Cover Page. The handle holds various files of this Leiden University dissertation.

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1 Cover Page The handle holds various files of this Leiden University dissertation. Author: Di, Zi Title: Development of automatic image analysis methods for high-throughput and highcontent screening Issue Date:

2 English summary High-throughput and high-content image analysis is a powerful tool to associate phenotypic responses of cells and multi-cellular structures, with modulation of their functional components by small molecules, peptides or RNA interference (RNAi), ultimately enabling the identification of novel drug targets and/or novel drug molecules. However, several limitations of current image analysis methods restrict the application of high-throughput and high-content screening to the identification of biologically active compounds or genetic players only. The full potential of high-throughput and high-content screening to systematically study phenotypes, for example to characterize phenotypes and associate them with modulation of cellular signaling pathways, is underestimated as yet. A major limitation is that current image analysis methods only measure a few representative features which are based on known phenotypic changes that occur under similar experimental conditions. Consequently, novel phenotypes and subtle changes remain undetected and a comprehensive characterization of phenotypes cannot be achieved. Another major limitation of current image analysis methods is lack of robustness as many of them are designed only for specific phenotypes and cell types. More importantly, these methods are highly dependent on the resolution of microscopy images. When image resolution and magnification are relatively low, these methods cannot profile phenotypes at a single cell level. As a consequence, heterogeneous cell behavior is obscured. One of the goals of the research presented in this thesis is to develop robust and efficient image analysis methods suitable for single-cell studies that should not be limited to specific cell types, cell densities or image quality. More importantly, heterogeneity between cell subpopulations should be taken into account and relevant information should be collected for phenotype characterization. In chapter 2, we developed such a methodology to investigate the NF-κB nuclear translocation dynamics in single cells after activating the NF-κB pathway with external stimuli. It is shown that this methodology can be applied to confocal fluorescence microscopy with relatively low magnification (20 ) and low numerical aperture (0.75NA) objective. Although the cell line (HepG2) we used for the method validation showed clustered and stacked cell growth which increased the difficulty to separate individual cells, by introducing two novel segmentation methods BEVC and WMC we can still define single cellular and nuclear regions accurately. In this research, different cell subpopulations were defined according to the number of NF-κB nuclear translocation peaks. Comparing the size of different subpopulations revealed that BMS (an 157

3 IKK-inhibitor which prevents NF-κB nuclear translocation) has an impact on NF-κB nuclear translocation by decreasing the percentage of cells with three translocation peaks, and increasing the percentage of cells with only one translocation peak. This impact would not have been resolved in the average nuclear translocation response over the whole cell population. Another important goal of this research is the development of image analysis platforms for the detailed and comprehensive characterization of cellular phenotypes from low-resolution images. In chapters 3, 4, and 5, two image analysis platforms were developed to profile phenotype of 3D cultured micro-tissues from image stacks which were generated by conventional wide-field microscopes. One platform presented in chapter 3 is based on the 2D projection of image stacks. It starts with composing a single image slice by projecting only in-focus regions from each slice of image stacks. Next, WMC was applied on the projected 2D slice to segment individual nuclei, while a local Niblack algorithm was used to define multi-cellular regions. Finally, phenotype profiling was carried out on the segmentation results and the projected images. In chapter 4, we extended the 2D projection based analysis platform to enable 3D phenotypic profiling of micro-tissues. Firstly, a deconvolution technique was applied to each image stack to remove out-of-focus information. Secondly, a 3D WMC method was introduced to define individual nuclei, and a novel sharpness estimation method was incorporated in the segmentation of multi-cellular regions. After correcting the elongation effect of nuclei in z-direction, 3D geometric models of nuclei and multi-cellular structures were reconstructed to perform phenotype profiling. Both the 2D projection based analysis platform and the 3D phenotypic profiling platform (chapters 3 and 5) embed an automated classification system to automatically distinguish the spherical cell clusters and branched cell networks. We statistically proved that extracting subpopulation related parameters and including them for phenotype classification increased the classification accuracy, confirming the importance of subpopulation information in the phenotype characterization. To investigate the robustness of our image analysis platforms, we applied the platform presented in chapter 2 to classify 44 known human breast cancer cell lines based on the phenotype profiles. The result showed high classification accuracy, indicating a wider applicability of our method. We also successfully applied both platforms in various other screens, such as compound screens in invasive PC3 cells and invasive lung cancer cells. This further confirms the robustness of our methods. In this research, we aim to develop ultra-high content analysis platforms which should be able to collect maximum information of phenotypes from images. In chapter 2, we have extended our image analysis so that it can automatically quantify 26 analogue parameters for each individual NF-κB nuclear translocation profile, such as the number of translocation peaks and time between consecutive peaks. These parameters were used to distinguish different cell subpopulations within an inhomogeneous population, representing a powerful tool to study heterogeneous cell behavior in the future. Both image analysis platforms we developed in chapter 3 and chapter 5 can measure not only classic morphological parameters, but also topological parameters, intensity properties, texture, moments, as well as subpopulation informa- 158

4 tion, therefore providing the full spectrum of phenotypic information. We analyzed the contribution of different parameter classes to the phenotype characterization of the 4T1 cell cultures exposed to various protein kinase inhibitors. The result showed that excluding any parameter class decreased the accuracy of phenotype classification, confirming the importance of quantifying images with a full spectrum of phenotypic information. Due to a wealth of information that can be extracted by our image analysis methods, more systematical approaches can be used to characterize phenotypes or associate phenotypes with modulation of different cellular pathways. In chapter 3, we introduced a regression modeling method to construct the concentration dependent trajectories in multi-phenotypic parameter space for biologically active compounds that affect cellular phenotypes. By means of a novel clustering method which is based on the distance between two trajectories, we obtained data suggesting that the phenotypic responses of 4T1 cells to protein kinase inhibitors are specific for the biological target that is inhibited. To validate this result, we categorized tested compounds into different classes according to their biological target. Classifiers were trained to successfully classify those compounds based on their corresponding phenotype profiles (2D projection based profiles or 3D profiles). This result may lead to a new strategy to study activity of new pharmacologically active compounds or identify off-target effects of existing compounds. 159

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