Search

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics., Comment: 15 pages, 6 figures

Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to rare cell populations and leads to low reproducibility. Moreover, the human eye is not suited to detect shifts of cellular properties of entire populations. Hence, quantitative image analysis could improve the accuracy and reproducibility of MDS diagnosis. We used real-time deformability cytometry (RT-DC) to measure bone marrow biopsy samples of MDS patients and age-matched healthy individuals. RT-DC is a high-throughput (1000 cells/s) imaging flow cytometer capable of recording morphological and mechanical properties of single cells. Properties of single cells were quantified using automated image analysis, and machine learning was employed to discover morpho-mechanical patterns in thousands of individual cells that allow to distinguish healthy vs. MDS samples. We found that distribution properties of cell sizes differ between healthy and MDS, with MDS showing a narrower distribution of cell sizes. Furthermore, we found a strong correlation between the mechanical properties of cells and the number of disease-determining mutations, inaccessible with current diagnostic approaches. Hence, machine-learning assisted RT-DC could be a promising tool to automate sample analysis to assist experts during diagnosis or provide a scalable solution for MDS diagnosis to regions lacking sufficient medical experts.

Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the cells’ properties and should be avoided, especially when purifying cells for clinical applications. A promising alternative is the label-free identification of cells based on physical properties. Sorting real-time deformability cytometry (soRT-DC) is a microfluidic technique for label-free analysis and sorting of single cells. In soRT-FDC, bright-field images of cells are analyzed by a deep neural net (DNN) to obtain a sorting decision, but sorting was so far only demonstrated for blood cells which show clear morphological differences and are naturally in suspension. Most cells, however, grow in tissues, requiring dissociation before cell sorting which is associated with challenges including changes in morphology, or presence of aggregates. Here, we introduce methods to improve robustness of analysis and sorting of single cells from nervous tissue and provide DNNs which can distinguish visually similar cells. We employ the DNN for image-based sorting to enrich photoreceptor cells from dissociated retina for transplantation into the mouse eye.

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.

Adipose tissue expansion involves both differentiation of new precursors and size increase of mature adipocytes. While the two processes are well balanced in healthy tissues, obesity and diabetes type II are associated with abnormally enlarged adipocytes and excess lipid accumulation. Previous studies suggested a link between cell stiffness, volume and stem cell differentiation, although in the context of preadipocytes, there have been contradictory results regarding stiffness changes with differentiation. Thus, we set out to quantitatively monitor adipocyte shape and size changes with differentiation and lipid accumulation. We quantified by optical diffraction tomography that differentiating preadipocytes increased their volumes drastically. Atomic force microscopy (AFM)-indentation and -microrheology revealed that during the early phase of differentiation, human preadipocytes became more compliant and more fluid-like, concomitant with ROCK-mediated F-actin remodelling. Adipocytes that had accumulated large lipid droplets were more compliant, and further promoting lipid accumulation led to an even more compliant phenotype. In line with that, high fat diet-induced obesity was associated with more compliant adipose tissue compared to lean animals, both for drosophila fat bodies and murine gonadal adipose tissue. In contrast, adipose tissue of diabetic mice became significantly stiffer as shown not only by AFM but also magnetic resonance elastography. Altogether, we dissect relative contributions of the cytoskeleton and lipid droplets to cell and tissue mechanical changes across different functional states, such as differentiation, nutritional state and disease. Our work therefore sets the basis for future explorations on how tissue mechanical changes influence the behaviour of mechanosensitive tissue-resident cells in metabolic disorders.

Adipose tissue expansion involves both differentiation of new precursors and size increase of mature adipocytes. While the two processes are well balanced in healthy tissues, obesity and diabetes type II are associated with abnormally enlarged adipocytes and excess lipid accumulation. Previous studies suggested a link between cell stiffness, volume and stem cell differentiation, although in the context of preadipocytes, there have been contradictory results regarding stiffness changes with differentiation. Thus, we set out to quantitatively monitor adipocyte shape and size changes with differentiation and lipid accumulation. We quantified by optical diffraction tomography that differentiating preadipocytes increased their volumes drastically. Atomic force microscopy (AFM)-indentation and -microrheology revealed that during the early phase of differentiation, human preadipocytes became more compliant and more fluid-like, concomitant with ROCK-mediated F-actin remodelling. Adipocytes that had accumulated large lipid droplets were more compliant, and further promoting lipid accumulation led to an even more compliant phenotype. In line with that, high fat diet-induced obesity was associated with more compliant adipose tissue compared to lean animals, both for drosophila fat bodies and murine gonadal adipose tissue. In contrast, adipose tissue of diabetic mice became significantly stiffer as shown not only by AFM but also magnetic resonance elastography. Altogether, we dissect relative contributions of the cytoskeleton and lipid droplets to cell and tissue mechanical changes across different functional states, such as differentiation, nutritional state and disease. Our work therefore sets the basis for future explorations on how tissue mechanical changes influence the behaviour of mechanosensitive tissue-resident cells in metabolic disorders.

