Increasing the Content of High-Content Screening An Overview

Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising. In this article, we assess whether...

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Published inJournal of biomolecular screening Vol. 19; no. 5; pp. 640 - 650
Main Authors Singh, Shantanu, Carpenter, Anne E., Genovesio, Auguste
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.06.2014
Subjects
Biological Assay
Chemistry, Pharmaceutical - methods
Drug Discovery
High-Throughput Screening Assays - methods
Multivariate Analysis
Normal Distribution
Phenotype
Support Vector Machine
statistical analyses
phenotypic drug discovery
Cell-based assays
image analysis
high-content screening
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Abstract Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising. In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60−80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information. We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.
AbstractList Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising. In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60-80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information. We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.
Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising. In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60−80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information. We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.
Author Singh, Shantanu
Carpenter, Anne E.
Genovesio, Auguste
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Issue 5
Keywords statistical analyses
phenotypic drug discovery
Cell-based assays
image analysis
high-content screening
Language English
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Snippet Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of...
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sage
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SubjectTerms Biological Assay
Chemistry, Pharmaceutical - methods
Drug Discovery
High-Throughput Screening Assays - methods
Multivariate Analysis
Normal Distribution
Phenotype
Support Vector Machine
Subtitle An Overview
Title Increasing the Content of High-Content Screening
URI https://journals.sagepub.com/doi/full/10.1177/1087057114528537
https://www.ncbi.nlm.nih.gov/pubmed/24710339
Volume 19
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