↓ Skip to main content

High Content Screening

Overview of attention for book
Cover of 'High Content Screening'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Applications and Caveats on the Utilization of DNA-Specific Probes in Cell-Based Assays
  3. Altmetric Badge
    Chapter 2 General Staining and Segmentation Procedures for High Content Imaging and Analysis
  4. Altmetric Badge
    Chapter 3 Tools to Measure Cell Health and Cytotoxicity Using High Content Imaging and Analysis
  5. Altmetric Badge
    Chapter 4 Cell-Based High Content Analysis of Cell Proliferation and Apoptosis
  6. Altmetric Badge
    Chapter 5 Tools to Measure Autophagy Using High Content Imaging and Analysis
  7. Altmetric Badge
    Chapter 6 Guidelines for Microplate Selection in High Content Imaging
  8. Altmetric Badge
    Chapter 7 Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler
  9. Altmetric Badge
    Chapter 8 High-Content Screening Approaches That Minimize Confounding Factors in RNAi, CRISPR, and Small Molecule Screening
  10. Altmetric Badge
    Chapter 9 Strategies and Solutions to Maintain and Retain Data from High Content Imaging, Analysis, and Screening Assays
  11. Altmetric Badge
    Chapter 10 Live-Cell High Content Screening in Drug Development
  12. Altmetric Badge
    Chapter 11 Challenges and Opportunities in Enabling High-Throughput, Miniaturized High Content Screening
  13. Altmetric Badge
    Chapter 12 Translocation Biosensors—Versatile Tools to Probe Protein Functions in Living Cells
  14. Altmetric Badge
    Chapter 13 High Content Positional Biosensor Assay to Screen for Compounds that Prevent or Disrupt Androgen Receptor and Transcription Intermediary Factor 2 Protein-Protein Interactions
  15. Altmetric Badge
    Chapter 14 High Content Imaging Assays for IL-6-Induced STAT3 Pathway Activation in Head and Neck Cancer Cell Lines
  16. Altmetric Badge
    Chapter 15 Single Cell and Population Level Analysis of HCA Data
  17. Altmetric Badge
    Chapter 16 Utilization of Multidimensional Data in the Analysis of Ultra-High-Throughput High Content Phenotypic Screens
  18. Altmetric Badge
    Chapter 17 High Content Screening of Mammalian Primary Cortical Neurons
  19. Altmetric Badge
    Chapter 18 Human-Derived Neurons and Neural Progenitor Cells in High Content Imaging Applications
  20. Altmetric Badge
    Chapter 19 Determination of Hepatotoxicity in iPSC-Derived Hepatocytes by Multiplexed High Content Assays
  21. Altmetric Badge
    Chapter 20 The Generation of Three-Dimensional Head and Neck Cancer Models for Drug Discovery in 384-Well Ultra-Low Attachment Microplates
  22. Altmetric Badge
    Chapter 21 An Endothelial Cell/Mesenchymal Stem Cell Coculture Cord Formation Assay to Model Vascular Biology In Vitro
  23. Altmetric Badge
    Chapter 22 High-Throughput Automated Chemical Screens in Zebrafish
  24. Altmetric Badge
    Chapter 23 Erratum to: High Content Screening
Attention for Chapter 13: High Content Positional Biosensor Assay to Screen for Compounds that Prevent or Disrupt Androgen Receptor and Transcription Intermediary Factor 2 Protein-Protein Interactions
Altmetric Badge

Readers on

mendeley
7 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
High Content Positional Biosensor Assay to Screen for Compounds that Prevent or Disrupt Androgen Receptor and Transcription Intermediary Factor 2 Protein-Protein Interactions
Chapter number 13
Book title
High Content Screening
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7357-6_13
Pubmed ID
Book ISBNs
978-1-4939-7355-2, 978-1-4939-7357-6
Authors

Yun Hua, Daniel P. Camarco, Christopher J. Strock, Paul A. Johnston

Abstract

Transcriptional Intermediary Factor 2 (TIF2) is a key Androgen receptor (AR) coactivator that has been implicated in the development and progression of castration resistant prostate cancer (CRPC). This chapter describes the implementation of an AR-TIF2 protein-protein interaction (PPI) biosensor assay to screen for small molecules that can induce AR-TIF2 PPIs, inhibit the DHT-induced formation of AR-TIF2 PPIs, or disrupt pre-existing AR-TIF2 PPIs. The biosensor assay employs high content imaging and analysis to quantify AR-TIF2 PPIs and integrates physiologically relevant cell-based assays with the specificity of binding assays by incorporating structural information from AR and TIF2 functional domains along with intracellular targeting sequences using fluorescent protein reporters. Expression of the AR-Red Fluorescent Protein (RFP) "prey" and TIF2-Green Fluorescent Protein (GFP) "bait" components of the biosensor is directed by recombinant adenovirus (rAV) expression constructs that facilitated a simple co-infection protocol to produce homogeneous expression of both biosensors that is scalable for screening. In untreated cells, AR-RFP expression is localized predominantly to the cytoplasm and TIF2-GFP expression is localized only in the nucleoli of the nucleus. Exposure to DHT induces the co-localization of AR-RFP within the TIF2-GFP positive nucleoli of the nucleus. The AR-TIF2 biosensor assay therefore recapitulates the ligand-induced translocation of latent AR from the cytoplasm to the nucleus, and the PPIs between AR and TIF2 result in the colocalization of AR-RFP within TIF2-GFP expressing nucleoli. The AR-TIF2 PPI biosensor approach offers significant promise for identifying molecules with potential to modulate AR transcriptional activity in a cell-specific manner that may overcome the development of resistance and progression to CRPC.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Researcher 2 29%
Unknown 3 43%
Readers by discipline Count As %
Medicine and Dentistry 2 29%
Agricultural and Biological Sciences 1 14%
Unknown 4 57%