Background Cell deformability of all major blood cell types is increased in depressive disorders (DD). Furthermore, impaired glucocorticoid secretion is causally related to DD. Nevertheless, there are no longitudinal studies examining changes in glucocorticoid output and depressive symptoms regarding cell deformability in DD. Aim To investigate, whether changes in depressive symptoms or hair glucocorticoids predict cell deformability in DD. Methods In 136 individuals, depressive symptoms (PHQ-9) and hair glucocorticoids (cortisol and cortisone) were measured at timepoint one (T1), while one year later (T2) depressive symptoms and hair glucocorticoids were remeasured and additionally cell deformability of peripheral blood cells was assessed and DD status was determined by clinical interview. Results Depression severity at T1 predicted higher cell deformability in monocytes and lymphocytes over the entire sample. Subjects with continuously high depressive symptoms at T1 and T2 showed elevated monocyte deformability as compared to subjects with low depressive symptoms. Depression severity at T1 of subjects with a lifetime persistent depressive disorder (PDD) was associated with elevated monocyte, neutrophil, and granulo-monocyte deformability. Depression severity at T1 of subjects with a 12-month PDD was positively associated with monocyte deformability. Furthermore, increases in glucocorticoid concentrations from T1 to T2 tended to be associated with higher immune cell deformability, while strongest associations emerged for the increase in cortisone with elevated neutrophil and granulo-monocyte deformability in the 12-month PDD group. Conclusion Continuously elevated depressive symptomatology as well as an increase in glucocorticoid levels over one year are associated with higher immune cell deformability, particularly in PDD. These findings suggest, that persistent depressive symptomatology associated with increased glucocorticoid secretion may lead to increased immu

Pathophysiological landmarks of depressive disorders are chronic low-grade inflammation and elevated glucocorticoid output. Both can potentially interfere with cytoskeleton organization, cell membrane bending and cell function, suggesting altered cell morpho-rheological properties like cell deformability and other cell mechanical features in depressive disorders. We performed a cross-sectional case-control study using the image-based morpho-rheological characterization of unmanipulated blood samples facilitating real-time deformability cytometry (RT-DC). Sixty-nine pre-screened individuals at high risk for depressive disorders and 70 matched healthy controls were included and clinically evaluated by Composite International Diagnostic Interview leading to lifetime and 12-month diagnoses. Facilitating deep learning on blood cell images, major blood cell types were classified and morpho-rheological parameters such as cell size and cell deformability of every individual cell was quantified. We found peripheral blood cells to be more deformable in patients with depressive disorders compared to controls, while cell size was not affected. Lifetime persistent depressive disorder was associated with increased cell deformability in monocytes and neutrophils, while in 12-month persistent depressive disorder erythrocytes deformed more. Lymphocytes were more deformable in 12-month major depressive disorder, while for lifetime major depressive disorder no differences could be identified. After correction for multiple testing, only associations for lifetime persistent depressive disorder remained significant. This is the first study analyzing morpho-rheological properties of entire blood cells and highlighting depressive disorders and in particular persistent depressive disorders to be associated with increased blood cell deformability. While all major blood cells tend to be more deformable, lymphocytes, monocytes, and neutrophils are mostly affected. This indicates that immune ce

Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to rare cell populations and leads to low reproducibility. Moreover, the human eye is not suited to detect shifts of cellular properties of entire populations. Hence, quantitative image analysis could improve the accuracy and reproducibility of MDS diagnosis. We used real-time deformability cytometry (RT-DC) to measure bone marrow biopsy samples of MDS patients and age-matched healthy individuals. RT-DC is a high-throughput (1000 cells/s) imaging flow cytometer capable of recording morphological and mechanical properties of single cells. Properties of single cells were quantified using automated image analysis, and machine learning was employed to discover morpho-mechanical patterns in thousands of individual cells that allow to distinguish healthy vs. MDS samples. We found that distribution properties of cell sizes differ between healthy and MDS, with MDS showing a narrower distribution of cell sizes. Furthermore, we found a strong correlation between the mechanical properties of cells and the number of disease-determining mutations, inaccessible with current diagnostic approaches. Hence, machine-learning assisted RT-DC could be a promising tool to automate sample analysis to assist experts during diagnosis or provide a scalable solution for MDS diagnosis to regions lacking sufficient medical experts.

Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the cells’ properties and should be avoided, especially when purifying cells for clinical applications. A promising alternative is the label-free identification of cells based on physical properties. Sorting real-time deformability cytometry (soRT-DC) is a microfluidic technique for label-free analysis and sorting of single cells. In soRT-FDC, bright-field images of cells are analyzed by a deep neural net (DNN) to obtain a sorting decision, but sorting was so far only demonstrated for blood cells which show clear morphological differences and are naturally in suspension. Most cells, however, grow in tissues, requiring dissociation before cell sorting which is associated with challenges including changes in morphology, or presence of aggregates. Here, we introduce methods to improve robustness of analysis and sorting of single cells from nervous tissue and provide DNNs which can distinguish visually similar cells. We employ the DNN for image-based sorting to enrich photoreceptor cells from dissociated retina for transplantation into the mouse eye.

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.

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

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

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

Organisms are frequently exposed to fluctuating environmental conditions and might consequently experience stress. Environmental stress can damage cellular components, which can threaten especially single-celled organisms, such as yeast, as they cannot escape. To survive, cells mount protective stress responses, which serve to preserve cellular components and architecture. Recent findings in yeast show that the stress response upon energy depletion stress involves a gelation of the cytoplasm due to macromolecular protein assembly, characterized by drastic changes in cytoplasmic material properties. Remarkably, the stress-induced cytoplasmic gelation is protective, raising the question whether this could be a common strategy of cells to cope with severe stress. I hypothesized that protein aggregation induced by another common stress, severe heat shock, might cause a similar cytoplasmic gelation in yeast. Furthermore, I hypothesized that the reversibility of cytoplasmic gelation is provided by molecular chaperones, which are known regulators of protein aggregation. In this thesis, I therefore aimed to characterize the changes in the material properties of the cytoplasm upon severe heat shock as well as their underlying causes and how molecular chaperones affect these changes. To characterize heat-induced changes in the material properties of the cytoplasm, I monitored Schizosaccharomyces pombe cells during recovery from severe heat shock using a combination of cell mechanical assays, time-lapse microscopy and single-particle tracking. I found that the cells entered a prolonged growth arrested state upon stress, which coincided with significant cell stiffening and a long-range motion arrest of lipid droplets in the cytoplasm, while smaller cytoplasmic nanoparticles remained mostly mobile. At the same time, a significant fraction of proteins aggregated in the cytoplasm, forming insoluble inclusions such as heat shock granules. After stress cessation, the observed change

