Mechanical properties of cells and their environment have an undeniable impact on physiological and pathological processes such as tissue development or cancer metastasis. Hence, there is a pressing need for establishing and validating methodologies for measuring the mechanical properties of cells, as well as for deciphering the molecular underpinnings that govern the mechanical phenotype. During my doctoral research, I addressed these needs by pushing the boundaries of the field of single-cell mechanics in four projects, two of which were method-oriented and two explored important biological questions. First, I consolidated real-time deformability cytometry as a method for high-throughput single-cell mechanical phenotyping and contributed to its transformation into a versatile image-based cell characterization and sorting platform. Importantly, this platform can be used not only to sort cells based on image-derived parameters, but also to train neural networks to recognize and sort cells of interest based on raw images. Second, I performed a cross-laboratory study comparing three microfluidics-based deformability cytometry approaches operating at different timescales in two standardized assays of osmotic shock and actin disassembly. This study revealed that while all three methods are sensitive to osmotic shock-induced changes in cell deformability, the method operating at the shortest timescale is not suited for detection of actin cytoskeleton changes. Third, I demonstrated changes in cell mechanical phenotype associated with cell fate specification on the example of differentiation and de-differentiation along the neural lineage. In the process of reprogramming to pluripotency, neural precursor cells acquired progressively stiffer phenotype, that was reversed in the process of neural differentiation. The stiff phenotype of induced pluripotent stem cells was equivalent to that of embryonic stem cells, suggesting that mechanical properties of cells are inherent to their developmental stage. Finally, I identified and validated novel target genes involved in the regulation of mechanical properties of cells. The targets were identified using machine learning-based network analysis of transcriptomic profiles associated with mechanical phenotype change, and validated computationally as well as in genetic perturbation experiments. In particular, I showed that the gene with the best in silico performance, CAV1, changes the mechanical properties of cells when silenced or overexpressed. Identification of novel targets for mechanical phenotype modification is crucial for future explorations of physiological and pathological roles of cell mechanics. Together, this thesis encompasses a collection of contributions at the frontier of single-cell mechanical characterization across timescales and cell state transitions, and lays ground for turning cell mechanics from a correlative phenomenological parameter to a controllable property.:Abstract
Kurzfassung
List of Publications
Contents
Introduction
Chapter 1 — Background
1.1. Mechanical properties as a marker of cell state in health and disease
1.2. Functional relevance of single-cell mechanical properties
1.3. Internal structures determining mechanical properties of cells
1.4. Cell as a viscoelastic material
1.5. Methods to measure single-cell mechanical properties
Aims and scope of this thesis
Chapter 2 — RT-DC as a versatile method for image-based cell characterization and sorting
2.1. RT-DC for mechanical characterization of cells
2.1.1. Operation of the RT-DC setup
2.1.2. Extracting Young’s modulus from RT-DC data
2.2. Additional functionalities implemented to the RT-DC setup
2.2.1. 1D fluorescence readout in three spectral channels
2.2.2. SSAW-based active cell sorting
2.3. Beyond assessment of cell mechanics — emerging applications
2.3.1. Deformation-assisted population separation and sorting
2.3.2. Brightness-based identification and sorting of blood cells
2.3.3. Transferring molecular specificity into label-free cell sorting
2.4. Discussion
2.5. Key conclusions
2.6. Materials and experimental procedures
2.7. Data analysis
Chapter 3 — A comparison of three deformability cytometry classes operating at different timescales
3.1. Results
3.1.1. Representatives of the three deformability cytometry classes
3.1.2. Osmotic shock-induced deformability changes are detectable in all three methods
3.1.3. Ability to detect actin disassembly is method-dependent
3.1.4. Strain rate increase decreases the range of deformability response to actin disassembly in sDC
3.2. Discussion
3.3. Key conclusions
3.4. Materials and methods
Chapter 4 — Mechanical journey of neural progenitor cells to pluripotency and back
4.1. Results
4.1.1. fNPCs become progressively stiffer during reprogramming to pluripotency
4.1.2. Transgene-dependent F-class cells are more compliant than ESC-like iPSCs
4.1.3. Surface markers unravel mechanical subpopulations at intermediate reprogramming stages
4.1.4. Neural differentiation of iPSCs mechanically mirrors reprogramming of fNPCs
4.1.5. The closer to the pluripotency, the higher the cell stiffness
4.2. Discussion
4.3. Key conclusions
4.4. Materials and methods
Chapter 5 — Data-driven approach for de novo identification of cell mechanics regulators
5.1. Results
5.1.1. An overview of the mechanomics approach
5.1.2. Model systems characterized by mechanical phenotype changes
5.1.3. Discriminative network analysis on discovery datasets
5.1.4. Conserved functional network module comprises five genes
5.1.5. CAV1 performs best at classifying soft and stiff cell states in validation datasets
5.1.6. Perturbing expression levels of CAV1 changes cells stiffness
5.2. Discussion
5.3. Key conclusions
5.4. Materials and methods
Conclusions and Outlook
Appendix A
Appendix B
Supplementary Tables B.1 – B.2
Supplementary Figures B.1 – B.9
Appendix C
Supplementary Tables C.1 – C.2.
Supplementary Figures C.1 – C.5
Appendix D
Supplementary Tables D.1 – D.6
Supplementary Figures D.1 – D.7
List of Figures
List of Tables
List of Abbreviations.
List of Symbols
References
Acknowledgements
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:77563 |
| Date | 25 January 2022 |
| Creators | Urbanska, Marta |
| Contributors | Guck, Jochen, Betz, Timo, Grill, Stephan Wolfgang, Technische Universität Dresden |
| 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 | 10.1242/dev.155218, 10.1016/bs.mcb.2018.06.009, 10.1038/s41592-020-0818-8, 10.1038/s41592-020-0831-y, 10.1101/2021.04.26.441418, 10.6084/m9.figshare.11704119, 10.6084/m9.figshare.11302595, 10.6084/m9.figshare.c.5399826 |
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