The ability of tissues and cells to move and rearrange is central to a broad range of diverse biological processes such as tissue remodeling and rearrangement in embryogenesis, cell migration in wound healing, or cancer progression. These processes are linked to a solidlike to fluid-like transition, also known as unjamming transition, a not rigorously defined framework that describes switching between a stable, resting state and an active, moving state. Various mechanisms, that is, proliferation and motility, are critical drivers for the (un) jamming transition on the cellular scale. However, beyond the scope of these fundamental mechanisms of cells, a unifying understanding remains to be established. During embryogenesis, the proliferation rate of cells is high, and the number density is continuously increasing, which indicates number-density-driven jamming. In contrast, cells have to unjam in tissues that are already densely packed during tumor progression, pointing toward a shape-driven unjamming transition. Here, we review recent investigations of jamming transitions during embryogenesis and cancer progression and pursue the question of how they might be interlinked. We discuss the role of density and shape during the jamming transition and the different biological factors driving it.

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

Zellmembranen sind hochspezialisierte Mehrkomponentenlegierungen, welche sowohl die Zelle selbst als auch ihre Organellen umgeben. Sie spielen eine entscheidende Rolle bei vielen biologisch relevanten Prozessen wie die Signaltransduktion und die Zellbewegung. Aus diesem Grund ist eine genaue Charakterisierung ihrer Eigenschaften der Schlüssel zum Verständnis der Bausteine des Lebens sowie ihrer Erkrankungen. Besonders Krebs steht im engen Zusammenhang mit Veränderungen der biomechanischen Eigenschaften vom Gewebe, Zellen und ihren Organellen. Während Veränderungen des Zytoskeletts von Krebszellen im Fokus vieler Biophysiker stehen, ist die Bedeutung der Biomechanik von Zellmembran weitgehend unklar. Zellmembranen faszinieren Wissenschaftler jedoch nicht nur wegen ihrer biomechanischen Eigenschaften. Sie sind auch Beispiele für eine selbstorganisierte und heterogene Landschaft, in der Prozesse fernab des Gleichgewichtes, wie z.B. räumliche und zeitliche Musterbildungen, auftreten. Die vorgelegte Dissertation untersucht erstmals umfassend die zentrale Rolle der Zellmembran und ihrer molekularen Architektur für die Signalübertragung, die Biomechanik und die Zellmigration. Hierfür werden einfache Modellmembranen aber auch komplexere Vesikel und ganze Zellen mittels etablierter physikalischer Methoden analysiert. Diese reichen von Fourier- Analysen zur Charakterisierung von thermisch angeregten Membranundulationen über Massenspektrometrie und ‘Optical Stretcher’ Messungen von ganzen Zellen bis hin zur Filmwaagentechnik. Des Weiteren wird ein Modellsystem vorgestellt, welches sowohl einen experimentellen als auch einen mathematischen Zugang zum ‘ME-switch’ ermöglicht. Die vorgelegte Dissertation bietet neue Einblicke in wichtige Funktionen von Zellmembranen und zeigt neue therapeutische Perspektiven in der Membran- und Krebsforschung auf.