Organisms are frequently exposed to fluctuating environmental conditions and might consequently experience stress. Environmental stress can damage cellular components, which can threaten especially single-celled organisms, such as yeast, as they cannot escape. To survive, cells mount protective stress responses, which serve to preserve cellular components and architecture. Recent findings in yeast show that the stress response upon energy depletion stress involves a gelation of the cytoplasm due to macromolecular protein assembly, characterized by drastic changes in cytoplasmic material properties. Remarkably, the stress-induced cytoplasmic gelation is protective, raising the question whether this could be a common strategy of cells to cope with severe stress. I hypothesized that protein aggregation induced by another common stress, severe heat shock, might cause a similar cytoplasmic gelation in yeast. Furthermore, I hypothesized that the reversibility of cytoplasmic gelation is provided by molecular chaperones, which are known regulators of protein aggregation. In this thesis, I therefore aimed to characterize the changes in the material properties of the cytoplasm upon severe heat shock as well as their underlying causes and how molecular chaperones affect these changes. To characterize heat-induced changes in the material properties of the cytoplasm, I monitored Schizosaccharomyces pombe cells during recovery from severe heat shock using a combination of cell mechanical assays, time-lapse microscopy and single-particle tracking. I found that the cells entered a prolonged growth arrested state upon stress, which coincided with significant cell stiffening and a long-range motion arrest of lipid droplets in the cytoplasm, while smaller cytoplasmic nanoparticles remained mostly mobile. At the same time, a significant fraction of proteins aggregated in the cytoplasm, forming insoluble inclusions such as heat shock granules. After stress cessation, the observed change

The mechanical phenotype of a cell is an inherent biophysical marker of its state and function, with many applications in basic and applied biological research. Microfluidics-based methods have enabled single-cell mechanophenotyping at throughputs comparable to those of flow cytometry. Here, we present a standardized cross-laboratory study comparing three microfluidics-based approaches for measuring cell mechanical phenotype: constriction-based deformability cytometry (cDC), shear flow deformability cytometry (sDC) and extensional flow deformability cytometry (xDC). All three methods detect cell deformability changes induced by exposure to altered osmolarity. However, a dose-dependent deformability increase upon latrunculin B-induced actin disassembly was detected only with cDC and sDC, which suggests that when exposing cells to the higher strain rate imposed by xDC, cellular components other than the actin cytoskeleton dominate the response. The direct comparison presented here furthers our understanding of the applicability of the different deformability cytometry methods and provides context for the interpretation of deformability measurements performed using different platforms.

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

The mechanical phenotype of a cell is an inherent biophysical marker of its state and function, with many applications in basic and applied biological research. Microfluidics-based methods have enabled single-cell mechanophenotyping at throughputs comparable to those of flow cytometry. Here, we present a standardized cross-laboratory study comparing three microfluidics-based approaches for measuring cell mechanical phenotype: constriction-based deformability cytometry (cDC), shear flow deformability cytometry (sDC) and extensional flow deformability cytometry (xDC). All three methods detect cell deformability changes induced by exposure to altered osmolarity. However, a dose-dependent deformability increase upon latrunculin B-induced actin disassembly was detected only with cDC and sDC, which suggests that when exposing cells to the higher strain rate imposed by xDC, cellular components other than the actin cytoskeleton dominate the response. The direct comparison presented here furthers our understanding of the applicability of the different deformability cytometry methods and provides context for the interpretation of deformability measurements performed using different platforms.

The mechanical phenotype of a cell is an inherent biophysical marker of its state and function, with many applications in basic and applied biological research. Microfluidics-based methods have enabled single-cell mechanophenotyping at throughputs comparable to those of flow cytometry. Here, we present a standardized cross-laboratory study comparing three microfluidics-based approaches for measuring cell mechanical phenotype: constriction-based deformability cytometry (cDC), shear flow deformability cytometry (sDC) and extensional flow deformability cytometry (xDC). All three methods detect cell deformability changes induced by exposure to altered osmolarity. However, a dose-dependent deformability increase upon latrunculin B-induced actin disassembly was detected only with cDC and sDC, which suggests that when exposing cells to the higher strain rate imposed by xDC, cellular components other than the actin cytoskeleton dominate the response. The direct comparison presented here furthers our understanding of the applicability of the different deformability cytometry methods and provides context for the interpretation of deformability measurements performed using different platforms.

