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
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:92133 |
| Date | 19 June 2024 |
| Creators | Sauer, Frank |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1002/advs.202303523, 10.1039/D1SM01291F, 10.1039/C8SM02264J, 10.1038/s41567-022-01755-0, 10.1088/1367-2630/ac254e, 10.1063/5.0188186, 10.1039/D3SM00630A |
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