Browse
Submit Data
Databases
API
Help

Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Differential expression of GFP+ and RFP+ TriPyMT cells

ABSTRACT: Tri-PyMT cell is a breast tumor cell line established from Tri-PyMT (MMTV-PyMT/fsp1-Cre/Rosa26-RGFP) primary breast tumors. The cells switch from RFP+ to GFP+ during epithelial to mesenchymal transition (EMT). Differential expressed genes between EMT and non-EMT tumor cells will reveal interesting targets for anti-EMT approaches. Tri-PyMT cells were culture in medium with serum (10%FBS). Cells were treated with or without 4-hydroperoxy-cyclophosphamide (4µM) for 7 days and sorted by flow cytometry into RFP+ and GFP+ subpopulations for RNA extraction and subsequent RNA-Seq analysis.

ORGANISM(S): Mus musculus

SUBMITTER: Dingcheng Gao

PROVIDER: E-GEOD-72480 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Differential expression of GFP+ and RFP+ TriPyMT cells

Project description:Tri-PyMT cell is a breast tumor cell line established from Tri-PyMT (MMTV-PyMT/fsp1-Cre/Rosa26-RGFP) primary breast tumors. The cells switch from RFP+ to GFP+ during epithelial to mesenchymal transition (EMT). Differential expressed genes between EMT and non-EMT tumor cells will reveal interesting targets for anti-EMT approaches.

2015-09-01 | GSE72480 | GEO

Single cell sequencing of Tri-PyMT cells

Project description:To study the contribution of epithelial-to-mesenchymal transition (EMT) in tumor metastasis, we established an EMT lineage tracing model in a multiple-transgenic mouse (MMTV-PyMT/Fsp1-Cre/Rosa26mT/mG, Tri-PyMT). In this model, breast tumor cells that underwent EMT would irreversibly switch their expression of the fluorescent marker from RFP+ to GFP+ due to the mesenchymal specific Cre expression. Surprisingly, we found that metastatic tumor cells did not convert from RFP+ to GFP+. Lung metastases were predominantly composed of RFP+ tumor cells persistently exhibiting epithelial phenotypes. These findings challenge the concept that EMT is required for metastasis, and have attracted vigorous discussion about its true contributions to metastasis. One of the major concerns with the EMT lineage tracing model is that expression of GFP only indicates the complete EMT transition. The partial-EMT programming, a novel concept describing tumor cells exhibiting both epithelial and mesenchymal features, could possibly fail to launch the Fsp1-Cre-mediated fluorescent marker switch. To address this concern, we evaluated the EMT status of Tri-PyMT cells on the single cell level by performing single cell RNA-sequencing (scRNA-seq). We found that the expression of epithelial markers (i.e. Epcam and Krt18) and mesenchymal markers (i.e. Vim and S110a4) were largely confined to RFP+ and GFP+ subpopulations, respectively. Based on the differentially expressed EMT related genes, we observed an EMT spectrum in which the low EMT score end was enriched with RFP+ cells, whereas the high EMT score end was enriched with GFP+ cells. Indeed, the fluorescent marker switch in Tri-PyMT cells predicted their EMT status with high specificity and sensitivity. Tri-PyMT cells which were derived from the primary breast tumor of triple transgenic mouse (MMTV-PyMT:Fsp1-cre:Rosa26mT/mG) were sorted into RFP+ or GFP+ populations by FACS and subjected for Drop-seq analysis.

2022-02-01 | GSE126374 | GEO

Sorted MDMs with RFP+GFP+ or RFP+GFP- Mtb

Project description:Human serum derived macrophages were infected with Mtb expressing a transcriptional reporter of viability and on day 5, the cells were sorted for RFP+GFP+ macrophages or RFP+GFP- macrophages.