Die Metaphasenspindel ist eine selbstorganisierende molekulare Maschine, die die entscheidende Funktion erfüllt, das Genom während der Zellteilung gleichmäßig zu trennen. Spindellänge und -form sind emergente Eigenschaften, die durch komplexe Wechselwirkungsnetzwerke zwischen Molekülen hervorgerufen werden. Obwohl erhebliche Fortschritte beim Verständnis der einzelnen molekularen Akteure erzielt wurden, die ihre Länge und Form beeinflussen, haben wir erst kürzlich damit begonnen, die Zusammenhänge zwischen Spindelmorphologie, Dynamik und Materialeigenschaften zu untersuchen. In dieser Arbeit untersuchte ich zunächst quantitativ die Rolle zweier molekularer Kraftgeneratoren - Kinesin-5 und Dynein - bei der Regulierung der Spindelform von Xenopus-Eiextrakt. Eine Störung ihrer Aktivität veränderte die Spindelmorphologie, ohne die Gesamtmasse der Mikrotubuli zu beeinflussen. Um die Spindelform physikalisch zu stören, wurde ein Optical Stretcher (OS) -Aufbau entwickelt. Obwohl das OS Vesikel in Extrakten verformen könnte, konnte keine Kraft auf Spindeln ausgeübt werden. Die Untersuchung des Brechungsindex der Struktur mittels optischer Beugungstomographie (ODT) ergab, dass es keinen Unterschied zwischen Spindel und Zytoplasma gab. Korrelative Fluoreszenz- und ODT-Bildgebung zeigten, wie sich die Materialeigenschaften innerhalb verschiedener Biomoleküle räumlich unterschieden. Die Gesamttrockenmasse der Spindel skalierte mit der Länge, während die Gesamtdichte konstant blieb. Interessanterweise waren die Spindeln in HeLa-Zellen dichter als das Zytoplasma. Schließlich deckte eine störende Mikrotubulusdichte auf, wie die Gesamttubulinkonzentration die Spindelgröße, die Gesamtmasse und die Materialeigenschaften regulierte. Insgesamt bietet diese Studie eine grundlegende Charakterisierung der physikalischen Eigenschaften der Spindel und hilft dabei, Zusammenhänge zwischen der Biochemie und der Biophysik einer aktiven Form weicher Materie zu beleuchten., The metaphase spindle is a self-organising molecular machine that performs the critical function of segregating the genome equally during cell division. Spindle length and shape are emergent properties brought about by complex networks of interactions between molecules. Although significant progress has been made in understanding the individual molecular players influencing its length and shape, we have only recently started exploring the links between spindle morphology, dynamics, and material properties. A thorough analysis of spindle material properties is essential if we are to comprehend how such a dynamic structure responds to forces, and maintains its steady-state length and shape. In this work, I first quantitatively investigated the role of two molecular force generators– Kinesin-5 and Dynein in regulating Xenopus egg extract spindle shape. Perturbing their activity altered spindle morphology without impacting total microtubule mass. To physically perturb spindle shape, an Optical Stretcher (OS) setup was developed. Although the OS could deform vesicles in extracts, force could not be exerted on spindles. Investigating the structure’s refractive index using Optical Diffraction Tomography (ODT) revealed that there was no difference between the spindle and cytoplasm. Correlative fluorescence and ODT imaging revealed how material properties varied spatially within different biomolecules. Additionally, spindle mass density and the microtubule density were correlated. The total dry mass of the spindle scaled with length while overall density remained constant. Interestingly, spindles in HeLa cells were denser than the cytoplasm. Finally, perturbing microtubule density uncovered how total tubulin concentration regulated spindle size, overall mass and material properties. Overall, this study provides a fundamental characterisation of the spindle’s physical properties and helps illuminate links between the biochemistry and biophysics of an active form of soft matte

Identification of different cell types is an indispensable task of biomedical research and clinical application. During the last decades, much attention was given to molecular characterization, and many cell types can now be identified using established markers that bind to cell-specific antigens. The required staining process is a lengthy and costly treatment, which can cause alterations of cellular properties, contaminate the sample and therefore limit its subsequent use. For example, for photoreceptor transplantations, highly pure samples of photoreceptor cells are required, which can currently only be obtained using molecular labelling, rendering the resulting sample incompatible for clinical application. A promising alternative to molecular markers is the label-free identification of cells using mechanical or morphological features. Real-time deformability cytometry (RT DC) is a microfluidic technique, which allows capturing both types of information simultaneously for single cells at high-throughput. In this thesis, I present machine learning methods which allow identifying different cell types, based on bright-field images from RT DC. In particular, I introduce algorithms that are fast enough to be applied in real-time during the measurement (at >1000 cells/s), which can be used for image-based cell sorting. The performance of the algorithms is shown for the identification of rod precursor cells in retina-samples, indicating that image-based sorting based on those algorithms would allow enriching photoreceptors to a final concentration, applicable for transplantation purposes.:Contents Abstract iii Kurzfassung iv List of figures viii List of tables x 1. Introduction 1 1.1. Texture and mechanical properties: label-free markers 4 1.2. The retina, diseases and cure by photoreceptor transplantation 5 1.3. Technologies for label-free assessment of cells 8 2. Materials and Methods 10 2.1. Experimental setup 10 2.1.1. Chip design for RT DC and RT-FDC 10 2.1.2. Chip

Identification of different cell types is an indispensable task of biomedical research and clinical application. During the last decades, much attention was given to molecular characterization, and many cell types can now be identified using established markers that bind to cell-specific antigens. The required staining process is a lengthy and costly treatment, which can cause alterations of cellular properties, contaminate the sample and therefore limit its subsequent use. For example, for photoreceptor transplantations, highly pure samples of photoreceptor cells are required, which can currently only be obtained using molecular labelling, rendering the resulting sample incompatible for clinical application. A promising alternative to molecular markers is the label-free identification of cells using mechanical or morphological features. Real-time deformability cytometry (RT DC) is a microfluidic technique, which allows capturing both types of information simultaneously for single cells at high-throughput. In this thesis, I present machine learning methods which allow identifying different cell types, based on bright-field images from RT DC. In particular, I introduce algorithms that are fast enough to be applied in real-time during the measurement (at >1000 cells/s), which can be used for image-based cell sorting. The performance of the algorithms is shown for the identification of rod precursor cells in retina-samples, indicating that image-based sorting based on those algorithms would allow enriching photoreceptors to a final concentration, applicable for transplantation purposes.:Contents Abstract iii Kurzfassung iv List of figures viii List of tables x 1. Introduction 1 1.1. Texture and mechanical properties: label-free markers 4 1.2. The retina, diseases and cure by photoreceptor transplantation 5 1.3. Technologies for label-free assessment of cells 8 2. Materials and Methods 10 2.1. Experimental setup 10 2.1.1. Chip design for RT DC and RT-FDC 10 2.1.2. Chip

