3D ultrastructural organization of whole Chlamydomonas reinhardtii cells studied by nanoscale soft x-ray tomography

The complex architecture of their structural elements and compartments is a hallmark of eukaryotic cells. The creation of high resolution models of whole cells has been limited by the relatively low resolution of conventional light microscopes and the requirement for ultrathin sections in transmission electron microscopy. We used soft x-ray tomography to study the 3D ultrastructural organization of whole cells of the unicellular green alga Chlamydomonas reinhardtii at unprecedented spatial resolution. Intact frozen hydrated cells were imaged using the natural x-ray absorption contrast of the sample without any staining. We applied different fiducial-based and fiducial-less alignment procedures for the 3D reconstructions. The reconstructed 3D volumes of the cells show features down to 30 nm in size. The whole cell tomograms reveal ultrastructural details such as nuclear envelope membranes, thylakoids, basal apparatus, and flagellar microtubule doublets. In addition, the x-ray tomograms provide quantitative data from the cell architecture. Therefore, nanoscale soft x-ray tomography is a new valuable tool for numerous qualitative and quantitative applications in plant cell biology

Correlative cellular ptychography with functionalized nanoparticles at the Fe L-edge

Precise localization of nanoparticles within a cell is crucial to the understanding of cell-particle interactions and has broad applications in nanomedicine. Here, we report a proof-of-principle experiment for imaging individual functionalized nanoparticles within a mammalian cell by correlative microscopy. Using a chemically-fixed HeLa cell labeled with fluorescent core-shell nanoparticles as a model system, we implemented a graphene-oxide layer as a substrate to significantly reduce background scattering. We identified cellular features of interest by fluorescence microscopy, followed by scanning transmission X-ray tomography to localize the particles in 3D, and ptychographic coherent diffractive imaging of the fine features in the region at high resolution. By tuning the X-ray energy to the Fe L-edge, we demonstrated sensitive detection of nanoparticles composed of a 22 nm magnetic Fe$_3$O$_4$ core encased by a 25-nm-thick fluorescent silica (SiO$_2$) shell. These fluorescent core-shell nanoparticles act as landmarks and offer clarity in a cellular context. Our correlative microscopy results confirmed a subset of particles to be fully internalized, and high-contrast ptychographic images showed two oxidation states of individual nanoparticles with a resolution of ~16.5 nm. The ability to precisely localize individual fluorescent nanoparticles within mammalian cells will expand our understanding of the structure/function relationships for functionalized nanoparticles

Dynamics and Structure of Cellular Aggregation

This work provides new insights into the dynamics and structure of cellular aggregation. Starting from cell motility which is necessary to get the cells into close proximity it presents new tools for visualization, analysis and modeling of aggregation processes. While a lot of work has been done in the field of microbial and amoeboid motility, there is a lack in theoretical understanding of mammalian cell motion, especially concerning directed migration stirred by external cues. To close this gap I developed a two-dimensional generic model based on mechanical cell-substrate interactions. This model facilitates the discrete nature of the motion cycle of mammalian cells by a randomized growth of protrusions and their retraction depending on the strength of an external cue. This model is capable of reproducing most experimental observations, especially the behavior at sharp changes in strength of the external cues, and provides an explanation for the attachment of the lagging cell pole as it increases the efficiency of gradient sensing. Furthermore, I introduce new experimental methods to visualize and analytical toolkits to analyze the structure of the highly irregular cell aggregates. These approaches were tested in two example cases: the two dimensional aggregation of mouse embryonic fibroblast (MEF)cells and the flocculation of S. cerevisiae mediated by the sugar-dependent adhesion protein Flo5. While it was possible to achieve temporal information of the MEF cell aggregation, the flocculation of S. cerevisiae is not accessible in this way. The time-lapse microscopy series indicate a subdivision of MEF cell aggregation into a spreading and a contraction phase. In addition, the data shows that there is a dependency of the aggregate’s structure on its size with a sharp transition from a linear dependency to a constant structure. The three-dimensional imaging of immobilized flocs using a confocal laser scanning microscope provided information about the structural properties of yeast flocs. The most important findings are that the flocs are self similar fractal structures and that cheater cells, i.e. cells that do not produce the necessary binding proteins but benefit from the altruistic behavior of producing cells, are largely underprivileged in the process. This indicates that, even though flo5 does not qualify as a “green beard gene” by definition, the benefits of the resulting altruistic behavior are strongly shifted in favor of the producing cells by the aggregation mechanism

Cheetah:a computational toolkit for cybergenetic control

Abstract Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah’s core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah’s segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells

Accessible software frameworks for reproducible image analysis of host-pathogen interactions

Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird

Modeling and control of gene expression dynamics in yeast

Synthetic biology is a novel research field which aims to engineer new functionalities in living cells with the final goal of controlling cellular behavior for a number of uses, ranging from energy, to environment, to medicine. Here, I contributed to the emerging role of control theory in synthetic biology. Exploring the concept of negative feedback loop, I extended the field of controlling cellular processes by devising novel approaches to model and control gene expression dynamics in a population of living cells