2019-06-10 | GSE131220 | GEO

Sorted MDMs with RFP+GFP+ or RFP+GFP- Mtb

Project description:Sorted MDMs with RFP+GFP+ or RFP+GFP- Mtb

| PRJNA542934 | ENA

Transcriptome analysis of isolated stormal cells and tumor epithelial cells in mouse lung cancer by RNA-Seq

Project description:We sequenced mRNA from individual stormal cells (Macrophages, Monocytes, and Neutrophils) and tumor epithelial cells from KrasG12dD; p53-/- murine lung cancer model and WT control mouse to compare gene expressio profiles of lung cancer stroma and tumor cells to their counterparts of WT lugns. The tumor was generated by injecting HKP1 lung cancer cell line, which was driven by KrasG12D activation and loss of p53, via tail vein. The cells were sorted by their specific surface markers at day 20-25 after orthortopic lung cancer formation. Examination of mRNA levels in individual stormal cells and tumor cells from tumor lungs compared to their counterparts from WT lungs

2015-03-06 | E-GEOD-59831 | biostudies-arrayexpress

Comparison of Hob6-RFP positive and negative Flk-1+ cells (RFP reporter)

Project description:ES derived Flk-1+ cells were separated by sorting into Hoxb6 positive and negative populations by RFP Hoxb6 reporter. Gene expression was compared between these two groups. Duplicate analysis of Hoxb6-RFP positive and negative Flk-1+ cells.

2013-04-16 | E-GEOD-45192 | biostudies-arrayexpress

Comparison of Hob6-RFP positive and negative Flk-1+ cells (RFP reporter)

Project description:ES derived Flk-1+ cells were separated by sorting into Hoxb6 positive and negative populations by RFP Hoxb6 reporter. Gene expression was compared between these two groups.

2013-04-16 | GSE45192 | GEO

Cellular and extracellular vesicle RNA sequence for E-cadherin-RFP EMT reporter system expressing mouse breast cancer cell line Py2T during EMT/MET

Project description:Epithelial-mesenchymal transition (EMT) is a process by which epithelial cells lose cell-cell contact and gain cancer malignancy such as invasion, stemness, chemoresistance and metastasis. Reverse precess, mesenchymal-epithelial transition (MET) is also important for colonization. Extracellular vesicles (EVs) secreted from cancer cells are also important for cancer malignancy. To analyze RNAs from cells and EVs during EMT and MET, RNA sequencing was performed using E-cadherin-RFP/Py2T reporter system.

2020-12-31 | E-MTAB-9750 | biostudies-arrayexpress

Colletotrichum higginsianum strain:MAFF305635-RFP

Project description:Colletotrichum higginsianum strain:MAFF305635-RFP Genome sequencing and assembly

| PRJNA352900 | ENA

Flow activated cell sorting and genome-wide analysis of hybrids cells (RFP+YFP+) and non-hybrid cells (RFP+)

Project description:Cellular functions in mouse and human tissues can be restored after fusion of bone marrow (BM)-derived cells with a variety of somatic cells. Here, after transplantation of hematopoietic stem and progenitor cells (HSPCs) in the substantia nigra pars compacta (SNpc) of two different mouse models of Parkinson’s disease, we significantly ameliorated the dopaminergic neuron loss and function. We show fusion of transplanted HSPCs with neurons and with glial cells in the ventral midbrain of Parkinson’s disease mice. Interestingly, the hybrids can undergo reprogramming in vivo and survived up to 4 weeks after transplantation, while acquiring features of mature astroglia. These newly generated astroglia produced Wnt1 and were essential for functional rescue of the dopaminergic neurons. Our data suggest that glial-derived hybrids produced upon fusion of transplanted HSPCs in the SNpc can rescue the Parkinson’s disease phenotype via a niche-mediated effect, and can be exploited as an efficient cell-therapy approach. To further investigate the reprogrammed phenotype of the hybrids genome-wide, we analyzed the transcriptome of the RFP+YFP+ fused cells compared to the RFP+ unfused cells using microarrays in the R26Y, GFAP-Cre and FoxA2-Cre models

2016-04-30 | GSE77527 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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