted and re-fed conditions. Experiment Set C compared lysosomal compositions between germ-free and conventional microbiome states. Experiment Set G compared ad libitum and 24h fasted conditions within stem cells. In each case, these RAWs represent the inputs to lyso-IP, not the lysosomally-enriched samples that are produced from lyso-IP. Sample preparation was performed using the S-TRAP method, with MMTS (methyl methanethiosulfonate) alkalyting free Cys side chains and trypsin digesting C-terminal to lysines and arginines. DDA experiments collected spectra for all 120 minutes of a two hour run, while DIA experiments using the high resolution MS1 (HRMS1) technique collected scans at two different FAIMS compensation voltages for 75 minutes during each 105 minute run, with scan events ending when the B solvent of the gradient reached 45%.
2024-07-26 | MSV000095444 | MassIVE