Browse
Submit Data
Databases
API
Help

Dataset Information

17 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Narrow Precursor Mass Range for DIA-MS Enhances Protein Identification and Quantification in Arabidopsis.

ABSTRACT: Data independent acquisition-mass spectrometry (DIA-MS) is becoming widely utilised for robust and accurate quantification of samples in quantitative proteomics. Here, we describe the systematic evaluation of the effects of DIA precursor mass range on total protein identification and quantification. We show that a narrow mass range of precursors (~250 m/z) for DIA-MS enables a higher number of protein identifications. Subsequent application of DIA with narrow precursor range (from 400 to 650 m/z) on an Arabidopsis sample with spike-in known proteins identified 34.7% more proteins than in conventional DIA (cDIA) with a wide precursor range of 400-1200 m/z. When combining several DIA-MS analyses with narrow precursor ranges (i.e., 400-650, 650-900 and 900-1200 m/z), we were able to quantify 10,099 protein groups with a median coefficient of variation of <6%. These findings represent a 54.7% increase in the number of proteins quantified than with cDIA analysis. This is particularly important for low abundance proteins, as exemplified by the six-protein mix spike-in. In cDIA only five out of the six-protein mix were quantified while our approach allowed accurate quantitation of all six proteins.

SUBMITTER: Zhang H

PROVIDER: S-EPMC8469718 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Narrow precursor window for DIA-MS enhances protein identification and quantification

Project description:Data independent acquistion (DIA) is becoming widely as a method of choice in quantitation proteomics due to its high reproducibility and quantitation accuracy at high-throughput fashion. In this project, we systematically evaluated the effect of the selected precursor mass range for DIA-MS on the protein identification and quantification. We show that a narrow precursor window (400-650 m/z) DIA could identify a 34.7% more proteins than a conventional DIA with a wide precursor window of 400-1200 m/z. When combining the three narrow precursor windows (400-650, 650-900, and 900-1200 m/z) DIA-MS analyses, we obtained a 59.4% increase in the number of proteins quantified than a conventional DIA-MS analysis.

2021-10-01 | PXD026965 | Pride

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides.

Project description:Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

| S-EPMC10190159 | biostudies-literature

Identification and Quantification of Coumarins by UHPLC-MS in Arabidopsis thaliana Natural Populations.

Project description:Coumarins are phytochemicals occurring in the plant kingdom, which biosynthesis is induced under various stress factors. They belong to the wide class of specialized metabolites well known for their beneficial properties. Due to their high and wide biological activities, coumarins are important not only for the survival of plants in changing environmental conditions, but are of great importance in the pharmaceutical industry and are an active source for drug development. The identification of coumarins from natural sources has been reported for different plant species including a model plant Arabidopsis thaliana. In our previous work, we demonstrated a presence of naturally occurring intraspecies variation in the concentrations of scopoletin and its glycoside, scopolin, the major coumarins accumulating in Arabidopsis roots. Here, we expanded this work by examining a larger group of 28 Arabidopsis natural populations (called accessions) and by extracting and analysing coumarins from two different types of tissues-roots and leaves. In the current work, by quantifying the coumarin content in plant extracts with ultra-high-performance liquid chromatography coupled with a mass spectrometry analysis (UHPLC-MS), we detected a significant natural variation in the content of simple coumarins like scopoletin, umbelliferone and esculetin together with their glycosides: scopolin, skimmin and esculin, respectively. Increasing our knowledge of coumarin accumulation in Arabidopsis natural populations, might be beneficial for the future discovery of physiological mechanisms of action of various alleles involved in their biosynthesis. A better understanding of biosynthetic pathways of biologically active compounds is the prerequisite step in undertaking a metabolic engineering research.

| S-EPMC8005220 | biostudies-literature

Segmentation of precursor mass range using "tiling" approach increases peptide identifications for MS1-based label-free quantification.

