Focus on Bio-Image Informatics by Winnok H. De Vos Sebastian Munck & Jean-Pierre Timmermans

Author:Winnok H. De Vos, Sebastian Munck & Jean-Pierre Timmermans
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

5.1.3 From Snapshots to Numbers: Towards High-Content Neuro-Imaging

Both primary and iPSC-derived neuronal networks can be cultivated in multi-well plates, starting from a limited amount of biological material. In combination with automated fluorescence microscopy, these networks make an attractive model for upscaling to a high-content screening (HCS) platform (Cornelissen et al. 2013; Schmitz et al. 2011). Of vital importance for such a platform is robust measurement of the endpoint of interest. Manual quantification is not only labour-intensive, but also prone to observer bias, which hampers reproducibility of the data. To eliminate this bias and boost throughput, automation of image analysis is inevitable. However, the design and implementation of generic automated image analyses are non-trivial since the experimental conditions, such as microscope settings, type of stains, cell type and cell densities that are used, introduce a strong variability in image quality (Meijering 2010). Nevertheless, with sufficient standardization of the sample preparation and image acquisition protocols, and adequate pre-processing of the raw image datasets, the major correlates of neuronal connectivity can be quantified in an unbiased way. In the following paragraphs, we discuss the main image analysis strategies for quantification of morphological and functional endpoints.

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