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High Content Screening

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Cover of 'High Content Screening'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Applications and Caveats on the Utilization of DNA-Specific Probes in Cell-Based Assays
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    Chapter 2 General Staining and Segmentation Procedures for High Content Imaging and Analysis
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    Chapter 3 Tools to Measure Cell Health and Cytotoxicity Using High Content Imaging and Analysis
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    Chapter 4 Cell-Based High Content Analysis of Cell Proliferation and Apoptosis
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    Chapter 5 Tools to Measure Autophagy Using High Content Imaging and Analysis
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    Chapter 6 Guidelines for Microplate Selection in High Content Imaging
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    Chapter 7 Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler
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    Chapter 8 High-Content Screening Approaches That Minimize Confounding Factors in RNAi, CRISPR, and Small Molecule Screening
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    Chapter 9 Strategies and Solutions to Maintain and Retain Data from High Content Imaging, Analysis, and Screening Assays
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    Chapter 10 Live-Cell High Content Screening in Drug Development
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    Chapter 11 Challenges and Opportunities in Enabling High-Throughput, Miniaturized High Content Screening
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    Chapter 12 Translocation Biosensors—Versatile Tools to Probe Protein Functions in Living Cells
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    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
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    Chapter 14 High Content Imaging Assays for IL-6-Induced STAT3 Pathway Activation in Head and Neck Cancer Cell Lines
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    Chapter 15 Single Cell and Population Level Analysis of HCA Data
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    Chapter 16 Utilization of Multidimensional Data in the Analysis of Ultra-High-Throughput High Content Phenotypic Screens
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    Chapter 17 High Content Screening of Mammalian Primary Cortical Neurons
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    Chapter 18 Human-Derived Neurons and Neural Progenitor Cells in High Content Imaging Applications
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    Chapter 19 Determination of Hepatotoxicity in iPSC-Derived Hepatocytes by Multiplexed High Content Assays
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    Chapter 20 The Generation of Three-Dimensional Head and Neck Cancer Models for Drug Discovery in 384-Well Ultra-Low Attachment Microplates
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    Chapter 21 An Endothelial Cell/Mesenchymal Stem Cell Coculture Cord Formation Assay to Model Vascular Biology In Vitro
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    Chapter 22 High-Throughput Automated Chemical Screens in Zebrafish
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    Chapter 23 Erratum to: High Content Screening
Attention for Chapter 2: General Staining and Segmentation Procedures for High Content Imaging and Analysis
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Chapter title
General Staining and Segmentation Procedures for High Content Imaging and Analysis
Chapter number 2
Book title
High Content Screening
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7357-6_2
Pubmed ID
Book ISBNs
978-1-4939-7355-2, 978-1-4939-7357-6
Authors

Kevin M. Chambers, Bhaskar S. Mandavilli, Nick J. Dolman, Michael S. Janes

Abstract

Automated quantitative fluorescence microscopy, also known as high content imaging (HCI), is a rapidly growing analytical approach in cell biology. Because automated image analysis relies heavily on robust demarcation of cells and subcellular regions, reliable methods for labeling cells is a critical component of the HCI workflow. Labeling of cells for image segmentation is typically performed with fluorescent probes that bind DNA for nuclear-based cell demarcation or with those which react with proteins for image analysis based on whole cell staining. These reagents, along with instrument and software settings, play an important role in the successful segmentation of cells in a population for automated and quantitative image analysis. In this chapter, we describe standard procedures for labeling and image segmentation in both live and fixed cell samples. The chapter will also provide troubleshooting guidelines for some of the common problems associated with these aspects of HCI.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 31%
Professor > Associate Professor 2 13%
Student > Bachelor 1 6%
Student > Doctoral Student 1 6%
Other 1 6%
Other 2 13%
Unknown 4 25%
Readers by discipline Count As %
Computer Science 2 13%
Biochemistry, Genetics and Molecular Biology 2 13%
Engineering 2 13%
Business, Management and Accounting 1 6%
Agricultural and Biological Sciences 1 6%
Other 4 25%
Unknown 4 25%