Zellmembranen sind hochspezialisierte Mehrkomponentenlegierungen, welche sowohl die Zelle selbst als auch ihre Organellen umgeben. Sie spielen eine entscheidende Rolle bei vielen biologisch relevanten Prozessen wie die Signaltransduktion und die Zellbewegung. Aus diesem Grund ist eine genaue Charakterisierung ihrer Eigenschaften der Schlüssel zum Verständnis der Bausteine des Lebens sowie ihrer Erkrankungen. Besonders Krebs steht im engen Zusammenhang mit Veränderungen der biomechanischen Eigenschaften vom Gewebe, Zellen und ihren Organellen. Während Veränderungen des Zytoskeletts von Krebszellen im Fokus vieler Biophysiker stehen, ist die Bedeutung der Biomechanik von Zellmembran weitgehend unklar. Zellmembranen faszinieren Wissenschaftler jedoch nicht nur wegen ihrer biomechanischen Eigenschaften. Sie sind auch Beispiele für eine selbstorganisierte und heterogene Landschaft, in der Prozesse fernab des Gleichgewichtes, wie z.B. räumliche und zeitliche Musterbildungen, auftreten. Die vorgelegte Dissertation untersucht erstmals umfassend die zentrale Rolle der Zellmembran und ihrer molekularen Architektur für die Signalübertragung, die Biomechanik und die Zellmigration. Hierfür werden einfache Modellmembranen aber auch komplexere Vesikel und ganze Zellen mittels etablierter physikalischer Methoden analysiert. Diese reichen von Fourier- Analysen zur Charakterisierung von thermisch angeregten Membranundulationen über Massenspektrometrie und ‘Optical Stretcher’ Messungen von ganzen Zellen bis hin zur Filmwaagentechnik. Des Weiteren wird ein Modellsystem vorgestellt, welches sowohl einen experimentellen als auch einen mathematischen Zugang zum ‘ME-switch’ ermöglicht. Die vorgelegte Dissertation bietet neue Einblicke in wichtige Funktionen von Zellmembranen und zeigt neue therapeutische Perspektiven in der Membran- und Krebsforschung auf.:1 Introduction 2 Background 2.1 The Cell Membrane 2.1.1 Lipids in Cell Membranes 2.1.2 Membrane Proteins 2.1.3 An Overview on Membrane Models 2.1.4 Lipid Rafts 2.2 Model Membranes – An Experimental Access to Cell Membranes 2.2.1 Surface Tension and Thermodynamic Equilibrium 2.2.2 Langmuir Monolayer 2.2.3 The Polymorphism of Langmuir Monolayers 2.2.4 Membrane Vesicles 2.3 Biological Membranes as Semiflexible Shells 2.3.1 Elasticity of Soft Shells 2.3.2 Helfrichs Theory About Bending Deformations 2.3.3 Membrane Undulation 2.4 Membranes in Cell Signaling 2.4.1 Signal Transduction Fundamentals 2.4.2 Phosphoinositides 2.4.3 Phosphatidylinositol Signaling Pathway 2.4.4 The Myristoyl-Electrostatic Switch 2.5 Reaction-Diffusion Systems 2.5.1 Diffusion 2.5.2 Michaelis-Menten Kinetics 2.5.3 Reaction-Diffusion Systems 3 Methods, Materials and Theory 3.1 Optical Microscopy 3.1.1 Fluorescence Microscopy 3.1.2 Phase Contrast Microscopy 3.2 Cell Culture and GPMV Formation 3.2.1 Tumor Dissociation and Cell Culturing of Primary Cells 3.2.2 Cell Lines and Cell Culturing 3.2.3 Preparation of Giant Plasma Membrane Vesicles 3.3 Optical Stretcher 3.4 Fourier Analysis of Thermally Excited Membrane Fluctuations 3.4.1 The Quasi-Spherical Model – Membrane Fluctuations 3.4.2 Determination of the Bending Rigidity 3.5 Mass Spectrometry 3.5.1 MALDI-TOF Mass Spectrometry 3.5.2 ESI Mass Spectrometry 3.6 Migration, Invasion and Cell Death Assays 3.7 Langmuir-Blodgett Technique 3.7.1 Langmuir Troughs and Film Balances 3.7.2 Experimental Setup and Monolayer Preperation 3.7.3 Phospholipids, Dyes and Buffer Solutions 4 Fluctuations in Cell Membranes 4.1 Cell Membrane Softening in Human Breast and Cervical Cancer Cells 4.1.1 Bending Rigidity of Human Beast and Cervical Cell Membranes 4.1.2 MALDI-TOF Analysis of Lipid Composition 4.1.3 Summary and Discussion 4.2 Targeting of Membrane Rigidity – Implications on Migration 4.2.1 ESI Tandem Analysis of Lipid Composition 4.2.2 Biomechanical Behavior of Whole Cells and Membranes 4.2.3 Migration and Invasion Behavior 4.2.4 Summary and Discussion 5 Oscillations in Cell Membranes 5.1 Mimicking the ME-switch 5.1.1 DPPC/PIP2 monolayers at the presence of MARCKS 5.1.2 Lateral organization of PIP2 in DPPC/PIP2 monolayers 5.1.3 Translocation of MARCKS 5.1.4 Phosphorylation of MARCKS by PKC 5.1.5 Summary and Discussion 5.2 Dynamic Membrane Structure Induces Temporal Pattern Formation 5.2.1 Mechanism of the Oscillation 5.2.2 Modeling the ME-switch 5.2.3 Time Evolution 5.2.4 Phase Diagrams and Open Systems 5.2.5 Summary and Discussion 6 Conclusion and Outlook Appendix Bibliography List of Figures List of Abbreviations Acknowledgement