Understanding the conformational and dynamic changes of biomacromolecular complexes in different states, such as the membrane protein photoreceptor-transducer complex NpSRII/NpHtrII, is a key step to gaining insight into the functional mechanism of these important classes of protein complexes, since ~30 % of the human proteome are membrane proteins, yet they are largely underrepresented in terms of structural information with <1 % of all structures in the protein data bank. Hence for the development of methods suitable to study the conformation and dynamics of such complexes there is a strong demand and a vast potential field of applications. Here we combined method development at the interface between biomolecular simulations and model-based analysis of EPR- and fluorescence spectroscopic data with application studies using state-of-the-art spectroscopic techniques in conjunction with site-directed spin- or fluorescence labeling. In an initial benchmark study on the rigid globular protein complex Rpo4/7, we compared experimental inter fluorescence label distances or spin label distance distributions to a variety of predicted inter label distances based on molecular dynamics simulations, Monte Carlo sampling and a discrete rotamer library analysis. We found that while for the molecular dynamics simulations with explicit solvent considerable sampling challenges have to be overcome to reproduce the experimentally observed inter label distance distributions, the Monte Carlo sampling performed well when compared to the experimental data and was computationally less demanding. Significantly more efficient and equally accurate for our examples was the so-called rotamer library analysis available for the spin labels since it relies on a pre-calculated set of rotational isomers. In general, predictions for the mean distances were in agreement within the error margins while distribution shapes were more challenging to reproduce. Overall this study shows a positive evaluation for the assessed tools and the developed simulation protocols as well as their potential applications. Using the combination of EPR and fluorescence spectroscopy for distance determination we studied the structural influence of RNA binding on Rpo4/7, and showed that the protein complex stays conformationally rigid and thereby serves as a guiding rail for the nascent RNA chain that leaves the RNA polymerase along the Rpo4/7 RNA binding interface. To enhance the interpretation of experimentally determined changes of conformation and dynamics in protein complexes and to discuss the observed changes in terms of structural information, we built models of the two transcription factors TFE and the Spt4/5 complex, as well as of Argonaute, a 713 amino acid four-domain protein nuclease from Methanocaldococcus jannaschii. These structural models not only allowed a more accurate planning of fluorescence or EPR labeling experiments, but also the models enabled the discussion of the experimental data in structural terms. Based on such an initial structure further computational analysis techniques may be applied to identify putative structural changes or dynamic modes. This was shown for the histidine transporter HisQMP2, where we combined normal mode analysis to model protein flexibility with the rotamer library analysis to screen for possible conformational changes in comparison to experimental inter spin distance data. The most prominent agreement with one mode led to a working hypothesis of a conformational change and provides the basis for validation in future experiments. Due to the inherent synergy effects, we applied a combined experimental and simulation approach for the EPR-based distance determination in the globular DNA-binding protein LexA to probe conformation and dynamics of the N-terminal DNA-binding domains with respect to the C-terminal domains within the LexA homodimer. While the C-terminal dimerization domains exhibit a well-defined conformation that proved to be independent of DNA-binding, large-scale changes in conformation and dynamics were detected for the N-terminal domains. They were only found in a defined conformation when bound to DNA while in its absence a large rotational freedom of the entire N-terminal domains contributed to the conformational ensemble. Combined with a biochemical characterization of the autocatalytic cleavage of LexA, our data explains how LexA induces the SOS response after DNA damage or under latent antibiotic stress. We further studied the membrane photoreceptor-transducer complex NpSRII/NpHtrII that governs the light-dependent swimming behavior in Natronomonas pharaonis by a two-component signaling system. This system comprises extraordinary features of sensitivity, signal amplification, integration and transducer cooperativity, yet the molecular details of these features are poorly understood, as is signal propagation itself. By combining time-resolved cw EPR spectroscopy of NpSRII/NpHtrII variants spin labeled in the HAMP1 domain with time-resolved optical absorbance spectroscopy to report on the receptor signaling state, we found a tight kinetic coupling of receptor and transducer during the relaxation back to the ground state and hence a prolonged activation period, that with ~500 - ~700 ms is sufficiently long to cause phosphorylation bursts of the cognate kinase CheA. This explains signal amplification already on the level of the NpSRII/NpHtrII dimers. We further determined the transient difference spectra from the time-resolved EPR data that show local differences in dynamics and steric restrictions upon light-activation. Comparing these experimentally observed differences to predictions confirms the assumed two-state structural model and shows this transition between the two states for a single HAMP domain in a light-dependent manner. Additionally, our approach integrates a dynamic view into the model, since the two states are shown to exhibit different local dynamics in a fashion described previously as a competing model for signaling by dynamic differences based on biochemical studies. Here we show unification of the two models into one congruent description encompassing a transition between the two previously suggested states by concerted structural and dynamic changes. In an independent analysis using all-atom and coarse grained molecular dynamics of the NpSRII/NpHtrII complex in the minimal unit that can exert kinase control, the trimer of receptor-transducer dimers, we revealed a distinct dynamical pattern encoded in the primary sequence of the coiled-coil heptad-repeats. Upon receptor activation, these segments alter their dynamics in a concerted fashion with regions such as HAMP1 and the adaptation region becoming more compact, while HAMP2 and the tip become more dynamic, leading to dynamic and to limited structural changes at the CheA-kinase binding sites. Together with an extensive validation against experimental data, these findings suggest the altered dynamics as the mechanism for signal propagation along the extended coiled-coil structure of NpHtrII. This working model, that explains the current body of experimental data, allows for further refinement by all-atom molecular dynamics and provides a basis to devise future experiments for validation. The presented studies outline the versatile methodology of combined experimental and simulation approaches to analyze the conformation and dynamics of biomacromolecules including membrane protein complexes.

Physical processes rely on the transmission of energy and information across scales. In the last century, theoretical tools have been developed in the field of statistical physics to infer macroscopic properties starting from a microscopic description of the system. However, less attention has been devoted to the remodelling of microscopic degrees of freedom by macroscopic feedback. In recent years, ideas from non-equilibrium physics have been applied to characterise biological and artificial intelligence systems. These systems share in common their structure in discrete scales of organisation that perform specialised functions. To correctly regulate these functions, the accurate transmission of information across scales is crucial. In this thesis we study the role of macroscopic feedback in the remodelling of microscopic degrees of freedom in two paradigmatic examples, one taken from the field of biology, the self-organisation of specialisation and plasticity in a social wasp, and one from artificial intelligence, the remodelling of deep neural networks in a stochastic many-particle system. In the first part of this thesis we study how the primitively social wasp Polistes canadensis simultaneously achieves robust specialization and rapid plasticity. Combining a unique experimental strategy correlating time-resolved measurements across vastly different scales with a theoretical approach, we characterise the re-establishment of the social steady state after queen removal. We show that Polistes integrates antagonistic processes on multiple scales to distinguish between extrinsic and intrinsic perturbations and thereby achieve both robust specialisation and rapid plasticity. Furthermore, we show that the long-term stability of the social structure relies on the regulation of transcriptional noise by dynamic DNA methylation. In the second part of this thesis, we ask whether emergent collective interactions can be used to remodel deep neural networks. To this end, we study a paradigmatic stochastic manyparticle model where the dynamics are defined by the reaction rates of single particles, given by the output of distinct deep neural networks. The neural networks are in turn dynamically remodelled using deep reinforcement learning depending on the previous history of the system. In particular, we implement this model as a one dimensional stochastic lattice gas. Our results show the formation of two groups of particles that move in opposite directions, diffusively at early times and ballistically over longer time-scales, with the transition between these regimes corresponding to the time-scale of left/right symmetry breaking at the level of individual particles. Over a hierarchy of characteristic time-scales these particles develop emergent, increasingly complex interactions characterised by short-range repulsion and long-range attraction. As a result, the system asymptotically converges to a regime characterised by the presence of anti-ferromagnetic particle clusters. To conclude, we characterise the impact of memory effects and demographic disorder on the dynamics. Together, our results shed light on how non-equilibrium systems can employ macroscopic feedback to regulate the propagation of fluctuations across scales.

