Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

High content imaging-based assay to classify estrogen receptor-? ligands based on defined mechanistic outcomes.

ABSTRACT: Estrogen receptor-? (ER) is an important target both for therapeutic compounds and endocrine disrupting chemicals (EDCs); however, the mechanisms involved in chemical modulation of regulating ER transcriptional activity are inadequately understood. Here, we report the development of a high content analysis-based assay to describe ER activity that uniquely exploits a microscopically visible multi-copy integration of an ER-regulated promoter. Through automated single-cell analyses, we simultaneously quantified promoter occupancy, recruitment of transcriptional cofactors and large-scale chromatin changes in response to a panel of ER ligands and EDCs. Image-derived multi-parametric data was used to classify a panel of ligand responses at high resolution. We propose this system as a novel technology providing new mechanistic insights into EDC activities in a manner useful for both basic mechanistic studies and drug testing.

SUBMITTER: Ashcroft FJ

PROVIDER: S-EPMC3086628 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

High content imaging-based assay to classify estrogen receptor-α ligands based on defined mechanistic outcomes.

Ashcroft F J FJ Newberg J Y JY Jones E D ED Mikic I I Mancini M A MA

Gene 20110120 1-2

Estrogen receptor-α (ER) is an important target both for therapeutic compounds and endocrine disrupting chemicals (EDCs); however, the mechanisms involved in chemical modulation of regulating ER transcriptional activity are inadequately understood. Here, we report the development of a high content analysis-based assay to describe ER activity that uniquely exploits a microscopically visible multi-copy integration of an ER-regulated promoter. Through automated single-cell analyses, we simultaneous ...[more]

PMID: 21256200

Similar Datasets

High-Content Imaging-Based Assay for SARS-CoV-2-Neutralizing Antibodies.

Project description:The COVID-19 pandemic and the consequent emergence of new SARS-CoV-2 variants of concern necessitates the determination of populational serum potency against the virus. Here, we standardized and validated an imaging-based method to quantify neutralizing antibodies against lentiviral particles expressing the spike glycoprotein (pseudovirus). This method was found to efficiently quantify viral titers based on ZsGreen-positive cells and detect changes in human serum neutralization capacity induced by vaccination with up to two doses of CoronaVac, Comirnaty, or Covishield vaccines. The imaging-based protocol was also used to quantify serum potency against pseudoviruses expressing spikes from Delta, Omicron BA.1.1.529, and BA.4/5. Our results revealed increases in serum potency after one and two doses of the vaccines evaluated and demonstrated that Delta and Omicron variants escape from antibody neutralization. The method presented herein represents a valuable tool for the screening of antibodies and small molecules capable of blocking viral entry and could be used to evaluate humoral immunity developed by different populations and for vaccine development.

| S-EPMC10974448 | biostudies-literature

Research resource: The estrogen receptor α cistrome defined by DamIP.

Project description:Gene expression is tightly regulated by transcription factors and cofactors that function by directly or indirectly interacting with DNA of the genome. Understanding how and where these proteins bind provides essential information to uncover genetic regulatory mechanisms. We have developed a new method to study DNA-protein interaction in vivo called DNA adenine methyltransferase (Dam)IP, which is based on fusing a protein of interest to a mutant form of Dam from Escherichia coli. We showed previously that DamIP can efficiently identify in vivo binding sites of Dam-tethered human estrogen receptor (hER)α. In current study, we present the cistrome of hERα determined by DamIP and high throughput sequencing (DamIP-seq). The DamIP-seq-defined hERα cistrome identifies many new binding regions and overlaps with those determined by chromatin immunoprecipitation (ChIP)-chip or ChIP-seq. Elements uniquely identified by DamIP-seq include a unique class of elements that show low, but persistent, hERα binding when reexamined by conventional ChIP. In contrast, DamIP-seq fails to detect some elements with very transient hERα binding. The methyl-adenine modifications introduced by Dam are stable and do not decrease over 12 d. In summary, the current study provides both an alternate view of the hERα cistrome to further understand the mechanism of hERα-mediated transcription and a new tool to explore other transcriptional factors and cofactors that is very different from conventional ChIP.

| S-EPMC3384088 | biostudies-literature

High-Content Imaging-Based Pooled CRISPR Screens in Mammalian Cells

Project description:We developed a microscopy-based approach, which we name optical enrichment, to select cells displaying a particular CRISPR-induced phenotype by automated imaging-based computation, mark them by photo-activation of an expressed photo-activatable fluorescent protein, and then isolate the fluorescent cells using fluorescence-activated cell sorting (FACS). One proof-of-principle screen, a nuclear size screen, a FSC screen and a H2B-mGFP screen were performed to validate the method.

2021-01-12 | GSE156623 | GEO

Novel Approach to Classify Plants Based on Metabolite-Content Similarity.

