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Three-dimensional multicellular aggregates such as spheroids provide reliable in vitro substitutes for tissues. Quantitative characterization of spheroids at the cellular level is fundamental. We present the first pipeline that provides three-dimensional, high-quality images of intact spheroids at cellular resolution and a comprehensive image analysis that completes traditional image segmentation by algorithms from other fields. The pipeline combines light sheet-based fluorescence microscopy of optically cleared spheroids with automated nuclei segmentation (F score: 0.88) and concepts from graph analysis and computational topology. Incorporating cell graphs and alpha shapes provided more than 30 features of individual nuclei, the cellular neighborhood and the spheroid morphology. The application of our pipeline to a set of breast carcinoma spheroids revealed two concentric layers of different cell density for more than 30,000 cells. The thickness of the outer cell layer depends on a spheroid’s size and varies between 50% and 75% of its radius. In differently-sized spheroids, we detected patches of different cell densities ranging from 5 × 105 to 1 × 106 cells/mm3. Since cell density affects cell behavior in tissues, structural heterogeneities need to be incorporated into existing models. Our image analysis pipeline provides a multiscale approach to obtain the relevant data for a system-level understanding of tissue architecture.
Research in cell and developmental biology requires the application of three-dimensional model systems that reproduce the natural environment of cells. Processes in developmental biology are therefore studied in entire systems like insects or plants. In cell biology, three-dimensional cell cultures (e.g. spheroids or organoids) model the physiology and pathology of cells, tissues or organs. In all systems, the cellular neighborhood and interactions, but also physicochemical influences, are realistically presented. The production and handling of these model systems is rather simple and allows for reproducible characterization.
Confocal and light sheet-based fluorescence microscopy (LSFM) enable the observation of these systems while maintaining their three-dimensional integrity. LSFM is applicable to imaging live samples at high spatio-temporal resolution over long periods of time. The quality of the acquired datasets enables the extraction of quantitative features about morphology, functionality and dynamics in the context of the complete system. This approach is referred to as image-based systems biology. Exploiting the potential of the generated datasets requires an image analysis pipeline for data management, visualization and the retrieval of biologically meaningful values.
The goal of this thesis was to identify, develop and optimize modules of the image analysis pipeline. The modules cover data management and reduction, visualization, reconstruction of multiview image datasets, the segmentation and tracking of cell nuclei and the extraction of quantitative features. The modules were developed in an application-driven manner to test and ensure their applicability to real datasets from three-dimensional fluorescence microscopy. The underlying datasets were taken from research projects in developmental biology in insects and plants, as well as from cell biology.
The datasets acquired in fluorescence microscopy are typically complex and require common image processing steps in order to manage, visualize, and analyze the datasets. The first module accomplishes automatic structuring of large image datasets, reduces the data amount by image cropping and compression and computes maximum projection images along different spatial directions. The second module corrects for intensity variations in the generated maximum projection images that occur as a function of time. The program was published as a part of an article in Nature Protocols. Another developed module named BugCube provides a web-based platform to visualize and share the processed image datasets.
In LSFM, samples can be rotated in-between two acquisitions enabling the generation of multiview image datasets. Prior to my work, Frederic Strobl and Alexander Ross acquired the complete embryogenesis of the red flour beetle, Tribolium castaneum, and the field cricket, Gryllus bimaculatus, with LSFM. I evaluated a plugin for the software FIJI as a module for the reconstruction of such datasets. The plugin was optimized for automation and efficiency. We obtained the first high quality three-dimensional reconstructions of Tribolium and Gryllus datasets.
Optical clearing increases the penetration depth into samples, thus providing endpoint images of entire three-dimensional objects with cellular detail. This work contributes a quantitative characterization module that was applied to endpoint images of optically cleared spheroids. A program for the generation of ground truth datasets was developed in order to evaluate the cell nuclei segmentation performance. The program was part of a paper that was published in BMC Bioinformatics. Using the program, I could show that the cell nuclei segmentation is robust and accurate. Approaches from computational topology and graph theory complete the segmentation of cell nuclei. Thus, the developed module provides a comprehensive quantitative characterization of spheroids on the level of the individual cell, the cell neighborhood and the whole cell aggregate. The module was employed in four applications to analyze the influence of different stress conditions on the morphology and cellular arrangement of cells in spheroids. The module was accepted for publication in Scientific Reports along with the results for one application. The cell nuclei segmentation further provided a data source for simulation models that used correlation functions to identify structural zones in spheroids. These results were published in Royal Society Interface.
The final part of this work presents a module for cell tracking and lineage reconstruction. In collaboration with Dr. Alexis Maizel, Dr. Jens Fangerau and Dr. Daniel von Wangenheim, I developed a module to track the positions of all cells involved in lateral root formation in Arabidopsis thaliana and used the extracted positions for extensive data analysis. We reconstructed the cell lineages and established the first atlas of all founder cells that contribute to the formation. The analysis of the retrieved data allowed us to study conserved and individual patterns in lateral root formation. The atlas and parts of the analysis presented in this thesis were published in Current Biology.
In this thesis, I developed modules for an image analysis pipeline in three-dimensional fluorescence microscopy and applied them in interdisciplinary research projects. The modules enabled the organization, processing, visualization and analysis of the datasets. The perspective of the image analysis pipeline is not restricted to image-based systems biology. With ongoing development of the image analysis pipeline, it can also be a valuable tool for medical diagnostics or industrial high-throughput approaches.
Background: Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset.
Results: We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91%) whereas the other methods each failed for at least one dataset (F-measure≤69%). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values.
Conclusion: We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology.
"Mehr Licht!" – so lauteten, glaubt man seinem Arzt Carl Vogel, die letzten Worte des größten deutschen Dichters und Denkers Johann Wolfgang Goethe. Aus der Sicht der Fluoreszenzmikroskopie ist das kein guter Grundsatz. Die Kernidee der Lichtscheiben-Fluoreszenzmikroskopie (LSFM) liegt in der Macht der dunklen Seite. Anders gesagt: Sie folgt dem Prinzip, dass weniger manchmal viel mehr sein kann. Die schonende Beleuchtung empfindlicher Proben bei der LSFM birgt großes Potenzial für die moderne Zell- und Entwicklungsbiologie.