The mechanical phenotype of a cell is an inherent biophysical marker of its state and function, with many applications in basic and applied biological research. Microfluidics-based methods have enabled single-cell mechanophenotyping at throughputs comparable to those of flow cytometry. Here, we present a standardized cross-laboratory study comparing three microfluidics-based approaches for measuring cell mechanical phenotype: constriction-based deformability cytometry (cDC), shear flow deformability cytometry (sDC) and extensional flow deformability cytometry (xDC). All three methods detect cell deformability changes induced by exposure to altered osmolarity. However, a dose-dependent deformability increase upon latrunculin B-induced actin disassembly was detected only with cDC and sDC, which suggests that when exposing cells to the higher strain rate imposed by xDC, cellular components other than the actin cytoskeleton dominate the response. The direct comparison presented here furthers our understanding of the applicability of the different deformability cytometry methods and provides context for the interpretation of deformability measurements performed using different platforms.

Identification of different cell types is an indispensable task of biomedical research and clinical application. During the last decades, much attention was given to molecular characterization, and many cell types can now be identified using established markers that bind to cell-specific antigens. The required staining process is a lengthy and costly treatment, which can cause alterations of cellular properties, contaminate the sample and therefore limit its subsequent use. For example, for photoreceptor transplantations, highly pure samples of photoreceptor cells are required, which can currently only be obtained using molecular labelling, rendering the resulting sample incompatible for clinical application. A promising alternative to molecular markers is the label-free identification of cells using mechanical or morphological features. Real-time deformability cytometry (RT DC) is a microfluidic technique, which allows capturing both types of information simultaneously for single cells at high-throughput. In this thesis, I present machine learning methods which allow identifying different cell types, based on bright-field images from RT DC. In particular, I introduce algorithms that are fast enough to be applied in real-time during the measurement (at >1000 cells/s), which can be used for image-based cell sorting. The performance of the algorithms is shown for the identification of rod precursor cells in retina-samples, indicating that image-based sorting based on those algorithms would allow enriching photoreceptors to a final concentration, applicable for transplantation purposes.:Contents Abstract iii Kurzfassung iv List of figures viii List of tables x 1. Introduction 1 1.1. Texture and mechanical properties: label-free markers 4 1.2. The retina, diseases and cure by photoreceptor transplantation 5 1.3. Technologies for label-free assessment of cells 8 2. Materials and Methods 10 2.1. Experimental setup 10 2.1.1. Chip design for RT DC and RT-FDC 10 2.1.2. Chip

Die Metaphasenspindel ist eine selbstorganisierende molekulare Maschine, die die entscheidende Funktion erfüllt, das Genom während der Zellteilung gleichmäßig zu trennen. Spindellänge und -form sind emergente Eigenschaften, die durch komplexe Wechselwirkungsnetzwerke zwischen Molekülen hervorgerufen werden. Obwohl erhebliche Fortschritte beim Verständnis der einzelnen molekularen Akteure erzielt wurden, die ihre Länge und Form beeinflussen, haben wir erst kürzlich damit begonnen, die Zusammenhänge zwischen Spindelmorphologie, Dynamik und Materialeigenschaften zu untersuchen. In dieser Arbeit untersuchte ich zunächst quantitativ die Rolle zweier molekularer Kraftgeneratoren - Kinesin-5 und Dynein - bei der Regulierung der Spindelform von Xenopus-Eiextrakt. Eine Störung ihrer Aktivität veränderte die Spindelmorphologie, ohne die Gesamtmasse der Mikrotubuli zu beeinflussen. Um die Spindelform physikalisch zu stören, wurde ein Optical Stretcher (OS) -Aufbau entwickelt. Obwohl das OS Vesikel in Extrakten verformen könnte, konnte keine Kraft auf Spindeln ausgeübt werden. Die Untersuchung des Brechungsindex der Struktur mittels optischer Beugungstomographie (ODT) ergab, dass es keinen Unterschied zwischen Spindel und Zytoplasma gab. Korrelative Fluoreszenz- und ODT-Bildgebung zeigten, wie sich die Materialeigenschaften innerhalb verschiedener Biomoleküle räumlich unterschieden. Die Gesamttrockenmasse der Spindel skalierte mit der Länge, während die Gesamtdichte konstant blieb. Interessanterweise waren die Spindeln in HeLa-Zellen dichter als das Zytoplasma. Schließlich deckte eine störende Mikrotubulusdichte auf, wie die Gesamttubulinkonzentration die Spindelgröße, die Gesamtmasse und die Materialeigenschaften regulierte. Insgesamt bietet diese Studie eine grundlegende Charakterisierung der physikalischen Eigenschaften der Spindel und hilft dabei, Zusammenhänge zwischen der Biochemie und der Biophysik einer aktiven Form weicher Materie zu beleuchten., The metaphase spindle is a self-organising molecular machine that performs the critical function of segregating the genome equally during cell division. Spindle length and shape are emergent properties brought about by complex networks of interactions between molecules. Although significant progress has been made in understanding the individual molecular players influencing its length and shape, we have only recently started exploring the links between spindle morphology, dynamics, and material properties. A thorough analysis of spindle material properties is essential if we are to comprehend how such a dynamic structure responds to forces, and maintains its steady-state length and shape. In this work, I first quantitatively investigated the role of two molecular force generators– Kinesin-5 and Dynein in regulating Xenopus egg extract spindle shape. Perturbing their activity altered spindle morphology without impacting total microtubule mass. To physically perturb spindle shape, an Optical Stretcher (OS) setup was developed. Although the OS could deform vesicles in extracts, force could not be exerted on spindles. Investigating the structure’s refractive index using Optical Diffraction Tomography (ODT) revealed that there was no difference between the spindle and cytoplasm. Correlative fluorescence and ODT imaging revealed how material properties varied spatially within different biomolecules. Additionally, spindle mass density and the microtubule density were correlated. The total dry mass of the spindle scaled with length while overall density remained constant. Interestingly, spindles in HeLa cells were denser than the cytoplasm. Finally, perturbing microtubule density uncovered how total tubulin concentration regulated spindle size, overall mass and material properties. Overall, this study provides a fundamental characterisation of the spindle’s physical properties and helps illuminate links between the biochemistry and biophysics of an active form of soft matte