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

Image Segmentation of Bacterial Cells in Biofilms

Bacterial biofilms are three-dimensional cell communities that live embedded in a self-produced extracellular matrix. Due to the protective properties of the dense coexistence of microorganisms, single bacteria inside the communities are hard to eradicate by antibacterial agents and bacteriophages. This increased resilience gives rise to severe problems in medical and technological settings. To fight the bacterial cells, an in-detail understanding of the underlying mechanisms of biofilm formation and development is required. Due to spatio-temporal variances in environmental conditions inside a single biofilm, the mechanisms can only be investigated by probing single-cells at different locations over time. Currently, the mechanistic information is primarily encoded in volumetric image data gathered with confocal fluorescence microscopy. To quantify features of the single-cell behaviour, single objects need to be detected. This identification of objects inside biofilm image data is called segmentation and is a key step for the understanding of the biological processes inside biofilms. In the first part of this work, a user-friendly computer program is presented which simplifies the analysis of bacterial biofilms. It provides a comprehensive set of tools to segment, analyse, and visualize fluorescent microscopy data without writing a single line of analysis code. This allows for faster feedback loops between experiment and analysis, and allows fast insights into the gathered data. The single-cell segmentation accuracy of a recent segmentation algorithm is discussed in detail. In this discussion, points for improvements are identified and a new optimized segmentation approach presented. The improved algorithm achieves superior segmentation accuracy on bacterial biofilms when compared to the current state-of-the-art algorithms. Finally, the possibility of deep learning-based end-to-end segmentation of biofilm data is investigated. A method for the quick generation of training data is presented and the results of two single-cell segmentation approaches for eukaryotic cells are adapted for the segmentation of bacterial biofilm segmentation.Bakterielle Biofilme sind drei-dimensionale Zellcluster, welche ihre eigene Matrix produzieren. Die selbst-produzierte Matrix bietet den Zellen einen gemeinschaftlichen Schutz vor äußeren Stressfaktoren. Diese Stressfaktoren können abiotischer Natur sein wie z.B. Temperatur- und Nährstoff\- schwankungen, oder aber auch biotische Faktoren wie z.B. Antibiotikabehandlung oder Bakteriophageninfektionen. Dies führt dazu, dass einzelne Zelle innerhalb der mikrobiologischen Gemeinschaften eine erhöhte Widerstandsfähigkeit aufweisen und eine große Herausforderung für Medizin und technische Anwendungen darstellen. Um Biofilme wirksam zu bekämpfen, muss man die dem Wachstum und Entwicklung zugrundeliegenden Mechanismen entschlüsseln. Aufgrund der hohen Zelldichte innerhalb der Gemeinschaften sind die Mechanismen nicht räumlich und zeitlich invariant, sondern hängen z.B. von Metabolit-, Nährstoff- und Sauerstoffgradienten ab. Daher ist es für die Beschreibung unabdingbar Beobachtungen auf Einzelzellebene durchzuführen. Für die nicht-invasive Untersuchung von einzelnen Zellen innerhalb eines Biofilms ist man auf konfokale Fluoreszenzmikroskopie angewiesen. Um aus den gesammelten, drei-dimensionalen Bilddaten Zelleigenschaften zu extrahieren, ist die Erkennung von den jeweiligen Zellen erforderlich. Besonders die digitale Rekonstruktion der Zellmorphologie spielt dabei eine große Rolle. Diese erhält man über die Segmentierung der Bilddaten. Dabei werden einzelne Bildelemente den abgebildeten Objekten zugeordnet. Damit lassen sich die einzelnen Objekte voneinander unterscheiden und deren Eigenschaften extrahieren. Im ersten Teil dieser Arbeit wird ein benutzerfreundliches Computerprogramm vorgestellt, welches die Segmentierung und Analyse von Fluoreszenzmikroskopiedaten wesentlich vereinfacht. Es stellt eine umfangreiche Auswahl an traditionellen Segmentieralgorithmen, Parameterberechnungen und Visualisierungsmöglichkeiten zur Verfügung. Alle Funktionen sind ohne Programmierkenntnisse zugänglich, sodass sie einer großen Gruppe von Benutzern zur Verfügung stehen. Die implementierten Funktionen ermöglichen es die Zeit zwischen durchgeführtem Experiment und vollendeter Datenanalyse signifikant zu verkürzen. Durch eine schnelle Abfolge von stetig angepassten Experimenten können in kurzer Zeit schnell wissenschaftliche Einblicke in Biofilme gewonnen werden.\\ Als Ergänzung zu den bestehenden Verfahren zur Einzelzellsegmentierung in Biofilmen, wird eine Verbesserung vorgestellt, welche die Genauigkeit von bisherigen Filter-basierten Algorithmen übertrifft und einen weiteren Schritt in Richtung von zeitlich und räumlich aufgelöster Einzelzellverfolgung innerhalb bakteriellen Biofilme darstellt. Abschließend wird die Möglichkeit der Anwendung von Deep Learning Algorithmen für die Segmentierung in Biofilmen evaluiert. Dazu wird eine Methode vorgestellt welche den Annotationsaufwand von Trainingsdaten im Vergleich zu einer vollständig manuellen Annotation drastisch verkürzt. Die erstellten Daten werden für das Training von Algorithmen eingesetzt und die Genauigkeit der Segmentierung an experimentellen Daten untersucht

Probing Cellular Uptake of Nanoparticles, One at a Time

Advanced fluorescence microscopy is the method of choice to study cellular uptake of nanoparticles with molecular specificity and nanoscale resolution; yet, direct visualization of nanoparticles entry into cells poses severe technical challenges. Here, we have combined super-resolution photoactivation localization microscopy (PALM) with single particle tracking (SPT) to visualize clathrin-mediated endocytosis (CME) of polystyrene nanoparticles at very high spatial and temporal resolution