Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

ABSTRACT: Cellular heterogeneity is important to biological processes, including cancer and development. However, proteome heterogeneity is largely unexplored because of the limitations of existing methods for quantifying protein levels in single cells. To alleviate these limitations, we developed Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS), and validated its ability to identify distinct human cancer cell types based on their proteomes. We used SCoPE-MS to quantify thousands of proteins in differentiating mouse embryonic stem (ES) cells. The single-cell proteomes enabled us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicated coordinated mRNA and protein covariation. Yet many genes exhibited functionally concerted and distinct regulatory patterns at the mRNA and the protein levels, suggesting that post-transcriptional regulatory mechanisms contribute to proteome remodeling during lineage specification, especially for developmental genes. SCoPE-MS is broadly applicable to measuring proteome configurations of single cells and linking them to functional phenotypes, such as cell type and differentiation potentials.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (ncbitaxon:9606) Mus Musculus (ncbitaxon:10090)

SUBMITTER: Nikolai Slavov

PROVIDER: MSV000080489 | MassIVE | Mon Jan 23 08:00:00 GMT 2017

SECONDARY ACCESSION(S): PXD005770

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

Project description:Cellular heterogeneity is important to biological processes, including cancer and development. However, proteome heterogeneity is largely unexplored because of the limitations of existing methods for quantifying protein levels in single cells. To alleviate these limitations, we developed Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS), and validated its ability to identify distinct human cancer cell types based on their proteomes. We used SCoPE-MS to quantify thousands of proteins in differentiating mouse embryonic stem (ES) cells. The single-cell proteomes enabled us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicated coordinated mRNA and protein covariation. Yet many genes exhibited functionally concerted and distinct regulatory patterns at the mRNA and the protein levels, suggesting that post-transcriptional regulatory mechanisms contribute to proteome remodeling during lineage specification, especially for developmental genes. SCoPE-MS is broadly applicable to measuring proteome configurations of single cells and linking them to functional phenotypes, such as cell type and differentiation potentials.

2018-02-19 | MSV000082077 | MassIVE

Single Cell Proteomics and the Carrier Proteome Effect

Project description:Single cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method that utilizes isobaric labels to quantify multiplexed single cell proteomes. While this technique has generated great excitement, the technologies underlying SCoPE-MS - isobaric labels and mass spectrometry - comprise technical limitations with the potential to unfavorably impact data quality and biological interpretation. These limitations are due to the carrier proteome, a sample added at 25-500x single cell proteomes to enable peptide identifications. Here, we perform SCoPE-MS experiments with increasing amounts of carrier proteome and evaluate quantitative accuracy as it relates to mass analyzer dynamic range, multiplexing level, and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy – we term this the carrier proteome effect. Based on our findings, we provide guidance on experimental design, data collection, and data analysis to limit the impact of the carrier proteome effect within SCoPE-MS measurements.

2020-12-16 | PXD016573 | Pride

Single Cell Proteomics and the Carrier Proteome Effect

Project description:Single cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry) comprise technical limitations with the potential to effect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500x single cell proteomes, is used to enable peptide identifications. Here, we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy as it relates to mass analyzer dynamic range, multiplexing level, and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single Cell Proteomics Companion a program that enables rapid evaluation of single cell proteomics data and recommends instrument and data analysis parameters for improved data quality.

2020-08-16 | MSV000085959 | MassIVE

Single-cell multi-omics dataset of hepatocellular carcinoma cell lines

Project description:We interrogated single-cell transcriptomes, proteomes and epigenetic information revealing metastasis potential heterogeneity in 5 HCC cell lines across metastasis capacity.

2022-05-31 | GSE188289 | GEO

Metabolic switches and adaptations deduced from the proteomes of Streptomyces coelicolor wild type and phoP mutant grown in batch culture

Project description:Bacteria in the genus Streptomyces are soil dwelling oligotrophs and important producers of secondary metabolites. Previously we showed that global mRNA expression was subject to a series of metabolic and regulatory switches during the life time of a fermentor batch culture of S. coelicolor M145. Here we analyse the proteome from eight time points from the same fermentor culture and, as phosphate availability is an important regulator of secondary metabolite production, compare this to the proteome of a similar time course from an S. coelicolor mutant, INB201 (M-NM-^TphoP), defective in the control of phosphate utilisation. The proteomes provide a detailed view of enzymes involved in central carbon and nitrogen metabolism. Trends in protein expression over the time courses were deduced from a protein abundance index which also revealed the importance of stress pathway proteins in both cultures. As expected the M-NM-^TphoP mutant was deficient in expression of PhoP-dependent genes and several putatively compensatory metabolic and regulatory pathways for phosphate scavenging were detected. Notably there is a succession of switches that co-ordinately induce the production of enzymes for five different secondary metabolite biosynthesis pathways over the course of the batch cultures and these were not confined to the stationary phase. 36 samples, no replicates; one hour resolution from 23-36h and 41-48h; half hour resolution from 36-41h; two hour resolution 48-60h; sample missing for 34 h

