3D-surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semi-supervised deep learning

Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nm range and strong contrast for membranous structures without requirement for labeling or chemical fixation. The short acquisition time and the relatively large volumes acquired allow for fast acquisition of large amounts of tomographic image data. Segmentation of these data into accessible features is a necessary step in gaining biologically relevant information from cryo-soft X-ray tomograms. However, manual image segmentation still requires several orders of magnitude more time than data acquisition. To address this challenge, we have here developed an end-to-end automated 3D-segmentation pipeline based on semi-supervised deep learning. Our approach is suitable for high-throughput analysis of large amounts of tomographic data, while being robust when faced with limited manual annotations and variations in the tomographic conditions. We validate our approach by extracting three-dimensional information on cellular ultrastructure and by quantifying nanoscopic morphological parameters of filopodia in mammalian cells

Interaction of Biologically Relevant Nanoparticles with Cells Studied by Cryo Soft X Ray Tomography

In dieser Arbeit, wurden Endozytose und intrazellulären Transportwegen für zwei verschiedene biologisch relevante Nanopartikel (dendritisch Polyglycerolsulfat (dPGS-np) und Polyethylenimin beschichtete Goldnanopartikel (PEI-np)) mit Kryo-Röntgentomographie untersucht. Diese Untersuchungen zeigten, beide Nanopartikeln über Makropinozytose endozytiert werden und dass die meisten Nanopartikel in Endosomen, multivesikulären Körpern und Lysosomen lokalisiert sind. Trotz dieser Ähnlichkeiten im Verhalten beider Partikel gab es auch Unterschiede die zeigten. Im Gegensatz zu PEI-np, wurden einige dPGS-np in Lipidtröpfchen gefunden. Weiterhin verursachen die PEI-np bei einer Konzentration von 0,13 nM ein Zerplatzen von Lysosomen in erheblichem Ausmaß wodurch die Nanopartikel in das Zytoplasma entweichen und teilweise in den Zellkern eindringen. Im Unterschied dazu konnte kein Nachweis für ein Zerplatzen von Lysosomen durch dPGS-np erbracht werde. Interessanterweise wurde ein Wechsel des Endocytose-Weges von der Makropinozytose zu einer mutmaßlichen Caveolae-vermittelten Endozytose beobachtet, wenn die PEI-np-Konzentration um das Zehnfache reduziert wurde. Unter diesen Bedingungen induzierten PEI-np kein Zerplatzen von Lysosomen mehr. Das Ausbleiben von zerplatzten Lysosomen bei dieser niedrigeren Konzentration korreliert mit einer verringerten Zellschädigung und hat somit wichtige Auswirkungen auf die Verwendung von PEI beim Gentransfer. Überraschenderweise stellte sich heraus, dass beide Nanopartikelarten eine bisher nicht beobachtete umfassende zytoplasmatische Veränderungen in den Zellen induzierten. Insbesondere wurden nach der Inkubation mit beiden Diese zytoplasmatischen Veränderungen könnten wichtige physiologische Veränderungen widerspiegeln, jedoch ist hierzu zusätzliche Forschung erforderlich. Diese Beobachtungen deuten darauf hin, dass das Verhalten biologisch relevanter Nanopartikel in Zukunft vorhersehbarer werden kann durch weitere systematische Studien.In this thesis, the endocytosis and trafficking pathways of two different biologically relevant nanoparticles, namely dendritic polyglycerol sulfate coated gold nanoparticles (dPGS-np) and polyethyleneimine coated gold nanoparticles (PEI-np) were investigated via cryo soft X-ray tomography. Both nanoparticles were found to be endocytosed predominantly via macropinocytosis, and most nanoparticles became localized to endosomes, multivesicular bodies and lysosomes. Despite these similarities in trafficking, there were also key differences. Some dPGS-np were found in lipid droplets but no PEI nanoparticles were found in this compartment. At a concentration of 0.13 nM, PEI-np were observed to induce extensive rupture of lysosomes, leading to significant levels of cytoplasmic escape and nuclear entry. In contrast, no evidence for lysosomal rupture with dPGS-np was detected, and concomitantly very low levels of cytoplasmic escape and no nuclear entry were found. Interestingly, when the PEI-np concentration was reduced by ten-fold, a switch in the endocytosis pathway from macropinocytosis to a putative caveolae-mediated endocytosis was observed. Importantly, under these conditions, PEI nanoparticles no longer induced lysosomal rupture. This lack of lysosomal rupture at the lower concentration was correlated with reduced cellular damage, and so has important implications for use of PEI in gene delivery. Most surprisingly, both nanoparticle types were found to induce similar global cytoplasmic alterations in the cells. These cytoplasmic alterations could reflect important physiological changes, but further work is required to determine this. Our observations suggest that the behavior of biologically relevant nanoparticles might become more predictable in the future by further application of systematic studies

Datasets for training, validation and hyperparameter optimization of the deep network

Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nm range and strong contrast for membranous structures without requirement for labeling or chemical fixation. The short acquisition time and the relatively large volumes acquired allow for fast acquisition of large amounts of tomographic image data. Segmentation of these data into accessible features is a necessary step in gaining biologically relevant information from cryo-soft X-ray tomograms. However, manual image segmentation still requires several orders of magnitude more time than data acquisition. To address this challenge, we have here developed an end-to-end automated 3D-segmentation pipeline based on semi-supervised deep learning. Our approach is suitable for high-throughput analysis of large amounts of tomographic data, while being robust when faced with limited manual annotations and variations in the tomographic conditions. We validate our approach by extracting three-dimensional information on cellular ultrastructure and by quantifying nanoscopic morphological parameters of filopodia in mammalian cells