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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
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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
  24. Altmetric Badge
    Chapter 23 Erratum to: High Content Screening
Attention for Chapter 7: Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler
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Chapter title
Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler
Chapter number 7
Book title
High Content Screening
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7357-6_7
Pubmed ID
29082489
Book ISBNs
978-1-4939-7355-2, 978-1-4939-7357-6
Authors

Mark-Anthony Bray, Anne E. Carpenter

Abstract

Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus images, debris, and auto-fluorescing samples can cause artifacts such as focus blur and image saturation, contaminating downstream analysis and impairing identification of subtle phenotypes. Here, we describe an automated quality control protocol implemented in validated open-source software, leveraging the suite of image-based measurements generated by CellProfiler and the machine-learning functionality of CellProfiler Analyst.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 74 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 20%
Student > Ph. D. Student 15 20%
Student > Master 12 16%
Student > Bachelor 5 7%
Student > Doctoral Student 3 4%
Other 6 8%
Unknown 18 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 27%
Engineering 7 9%
Agricultural and Biological Sciences 7 9%
Computer Science 5 7%
Medicine and Dentistry 4 5%
Other 10 14%
Unknown 21 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 January 2018.
All research outputs
#20,451,228
of 23,007,053 outputs
Outputs from Methods in molecular biology
#9,941
of 13,159 outputs
Outputs of similar age
#378,112
of 442,275 outputs
Outputs of similar age from Methods in molecular biology
#1,193
of 1,498 outputs
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So far Altmetric has tracked 13,159 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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