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Softening of Tumor Cells in Aggressive Carcinomas
Get PDFZellen 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 186Carcinoma 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 18
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Analyses of Adhesion Topography and Fluctuations in Bio-Membranes by Advanced Optical Microscopy
Get PDFThe cell membrane not only mechanically separates the interior of the cell from the exterior environment but is also an active organelle involved in a host of functions like maintaining osmotic balance, aiding in endo/exocytosis, regulating cell shape and mediating cell adhesion. The cell membrane is soft and easily deformable and hence may be subject to active deformations or even thermal fluctuations. The present work aims at a quantitative understanding of fluctuations of model cell membranes which are either adhered or free. The model system consisted of giant unilamellar vesicles (GUVs). Adhesion was effected via specific ligand-receptor interaction of biotin-neutravidin. Special structured adhesive substrates were developed where the receptors were presented in the form of grids or lines. Two light microscopic techniques were employed to probe the membrane (i) Dual-Wavelength Reflection Interference Contrast Microscopy (DW-RICM), which measures membrane-substrate distances with an accuracy of few nanometer and up to a range of one micrometer, with temporal resolution of about 50 ms. Here, significant progress in automating and refining the analysis of DWRICM data was made. (ii) Fluctuation Correlation Spectroscopy (FluCS), which is an entirely novel method developed during this thesis and measures membrane fluctuations with spatial and temporal resolution of 20 nm and 10 µs, respectively. It is based on the set up of Fluorescence Correlation Spectroscopy, and like FCS measures the decay of the correlation in fluorescence signal, but in FluCS the decay is due to membrane fluctuations rather than diffusion. DW-RICM measurements revealed that GUVs on the structured adhesive substrates exhibit regions of bound and fluctuating membrane, in accordance with the underlying pattern. In the fluctuating zone, the membrane presented itself as a flat-topped hill with the membrane-substrate distance saturated in a plateau of 79 +/- 9 nm. In this plateau, the fluctuation amplitude was found to be 12 +/- 2 nm. Variation of the shape (grid verses lines) or size (grids of 3.5 or 7 µm lattice constant) influenced neither the height nor the fluctuation amplitude in the plateau. Theoretical analysis (collaboration with Daniel Schmidt, Universität Stuttgart/Prof. Udo Seifert, Universität Stuttgart/Prof. Ana Smith, Universität Erlangen-Nürnberg) of the membrane shape and fluctuations permitted us to infer the membrane tension (3.7 +/- 0.7 µJ/m²) and the stiffness of the unspecific interaction potential (equivalent to the double derivative at minimum ; 2.3+/- 0.2 x 10^8 J/m^4). Fourier analysis revealed that modes of preferentially 2 µm wavelength developed. The plateau height could be tuned from 0 to 538 nm by changing the effective membrane tension via a change in the osmotic gradient between the inside and outside of the GUV. Corresponding fluctuation amplitude ranges from non-detectable to a maximum of 16 nm. FluCS can probe fluctuation far away as well as near a substrate. Using FluCS, the tension far from the substrate was measured to be 0.4 +/- 0.2 µJ/m² in non-adhered vesicles, and 0.5 +/- 0.4 µJ/m² in vesicles very weakly adhering to structured substrates. Compared with RICM measurements, where the vesicles studied adhered relatively more strongly and exhibited higher tension and smaller amplitudes, FluCS allowed for quantification of adhesion in the limit of soft, weakly adhering vesicles. The stiffness of the unspecific interaction potential was measured to be 7 +/- 3 x 10^5 J/m^4 for the case of free vesicles. Analysis of the decay of correlation with FluCS can also give information about damping which we interpreted as an effective viscosity amounting to 1.3+/-0.1 x 10^{-3} kg/(m s) in both free and adhered vesicles. As expected, close to the substrate, damping was even higher. Fluctuations in membranes of living cells (HEK and macrophages) was probed with FluCS. A novel liposome-fusion method was developed to over-saturate the cell membrane with fluorescent dye molecules (developed in our institute by Dr. Agnes Csiszár) ; a prerequisite for FluCS. Fluctuation amplitudes above the resolution limit were detected. The decay in correlation was found to be different from those measured for vesicles and varied from cell to cell. We believe that this variation arises due to active and directed cell motion. We present some possible future advancement of FluCS including better acquisition and treatment of data in particular for application to out of equilibrium systems like cells
Thermische Fluktuationen zweidimensionaler, flexibler Objekte - eine interferometrische Untersuchung an strukturiert adhärierten Lipidmembranen
No full textVariations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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