Identification of different cell types is an indispensable task of biomedical research and clinical application. During the last decades, much attention was given to molecular characterization, and many cell types can now be identified using established markers that bind to cell-specific antigens. The required staining process is a lengthy and costly treatment, which can cause alterations of cellular properties, contaminate the sample and therefore limit its subsequent use. For example, for photoreceptor transplantations, highly pure samples of photoreceptor cells are required, which can currently only be obtained using molecular labelling, rendering the resulting sample incompatible for clinical application. A promising alternative to molecular markers is the label-free identification of cells using mechanical or morphological features. Real-time deformability cytometry (RT DC) is a microfluidic technique, which allows capturing both types of information simultaneously for single cells at high-throughput. In this thesis, I present machine learning methods which allow identifying different cell types, based on bright-field images from RT DC. In particular, I introduce algorithms that are fast enough to be applied in real-time during the measurement (at >1000 cells/s), which can be used for image-based cell sorting. The performance of the algorithms is shown for the identification of rod precursor cells in retina-samples, indicating that image-based sorting based on those algorithms would allow enriching photoreceptors to a final concentration, applicable for transplantation purposes.:Contents Abstract iii Kurzfassung iv List of figures viii List of tables x 1. Introduction 1 1.1. Texture and mechanical properties: label-free markers 4 1.2. The retina, diseases and cure by photoreceptor transplantation 5 1.3. Technologies for label-free assessment of cells 8 2. Materials and Methods 10 2.1. Experimental setup 10 2.1.1. Chip design for RT DC and RT-FDC 10 2.1.2. Chip

Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for and development of label-free methodologies to classify cells is strong and its impact on precision medicine is relevant. Towards this end, high-throughput techniques for cell mechanical phenotyping have been proposed to get a multidimensional biophysical characterization of single cells. With this motivation, our goal here is to investigate the extent to which an unsupervised machine learning methodology, which is applied exclusively on morpho-rheological markers obtained by real-time deformability and fluorescence cytometry (RT-FDC), can address the difficult task of providing label-free discrimination of reticulocytes from mature red blood cells. We focused on this problem, since the characterization of reticulocytes (their percentage and cellular features) in the blood is vital in multiple human disease conditions, especially bone-marrow disorders such as anemia and leukemia. Our approach reports promising label-free results in the classification of reticulocytes from mature red blood cells, and it represents a step forward in the development of high-throughput morpho-rheological-based methodologies for the computational categorization of single cells. Besides, our methodology can be an alternative but also a complementary method to integrate with existing cell-labelling techniques., Comment: 13 pages, 3 figures, 4 tables

Osteosarcoma is the most frequent primary tumor of bone and is characterized by its high tendency to metastasize in lungs. Although treatment in cases of early diagnosis results in a 5-yr survival rate of nearly 60%, the prognosis for patients with secondary lesions at diagnosis is poor, and their 5-yr survival rate remains below 30%. In the present work, we have used a number of analytical methods to investigate the impact of increased metastatic potential on the biophysical properties and force generation of osteosarcoma cells. With that aim, we used two paired osteosarcoma cell lines, with each one comprising a parental line with low metastatic potential and its experimentally selected, highly metastatic form. Mechanical characterization was performed by means of atomic force microscopy, tensile biaxial deformation, and real-time deformability, and cell traction was measured using two-dimensional and micropost-based traction force microscopy. Our results reveal that the low metastatic osteosarcoma cells display larger spreading sizes and generate higher forces than the experimentally selected, highly malignant variants. In turn, the outcome of cell stiffness measurements strongly depends on the method used and the state of the probed cell, indicating that only a set of phenotyping methods provides the full picture of cell mechanics.

The shortest path (SP) and shortest paths tree (SPT) problems arise both as direct applications and as subroutines of overlay algorithms solving more complex problems such as the constrained shortest path (CSP) or the constrained minimum Steiner tree (CMST) problems. Often, such algorithms do not use the result of an SP subroutine if its total cost is greater than a given bound. For example, for delay-constrained problems, paths resulting from a least-delay SP run and whose delay is greater than the delay constraint of the original problem are not used by the overlay algorithm to construct its solution. As a result of the existence of these bounds, and because the Dijkstra SP algorithm discovers paths in increasing order of cost, we can terminate the SP search earlier, i.e., once it is known that paths with a greater total cost will not be considered by the overlay algorithm. This early termination allows to reduce the runtime of the SP subroutine, thereby reducing the runtime of the overlay algorithm without impacting its final result. We refer to this adaptation of Dijkstra for centralized implementations as bounded Dijkstra (BD). On the example of CSP algorithms, we confirm the usefulness of BD by showing that it can reduce the runtime of some algorithms by 75% on average., Comment: 9 pages -- accompanying web page: https://lora.lkn.ei.tum.de

State synchronisation in clustered Software Defined Networking controller deployments ensures that all instances of the controller have the same state information in order to provide redundancy. Current implementations of controllers use a strong consistency model, where configuration changes must be synchronised across a number of instances before they are applied on the network infrastructure. For large deployments, this blocking process increases the delay of state synchronisation across cluster members and consequently has a detrimental effect on network operations that require rapid response, such as fast failover and Quality of Service applications. In this paper, we introduce an adaptive consistency model for SDN Controllers that employs concepts of eventual consistency models along with a novel `cost-based' approach where strict synchronisation is employed for critical operations that affect a large portion of the network resources while less critical changes are periodically propagated across cluster nodes. We use simulation to evaluate our model and demonstrate the potential gains in performance., Comment: 7 pages