Zellen aus Karzinomen sind erwiesenermaßen weicher als Epithelzellen ihres Ursprungsgewebes. Es wurde vermutet, dass dieses Weicherwerden Zellen dabei hilft, aus dem Primärtumor auszubrechen und Metastasen zu bilden, was allerdings erst von wenigen Belegen bestärkt wird. Weiterhin wird die Entwicklung von Karzinomen allgemein als von einer epithelial-mesenchymalen Transition (EMT) angetrieben angesehen, ein Prozess, der die Umformung von Epithelgeweben steuert und stark in das Zytoskelett eingreift. Ich habe daher die Hypothese aufgestellt, dass EMT Karzinomzellen weicher macht und somit aggressive und invasive Tumore erzeugt. In der vorgelegten Arbeit gehe ich dem Nachweis dieser Hypothese nach. Ich habe den Einfluss der EMT auf Zellweichheit in vitro untersucht, allerdings kein gerichtetes Weicherwerden mit Fortschritt der EMT feststellen können. Mit vitalen Einzelzellen, die ich aus Operationsresektaten isoliert habe, verglich ich die mechanischen Eigenschaften von invasiven und nicht-invasiven Tumoren ex vivo und konnte eine klare Korrelation von Aggressivität mit Zellweichheit in vier verschiedenen Arten von Karzinomen aufzeigen. Membrangebundenes E-cadherin, das mir als Marker für den Fortschritt der EMT diente, war jedoch weder mit der Aggressivität der Karzinome noch mit der Weichheit derer Zellen korreliert. Ich benutzte maschinelles Lernen (ML), um Krebs-zellen in silico auf Basis ihrer mechanischer Eigenschaften zu klassifizieren, stieß aber auf klare Grenzen. In dieser Arbeit habe ich zum ersten Mal ex vivo gezeigt, dass das Weicherwerden von Krebszellen ein kontinuierlicher Prozess in Karzinomen ist, und dass erhöhte Aggressivität mit erhöhter Zellweichheit einhergeht. Ich habe außerdem EMT, die lange Zeit als entscheidend für Zellinvasion galt, als mögliche Ursache für dieses Weicherwerden ausgeschlossen. Zusammengenommen mit meinen Resultaten der ML Klassifikation deutet dies darauf hin, dass eine erhöhte Heterogenität von mechanischen Eigenschaften von Krebszellen, ausgelöst von allgemeiner Deregulation, die Invasion von Karzinomen antreibt.:1 Introduction 1 2 Background 11 2.1 The cytoskeleton of eukaryotic cells 12 2.2 The actin-E-cadherin-complex 17 2.2.1 E-cadherin 17 2.2.2 The Wnt/β-catenin pathway 18 2.2.3 Actin-E-cadherin dynamics 19 2.3 The epithelial to mesenchymal transition (EMT) 21 2.3.1 Epithelial and mesenchymal cells 21 2.3.2 Classical EMT 22 2.3.3 EMT in carcinoma development 23 2.4 Carcinoma development 25 2.4.1 Growth and spread 25 2.4.2 Tumor grading and staging 26 2.4.3 Carcinoma development outside of EMT 29 2.5 Cell mechanics in migration and invasion 31 3 Materials & methods 37 3.1 The Optical Stretcher as a main measurement device for cellular softness and E-cadherin level 38 3.1.1 Deformation by radiation pressure 39 3.1.2 Viability in an OS 43 3.1.3 Data acquisition and evaluation 46 3.2 Kelvin Voigt (KV) modeling 50 3.3 Machine learning 53 3.3.1 Interpreting and evaluating classications 54 3.3.2 Data preparation 58 3.3.3 Support vector machines (SVM) 58 3.3.4 Random forest (RF) 64 3.3.5 Permutation importance 67 3.4 Statistical analysis 68 3.4.1 Two one-sided tests (TOST) as a statistical test for equivalence 69 3.5 In vitro model systems for eukaryotic cells, their culture, and preparation 71 3.5.1 Cell lines 71 3.5.2 Cell culture 73 3.5.3 Fluorescent labeling of E-cadherin 73 3.6 Isolation of cancer cells from primary samples 75 3.6.1 Isolation of cancer cells from blood samples 75 3.6.2 Isolation of cancer cells from surgical resections 77 4 Results & discussion 79 4.1 In vitro growth factor induced EMT 81 4.1.1 EGF induced EMT is not correlated to cell softening in MCF 10A epithelial cells 82 4.1.2 TGFβ1 induced EMT is not correlated to cell softening in MCF 10A epithelial cells 87 4.1.3 Summary 91 4.2 Ex vivo vital tumor cells from liquid biopsies and surgical resections 94 4.2.1 Database analysis reveals that there is no systematic change of EMT related markers over the course of carcinoma progression 96 4.2.2 Vital single cells isolated from liquid biopsies of breast cancer patients can be distinguished from healthy cells of their natural surrounding 99 4.2.3 Cell softening is correlated to aggressiveness in tumor cells isolated from surgical resections 110 4.2.4 EMT progression is connected to neither cell softening nor aggressiveness in tumor cells isolated from surgical resections 120 4.2.5 Summary 123 4.3 In silico Machine learning as means to assess the predictive power of cell mechanics 127 4.3.1 Parameters from OS measurements 128 4.3.2 In vitro discrimination of cell types in a breast cancer cell line panel 129 4.3.3 Ex vivo discrimination of breast cancer cells and PBMC isolated from liquid biopsies 136 4.3.4 Summary 143 5 Conclusion & outlook 147 A Additional data and information 161 A.1 Optimization of support vector machines (SVM) and random forest (RF) machine learning approaches 161 A.1.1 Optimization of the training set size in SVM and RF machine learning approaches 161 A.1.2 Optimization of the SVM machine learning algorithm 161 A.1.3 Optimization of the RF machine learning algorithm 163 A.2 List of features for machine learning based classication 164 A.2.1 List of features used for classication of my in vitro cell line panel 164 A.2.2 List of features for classication of circulating tumor cells isolated from the blood of patients with mamma carcinoma 166 A.3 Activity parameter A of cells isolated from the blood samples of breast cancer patients 170 B Materials and reagents 171 B.1 Cell culture media 171 B.1.1 Medium for MCF 10A cells 171 B.1.2 Medium for MDA-MB-436 and MDA-MB-231 cells 171 B.1.3 Medium for NIH/3T3 cells 172 B.2 Ringer lactate buer for tissue transport and storage 172 B.3 MACS buffer 172 C Protocols 173 C.1 In vitro culture of cell lines 173 C.1.1 Passage of cell lines cultured in vitro 173 C.1.2 Cryogenic storage and thawing of cell lines 174 C.2 Immunouorescent labeling of E-cadherin 174 C.3 Growth factor treatment of MCF 10A epithelial cells 175 C.3.1 Treatment with increasing concentrations of epidermal growth factor (EGF) 175 C.3.2 Treatment with constant concentration of epidermal growth factor (EGF) 176 C.3.3 Treatment with transforming growth factor β1 (TGFβ1) 177 C.4 Isolation of vital cells from patient samples 178 C.4.1 Negative depletion of specic populations from cell suspensions by magnetic bead sorting 178 C.4.2 Isolation of vital circulating tumor cells (CTC) from the blood of patients with mamma carcinoma 179 C.4.3 Isolation of healthy peripheral blood mononuclear cells (PBMC) from the blood of patients and donors 179 C.4.4 Isolation of vital cancer cells from tumor samples of surgical resections of various carcinomas 180 C.5 Immunohistochemical staining of paranized tissue slices of tumor tissue 82 Bibliography 