Project description:Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward's method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.

| S-EPMC5253511 | biostudies-literature

The Estrogen receptor alpha cistrome defined by DamIP

Project description:We have developed a new method to study DNA-protein interaction in vivo called DamIP, which is based on fusing a protein of interest to a mutant form of DNA adenine methylatransferase (Dam) from E. coli. We showed previously that DamIP can efficiently identify in vivo binding sites of Dam-tethered human estrogen receptor alpha (hERα). In current study, we present the cistrome of hERα determined by DamIP and high throughput sequencing (DamIP-seq). The DamIP-seq defined hERα cistrome overlaps significantly with those determined by ChIP-chip or ChIP-seq, but identifies many new binding regions. As shown by conventional ChIP assay, many DamIP-seq unique hERα binding regions show relatively stable hERα binding, whereas DamIP-seq misses some regions with very transient hERα binding. The methyl-adenine modifications introduced by Dam are stable and do not decrease over 12 days. In summary, the current study provides both an alternate view of the hERα cistrome to further understand the mechanism of hERα mediated transcription, and a new tool to explore other transcriptional factors and cofactors. MCF7 cells were transfected with DamK9A or DamK9A-hERalpha. DamIP were performed from each sample and subjected to solexa sequencing

2011-11-19 | E-GEOD-33804 | biostudies-arrayexpress

Pancreatic insulin content regulation by the estrogen receptor ER alpha.

Project description:The function of pancreatic beta-cells is the synthesis and release of insulin, the main hormone involved in blood glucose homeostasis. Estrogen receptors, ER alpha and ER beta, are important molecules involved in glucose metabolism, yet their role in pancreatic beta-cell physiology is still greatly unknown. In this report we show that both ER alpha and ER beta are present in pancreatic beta-cells. Long term exposure to physiological concentrations of 17beta-estradiol (E2) increased beta-cell insulin content, insulin gene expression and insulin release, yet pancreatic beta-cell mass was unaltered. The up-regulation of pancreatic beta-cell insulin content was imitated by environmentally relevant doses of the widespread endocrine disruptor Bisphenol-A (BPA). The use of ER alpha and ER beta agonists as well as ER alphaKO and ER betaKO mice suggests that the estrogen receptor involved is ER alpha. The up-regulation of pancreatic insulin content by ER alpha activation involves ERK1/2. These data may be important to explain the actions of E2 and environmental estrogens in endocrine pancreatic function and blood glucose homeostasis.

| S-EPMC2323613 | biostudies-literature

The Estrogen receptor alpha cistrome defined by DamIP

2011-11-19 | GSE33804 | GEO

RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes.

Project description:BackgroundBreast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification.MethodsWe propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER- breast cancer samples.ResultsThe evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches.

| S-EPMC3916021 | biostudies-literature

A Rhodopsin Transport Assay by High-Content Imaging Analysis.

Project description:Rhodopsin misfolding mutations lead to rod photoreceptor death that is manifested as autosomal dominant retinitis pigmentosa (RP), a progressive blinding disease that lacks effective treatment. We hypothesize that the cytotoxicity of the misfolded rhodopsin mutant can be alleviated by pharmacologically stabilizing the mutant rhodopsin protein. The P23H mutation, among the other Class II rhodopsin mutations, encodes a structurally unstable rhodopsin mutant protein that is accumulated in the endoplasmic reticulum (ER), whereas the wild type rhodopsin is transported to the plasma membrane in mammalian cells. We previously performed a luminescence-based high-throughput screen (HTS) and identified a group of pharmacological chaperones that rescued the transport of the P23H rhodopsin from ER to the plasma membrane. Here, using an immunostaining method followed by a high-content imaging analysis, we quantified the mutant rhodopsin protein amount in the whole cell and on the plasma membrane. This method is informative and effective to identify true hits from false positives following HTS. Additionally, the high-content image analysis enabled us to quantify multiple parameters from a single experiment to evaluate the pharmacological properties of each compound. Using this assay, we analyzed the effect of 11 different compounds towards six RP associated rhodopsin mutants, obtaining a 2-D pharmacological profile for a quantitative and qualitative understanding about the structural stability of these rhodopsin mutants and efficacy of different compounds towards these mutants.

| S-EPMC6518421 | biostudies-literature

A Cell-Based Assay Optimized for High-Content Cilia Imaging with Primary Rat Hippocampal Neurons.

Project description:Genetic manipulations of dissociated rodent neurons provide translatable in vitro models for disease-driven phenotypes. Cilia are cellular antenna with a role in neuronal maturation and function often perturbed in neurodevelopmental disorders. Efforts for automated imaging of these microscopic protrusions are crucial given the role of cilia in the brain. We developed a cell-based assay to monitor cilia in rat hippocampal neurons using lentiviral-mediated shRNA-based gene silencing. This optimized platform can be used for high-throughput cilia imaging, disease modeling, and drug screening. For complete details on the use and execution of this protocol, please refer to Di Nardo et al. (2020).

| S-EPMC7757556 | biostudies-literature

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