Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SILAC-based proteomic quantification of chemoattractant-induced cytoskeleton dynamics on a second to minute timescale.

ABSTRACT: Cytoskeletal dynamics during cell behaviours ranging from endocytosis and exocytosis to cell division and movement is controlled by a complex network of signalling pathways, the full details of which are as yet unresolved. Here we show that SILAC-based proteomic methods can be used to characterize the rapid chemoattractant-induced dynamic changes in the actin-myosin cytoskeleton and regulatory elements on a proteome-wide scale with a second to minute timescale resolution. This approach provides novel insights in the ensemble kinetics of key cytoskeletal constituents and association of known and novel identified binding proteins. We validate the proteomic data by detailed microscopy-based analysis of in vivo translocation dynamics for key signalling factors. This rapid large-scale proteomic approach may be applied to other situations where highly dynamic changes in complex cellular compartments are expected to play a key role.

SUBMITTER: Sobczyk GJ

PROVIDER: S-EPMC3971484 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

SILAC-based proteomic quantification of chemoattractant-induced cytoskeleton dynamics on a second to minute timescale.

Sobczyk Grzegorz J GJ Wang Jun J Weijer Cornelis J CJ

Nature communications 20140226

Cytoskeletal dynamics during cell behaviours ranging from endocytosis and exocytosis to cell division and movement is controlled by a complex network of signalling pathways, the full details of which are as yet unresolved. Here we show that SILAC-based proteomic methods can be used to characterize the rapid chemoattractant-induced dynamic changes in the actin-myosin cytoskeleton and regulatory elements on a proteome-wide scale with a second to minute timescale resolution. This approach provides ...[more]

PMID: 24569529

Similar Datasets

SILAC-based proteomic quantification of chemoattractant induced cytoskeleton dynamics on a second to minute timescale

Project description:Cytoskeletal dynamics during cell behaviours ranging from endocytosis and exocytosis to cell division and movement is controlled by a complex network of signalling pathways, the full details of which are as yet unresolved. Here we show that SILAC-based proteomic methods can be used to characterise the rapid chemoattractant induced dynamic changes in the actin-myosin cytoskeleton and regulatory elements on a proteome wide scale with a second to minute timescale resolution. This approach provides novel insights in the ensemble kinetics of key cytoskeletal constituents and association of known and novel identified binding proteins. We validate the proteomic data by detailed microscopy based analysis of in vivo translocation dynamics for key signalling factors. This rapid large-scale proteomic approach may be applied to other situations where highly dynamic changes in complex cellular compartments are expected to play a key role.

2014-02-26 | PXD000738 | Pride

Proteomics of microparticles with SILAC Quantification (PROMIS-Quan): a novel proteomic method for plasma biomarker quantification.

Project description:Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However, the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan-PROteomics of MIcroparticles with Super-Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification. PROMIS-Quan enables a two-step relative and absolute SILAC quantification. First, plasma microparticle proteomes are quantified relative to a super-SILAC mix composed of cell lines from distinct origins. Next, the absolute amounts of selected proteins of interest are quantified relative to the super-SILAC mix. We applied PROMIS-Quan to prostate cancer and compared plasma microparticle samples of healthy individuals and prostate cancer patients. We identified in total 5374 plasma-microparticle proteins, and revealed a predictive signature of three proteins that were elevated in the patient-derived plasma microparticles. Finally, PROMIS-Quan enabled determination of the absolute quantitative changes in prostate specific antigen (PSA) upon treatment. We propose PROMIS-Quan as an innovative platform for biomarker discovery, validation, and quantification in both the biomedical research and in the clinical worlds.

| S-EPMC4390257 | biostudies-literature

Exploring Market State and Stock Interactions on the Minute Timescale.

Project description:A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is limited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more stable. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective.

| S-EPMC4762888 | biostudies-literature

SILAC-based quantitative proteomic analysis of gastric cancer secretome.

Project description:Gastric cancer is a commonly occurring cancer in Asia and one of the leading causes of cancer deaths. However, there is no reliable blood-based screening test for this cancer. Identifying proteins secreted from tumor cells could lead to the discovery of clinically useful biomarkers for early detection of gastric cancer.A SILAC-based quantitative proteomic approach was employed to identify secreted proteins that were differentially expressed between neoplastic and non-neoplastic gastric epithelial cells. Proteins from the secretome were subjected to SDS-PAGE and SCX-based fractionation, followed by mass spectrometric analysis on an LTQ-Orbitrap Velos mass spectrometer. Immunohistochemical labeling was employed to validate a subset of candidates using tissue microarrays.We identified 2205 proteins in the gastric cancer secretome of which 263 proteins were overexpressed greater than fourfold in gastric cancer-derived cell lines as compared to non-neoplastic gastric epithelial cells. Three candidate proteins, proprotein convertase subtilisin/kexin type 9 (PCSK9), lectin mannose binding 2 (LMAN2), and PDGFA-associated protein 1 (PDAP1) were validated by immunohistochemical labeling.We report here the largest cancer secretome described to date. The novel biomarkers identified in the current study are excellent candidates for further testing as early detection biomarkers for gastric adenocarcinoma.