186 / Carcinoma cells have been shown to be softer than cells from their tissue of origin, healthy epithelia. This softening effect has been predicted to drive tumor cell migration and ergo metastases, but only circumstantial evidence exists for this. Carcinoma development is also generally viewed as driven by an epithelial to mesenchymal transition (EMT), a process that governs epithelial restructuring and heavily interferes with the cytoskeleton. I therefore hypothesized that EMT drives cell softening in carcinomas, which in turn leads to aggressive and invasive tumors. In the presented work, I pursue the verification of this hypothesis. I investigated the influence of EMT on cell softening in vitro, yet found no directed development of cell body softness with EMT progression. With vital single cancer cells that I isolated from surgical resections, I explored the mechanics of invasive, and non-invasive tumors ex vivo and saw a clear correlation of tumor aggressiveness with cell softness in four different types of carcinomas. There was however no correlation between E-cadherin in the cell membrane of isolated cancer cells, which I used as a marker for EMT progression, and the aggressiveness of the respective carcinomas or the softness of their cells. I employed machine learning (ML) to classify cancer cells based on their mechanical properties in silico, but found clear limits to that approach. In this work, I have shown for the very first time ex vivo how cell softening is an ongoing process during carcinoma development and increased aggressiveness is linked to increased softness. I also excluded EMT, which has long been deemed a driver of cell invasion, as a possible origin for cell softening. Together with results from ML classification, this points to increased heterogeneity in mechanical properties of cancer cells by deregulation as a main contributor to carcinoma invasion.:1 Introduction 1 2 Background 11 2.1 The cytoskeleton of eukaryotic cells 12 2.2 The actin-E-cadherin-complex 17 2.2.1 E-cadherin 17 2.2.2 The Wnt/β-catenin pathway 18 2.2.3 Actin-E-cadherin dynamics 19 2.3 The epithelial to mesenchymal transition (EMT) 21 2.3.1 Epithelial and mesenchymal cells 21 2.3.2 Classical EMT 22 2.3.3 EMT in carcinoma development 23 2.4 Carcinoma development 25 2.4.1 Growth and spread 25 2.4.2 Tumor grading and staging 26 2.4.3 Carcinoma development outside of EMT 29 2.5 Cell mechanics in migration and invasion 31 3 Materials & methods 37 3.1 The Optical Stretcher as a main measurement device for cellular softness and E-cadherin level 38 3.1.1 Deformation by radiation pressure 39 3.1.2 Viability in an OS 43 3.1.3 Data acquisition and evaluation 46 3.2 Kelvin Voigt (KV) modeling 50 3.3 Machine learning 53 3.3.1 Interpreting and evaluating classications 54 3.3.2 Data preparation 58 3.3.3 Support vector machines (SVM) 58 3.3.4 Random forest (RF) 64 3.3.5 Permutation importance 67 3.4 Statistical analysis 68 3.4.1 Two one-sided tests (TOST) as a statistical test for equivalence 69 3.5 In vitro model systems for eukaryotic cells, their culture, and preparation 71 3.5.1 Cell lines 71 3.5.2 Cell culture 73 3.5.3 Fluorescent labeling of E-cadherin 73 3.6 Isolation of cancer cells from primary samples 75 3.6.1 Isolation of cancer cells from blood samples 75 3.6.2 Isolation of cancer cells from surgical resections 77 4 Results & discussion 79 4.1 In vitro growth factor induced EMT 81 4.1.1 EGF induced EMT is not correlated to cell softening in MCF 10A epithelial cells 82 4.1.2 TGFβ1 induced EMT is not correlated to cell softening in MCF 10A epithelial cells 87 4.1.3 Summary 91 4.2 Ex vivo vital tumor cells from liquid biopsies and surgical resections 94 4.2.1 Database analysis reveals that there is no systematic change of EMT related markers over the course of carcinoma progression 96 4.2.2 Vital single cells isolated from liquid biopsies of breast cancer patients can be distinguished from healthy cells of their natural surrounding 99 4.2.3 Cell softening is correlated to aggressiveness in tumor cells isolated from surgical resections 110 4.2.4 EMT progression is connected to neither cell softening nor aggressiveness in tumor cells isolated from surgical resections 120 4.2.5 Summary 123 4.3 In silico Machine learning as means to assess the predictive power of cell mechanics 127 4.3.1 Parameters from OS measurements 128 4.3.2 In vitro discrimination of cell types in a breast cancer cell line panel 129 4.3.3 Ex vivo discrimination of breast cancer cells and PBMC isolated from liquid biopsies 136 4.3.4 Summary 143 5 Conclusion & outlook 147 A Additional data and information 161 A.1 Optimization of support vector machines (SVM) and random forest (RF) machine learning approaches 161 A.1.1 Optimization of the training set size in SVM and RF machine learning approaches 161 A.1.2 Optimization of the SVM machine learning algorithm 161 A.1.3 Optimization of the RF machine learning algorithm 163 A.2 List of features for machine learning based classication 164 A.2.1 List of features used for classication of my in vitro cell line panel 164 A.2.2 List of features for classication of circulating tumor cells isolated from the blood of patients with mamma carcinoma 166 A.3 Activity parameter A of cells isolated from the blood samples of breast cancer patients 170 B Materials and reagents 171 B.1 Cell culture media 171 B.1.1 Medium for MCF 10A cells 171 B.1.2 Medium for MDA-MB-436 and MDA-MB-231 cells 171 B.1.3 Medium for NIH/3T3 cells 172 B.2 Ringer lactate buer for tissue transport and storage 172 B.3 MACS buffer 172 C Protocols 173 C.1 In vitro culture of cell lines 173 C.1.1 Passage of cell lines cultured in vitro 173 C.1.2 Cryogenic storage and thawing of cell lines 174 C.2 Immunouorescent labeling of E-cadherin 174 C.3 Growth factor treatment of MCF 10A epithelial cells 175 C.3.1 Treatment with increasing concentrations of epidermal growth factor (EGF) 175 C.3.2 Treatment with constant concentration of epidermal growth factor (EGF) 176 C.3.3 Treatment with transforming growth factor β1 (TGFβ1) 177 C.4 Isolation of vital cells from patient samples 178 C.4.1 Negative depletion of specic populations from cell suspensions by magnetic bead sorting 178 C.4.2 Isolation of vital circulating tumor cells (CTC) from the blood of patients with mamma carcinoma 179 C.4.3 Isolation of healthy peripheral blood mononuclear cells (PBMC) from the blood of patients and donors 179 C.4.4 Isolation of vital cancer cells from tumor samples of surgical resections of various carcinomas 180 C.5 Immunohistochemical staining of paranized tissue slices of tumor tissue 82 Bibliography 186