Project description:Label-free quantification is a powerful tool for the measurement of protein abundances by mass spectrometric methods. To maximize quantifiable identifications, MS(1)-based methods must balance the collection of survey scans and fragmentation spectra while maintaining reproducible extracted ion chromatograms (XIC). Here we present a method which increases the depth of proteome coverage over replicate data-dependent experiments without the requirement of additional instrument time or sample prefractionation. Sampling depth is increased by restricting precursor selection to a fraction of the full MS(1) mass range for each replicate; collectively, the m/z segments of all replicates encompass the full MS(1) range. Although selection windows are narrowed, full MS(1) spectra are obtained throughout the method, enabling the collection of full mass range MS(1) chromatograms such that label-free quantitation can be performed for any peptide in any experiment. We term this approach "binning" or "tiling" depending on the type of m/z window utilized. By combining the data obtained from each segment, we find that this approach increases the number of quantifiable yeast peptides and proteins by 31% and 52%, respectively, when compared to normal data-dependent experiments performed in replicate.

| S-EPMC3607285 | biostudies-literature

Analytical Methods for Quantification and Identification of Intact Glucosinolates in Arabidopsis Roots Using LC-QqQ(LIT)-MS/MS.

Project description:Glucosinolates are biologically active secondary metabolites in Brassicaceae plants that play a critical role in positive and negative interactions between plants and root-associated microbial communities. The aim of this study was to develop a reversed-phase liquid chromatography method to quantify and identify intact glucosinolates in the root of Arabidopsis thaliana (Arabidopsis) grown in non-sterile natural soil by using liquid chromatography-hybrid triple quadruple-linear ion trap (LC-QqQ(LIT)) mass spectrometry. The Synergi Fusion C18-based column was found to be effective for sufficient retention and separation of nine intact glucosinolates without the need for time-consuming desulfation or ion-pairing steps. Method validation results showed satisfactory inter-day and intra-day precision for all glucosinolates except for 4-hydroxyglucobrassicin. Good inter-day and intra-day accuracy and recovery results were observed for glucoiberin, gluconapin, glucobrassicin, 4-methoxyglucobrassicin and neoglucobrassicin. However, for 4-hydroxyglucobrassicin, glucoraphanin and glucoerucin corrections to quantification results might be necessary since the recovery and accuracy results were not optimal. Matrix effects were shown to have a negligible effect on the ionization of all target analytes. The established liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was applied to quantify target intact glucosinolates in Arabidopsis root crude extract of four different wild-type accessions where differences in terms of concentration and composition of intact glucosinolates were observed. Employment of sensitive and selective precursor ion survey scan of m/z 97 in combination with the information-dependent acquisition (IDA) of the enhanced product ion (EPI) dependent scan (Prec97-IDA-EPI) using LC-QqQ(LIT) provided high confidence in structural characterization of diverse intact glucosinolate profiles in complex Arabidopsis root crude extract.

| S-EPMC7826904 | biostudies-literature

Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry.

Project description:The generation of antigen-specific reagents is a significant bottleneck in the study of complex pathogens that express many hundreds to thousands of different proteins or to emerging or new strains of viruses that display potential pandemic qualities and therefore require rapid investigation. In these instances the development of antibodies for example can be prohibitively expensive to cover the full pathogen proteome, or the lead time may be unacceptably long in urgent cases where new highly pathogenic viral strains may emerge. Because genomic information on such pathogens can be rapidly acquired this opens up avenues using mass spectrometric approaches to study pathogen antigen expression, host responses and for screening the utility of therapeutics. In particular, data-independent acquisition (DIA) modalities on high-resolution mass spectrometers generate spectral information on all components of a complex sample providing depth of coverage hitherto only seen in genomic deep sequencing. The spectral information generated by DIA can be iteratively interrogated for potentially any protein of interest providing both evidence of protein expression and quantitation. Here we apply a solely DIA mass spectrometry based methodology to profile the viral antigen expression in cells infected with vaccinia virus up to 9 h post infection without the need for antigen specific antibodies or other reagents. We demonstrate deep coverage of the vaccinia virus proteome using a SWATH-MS acquisition approach, extracting quantitative kinetics of 100 virus proteins within a single experiment. The results highlight the complexity of vaccinia protein expression, complementing what is known at the transcriptomic level, and provide a valuable resource and technique for future studies of viral infection and replication kinetics. Furthermore, they highlight the utility of DIA and mass spectrometry in the dissection of host-pathogen interactions.