| S-EPMC3804263 | biostudies-other

Improved SILAC Quantification with Data-Independent Acquisition to Investigate Bortezomib-Induced Protein Degradation.

Project description:Stable isotope labeling by amino acids in cell culture (SILAC) coupled to data-dependent acquisition (DDA) is a common approach to quantitative proteomics with the desirable benefit of reducing batch effects during sample processing and data acquisition. More recently, using data-independent acquisition (DIA/SWATH) to systematically measure peptides has gained popularity for its comprehensiveness, reproducibility, and accuracy of quantification. The complementary advantages of these two techniques logically suggests combining them. Here we develop a SILAC-DIA-MS workflow using free, open-source software. We empirically determine that using DIA achieves similar peptide detection numbers as DDA and that DIA improves the quantitative accuracy and precision of SILAC by an order of magnitude. Finally, we apply SILAC-DIA-MS to determine protein turnover rates of cells treated with bortezomib, an FDA-approved 26S proteasome inhibitor for multiple myeloma and mantle cell lymphoma. We observe that SILAC-DIA produces more sensitive protein turnover models. Of the proteins determined to be differentially degraded by both acquisition methods, we find known proteins that are degraded by the ubiquitin-proteasome pathway, such as HNRNPK, EIF3A, and IF4A1/EIF4A-1, and a slower turnover for CATD, a protein implicated in invasive breast cancer. With improved quantification from DIA, we anticipate that this workflow will make SILAC-based experiments like protein turnover more sensitive.

| S-EPMC8256668 | biostudies-literature

Chemoattractant-induced phosphatidylinositol 3,4,5-trisphosphate accumulation is spatially amplified and adapts, independent of the actin cytoskeleton.

Project description:Experiments in amoebae and neutrophils have shown that local accumulations of phosphatidylinositol 3,4,5-trisphosphate [PI(3,4,5)P(3)] mediate the ability of cells to migrate during gradient sensing. To define the nature of this response, we subjected Dictyostelium discoideum cells to measurable temporal and spatial chemotactic inputs and analyzed the accumulation of PI(3,4,5)P(3) on the membrane, as well as the recruitment of the enzymes phosphoinositide 3-kinase and PTEN. In latrunculin-treated cells, spatial gradients elicited a PI(3,4,5)P(3) response only on the front portion of the cell where the response increased more steeply than the gradient and did not depend on its absolute concentration. Phosphoinositide 3-kinase bound to the membrane only at the front, although it was less sharply localized than PI(3,4,5)P(3). Membrane-bound PTEN was highest at the rear and varied inversely with receptor occupancy. The localization of PI(3,4,5)P(3) was enhanced further in untreated polarized cells containing an intact cytoskeleton. Interestingly, the treated cells could respond to two independent gradients simultaneously, demonstrating that a response at the front does not necessarily inhibit the back. Combinations of temporal and spatial stimuli provided evidence of an inhibitory process and showed that a gradient generates a persistent steady-state response independent of a previous history of exposure to chemoattractant. These results support a local excitation/global inhibition model and argue against other schemes proposed to explain directional sensing.

| S-EPMC428453 | biostudies-literature

Hepatic SILAC proteomic data from PANDER transgenic model.

Project description:This article contains raw and processed data related to research published in "Quantitative Proteomic Profiling Reveals Hepatic Lipogenesis and Liver X Receptor Activation in the PANDER Transgenic Model" (M.G. Athanason, W.A. Ratliff, D. Chaput, C.B. MarElia, M.N. Kuehl, S.M., Jr. Stevens, B.R. Burkhardt (2016)) [1], and was generated by "spike-in" SILAC-based proteomic analysis of livers obtained from the PANcreatic-Derived factor (PANDER) transgenic mouse (PANTG) under various metabolic conditions [1]. The mass spectrometry output of the PANTG and wild-type B6SJLF mice liver tissue and resulting proteome search from MaxQuant 1.2.2.5 employing the Andromeda search algorithm against the UniprotKB reference database for Mus musculus has been deposited to the ProteomeXchange Consortium (http://www.proteomexchange.org) via the PRIDE partner repository with dataset identifiers PRIDE: PXD004171 and doi:10.6019/PXD004171. Protein ratio values representing PANTG/wild-type obtained by MaxQuant analysis were input into the Perseus processing suite to determine statistical significance using the Significance A outlier test (p<0.05). Differentially expressed proteins using this approach were input into Ingenuity Pathway Analysis to determined altered pathways and upstream regulators that were altered in PANTG mice.

| S-EPMC5018088 | biostudies-literature

Altered Motor Activity Patterns within 10-Minute Timescale Predict Incident Clinical Alzheimer's Disease.