The DNA origami method was established by Paul Rothemund in 2009. It allows to produce self-assembling 2D nanostructures with precise geometry and tunable mechanical properties that can be equipped with a broad range of functionalizations. It was extended to 3D by the group of William Shih in 2009 which also presented caDNAno, a software that made the design of nanostructures easier and more accessible. Since then, DNA origami nanostructures were utilized in a broad range of applications, which enabled unprecedented insight into mechanisms and processes of biological systems at the nanoscale. In this thesis multiple nanostructures were designed and manufactured to perform studies at the single-molecule level, which yielded a number of scientifically relevant contributions in the fields of biophysics and nanotechnology. Development of DNA origami nanostructures to mimic the properties and function of membrane proteins As a first application, DNA origami nanostructures with defined geometric and mechanical properties were designed, that mimic the behaviour and function of membrane proteins. To this end, rod-shaped nanostructures were equipped with precisely placed, lipid-integrating cholesterol modifications as well as fluorescent dyes. Subsequently their interaction with lipid membranes was studied. It was found that the prepared nanostructures specifically bound to lipid membranes and could diffuse on their surface, for which the rotational and translational diffusion coefficients were determined. The presence of magnesium thereby promoted the nanostructures to migrate into specific lipid domains in a reversible, switchable manner. Furthermore, their high aspect ratio allowed to investigate crowding effects, which are considered important mechanisms for the self-organisation of membrane proteins. In addition, block-shaped DNA origami nanostructures that organized into micrometre-sized super-structures were designed and produced. They were capable of deforming lipid membranes on the scale of micrometres in a similar fashion to biological counterparts. Establishing ultra-fast twist and torque measurements using DNA origami nanorotors In an additional application, DNA origami nanorotors were developed to perform ultra-fast single-molecule twist and torque measurements, allowing to resolve subtle changes in real-time. This also required the development of a new measurement setup that extended magnetic tweezers with the capability to detect the scattered light of gold nanoparticles. Hence, a complex setup was constructed and calibrated that enabled magnetic tweezers measurements with up to 4 kHz and simultaneously track gold nanoparticles at 4 kHz as well. In an alternative configuration the setup allowed simultaneous magnetic tweezers and single-molecule fluorescence and FRET measurements. DNA origami nanorotors which were embedded within DNA constructs and carried the gold nanoparticles were then obtained and used to perform ultra-fast twist and torque measurements. This constituted improvements in the spatio-temporal resolution over previous methods by one to three orders of magnitude, as demonstrated by direct measurements on the torsional response of DNA to external twists and the unwinding of DNA by an enzyme. Direct measurements of the energy landscape and dynamics of the R-loop formation by the CRISPR-Cas surveillance complex Cascade Using the DNA origami nanorotor enhanced ultra-fast twist measurements, the target recognition process of the CRISPR-Cas surveillance complex Cascade was directly observed. Effector complexes of CRISPR-Cas systems have been widely applied in genome editing recently, since they can be programmed to bind practically any genomic target by their intrinsic RNA (crRNA) component. They have, however, considerable tolerance for mismatches between their RNA and their intended DNA target. For Cascade, after binding with a protein motif to a DNA target, base-pairing between crRNA and the double-stranded DNA target is initiated, resulting in the formation of an R-loop structure which leads to unwinding of the DNA. This was directly measured using the nanorotor, which provided unprecedented insight in the R-loop formation by Cascade, allowing to determine the underlying energy landscape and the dynamics of the process. It was shown that R-loop progression occurs on 6-bp kinetic intermediate steps with an underlying single base pair stepping on fast time scales. Furthermore the effect of mutations in the target DNA on the R-loop formation process was investigated, indicating that the global shape of the energy landscape allows for a highly specific kinetic discrimination of mismatched targets. Investigations into the locking transition, a conformational change that occurs after the full formation of the R-loop and is a prerequisite for subsequent DNA degradation, completed the study. Overall, the findings provide a better understanding of the target recognition process of Cascade, which will contribute to the construction of more precise gene-editing tools in the future. Furthermore, the nanorotor-assisted measurements are applicable to many twist and torque inducing mechanisms and processes that can be investigated in further studies.:1. Introduction 2. Multifunctional magnetic tweezers 3. Applications of DNA origami 4. Ultra-Fast torque measurements on supercoiled DNA 5. R-loop dynamics of the CRISPR-Cas Cascade complex 6. Summary and Discussion Bibliography List of Figures List of Tables List of Publications A. Appendix / Die DNA Origami Methode wurde im Jahr 2006 durch Paul Rothemund begründet. Sie erlaubt es selbst-assemblierende 2D Nanostrukturen mit präzisen Geometrien und kalibrierbaren mechanischen Eigenschaften zu erstellen, die zudem mit einer Vielzahl an Funktionalisierungen ausgestattet werden können. Die Methode wurde 2009 in der Gruppe von William Shih auf 3D Nanostrukturen erweitert, wobei zudem caDNAno präsentiert wurde, eine Software die die Erstellung solcher Nanostrukturen wesentlich einfacher und zugänglicher machte. Seitdem wurden DNA Origami Nanostrukturen in vielfältigen Anwendungen genutzt, die nie dagewesene Einblicke in Mechanismen und Prozesse von biologischen Systemen auf der Nanoskala erlaubten. In dieser Arbeit wird anhand mehrerer Beispiele gezeigt, wie solche Nanostrukturen genutzt werden können, um Studien auf der Einzelmolekül-Ebene durchzuführen. Entwicklung von DNA Origami Nanostrukturen, welche die Eigenschaften und Funktionen von Membranproteinen imitieren In einer ersten Anwendung wurden DNA Origami Nanostrukturen mit definierten geometrischen und mechanischen Eigenschaften entworfen, welche das Verhalten und die Funktion von Membranproteinen nachahmten. Dazu wurden stabförmige Nanostrukturen mit präzise platzierten, lipidintegrierenden Cholesterinmodifikationen und fluoreszierenden Farbstoffen ausgestattet. Anschließend wurde ihre Interaktion mit Lipidmembranen untersucht. Es zeigte sich, dass die Nanostrukturen spezifisch an