Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Improved detection and quantitation of RNA-interactome using DIA SILAC

ABSTRACT: Detection and quantitation of the RNA-interacting proteome is commonly achieved applying SILAC labeling, following by data-dependent acquisition (DDA) of the proteomic data. However, the limited sensitivity of the DDA approach often restricts protein detection to those with higher expression in cells, necessitating peptide fractionation prior to mass spectrometry. Here we report a pipeline for SILAC analysis using data-independent acquisition (DIA), with a spectral library constructed using gas-phase window separation of light materials followed by in silico prediction of heavy spectra. The resulting DIA datasets had 30-40% more detected proteins compared to the same biological materials analyzed using DDA, while abolishing the requirement for pre-MS reverse-phase fractionation of peptides. Lower inter-replicate variations were seen with DIA for proteins detected in both acquisitions. As a test, we determined the effects of arsenite treatment on the RNA-bound proteome of HEK cells recovered following total RNA-associated proteome purification (TRAPP). The DIA dataset yielded clear GO term enrichment for RNA-binding proteins involved in cellular stress responses, while enrichment in the DDA dataset was relatively weak. Dataset normalization of with Cyclic Loess slightly improved the DDA-DIA correlation over median normalization for DIA datasets, but not for DDA dataset relative to default MaxQuant normalization. Overall, the DIA SILAC approach improved both protein detection and biological significance over conventional DDA SILAC.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture, Kidney

SUBMITTER: Thomas Tan

LAB HEAD: David Tollervey

PROVIDER: PXD045318 | Pride | 2023-12-19

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	Tan_etal_2023_C1_F1.raw	Raw
	Tan_etal_2023_C1_F2.raw	Raw
	Tan_etal_2023_C1_F3.raw	Raw
	Tan_etal_2023_C2_F1.raw	Raw
	Tan_etal_2023_C2_F2.raw	Raw

Items per page:

1 - 5 of 46

Similar Datasets

DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics

Project description:This dataset consists of 44 raw MS files, comprising 27 DIA (SWATH) and 15 DDA runs on a TripleTOF 5600 and of two raw mass spectrometry files acquired on a Q Exactive. The composition of the dataset is described in the manuscript by Tsou et al., titled: "DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics", Nature Methods, in press Raw files are deposited here in ProteomeXchange and are associated with the DIA-Umpire processed data. All DIA-Umpire processed results for each sample together with DDA results are deposited in separated folders. Also see the "DataSampleID.xlsx" associated with this Readme file. Internal reference from the Gingras lab ProHits implementation: Project 94, Export version VS2 (Tsou_DIA-Umpire)

2015-01-21 | PXD001587 | Pride

Yeast calibration curve dataset

Project description:The yeast calibration curve dataset was acquired to compare the accuracy of DIA tools with decreasing contents of target peptides. Four samples (Y1, Y2, Y3 and Y4) with decreasing contents (200, 100, 50 and 25 ng, respectively) of analytes (yeast tryptic peptides) and a high content of background peptides (800 ng human tryptic peptides constantly) were analyzed in triplicate using LC-DIA-MS/MS. The DIA data were processed by different DIA tools based on the spectral library generated from the DDA data. The accuracy of different DIA tools was compared.

2023-10-25 | PXD039399 | Pride

Benchmarking of DDA and plexDIA analysis with SILAC-labelled Hela cell samples

Project description:SILAC labelled Hela cell lysates were mixed in defined ratios and analysed using both DDA and DIA methods, to serve as a benchmark for the novel plexDIA features of DIA-NN.

2023-10-19 | PXD039580 | Pride

Reproducibility, specificity and accuracy of DIA quantification - DIA analysis

Project description:For the complete description see the manuscript. Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined.

2019-11-08 | PXD012988 | Pride

Benchmarking plexDIA analysis with SILAC-labelled Hela cell samples

Project description:SILAC labelled Hela cell lysates were mixed in defined ratios and analysed using both DDA and DIA methods, to serve as a benchmark for the novel plexDIA features of DIA-NN.

2023-10-19 | PXD039578 | Pride

Characterization of cerebrospinal fluid via data-independent acquisition mass spectrometry

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

Reproducibility, specificity and accuracy of DIA quantification - Spectral Libraries

2019-11-08 | PXD012987 | Pride

Profiling the proteomic inflammatory state of human astrocytes using DIA mass spectrometry

Project description:In the present study we used DIA MS to characterize the profile of over 4,000 proteins in immortalized human astrocytes exposed to TNF, IL-1β, and LPS, as well as in primary human astrocytes cocultured with brain endothelial cells and exposed to LPS. For both immortalized and primary astrocytes, DIA MS data were matched against a spectral library generated by merging DDA MS runs from both unfractionated and fractionated cell lysates.

2018-12-10 | PXD009891 | Pride

Specter - Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics

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

The phosphoproteome of the coral model system Aiptasia

Project description:The endosymbiotic interaction established by cnidarians and photosynthetic dinoflagellate algae is the foundation of coral reef ecosystems. This essential interaction is globally threatened to breakdown by anthropogenic disturbance. As such, it is compelling to understand the molecular mechanisms underpinning the cnidarian-algal association. We investigated phosphorylation-mediated protein signaling as a mechanism of regulation of the cnidarian-algal interaction, and we report on the generation of the first phosphoproteome for the coral model system Aiptasia. Using mass spectrometry-based phosphoproteomics in data-independent acquisition (DIA) allowed consistent quantification of over 3,000 phosphopeptides totaling more than 1,600 phosphoproteins across aposymbiotic (symbiont-free) and symbiotic anemones. Additionally, to allow for discrimination between translational regulation and post-translational phosphorylation, we generated a total proteome dataset from the same anemones and used it for phosphopeptide normalization against protein amount. While quantification of protein phosphorylation relied upon the generation of a spectrum library generated by data-dependent acquisition (DDA), total protein quantification in DIA was conducted "library-free" (directDIA) in SpectronautX. DirectDIA allowed consistent quantification of 20,215 peptides, totaling 4,121 proteins (3,518 protein groups) across biological samples.

2019-11-06 | PXD014076 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data