Project description:BackgroundFractal motor activity regulation (FMAR), characterized by self-similar temporal patterns in motor activity across timescales, is robust in healthy young humans but degrades with aging and in Alzheimer's disease (AD).ObjectiveTo determine the timescales where alterations of FMAR can best predict the clinical onset of AD.MethodsFMAR was assessed from actigraphy at baseline in 1,077 participants who had annual follow-up clinical assessments for up to 15 years. Survival analysis combined with deep learning (DeepSurv) was used to examine how baseline FMAR at different timescales from 3 minutes up to 6 hours contributed differently to the risk for incident clinical AD.ResultsClinical AD occurred in 270 participants during the follow-up. DeepSurv identified three potential regions of timescales in which FMAR alterations were significantly linked to the risk for clinical AD: <10, 20-40, and 100-200 minutes. Confirmed by the Cox and random survival forest models, the effect of FMAR alterations in the timescale of <10 minutes was the strongest, after adjusting for covariates.ConclusionsSubtle changes in motor activity fluctuations predicted the clinical onset of AD, with the strongest association observed in activity fluctuations at timescales <10 minutes. These findings suggest that short actigraphy recordings may be used to assess the risk of AD.

| S-EPMC10977378 | biostudies-literature

SILAC-Based Quantitative Proteomic Analysis Unveils Arsenite-Induced Perturbation of Multiple Pathways in Human Skin Fibroblast Cells.

Project description:Humans are exposed to arsenic species through inhalation, ingestion, and dermal contact, which may lead to skin, liver, and bladder cancers as well as cardiovascular and neurological diseases. The mechanisms underlying the cytotoxic and carcinogenic effects of arsenic species, however, remain incompletely understood. To exploit the mechanisms of toxicity of As(III), we employed stable isotope labeling by amino acids in cell culture (SILAC) together with LC/MS/MS analysis to quantitatively assess the As(III)-induced perturbation of the entire proteome of cultured human skin fibroblast cells. Shotgun proteomic analysis on an LTQ-Orbitrap Velos mass spectrometer facilitated the quantification of 3880 proteins, 130 of which were quantified in both forward and reverse SILAC-labeling experiments and displayed significant alterations (>1.5 fold) upon arsenite treatment. Targeted analysis on a triple-quadrupole mass spectrometer in multiple-reaction monitoring (MRM) mode confirmed the quantification results of some select proteins. Ingenuity pathway analysis revealed the arsenite-induced alteration of more than 10 biological pathways, including the Nrf2-mediated oxidative stress response pathway, which is represented by the upregulation of nine proteins in this pathway. In addition, arsenite induced changes in expression levels of a number of selenoproteins and metallothioneins. Together, the results from the present study painted a more complete picture regarding the biological pathways that are altered in human skin fibroblast cells upon arsenite exposure.

| S-EPMC5616127 | biostudies-literature

The SILAC fly allows for accurate protein quantification in vivo.

Project description:Stable isotope labeling by amino acids in cell culture (SILAC) is widely used to quantify protein abundance in tissue culture cells. Until now, the only multicellular organism completely labeled at the amino acid level was the laboratory mouse. The fruit fly Drosophila melanogaster is one of the most widely used small animal models in biology. Here, we show that feeding flies with SILAC-labeled yeast leads to almost complete labeling in the first filial generation. We used these "SILAC flies" to investigate sexual dimorphism of protein abundance in D. melanogaster. Quantitative proteome comparison of adult male and female flies revealed distinct biological processes specific for each sex. Using a tudor mutant that is defective for germ cell generation allowed us to differentiate between sex-specific protein expression in the germ line and somatic tissue. We identified many proteins with known sex-specific expression bias. In addition, several new proteins with a potential role in sexual dimorphism were identified. Collectively, our data show that the SILAC fly can be used to accurately quantify protein abundance in vivo. The approach is simple, fast, and cost-effective, making SILAC flies an attractive model system for the emerging field of in vivo quantitative proteomics.

| S-EPMC2953914 | 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