Lipidmembranen binden und auf deren Oberfläche diffundieren konnten. Hierbei wurden die Diffusionskoeffizienten der Rotations- und Translationsbewegungen bestimmt. Zudem bewirkte die An- oder Abwesenheit freier Magnesiumionen die steuerbare und reversible Anreicherung in verschiedenen Lipiddomänen. Die längliche Form der Nanostrukturen erlaubte es zudem, Verdrängungseffekte zu untersuchen, die als wichtiger Mechanismus für die Selbstorganisation von Membranproteinen gelten. Des weiteren wurden blockartige, multimerisierende DNA Origami Nanostrukturen entwickelt, die mikrometer-große Superstrukturen bilden konnten. Im ähnlichen Maße wie biologische Vorbilder, waren diese Strukturen in der Lage, Lipidmembranen über mehrere Mikrometer hinweg zu verformen. Etablierung ultraschneller Verdrehungs- und Torsionsmessungen mit DNA Origami Nanorotoren In einer weiteren Anwendung wurden DNA Origami Nanorotoren entwickelt, um ultraschnelle Einzelmolekül-Verdrehungs- und Torsionsmessungen durchzuführen, bei denen kleinste Veränderungen in Echtzeit beobachtet werden konnten. Dazu wurde eine neue Messapparatur entwickelt, bei der eine Magnetische Pinzette um die Fähigkeit Goldnanopartikeln zu detektieren erweitert wurde. Dies erlaubte die Konstruktion und Kalibrierung eines komplexen Messaufbaus, mit dem es möglich war Magnetische-Pinzetten-Messungen mit 4 kHz durchzuführen und gleichzeitig Goldnanopartikel mit ebenfalls 4 kHz zu verfolgen. Zudem konnten in einer alternativen Konfiguration Magnetische-Pinzetten-Messungen mit Einzelmolekül-Fluoreszenz- und FRET-Messungen kombiniert werden. Mit Goldnanopartikeln funktionalisierte DNA-Origami-Nanorotoren wurden anschließend in DNA-Konstrukte eingebettet und mit Hilfe des Messaufbaus für ultraschnelle Verdrehungs- und Torsionsmessungen genutzt. Gegenüber vorheriger Methoden wurde dadurch die räumlich-zeitliche Auflösung um eine bis drei Größenordnungen verbessert. Dies wurde anhand der Bestimmung der Torsionsreaktion von DNA auf Verdrehungen sowie deren Entwindung durch ein Enzym demonstriert. Direkte Bestimmung der Energielandschaft und Dynamiken der R-loop Entstehung des CRISPR-Cas Überwachungskomplexes Cascade Die entwickelten DNA Origami Nanorotoren ermöglichten zudem ultraschnelle Verdrehungsmessungen durchzuführen, um den Zielerkennungsprozess des CRISPR-Cas Überwachungskomplexes Cascade direkt zu beobachten. Effektorkomplexe von CRISPR-Cas Systemen werden zunehmend als Geneditierwerkzeuge eingesetzt, da sie aufgrund ihrer intrinsischen RNA-Komponente (crRNA) darauf programmiert werden können an praktisch jede DNA-Sequenz zu binden. Allerdings zeigen Sie eine beträchtliche Toleranz gegenüber Abweichungen zwischen der Sequenz ihrer RNA und der bestimmungsgemäßen DNA-Zielsequenz. Grundsätzlich bindet Cascade zunächst mit einem Protein-Motiv an eine kurze DNA-Sequenz, woraufhin es zur Basenpaarung zwischen der crRNA und der doppelsträngigen DNA-Zielsequenz kommt. Dabei entsteht ein R-loop, was zur Entwindung der DNA führt. Dies wurde direkt mit Hilfe der Nanorotoren gemessen, was nie dagewesene Einblicke in diesen Prozess erlaubte und die Bestimmung der zugrundeliegenden Energielandschaft und Dynamiken ermöglichte. Es wurde gezeigt, dass Längenänderungen des R-loops in kinetischen Zwischenschritten von 6 Basenpaaren erfolgen, denen Einzel-Basenpaar-Schritte auf schnelleren Zeitskalen zugrunde liegen. Des weiteren wurde der Effekt von Mutationen der DNA-Zielsequenz auf die R-loop Entstehung untersucht. Hierbei zeigte sich, dass die globale Form der Energielandschaft eine hochspezifische kinetische Differenzierung von inkongruenten Zielsequenzen erlaubt. Untersuchungen des 'locking' Mechanismus, ein struktureller Übergang der nach der vollständigen Ausbildung des R-loops erfolgt und eine Voraussetzung für die nachfolgende Zersetzung von DNA darstellt, rundeten die Untersuchungen ab. Insgesamt wurde gezeigt, dass mit Hilfe der ultraschnellen Verdrehungsmessungen, neue, detaillierte Einblicke in den Zielerkennungsprozess von Cascade gewonnen wurden, die zur Erstellung präziserer Genmanipulationswerkzeuge in der Zukunft beitragen können. Darüber hinaus eignen sich die Nanorotoren zur Untersuchung weiterer verdrehungs- und torsionserzeugender Mechanismen und Prozesse, die in weiteren Studien erforscht werden können.:1. Introduction 2. Multifunctional magnetic tweezers 3. Applications of DNA origami 4. Ultra-Fast torque measurements on supercoiled DNA 5. R-loop dynamics of the CRISPR-Cas Cascade complex 6. Summary and Discussion Bibliography List of Figures List of Tables List of Publications A. Appendix

The DNA origami method was established by Paul Rothemund in 2009. It allows to produce self-assembling 2D nanostructures with precise geometry and tunable mechanical properties that can be equipped with a broad range of functionalizations. It was extended to 3D by the group of William Shih in 2009 which also presented caDNAno, a software that made the design of nanostructures easier and more accessible. Since then, DNA origami nanostructures were utilized in a broad range of applications, which enabled unprecedented insight into mechanisms and processes of biological systems at the nanoscale. In this thesis multiple nanostructures were designed and manufactured to perform studies at the single-molecule level, which yielded a number of scientifically relevant contributions in the fields of biophysics and nanotechnology. Development of DNA origami nanostructures to mimic the properties and function of membrane proteins As a first application, DNA origami nanostructures with defined geometric and mechanical properties were designed, that mimic the behaviour and function of membrane proteins. To this end, rod-shaped nanostructures were equipped with precisely placed, lipid-integrating cholesterol modifications as well as fluorescent dyes. Subsequently their interaction with lipid membranes was studied. It was found that the prepared nanostructures specifically bound to lipid membranes and could diffuse on their surface, for which the rotational and translational diffusion coefficients were determined. The presence of magnesium thereby promoted the nanostructures to migrate into specific lipid domains in a reversible, switchable manner. Furthermore, their high aspect ratio allowed to investigate crowding effects, which are considered important mechanisms for the self-organisation of membrane proteins. In addition, block-shaped DNA origami nanostructures that organized into micrometre-sized super-structures were designed and produced. They were capable of deforming lipid membranes on the scale of micrometres in a similar fashion to biological counterparts. Establishing ultra-fast twist and torque measurements using DNA origami nanorotors In an additional application, DNA origami nanorotors were