Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mapping Nanoscale-to-Single Cell Phosphoproteomic Landscape by Chip-DIA

ABSTRACT: Mapping dynamic protein phosphorylation within trace samples deciphers cellular heterogeneity in signaling events and network activations for better understanding biology and disease mechanisms. We present a Chip-DIA strategy combining a microfluidic chip and data-independent acquisition mass spectrometry (DIA-MS) for sensitive nanoscale-to-single cell phosphoproteomic profiling.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Hsiung-Lin Tu, Yu-Ju Chen

PROVIDER: PXD045210 | JPOST Repository | Sun Sep 08 00:00:00 BST 2024

REPOSITORIES: jPOST

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	20220107_MDPhospho_DIA_50cells_TiO2_ND_R1.raw	Raw
	20220107_MDPhospho_DIA_50cells_TiO2_ND_R2.raw	Raw
	20220107_MDPhospho_DIA_50cells_TiO2_ND_R3.raw	Raw
	20220107_MDPhospho_DIA_50cells_TiO2_R1.raw	Raw
	20220107_MDPhospho_DIA_50cells_TiO2_R2.raw	Raw

Items per page:

1 - 5 of 20

Similar Datasets

Streamlined Single-cell Proteomics by an Integrated Microfluidic Chip and Data-independent Acquisition Mass Spectrometry

Project description:Single-cell proteomics can reveal cellular phenotypic heterogeneity and cell-specific functional networks underlying biological processes. Here, we present a streamlined workflow combining microfluidic chips for all-in-one proteomic sample preparation and data-independent acquisition (DIA) mass spectrometry (MS) for proteomics analysis down to the single-cell level. The proteomics chips enable multiplexed and automated cell isolation/counting/imaging and sample processing in a single device. Combining chip-based sample handling with DIA-MS using project-specific mass spectral libraries, we profile on average ~1,500 protein groups across 20 single mammalian cells. Applying the chip-DIA workflow to profile the proteomes of adherent and non-adherent malignant cells, we cover a dynamic range of 5 orders of magnitude with good reproducibility and <16% missing values between runs. Taken together, the chip-DIA workflow offers all-in-one cell characterization, analytical sensitivity and robustness, and the option to add additional functionalities in the future, thus providing a basis for advanced single-cell proteomics applications.

2021-11-12 | PXD023325 | JPOST Repository

Elstera litoralis strain:Dia-1

Project description:Elstera litoralis strain:Dia-1 Genome sequencing and assembly

| PRJNA278930 | ENA

scRNAseq RAT DIA WT and DMD 12months

Project description:scRNAseq RAT DIA WT and DMD 12months

| PRJNA814255 | ENA

PBMC_A data-independent acquisition (DIA)-based quantification workflow for proteome analysis of 5000 cells

Project description:An optimized workflow for nanoscale proteomics is applied to the proteomics profiling of 5000 PBMCs in DIA mode on Orbitrap Lumos.

2022-04-27 | PXD029721 | Pride

ProteomicAnalysis(DIA)ofmuscleinvasivebladdercancerpatient-derivedxenografts

Project description:Accesstoproteomicfiles(DIA)ofMIBCpatient-derivedxenografts(N=8)

| EGAD00010002512 | EGA

Benchmarking DIA-type Proteomics Using Large-Scale Inter-Patient Heterogeneity Dataset

Project description:Benchmarking Proteomics Quantitation in DIA-type data using real patient material to create a benchmark dataset comprising inter-patient heterogeneity

| EGAS00001005589 | EGA

Proteomicanalysis(DIA)ofpatientswithnon-muscleinvasiveandmuscleinvasivebladdercancer

Project description:Accesstoproteomicfiles(DIA)ofpatientswithtreatment-naiveMIBC(N=51),treatment-naiveNMIBC(N=17)andneoadjuvantMIBC(N=11)

| EGAD00010002513 | EGA

Ultrasensitive single cell proteomics based on an integrated stationary nanoliter-droplet chip

Project description:Single-cell proteomic analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems and heterogeneous populations. Herein, we developed an automated and multiplexed sample processing workflow combining integrated stationary nanoliter-droplet chips and a commercial liquid dispensing instrument for single-cell proteomics. Utilizing this robust workflow and data independent acquisition (DIA) mode, we can profile up to 3000 protein groups in single mammalian cell with a remarkable improvement, achieving a dynamic range of 6 orders of magnitude with good reproducibility. The chip-based system was further applied in single lymphocyte analysis, with protein groups of 617 and 1089 identified at the single B and T lymphocyte, demonstrating the promising applications of single-cell insights in clinical research, especially in cancer immunology.

2024-02-01 | PXD049099 | iProX

The regulatory landscape of the yeast phosphoproteome – quantitative phosphoproteomic DIA data

Project description:We systematically exposed the yeast Saccharomyces cerevisiae to a wide array of 101 perturbations and generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses. Perturbations covered broad classes of environmental, chemical, and drug perturbations and systematically targeted diverse aspects of yeast cell biology. Importantly, most perturbations focused on capturing early signaling responses (5 min treatments), with the reasoning that functional phosphorylation of specific substrates occurs rapidly following stimulation, but promiscuous phosphorylation and confounding changes in transcript and protein abundances takes place more slowly. Treatments were done in 6 biological replicates and large-scale phosphoproteomics was enabled by our recently developed R2-P2 (Rapid-Robotic Phosphoproteomics) sample preparation workflow in combination with DIA measurements. This submission includes 559 quantitative phospho DIA-MS raw files that went into the analysis of the systems-level phosphorylation stress response.

2023-08-27 | PXD035050 | Pride

Benchmark Dataset DIA Clinical Proteomics LymphNodes E.coli

Project description:Real patient variability Benchmark Dataset for Optimization of DIA data analysis workflows

| EGAD00010002223 | EGA