The main goal of industrial networks is to transmit mission critical messages. Deterministic real-time QoS is a key requirement for critical messages. In this work, the realization of a centralized deterministic QoS framework is presented and analyzed. The evaluation shows that this framework provides high performance in terms of maximum traffic intensity at reasonable computation costs., Der Hauptzweck industrieller Kommunikationsnetze besteht darin, funktionskritische Nachrichten zu übertragen. Deterministische Echtzeit QoS ist eine Schlüsselanforderung für diese Nachrichten. In dieser Arbeit wird die Realisierung eines zentralisierten deterministischen QoS Frameworks vorgestellt. Die Evaluierung zeigt, dass dieses Framework eine hohe Leistungsfähigkeit hinsichtlich der maximalen Verkehrsintensität zu einem vertretbaren Berechnungsaufwands aufweist.

Multicellular life is formed by an orchestrated interplay of processes on different scales in space and time. Observing and quantitatively measuring these processes in an intact, living organism requires gentle and adaptive imaging. One example of such a process is the scaling of the mitotic spindle during early development. The spindle segregates the chromosomes during cell division and the spindle length determines the positioning of the chromosomes in the successive daughter cells. Thus, adaptation of spindle size to cell size is crucial for proper functioning. Early development is an excellent phase to study spindle scaling since cells rapidly divide in the absence of growth. In this phase, the spindle can be studied in cells of the same organism changing its volume orders of magnitude. During early zebrafish embryogenesis, the mitotic spindle only appears for three minutes out of the fifteen minutes cell cycle. Quantifying these short-lived events in a living embryo requires flexible and adaptive multi-resolution recordings, which are impossible with any state-of-the-art microscope. In this thesis, I present two new techniques to adaptively image biological samples based on light sheet fluorescence microscopy (LSFM). First, I present a remote, contact-free positioning technique based on magnetic forces to orient the sample in the microscope. When imaging biological samples, there is often only one sample orientation that offers the best view on the region of interest. This preferred orientation typically changes over time as the specimen grows and develops. The contact-free positioning technique allows to always image specimens from the optimal viewing angle. I demonstrate the functionality of this method by 3D orientation of zebrafish embryos and zebrafish larvae. Second, I present a new type of LSFM that autonomously adapts its detection scheme to the sample state. This microscope contains an adaptable magnification module to map the development of the millim, Multizelluläres Leben wird durch ein orchestriertes Zusammenspiel von Prozessen auf verschiedenen Skalen in Raum und Zeit gebildet. Beobachtung und quantitative Messungen dieser Vorgänge in einem intakten, lebenden Organismus erfordern schonende und adaptive Bildgebung. Ein Beispiel für einen solchen Prozess ist die Größenanpassung der mitotischen Spindel während der frühen Entwicklung. Die Spindel trennt die Chromosomen während der Zellteilung und die Spindellänge bestimmt die Positionierung der Chromosomen in den Tochterzellen. Daher ist die Anpassung der Spindelgröße an die Zellgröße entscheidend für die ordnungsgemäße Funktion. Die Phase der frühen Entwicklung eignet sich hervorragend zur Untersuchung der Spindel-Skalierung, da die Zellen sich schnell teilen ohne zu wachsen. Während der frühen Zebrafischembryogenese erscheint die Spindel nur drei Minuten innerhalb des fünfzehnminütigen Zellzyklus. Die Quantifizierung dieser kurzlebigen Ereignisse in einem lebenden Embryo erfordert flexible und anpassungsfähige Aufnahmen mit variabler Auflösung, die mit keinem Mikroskop nach dem aktuellen Stand der Technik möglich sind. In dieser Arbeit präsentiere ich zwei neue Techniken zur adaptiven Abbildung biologischer Proben basierend auf der Lichtblatt-Fluoreszenzmikroskopie (LSFM). Zuerst stelle ich eine berührungslose Positionierungstechnik vor, die auf Magnetkräften basiert, um die Probe im Mikroskop zu orientieren. Bei der Abbildung biologischer Proben gibt es oft nur eine Probenorientierung, welche die beste Sicht auf die Region von Interesse bietet. Diese Vorzugsorientierung ändert sich typischerweise mit der Zeit, wenn die Probe wächst und sich entwickelt. Die Positionierungstechnik ermöglicht es, Proben immer aus dem optimalen Betrachtungswinkel abzubilden. Zweitens stelle ich einen neuen Typ von LSFM vor, der sein Detektionsschema autonom an den Probenzustand anpasst. Dieses Mikroskop enthält ein anpassbares Vergrößerungsmodul, um die Entwicklung des millimetergr