developed to perform ultra-fast single-molecule twist and torque measurements, allowing to resolve subtle changes in real-time. This also required the development of a new measurement setup that extended magnetic tweezers with the capability to detect the scattered light of gold nanoparticles. Hence, a complex setup was constructed and calibrated that enabled magnetic tweezers measurements with up to 4 kHz and simultaneously track gold nanoparticles at 4 kHz as well. In an alternative configuration the setup allowed simultaneous magnetic tweezers and single-molecule fluorescence and FRET measurements. DNA origami nanorotors which were embedded within DNA constructs and carried the gold nanoparticles were then obtained and used to perform ultra-fast twist and torque measurements. This constituted improvements in the spatio-temporal resolution over previous methods by one to three orders of magnitude, as demonstrated by direct measurements on the torsional response of DNA to external twists and the unwinding of DNA by an enzyme. Direct measurements of the energy landscape and dynamics of the R-loop formation by the CRISPR-Cas surveillance complex Cascade Using the DNA origami nanorotor enhanced ultra-fast twist measurements, the target recognition process of the CRISPR-Cas surveillance complex Cascade was directly observed. Effector complexes of CRISPR-Cas systems have been widely applied in genome editing recently, since they can be programmed to bind practically any genomic target by their intrinsic RNA (crRNA) component. They have, however, considerable tolerance for mismatches between their RNA and their intended DNA target. For Cascade, after binding with a protein motif to a DNA target, base-pairing between crRNA and the double-stranded DNA target is initiated, resulting in the formation of an R-loop structure which leads to unwinding of the DNA. This was directly measured using the nanorotor, which provided unprecedented insight in the R-loop formation by Cascade, allowing to determine the underlying energy landscape and the dynamics of the process. It was shown that R-loop progression occurs on 6-bp kinetic intermediate steps with an underlying single base pair stepping on fast time scales. Furthermore the effect of mutations in the target DNA on the R-loop formation process was investigated, indicating that the global shape of the energy landscape allows for a highly specific kinetic discrimination of mismatched targets. Investigations into the locking transition, a conformational change that occurs after the full formation of the R-loop and is a prerequisite for subsequent DNA degradation, completed the study. Overall, the findings provide a better understanding of the target recognition process of Cascade, which will contribute to the construction of more precise gene-editing tools in the future. Furthermore, the nanorotor-assisted measurements are applicable to many twist and torque inducing mechanisms and processes that can be investigated in further studies.:1. Introduction 2. Multifunctional magnetic tweezers 3. Applications of DNA origami 4. Ultra-Fast torque measurements on supercoiled DNA 5. R-loop dynamics of the CRISPR-Cas Cascade complex 6. Summary and Discussion Bibliography List of Figures List of Tables List of Publications A. Appendix / Die DNA Origami Methode wurde im Jahr 2006 durch Paul Rothemund begründet. Sie erlaubt es selbst-assemblierende 2D Nanostrukturen mit präzisen Geometrien und kalibrierbaren mechanischen Eigenschaften zu erstellen, die zudem mit einer Vielzahl an Funktionalisierungen ausgestattet werden können. Die Methode wurde 2009 in der Gruppe von William Shih auf 3D Nanostrukturen erweitert, wobei zudem caDNAno präsentiert wurde, eine Software die die Erstellung solcher Nanostrukturen wesentlich einfacher und zugänglicher machte. Seitdem wurden DNA Origami Nanostrukturen in vielfältigen Anwendungen genutzt, die nie dagewesene Einblicke in Mechanismen und Prozesse von biologischen Systemen auf der Nanoskala erlaubten. In dieser Arbeit wird anhand mehrerer Beispiele gezeigt, wie solche Nanostrukturen genutzt werden können, um Studien auf der Einzelmolekül-Ebene durchzuführen. Entwicklung von DNA Origami Nanostrukturen, welche die Eigenschaften und Funktionen von Membranproteinen imitieren In einer ersten Anwendung wurden DNA Origami Nanostrukturen mit definierten geometrischen und mechanischen Eigenschaften entworfen, welche das Verhalten und die Funktion von Membranproteinen nachahmten. Dazu wurden stabförmige Nanostrukturen mit präzise platzierten, lipidintegrierenden Cholesterinmodifikationen und fluoreszierenden Farbstoffen ausgestattet. Anschließend wurde ihre Interaktion mit Lipidmembranen untersucht. Es zeigte sich, dass die Nanostrukturen spezifisch an Lipidmembranen binden und auf deren Oberfläche diffundieren konnten. Hierbei wurden die Diffusionskoeffizienten der Rotations- und Translationsbewegungen bestimmt. Zudem bewirkte die An- oder Abwesenheit freier Magnesiumionen die steuerbare und reversible Anreicherung in verschiedenen Lipiddomänen. Die längliche Form der Nanostrukturen erlaubte es zudem, Verdrängungseffekte zu untersuchen, die als wichtiger Mechanismus für die Selbstorganisation von Membranproteinen gelten. Des weiteren wurden blockartige, multimerisierende DNA Origami Nanostrukturen entwickelt, die mikrometer-große Superstrukturen bilden konnten. Im ähnlichen Maße wie biologische Vorbilder, waren diese Strukturen in der Lage, Lipidmembranen über mehrere Mikrometer hinweg zu verformen. Etablierung ultraschneller Verdrehungs- und Torsionsmessungen mit DNA Origami Nanorotoren In einer weiteren Anwendung wurden DNA Origami Nanorotoren entwickelt, um ultraschnelle Einzelmolekül-Verdrehungs- und Torsionsmessungen durchzuführen, bei denen kleinste Veränderungen in Echtzeit beobachtet werden konnten. Dazu wurde eine neue Messapparatur entwickelt, bei der eine Magnetische Pinzette um die Fähigkeit Goldnanopartikeln zu detektieren erweitert wurde. Dies erlaubte die Konstruktion und Kalibrierung eines komplexen Messaufbaus, mit dem es möglich war Magnetische-Pinzetten-Messungen mit 4 kHz durchzuführen und gleichzeitig Goldnanopartikel mit ebenfalls 4 kHz zu verfolgen. Zudem konnten in einer alternativen Konfiguration Magnetische-Pinzetten-Messungen mit Einzelmolekül-Fluoreszenz- und FRET-Messungen kombiniert werden. Mit Goldnanopartikeln funktionalisierte DNA-Origami-Nanorotoren wurden anschließend in DNA-Konstrukte eingebettet und mit Hilfe des Messaufbaus für ultraschnelle Verdrehungs- und Torsionsmessungen genutzt. Gegenüber vorheriger Methoden wurde dadurch die räumlich-zeitliche Auflösung um eine bis drei Größenordnungen verbessert. Dies wurde anhand der Bestimmung der Torsionsreaktion von DNA auf Verdrehungen sowie deren Entwindung durch ein Enzym