Abstract

High content imaging combines automated microscopy with image analysis approaches to simultaneously quantify multiple phenotypic and/or functional parameters in biological systems. The technology has become an important tool in the fields of safety sciences and drug discovery, because it can be used for mode-of-action identification, determination of hazard potency and the discovery of toxicity targets and biomarkers. In contrast to conventional biochemical endpoints, high content imaging provides insight into the spatial distribution and dynamics of responses in biological systems. This allows the identification of signaling pathways underlying cell defense, adaptation, toxicity and death. Therefore, high content imaging is considered a promising technology to address the challenges for the "Toxicity testing in the 21st century" approach. Currently, high content imaging technologies are frequently applied in academia for mechanistic toxicity studies and in pharmaceutical industry for the ranking and selection of lead drug compounds or to identify/confirm mechanisms underlying effects observed in vivo. A recent workshop gathered scientists working on high content imaging in academia, pharmaceutical industry and regulatory bodies with the objective to compile the state-of-the-art of the technology in the different institutions. Together they defined technical and methodological gaps, proposed quality control measures and performance standards, highlighted cell sources and new readouts and discussed future requirements for regulatory implementation. This review summarizes the discussion, proposed solutions and recommendations of the specialists contributing to the workshop.

Highlights

  • The term high content imaging (HCI) is used to describe automated microscopy combined with image analysis approaches to simultaneously quantify multiple phenotypic and/or functional parameters in biological systems (Giuliano et al, 1997, 2003; Abraham et al, 2004)

  • HCI has become an important tool in the field of safety sciences, because it can be used for mode-of-action (MoA) identification, hazard potency determination and the discovery of predictive biomarkers for the mechanistic safety assessment of compounds (Young et al, 2008; Zanella et al, 2010)

  • To increase the scientific relevance of HCI data for the in vivo and human situation, the technology must be tailored for the analysis of more complex biological systems such as 3D cell models (Wenzel et al, 2014) and human stem cell models (Barbaric et al, 2010; Sherman et al, 2011)

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Summary

Summary

High content imaging combines automated microscopy with image analysis approaches to simultaneously quantify multiple phenotypic and/or functional parameters in biological systems. In contrast to conventional biochemical endpoints, high content imaging provides insight into the spatial distribution and dynamics of responses in biological systems. This allows the identification of signaling pathways underlying cell defense, adaptation, toxicity and death. Altex 31, 4/14 in academia, pharmaceutical industry and regulatory bodies with the objective to compile the stateof-the-art of the technology in the different institutions. Together they defined technical and methodological gaps, proposed quality control measures and performance standards, highlighted cell sources and new readouts and discussed future requirements for regulatory implementation.

Introduction
Characteristics and added value of the high content imaging approach
Current high content imaging applications in safety sciences
High content imaging case studies
Current gaps and challenges for high content imaging approaches
Technical limitations
Scientific relevance and standardization of the biological system
Lack of widely recognized reference compounds
Concentration issues
Benchmarking of high content imaging data
Imaging of dynamic responses
Quality control and performance standards
Data analysis and integration of read-out parameters
Integration of high content imaging with other technologies
Future potential of high content imaging for safety sciences
Development of HCI computational database systems for “Big Data”
Regulatory acceptance of high content imaging
Conclusions
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