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Cell state transitions regulated by Slug are critical for tissue regeneration and tumor initiation [Nanostring nCounter samples]

ABSTRACT: Gene expression data of MCF10A cell subpopulations: luminal, basal and luminal.

ORGANISM(S): Homo sapiens

PROVIDER: GSE51397 | GEO | 2014/06/23

SECONDARY ACCESSION(S): PRJNA222890

REPOSITORIES: GEO

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Cell state transitions regulated by Slug are critical for tissue regeneration and tumor initiation [Nanostring nCounter samples]

Project description:Gene expression data of MCF10A cell subpopulations: luminal, basal and luminal. Three samples were analized. Expression of 105 selected genes was evaluated, including 5 HK genes.

2014-06-23 | E-GEOD-51397 | biostudies-arrayexpress

Cell state transitions regulated by Slug are critical for tissue regeneration and tumor initiation [Agilent microarray samples]

Project description:Alterations that perturb differentiation and cell state transitions can lead to defects in development, function and the genesis of cancer. Studying cellular plasticity at high resolution and in real time has proven difficult using existing methods. Here, we use a quantitative approach to gain insights into cell state dynamics of normal mammary epithelial cells (MECs) and validate the model's predictions in vivo. In the absence of Slug/SNAI2, basal mammary progenitor cells transition into a luminal differentiation state, while luminal progenitor cells proliferate and expand; these changes result in abnormal mammary architecture and defects in tissue function. Loss of Slug also disrupts cellular plasticity leading to defects in tissue regeneration and the initiation of cancer. Mechanistically, Slug promotes cellular plasticity by recruiting the chromatin modifier, LSD1 (lysine specific demethylase 1), to promoters of lineage specific genes to represses transcription. Together, these finding demonstrate that Slug is necessary for cellular adaptation during tissue development and regeneration, and that transitioning back into a more primitive stem-like state is a prerequisite for tumor initiation. reference x sample

2014-06-23 | E-GEOD-54713 | biostudies-arrayexpress

Cell state transitions regulated by Slug are critical for tissue regeneration and tumor initiation [Agilent microarray samples]

2014-06-23 | GSE54713 | GEO

Cell-state transitions regulated by SLUG are critical for tissue regeneration and tumor initiation.

Project description:Perturbations in stem cell activity and differentiation can lead to developmental defects and cancer. We use an approach involving a quantitative model of cell-state transitions in vitro to gain insights into how SLUG/SNAI2, a key developmental transcription factor, modulates mammary epithelial stem cell activity and differentiation in vivo. In the absence of SLUG, stem cells fail to transition into basal progenitor cells, while existing basal progenitor cells undergo luminal differentiation; together, these changes result in abnormal mammary architecture and defects in tissue function. Furthermore, we show that in the absence of SLUG, mammary stem cell activity necessary for tissue regeneration and cancer initiation is lost. Mechanistically, SLUG regulates differentiation and cellular plasticity by recruiting the chromatin modifier lysine-specific demethylase 1 (LSD1) to promoters of lineage-specific genes to repress transcription. Together, these results demonstrate that SLUG plays a dual role in repressing luminal epithelial differentiation while unlocking stem cell transitions necessary for tumorigenesis.

| S-EPMC4050485 | biostudies-literature

Cell state transitions regulated by Slug are critical for tissue regeneration and tumor initiation

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2014-06-23 | E-GEOD-54735 | biostudies-arrayexpress

Cell state transitions regulated by Slug are critical for tissue regeneration and tumor initiation

Project description:This SuperSeries is composed of the SubSeries listed below.

2014-06-23 | GSE54735 | GEO

Genome Wide Expression of Mammary Epithelial Populations in Slug (SNAI2) WT vs Slug Null Mice

Project description:This experiment shows differential expression of genes in the luminal, basal and stromal subpopulations from SNAI2+/+ and SNAI2 LacZ/LacZ mammary epithelial cells. Luminal, basal and stromal populations were sorted from SNAI2+/+ and SNAI2 LacZ/LacZ mammary epithelial cells based on expression of CD49f and Epcam.

2014-03-08 | E-GEOD-55620 | biostudies-arrayexpress

Easy NanoString nCounter data analysis with the NanoTube.

Project description:SummaryThe NanoTube is an open-source pipeline that simplifies the processing, quality control, normalization and analysis of NanoString nCounter gene expression data. It is implemented in an extensible R library, which performs a variety of gene expression analysis techniques and contains additional functions for integration with other R libraries performing advanced NanoString analysis techniques. Additionally, the NanoTube web application is available as a simple tool for researchers without programming expertise.Availability and implementationThe NanoTube R package is available on Bioconductor under the GPL-3 license (https://www.bioconductor.org/packages/NanoTube/). The R-Shiny application can be downloaded at https://github.com/calebclass/Shiny-NanoTube, or a simplified version of this application can be run on all major browsers, at https://research.butler.edu/nanotube/.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9805552 | biostudies-literature

Genome Wide Expression of Mammary Epithelial Populations in Slug (SNAI2) WT vs Slug Null Mice

Project description:This experiment shows differential expression of genes in the luminal, basal and stromal subpopulations from SNAI2+/+ and SNAI2 LacZ/LacZ mammary epithelial cells.

2014-03-08 | GSE55620 | GEO

A new normalization for Nanostring nCounter gene expression data.

Project description:The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different datasets. We also offer suggestions on the design and analysis of studies involving this technology.

| S-EPMC6614807 | biostudies-literature

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