Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A genome editing approach to study cancer stem cells in human tumors

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE92962 | GEO | 2017/04/28

SECONDARY ACCESSION(S): PRJNA359182

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

A genome editing approach to study cancer stem cells in human tumors.

Project description:The analysis of stem cell hierarchies in human cancers has been hampered by the impossibility of identifying or tracking tumor cell populations in an intact environment. To overcome this limitation, we devised a strategy based on editing the genomes of patient-derived tumor organoids using CRISPR/Cas9 technology to integrate reporter cassettes at desired marker genes. As proof of concept, we engineered human colorectal cancer (CRC) organoids that carry EGFP and lineage-tracing cassettes knocked in the LGR5 locus. Analysis of LGR5-EGFP+ cells isolated from organoid-derived xenografts demonstrated that these cells express a gene program similar to that of normal intestinal stem cells and that they propagate the disease to recipient mice very efficiently. Lineage-tracing experiments showed that LGR5+ CRC cells self-renew and generate progeny over long time periods that undergo differentiation toward mucosecreting- and absorptive-like phenotypes. These genetic experiments confirm that human CRCs adopt a hierarchical organization reminiscent of that of the normal colonic epithelium. The strategy described herein may have broad applications to study cell heterogeneity in human tumors.

| S-EPMC5494503 | biostudies-literature

A genome editing approach to study cancer stem cells in human tumors

Project description:A genome editing approach to study cancer stem cells in human tumors

| PRJNA359182 | ENA

An Integrated Approach to Reconstructing Genome-scale Transcriptional Regulatory Networks