2011-12-31 | E-GEOD-31068 | biostudies-arrayexpress

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

Deep proteomic analysis of murine erythroid differentiation

Project description:Deciphering cellular proteomes is critical for our understanding of erythropoiesis. To better comprehend commonly used models of erythropoiesis, we obtained absolute quantification of proteins expressed in cultured primary murine bone-marrow derived erythroblasts throughout erythroid differentiation. These data were compared to proteomes from Friend murine erythroleukemia (MEL), G1ER, and MEDEP cells. Overall, more than 7200 proteins were quantified in at least one of these models of murine erythropoiesis. Protein expression during differentiation of MEDEP cells was most similar to that of differentiating primary murine erythroid cells, while proteomes of Friend MEL and G1ER cells were more distantly related. Comparison of the proteomes of murine and human erythroblasts revealed species-linked variability, but these differences were not as dramatic as those observed comparing human and murine transcriptomes during erythroid differentiation. To functionally validate the differences between transcriptional and translational control, heme synthesis was inhibited in MEDEP cells and the effects on the transcriptome and the proteome examined. While heme deficiency had minimal global effect on the cellular proteome, globin proteins were significantly down regulated, supporting observations heme regulation of globin synthesis is tightly regulated at the protein level to avoid deleterious over production of globin chains. As predicted, heme deficiency led to up regulation of -aminolevulinic acid synthase 2 (Alas2) protein. Interestingly, Alas1 was upregulated at both the transcriptional and protein levels. Characterization of cellular proteomes during erythropoiesis has potential to yield insight into mechanistic principles of human disease, as well as reveal novel therapeutic targets.

2020-04-20 | GSE120379 | GEO

Comparative single-cell transcriptional and proteomic atlas of clinical-grade injectable mesenchymal source tissues

Project description:Bone marrow aspirate concentrate (BMAC) and adipose-derived stromal vascular fraction (ADSVF) are the most marketed stem cell therapies to treat a variety of conditions in the general population and elite athletes. Both tissues have been used interchangeably clinically even though their detailed composition, heterogeneity, and mechanisms of action have neither been rigorously inventoried nor compared. This lack of information has prevented investigations into ideal dosages and has facilitated anecdata and misinformation. Here, we analyzed single-cell transcriptomes, proteomes, and flow cytometry profiles from paired clinical-grade BMAC and ADSVF. This comparative transcriptional atlas challenges the prevalent notion that there is one therapeutic cell type present in both tissues. We also provide data of surface markers that may enable isolation and investigation of cell (sub)populations. Furthermore, the proteome atlas highlights intertissue and interpatient heterogeneity of injected proteins with potentially regenerative or immunomodulatory capacities.

2024-07-22 | GSE217244 | GEO

Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors and selection for translation fidelity

Project description:The protein translation machinery and the genes it decodes co-evolved to achieve production throughput and accuracy. Nonetheless translation errors are frequent and they affect physiology, fitness and protein evolution. Mapping translation errors in proteomes and understanding their causes was hindered by lack of a proteome-wide experimental methodology. Here, we present the first methodology for systematic detection and quantification of errors in entire proteomes. Following proteome mass-spectrometry, we identify in E. coli and S. cerevisiae peptides whose mass indicates specific amino acid substitutions. Most substitutions occur due to codon-anticodon mispairing within the ribosome. we performed a deep RNA sequencing of the bacterial tRNA pool at the same time points in which we measured the proteome during E. coli culture growth. We compared ratio of abundances between congate and near-cognate tRNAs and found the changes corespond with changes in translation errors.

2019-04-18 | GSE128812 | GEO

Microscopy-based functional single cell proteomic profiling of cells with abnormal DNA damage response dynamics

Project description:Tumor heterogeneity is an important source of cancer therapy resistance. Single cell proteomics has the potential to decipher protein content leading to heterogeneous cellular phenotypes. Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) is a recently developed, promising, unbiased proteomic profiling techniques, which allows profiling several tens of single cells for >1000 proteins per cell. However, a method to link single cell proteomes with cellular behaviors is needed to advance this type of profiling technique. Here, we developed a microscopy-based functional single cell proteomic profiling technology, called FUNpro, to link the proteome of individual cells with phenotypes of interest, even if the phenotypes are dynamic or the cells of interest are sparse. FUNpro enables one i) to screen thousands of cells with subcellular resolution and monitor (intra)cellular dynamics using a custom-built microscope, ii) to real-time analyze (intra)cellular dynamics of individual cells using an integrated cell tracking algorithm, iii) to promptly isolate the cells displaying phenotypes of interest, and iv) to single cell proteomically profile the isolated cells. We applied FUNpro to proteomically profile a newly identified small subpopulation of U2OS osteosarcoma cells displaying an abnormal, prolonged DNA damage response (DDR) after ionizing radiation (IR). With this, we identified the PDS5A protein contributing to the abnormal DDR dynamics and helping the cells survive after IR.

2022-07-07 | PXD034370 | 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