demonstriert. Direkte Bestimmung der Energielandschaft und Dynamiken der R-loop Entstehung des CRISPR-Cas Überwachungskomplexes Cascade Die entwickelten DNA Origami Nanorotoren ermöglichten zudem ultraschnelle Verdrehungsmessungen durchzuführen, um den Zielerkennungsprozess des CRISPR-Cas Überwachungskomplexes Cascade direkt zu beobachten. Effektorkomplexe von CRISPR-Cas Systemen werden zunehmend als Geneditierwerkzeuge eingesetzt, da sie aufgrund ihrer intrinsischen RNA-Komponente (crRNA) darauf programmiert werden können an praktisch jede DNA-Sequenz zu binden. Allerdings zeigen Sie eine beträchtliche Toleranz gegenüber Abweichungen zwischen der Sequenz ihrer RNA und der bestimmungsgemäßen DNA-Zielsequenz. Grundsätzlich bindet Cascade zunächst mit einem Protein-Motiv an eine kurze DNA-Sequenz, woraufhin es zur Basenpaarung zwischen der crRNA und der doppelsträngigen DNA-Zielsequenz kommt. Dabei entsteht ein R-loop, was zur Entwindung der DNA führt. Dies wurde direkt mit Hilfe der Nanorotoren gemessen, was nie dagewesene Einblicke in diesen Prozess erlaubte und die Bestimmung der zugrundeliegenden Energielandschaft und Dynamiken ermöglichte. Es wurde gezeigt, dass Längenänderungen des R-loops in kinetischen Zwischenschritten von 6 Basenpaaren erfolgen, denen Einzel-Basenpaar-Schritte auf schnelleren Zeitskalen zugrunde liegen. Des weiteren wurde der Effekt von Mutationen der DNA-Zielsequenz auf die R-loop Entstehung untersucht. Hierbei zeigte sich, dass die globale Form der Energielandschaft eine hochspezifische kinetische Differenzierung von inkongruenten Zielsequenzen erlaubt. Untersuchungen des 'locking' Mechanismus, ein struktureller Übergang der nach der vollständigen Ausbildung des R-loops erfolgt und eine Voraussetzung für die nachfolgende Zersetzung von DNA darstellt, rundeten die Untersuchungen ab. Insgesamt wurde gezeigt, dass mit Hilfe der ultraschnellen Verdrehungsmessungen, neue, detaillierte Einblicke in den Zielerkennungsprozess von Cascade gewonnen wurden, die zur Erstellung präziserer Genmanipulationswerkzeuge in der Zukunft beitragen können. Darüber hinaus eignen sich die Nanorotoren zur Untersuchung weiterer verdrehungs- und torsionserzeugender Mechanismen und Prozesse, die in weiteren Studien erforscht werden können.:1. Introduction 2. Multifunctional magnetic tweezers 3. Applications of DNA origami 4. Ultra-Fast torque measurements on supercoiled DNA 5. R-loop dynamics of the CRISPR-Cas Cascade complex 6. Summary and Discussion Bibliography List of Figures List of Tables List of Publications A. Appendix

Während der Tumorprogression unterliegen Zellen und Gewebe mechanischen Veränderungen. Mittels Magnetresonanz-Elastographie (MRE) kann die Mechanik von Geweben in vivo untersucht werden. In der Klinik wird diese Technik jedoch bisher hauptsächlich als zusätzlicher Bildkontrast verwendet, wobei eine Verknüpfung mit der zugrunde liegenden Physik des Krebses bisher weitgehend fehlt. In meiner Arbeit skizziere ich einen in vivo Tumor-Marker, der auf biophysikalische Parametern beruht. Dazu liefere ich eine breite experimentelle Basis, die von der mechanischen Charakterisierung von Kollagen als Hauptbestandteil der extrazellulären Matrix bis zum Tracking lebender Zellen und ex vivo MRE in vitalen menschlichen Tumorexplantaten reicht. Eine anschließende Analyse der mechanischen Fingerabdrücke von Tumoren in vivo zeigt robuste Trends. Diese werden durch ein Gedankenexperiment zu den grundlegenden mechanischen Voraussetzungen für das Tumorwachstum weiter erläutert. Darauf aufbauend leite ich ein auf biophysikalischen Parametern basierendes Tumor-Klassifikationsschema ab. Abschließend fasse ich zusammen, wie tumorassoziierte Mechanismen die Mechanik von Gewebe beeinflussen, wobei ich auch emergente Effekte berücksichtige.:Contents iv List of Figures viii 1 Introduction 1 2 Background 5 2.1 Tissue architecture 5 2.1.1 The extracellular matrix 5 2.1.2 ECM in tumors 6 2.1.3 Focus: collagen 7 2.1.4 The neural ECM in the brain 9 2.1.5 Breast tissue 10 2.1.6 Cervix and uterus tissue 11 2.2 Cancer 13 2.2.1 Development and spreading 13 2.2.2 Clinical grading and staging 15 2.3 Cell mechanics 17 2.3.1 Contractility17 2.3.2 Unjamming and tissue Fluidization in cancer 19 2.4 Applied Magnetic Resonance Imaging 20 2.4.1 The necessary basics 20 2.4.2 Diffusion weighted imaging 24 2.4.3 MR Elastogprahy 25 2.5 Viscoelasticity and rheological models 28 2.5.1 Deformation and material response 28 2.5.2 Basic viscoelastic model components 30 2.5.3 Fractional element model 32 2.5.4 Kelvin-Voigt model 33 2.6 Stiffness and Fluidity 34 2.6.1 Stiffness and Fluidity in clinical in vivo MRE 35 3 Materials and Methods 36 3.1 Collagen Gels 36 3.1.1 Collagen preparation 36 3.1.2 Collagen crosslinking 37 3.2 Cell and tissue culture 37 3.2.1 Cell lines 37 3.2.2 Multicellular Spheroids 39 3.2.3 Primary tissues 40 3.2.4 Contractility and invasion assay 41 3.3 Optical imaging and analysis 43 3.3.1 Confocal microscopy for collagen pore size analysis 43 3.3.2 Optical clearing and imaging of fixated primary tissues 44 3.3.3 Live imaging scenarios for cell tracking and collagen displacement analysis 44 3.4 Oscillatory shear rheology 46 3.5 MR techniques 47 3.5.1 0.5 T Tabletop MRE device 48 3.5.2 NMR based diffusion measurements 49 3.5.3 MR Elastography with the tabletop device 52 3.5.4 Clinical in vivo MRE 55 3.6 Optical cell stretcher after in vivo MRE 58 3.6.1 Study design and sample handling 58 3.6.2 In vivo MRE on human brain tumors 59 3.6.3 OCS on cells from dissociated human brain tumors 61 3.6.4 Correlation analysis between OCS and in vivo MRE 61 3.7 Atomic force microscopy (AFM) 62 4 Results and Discussion 63 4.1 Elastic vs. viscoelastic behavior 63 4.2 The scalability of rheological methods 65 4.2.1 Quantitative comparison 65 4.2.2 Qualitative coherence in aortic tissues across all scales 66 4.2.3 Section-Discussion: Multiscale tissue analysis 71 4.3 Collagen as a tuneable ECM surrogate 73 4.3.1 Shear rheology on collagen gels 73 4.3.2 Crosslinking solidies collagen gels 74 4.3.3 Simplifying data interpretation with stiffness and Fluidity 79 4.3.4 Inuence of matrix architecture on stiffness and Fluidity 81 4.3.5 Section-Discussion: tabletop MRE and DWI on collagen gels 84 4.4 Single cell vs. bulk tissue mechanics 86 4.4.1 Surface and bulk mechanics of spheroids in context of their single cell properties 86 4.4.2 Soft cancer cells in rigid tumors (ex vivo) 88 4.4.3 Correlation of in vivo bulk tissue mechanics with single cell properties in human brain tumors 89 4.4.4 Section-Discussion: Single cell vs. bulk tissue mechanics 94 4.5 Cells in interaction with the ECM 97 4.5.1 Single cells on collagen 97 4.5.2 Cell aggregates and spheroids on collagen 102 4.5.3 Primary tumor tissue on collagen 106 4.5.4 Partial tissue fluidization in cancer