Abstract
ABSTRACT3D cell cultures enable the in vitro study of dynamic biological processes such as the cell cycle, but their use in high-throughput screens remains impractical with conventional fluorescent microscopy. Here, we present a screening workflow for the automated evaluation of mitotic phenotypes in 3D cell cultures by light-sheet microscopy. After sample preparation by a liquid handling robot, cell spheroids are imaged for 24 h in toto with a dual-view inverted selective plane illumination microscope (diSPIM) with a much improved signal-to-noise ratio, higher imaging speed, isotropic resolution and reduced light exposure compared to a spinning disc confocal microscope. A dedicated high-content image processing pipeline implements convolutional neural network-based phenotype classification. We illustrate the potential of our approach using siRNA knockdown and epigenetic modification of 28 mitotic target genes for assessing their phenotypic role in mitosis. By rendering light-sheet microscopy operational for high-throughput screening applications, this workflow enables target gene characterization or drug candidate evaluation in tissue-like 3D cell culture models.
Highlights
The cell cycle with its highly conserved and tightly regulated phases plays a key role in cancer development and progression
After sample preparation by a liquid handling robot, cell spheroids are imaged for 24 hours in toto with a dual-view inverted selective plane illumination microscope with a much improved signal-tonoise ratio, higher imaging speed, isotropic resolution and reduced light exposure compared to a spinning disc confocal microscope
Light-sheet imaging screen for high-content mitotic phenotype quantification To evaluate the applicability of selective plane illumination microscopy (SPIM) for high-throughput screening of mitotic phenotypes in 3D cell culture, we used an MCF10A breast epithelial cell line (Soule et al, 1990) stably expressing H2B-GFP to label DNA throughout the cell cycle
Summary
The cell cycle with its highly conserved and tightly regulated phases plays a key role in cancer development and progression. SPIM enables the evaluation of phenotypes at the subcellular level in wholespheroid or whole-organoid 3D cultures, with sufficient temporal resolution to visualize fast processes such as mitosis (Pampaloni et al, 2015; Strnad et al, 2016) While these features in principle make SPIM microscopes ideally suited to high-throughput or high-content screens, their distinct geometry and the large volumes of data generated pose new challenges for sample preparation as well as data processing and analysis (Preibisch et al, 2014; Schmied et al, 2016). A dedicated highthroughput image processing pipeline optimized for the diSPIM acquisition geometry combines convolutional neural network-based cell cycle phase detection with random forestbased classification to quantify phenotypic traits. Using this approach, we detect mitotic phenotypes in 3D cell culture models following modulation of gene expression by siRNA knock-down or epigenetic modification. Our fully automated workflow adapts light-sheet microscopy for applications in high-throughput screening in 3D cell culture models
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