Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single cell

ABSTRACT: Single cell

TISSUE(S): ['lung']

SUBMITTER: Shijie Tang

PROVIDER: OEX00015743 | NODE | 2022-01-19

REPOSITORIES: NODE

ACCESS DATA

Json Xml

Similar Datasets

GNPS - Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics

Project description:We combined the Single-probe single cell MS(SCMS) experimental technique with a bioinformatics software package, SinCHet-MS (Single Cell Heterogeneity for Mass Spectrometry), to characterize changes of tumor heterogeneity, quantify cell subpopulations, and prioritize the metabolite biomarkers of each subpopulation.

2023-06-15 | MSV000092178 | GNPS

GNPS - Single cell amine metabolomics raw files

Project description:Targeted and untargeted single cell amine metabolomics raw files and metadata

2024-06-15 | MSV000095047 | GNPS

GNPS - Single cell Co-culture with fluorescence microscopy project

Project description:Raw data for Metabolomics Studies of Cell-Cell Interactions using Single Cell Mass Spectrometry Combined with Fluorescence Microscopy

2022-05-19 | MSV000089496 | GNPS

single cell sequencing of mouse lung single cell suspensions

Project description:single cell sequencing of mouse lung single cell suspensions

| PRJNA1008562 | ENA

Facile sample preparation for quantitative single-cell proteomics

Project description:Large numbers of cells are generally required for quantitative global proteome profiling due to the significant surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations, such as circulating tumor cells (CTCs) and early metastatic cells. Herein we report a facile mass spectrometry (MS)-based single-cell proteomics method that capitalizes on a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, for greatly reducing the surface adsorption losses by ~20-fold for effective single-tube processing of single cells, thus significantly improving detection sensitivity for single-cell proteomic analysis. With standard MS platforms, the method allows for the first time precise, label-free, reliable quantification of hundreds of proteins from single human cells in a simple, convenient manner. When applied to a patient CTC-derived xenograft (PCDX) model, the method can reveal distinct protein signatures between primary tumor cells and early metastases to the lungs at the single-cell resolution. The approach paves the way for routine, precise quantitative single-cell proteomic analysis.

2021-06-07 | PXD017075 | JPOST Repository

Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a tumor cell

Project description:The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of ~1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a tumor cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 1804–3349, 1778–3049 and 1074–2487 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27), respectively. Benefiting from the flexible cell picking-up ability, we study tumor cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.

2023-10-30 | PXD041966 |