| S-EPMC4424405 | biostudies-literature

Fast Proteome Identification and Quantification from Data-Dependent Acquisition-Tandem Mass Spectrometry (DDA MS/MS) Using Free Software Tools.

Project description:The identification of nearly all proteins in a biological system using data-dependent acquisition (DDA) tandem mass spectrometry has become routine for organisms with relatively small genomes such as bacteria and yeast. Still, the quantification of the identified proteins may be a complex process and often requires multiple different software packages. In this protocol, I describe a flexible strategy for the identification and label-free quantification of proteins from bottom-up proteomics experiments. This method can be used to quantify all the detectable proteins in any DDA dataset collected with high-resolution precursor scans and may be used to quantify proteome remodeling in response to drug treatment or a gene knockout. Notably, the method is statistically rigorous, uses the latest and fastest freely-available software, and the entire protocol can be completed in a few hours with a small number of data files from the analysis of yeast.

| S-EPMC6469856 | biostudies-other

Comparison of database search strategies for high precursor mass accuracy MS/MS data.

Project description:In shotgun proteomics, the analysis of tandem mass spectrometry data from peptides can benefit greatly from high mass accuracy measurements. In this study, we have evaluated two database search strategies which use high mass accuracy measurements of the peptide precursor ion. Our results indicate that peptide identifications are improved when spectra are searched with a wide mass tolerance window and precursor mass is used as a filter to discard incorrect matches. Database searches with a peptide data set constrained to peptides within a narrow mass window resulted in fewer peptide identifications but a significantly faster database search time.

| S-EPMC2818881 | biostudies-literature

iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics.

Project description:SUMMARY:We present an R package called iq to enable accurate protein quantification for label-free data-independent acquisition (DIA) mass spectrometry-based proteomics, a recently developed global approach with superior quantitative consistency. We implement the popular maximal peptide ratio extraction module of the MaxLFQ algorithm, so far only applicable to data-dependent acquisition mode using the software suite MaxQuant. Moreover, our implementation shows, for each protein separately, the validity of quantification over all samples. Hence, iq exports a state-of-the-art protein quantification algorithm to the emerging DIA mode in an open-source implementation. AVAILABILITY AND IMPLEMENTATION:The open-source R package is available on CRAN, https://github.com/tvpham/iq/releases and oncoproteomics.nl/iq. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

| S-EPMC7178409 | biostudies-literature

Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM-DIA, and Untargeted DIA.

Project description:Data-independent acquisition (DIA) is an emerging technology for quantitative proteomics. Current DIA focusses on the identification and quantitation of fragment ions that are generated from multiple peptides contained in the same selection window of several to tens of m/z. An alternative approach is WiSIM-DIA, which combines conventional DIA with wide-SIM (wide selected-ion monitoring) windows to partition the precursor m/z space to produce high-quality precursor ion chromatograms. However, WiSIM-DIA has been underexplored; it remains unclear if it is a viable alternative to DIA. We demonstrate that WiSIM-DIA quantified more than 24 000 unique peptides over five orders of magnitude in a single 2 h analysis of a neuronal synapse-enriched fraction, compared to 31 000 in DIA. There is a strong correlation between abundance values of peptides quantified in both the DIA and WiSIM-DIA datasets. Interestingly, the S/N ratio of these peptides is not correlated. We further show that peptide identification directly from DIA spectra identified >2000 proteins, which included unique peptides not found in spectral libraries generated by DDA.

| S-EPMC5817406 | biostudies-literature

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