cell clusters in primary human tumor explants 111 4.5.5 Section-Discussion: Cell-ECM interactions 114 4.6 Tabletop MRE on tumor tissues 116 4.6.1 General remarks 116 4.6.2 Results 119 4.6.3 Correlations with patient data 125 4.6.4 Section-Discussion: Tabletop vs. clinical in vivo NMR 126 4.7 Stiffness and Fluidity as prognostic tumor markers 134 4.7.1 Rheological Fingerprints of tumors in vivo 134 4.7.2 Gedankenexperiment on tumor growth 139 4.7.3 Roadmap to a novel prognostic tumor marker 143 4.7.4 Section-Discussion: Stiffness and Fluidity in tumor progression 147 4.7.5 The limitations of in vivo MRE 155 5 Conclusions and Outlook 156 5.1 Conclusions 156 5.2 Outlook on a novel biophysical in vivo tumor marker 163 A Extended data 165 A.1 Extended tabletop results for aortic tissue 165 A.2 Supplementary Figures 168 A.3 Protocols 171 A.3.1 Data acquisition with the tabletop MRE 171 A.3.2 Data evaluation routines for the tabletop MRE 173 A.4 Additional information on breast tumor sample MCA200 175 A.5 Case-wise tumor classification scheme 176 B Video Attachments 178 B.1 Collagen synthesis 178 B.2 Single cells on collagen 178 B.3 Cell aggregates and spheroids on collagen 178 B.4 Primary tumor tissues on collagen 179 B.5 Live cell tracking in breast tumor MCA200 179 Bibliography 180 Acknowledgments 207 Zusammenfassung nach §11 209 / During cancer progression, cells and tissues undergo mechanical changes. Magnetic Resonance Elastography (MRE) can probe tissue mechanics in vivo, but currently, it is predominantly used as an additional contrast mode in clinical settings and the connection to the underlying physics of cancer is mostly lacking. In my thesis, I outline a roadmap towards an in vivo tumor marker that focuses on biophysical properties. I provide a diverse experimental background, which spans from the mechanical characterization of extracellular matrix surrogates to live cell tracking and ex vivo MRE in vital human tumor explants. A subsequent analysis of the mechanical Fingerprints of tumors in vivo reveals robust trends. These trends are elucidated further through a gedankenexperiment on the fundamental mechanical prerequisites for tumor growth. I propose a biophysics-based tumor classification scheme rooted in mechanical parameters. In conclusion, I consolidate how tumorassociated mechanisms impact bulk tissue mechanics, emphasizing emergent effects.:Contents iv List of Figures viii 1 Introduction 1 2 Background 5 2.1 Tissue architecture 5 2.1.1 The extracellular matrix 5 2.1.2 ECM in tumors 6 2.1.3 Focus: collagen 7 2.1.4 The neural ECM in the brain 9 2.1.5 Breast tissue 10 2.1.6 Cervix and uterus tissue 11 2.2 Cancer 13 2.2.1 Development and spreading 13 2.2.2 Clinical grading and staging 15 2.3 Cell mechanics 17 2.3.1 Contractility17 2.3.2 Unjamming and tissue Fluidization in cancer 19 2.4 Applied Magnetic Resonance Imaging 20 2.4.1 The necessary basics 20 2.4.2 Diffusion weighted imaging 24 2.4.3 MR Elastogprahy 25 2.5 Viscoelasticity and rheological models 28 2.5.1 Deformation and material response 28 2.5.2 Basic viscoelastic model components 30 2.5.3 Fractional element model 32 2.5.4 Kelvin-Voigt model 33 2.6 Stiffness and Fluidity 34 2.6.1 Stiffness and Fluidity in clinical in vivo MRE 35 3 Materials and Methods 36 3.1 Collagen Gels 36 3.1.1 Collagen preparation 36 3.1.2 Collagen crosslinking 37 3.2 Cell and tissue culture 37 3.2.1 Cell lines 37 3.2.2 Multicellular Spheroids 39 3.2.3 Primary tissues 40 3.2.4 Contractility and invasion assay 41 3.3 Optical imaging and analysis 43 3.3.1 Confocal microscopy for collagen pore size analysis 43 3.3.2 Optical clearing and imaging of fixated primary tissues 44 3.3.3 Live imaging scenarios for cell tracking and collagen displacement analysis 44 3.4 Oscillatory shear rheology 46 3.5 MR techniques 47 3.5.1 0.5 T Tabletop MRE device 48 3.5.2 NMR based diffusion measurements 49 3.5.3 MR Elastography with the tabletop device 52 3.5.4 Clinical in vivo MRE 55 3.6 Optical cell stretcher after in vivo MRE 58 3.6.1 Study design and sample handling 58 3.6.2 In vivo MRE on human brain tumors 59 3.6.3 OCS on cells from dissociated human brain tumors 61 3.6.4 Correlation analysis between OCS and in vivo MRE 61 3.7 Atomic force microscopy (AFM) 62 4 Results and Discussion 63 4.1 Elastic vs. viscoelastic behavior 63 4.2 The scalability of rheological methods 65 4.2.1 Quantitative comparison 65 4.2.2 Qualitative coherence in aortic tissues across all scales 66 4.2.3 Section-Discussion: Multiscale tissue analysis 71 4.3 Collagen as a tuneable ECM surrogate 73 4.3.1 Shear rheology on collagen gels 73 4.3.2 Crosslinking solidies collagen gels 74 4.3.3 Simplifying data interpretation with stiffness and Fluidity 79 4.3.4 Inuence of matrix architecture on stiffness and Fluidity 81 4.3.5 Section-Discussion: tabletop MRE and DWI on collagen gels 84 4.4 Single cell vs. bulk tissue mechanics 86 4.4.1 Surface and bulk mechanics of spheroids in context of their single cell properties 86 4.4.2 Soft cancer cells in rigid tumors (ex vivo) 88 4.4.3 Correlation of in vivo bulk tissue mechanics with single cell properties in human brain tumors 89 4.4.4 Section-Discussion: Single cell vs. bulk tissue mechanics 94 4.5 Cells in interaction with the ECM 97 4.5.1 Single cells on collagen 97 4.5.2 Cell aggregates and spheroids on collagen 102 4.5.3 Primary tumor tissue on collagen 106 4.5.4 Partial tissue fluidization in cancer cell clusters in primary human tumor explants 111 4.5.5 Section-Discussion: Cell-ECM interactions 114 4.6 Tabletop MRE on tumor tissues 116 4.6.1 General remarks 116 4.6.2 Results 119 4.6.3 Correlations with patient data 125 4.6.4 Section-Discussion: Tabletop vs. clinical in vivo NMR 126 4.7 Stiffness and Fluidity as prognostic tumor markers 134 4.7.1 Rheological Fingerprints of tumors in vivo 134 4.7.2 Gedankenexperiment on tumor growth 139 4.7.3 Roadmap to a novel prognostic tumor marker 143 4.7.4 Section-Discussion: Stiffness and Fluidity in tumor progression 147 4.7.5 The limitations of in vivo MRE 155 5 Conclusions and Outlook 156 5.1 Conclusions 156 5.2 Outlook on a novel biophysical in vivo tumor marker 163 A Extended data 165 A.1 Extended tabletop results for aortic tissue 165 A.2 Supplementary Figures 168 A.3 Protocols 171 A.3.1 Data acquisition with the tabletop MRE 171 A.3.2 Data evaluation routines for the tabletop MRE 173 A.4 Additional information on breast tumor sample MCA200 175 A.5 Case-wise tumor classification scheme 176 B Video Attachments 178 B.1 Collagen synthesis 178 B.2 Single cells on collagen 178 B.3 Cell aggregates and spheroids on collagen 178 B.4 Primary tumor tissues on collagen 179 B.5 Live cell tracking in breast tumor MCA200 179 Bibliography 180 Acknowledgments 207 Zusammenfassung nach §11 209

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