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High Content Screening

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

Table of Contents

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    Book Overview
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    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 9: Strategies and Solutions to Maintain and Retain Data from High Content Imaging, Analysis, and Screening Assays
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Chapter title
Strategies and Solutions to Maintain and Retain Data from High Content Imaging, Analysis, and Screening Assays
Chapter number 9
Book title
High Content Screening
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7357-6_9
Pubmed ID
29082491
Book ISBNs
978-1-4939-7355-2, 978-1-4939-7357-6
Authors

K. Kozak, B. Rinn, O. Leven, M. Emmenlauer

Abstract

Data analysis and management in high content screening (HCS) has progressed significantly in the past 10 years. The analysis of the large volume of data generated in HCS experiments represents a significant challenge and is currently a bottleneck in many screening projects. In most screening laboratories, HCS has become a standard technology applied routinely to various applications from target identification to hit identification to lead optimization. An HCS data management and analysis infrastructure shared by several research groups can allow efficient use of existing IT resources and ensures company-wide standards for data quality and result generation. This chapter outlines typical HCS workflows and presents IT infrastructure requirements for multi-well plate-based HCS.

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Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 20%
Professor > Associate Professor 2 20%
Professor 1 10%
Researcher 1 10%
Unknown 4 40%
Readers by discipline Count As %
Computer Science 2 20%
Business, Management and Accounting 1 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 10%
Immunology and Microbiology 1 10%
Medicine and Dentistry 1 10%
Other 0 0%
Unknown 4 40%

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