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Janos Kriston-Vizi: Automated Image Analysis Pipeline for the Identification of Autophagic Inducers by High Content Screening


Automated image processing in confocal microscopy is a critical and often rate-limiting step in pharmaceutical high-content screening (HCS) workflows. An ImageJ macro – R script high-content analysis framework is described here with emphasis on segmentation to identify novel selective pharmacological inducers of the cellular process autophagy.

Autophagy serves a critical housekeeping role in cellular homeostasis. During autophagy, autophagic vacuoles (AVs) sequester organelles and proteins that are targeted for removal. Dysregulation of autophagy has been recently linked to several human diseases such as cancer, Alzheimer’s and Huntington’s disease. Therefore, identifying therapeutic compounds that modulate autophagy is of clinical importance.

A549, a human alveolar cancer cell line was screened for autophagy against the commercially available LOPAC small molecule compound library, and more than 30 thousands images were evaluated by both local adaptive and global segmentation. At an individual cell level, region-growing segmentation was compared with histogram-derived segmentation. A key component of this analysis is the development of an ImageJ macro which enables precise identification of the features of sporadic and variably stained cellular AVs.

The histogram approach allowed segmentation of a sporadic-pattern foreground and hence the attainment of pixel-level precision. Single-cell phenotypic features were measured and reduced after assessing assay quality control. Hit compounds selected by machine learning corresponded well to the subjective threshold-based hits determined by expert analysis. Histogram-derived segmentation displayed robustness against image noise, a factor adversely affecting region growing segmentation.


high content screening, HCS, confocal, autophagy, segmentation

Administrative data

Presenting author: Janos Kriston-Vizi
Organisation: LMCB, Medical Research Council, UK


Type: Poster (portrait)

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