Gewebe sind nicht nur durch ihre biochemische Zusammensetzung definiert, sondern auch durch ihre individuellen mechanischen Eigenschaften. Inzwischen ist es weithin akzeptiert, dass Zellen ihre mechanische Umgebung spüren und darauf reagieren. Zum Beispiel werden Zellmigration und die Differenzierung von Stammzellen durch die Umgebungssteifigkeit beeinflusst. Um diese Effekte in vitro zu untersuchen, wurden viele Zellkulturstudien auf 2D Hydrogelsubstraten durchgeführt. Zusätzlich dazu steigt die Anzahl von Studien an, die hydrogelbasierte 3D-Scaffolds nutzen, um 2D Studien zu validieren und die experimentellen Bedingungen der Situation in vivo anzunähern. Jedoch erweist es sich weiterhin als schwierig den Effekt von Mechanik in 3D in vitro zu untersuchen, da in den gemeinhin genutzten 3D Hydrogelsystemen immer eine Kopplung zwischen Gelporosität und Steifigkeit besteht. Zusätzlich hängt die Konzentration der biologisch aktiven Bindungsstellen für Zellen oft ebenfalls von der Steifigkeit ab. Diese Arbeit präsentiert die Entwicklung und Optimierung neuer 3D Hydrogelkugel-Scaffolds, in denen die Steifigkeit von der Porosität schließlich entkoppelt wird. Mit Hydrogelkugeln als Scaffold-Bausteine ist es nun möglich 3D Scaffolds mit definierten mechanischen Eigenschaften und konstanter Porengröße zu generieren. Während der Methodenentwicklung wurden verschiedene Prinzipien und Kultivierungskammern konstruiert und überarbeitet, gefolgt von der theoretischen Betrachtung der Sauerstoffdiffusion, um die Eignung der gewählten Kammer hinsichtlich Zellvitalität und Zellwachstum zu überprüfen. Eine Kombination aus mehreren getesteten Filtern wurde ausgewählt um HydrogelkugelScaffolds erfolgreich in der ausgewählten Kammer zu generieren. Im Weiteren wurden verschiedene Hydrogelmaterialien untersucht hinsichtlich der erfolgreichen Produktion monodisperser Hydrogelkugeln und der Erzeugung stabiler Scaffolds. Hydrogelkugeln aus Polyacrylamid (PAAm) wurden als Scaffold-Bausteine ausg, Tissues are defined not only by their biochemical composition, but also by their distinct mechanical properties. It is now widely accepted that cells sense their mechanical environment and respond to it. For example, cell migration and stem cell differentiation is affected by stiffness. To study these effects in vitro, many cell culture studies have been performed on 2D hydrogel substrates. Additionally, the amount of 3D studies based on hydrogels as 3D scaffold is increasing to validate 2D in vitro studies and adjust experimental conditions closer to the situation in vivo. However, studying the effects of mechanics in vitro in 3D is still challenging as commonly used 3D hydrogel assays always link gel porosity with stiffness. Additionally, the concentration of biologically active adhesion sides often also depends on the stiffness. This work presents the development and optimization of novel 3D hydrogel bead scaffolds where the stiffness is finally decoupled from porosity. With hydrogel beads as scaffold building blocks it was possible to generate 3D scaffolds with defined mechanical properties and a constant pore size. During the method development, different culture devices were constructed and revised, followed by oxygen diffusion simulations to proof the suitability of the chosen device for cell survival and growth. A combination of different filter approaches was selected to generate hydrogel bead scaffolds in the culture device. Furthermore, different hydrogel materials were investigated regarding successful production of monodisperse beads and stable scaffold generation. Polyacrylamide (PAAm) hydrogel beads were chosen as scaffold building blocks to demonstrate live-cell imaging and successful cell survival over four days in differently compliant hydrogel bead scaffolds. Moreover, first cell migration experiments were performed by using plain PAAm hydrogel beads as well as PAAm hydrogel beads functionalized with adhesion molecules with differently stiff layer

Multicellular life is formed by an orchestrated interplay of processes on different scales in space and time. Observing and quantitatively measuring these processes in an intact, living organism requires gentle and adaptive imaging. One example of such a process is the scaling of the mitotic spindle during early development. The spindle segregates the chromosomes during cell division and the spindle length determines the positioning of the chromosomes in the successive daughter cells. Thus, adaptation of spindle size to cell size is crucial for proper functioning. Early development is an excellent phase to study spindle scaling since cells rapidly divide in the absence of growth. In this phase, the spindle can be studied in cells of the same organism changing its volume orders of magnitude. During early zebrafish embryogenesis, the mitotic spindle only appears for three minutes out of the fifteen minutes cell cycle. Quantifying these short-lived events in a living embryo requires flexible and adaptive multi-resolution recordings, which are impossible with any state-of-the-art microscope. In this thesis, I present two new techniques to adaptively image biological samples based on light sheet fluorescence microscopy (LSFM). First, I present a remote, contact-free positioning technique based on magnetic forces to orient the sample in the microscope. When imaging biological samples, there is often only one sample orientation that offers the best view on the region of interest. This preferred orientation typically changes over time as the specimen grows and develops. The contact-free positioning technique allows to always image specimens from the optimal viewing angle. I demonstrate the functionality of this method by 3D orientation of zebrafish embryos and zebrafish larvae. Second, I present a new type of LSFM that autonomously adapts its detection scheme to the sample state. This microscope contains an adaptable magnification module to map the development of the millim, Multizelluläres Leben wird durch ein orchestriertes Zusammenspiel von Prozessen auf verschiedenen Skalen in Raum und Zeit gebildet. Beobachtung und quantitative Messungen dieser Vorgänge in einem intakten, lebenden Organismus erfordern schonende und adaptive Bildgebung. Ein Beispiel für einen solchen Prozess ist die Größenanpassung der mitotischen Spindel während der frühen Entwicklung. Die Spindel trennt die Chromosomen während der Zellteilung und die Spindellänge bestimmt die Positionierung der Chromosomen in den Tochterzellen. Daher ist die Anpassung der Spindelgröße an die Zellgröße entscheidend für die ordnungsgemäße Funktion. Die Phase der frühen Entwicklung eignet sich hervorragend zur Untersuchung der Spindel-Skalierung, da die Zellen sich schnell teilen ohne zu wachsen. Während der frühen Zebrafischembryogenese erscheint die Spindel nur drei Minuten innerhalb des fünfzehnminütigen Zellzyklus. Die Quantifizierung dieser kurzlebigen Ereignisse in einem lebenden Embryo erfordert flexible und anpassungsfähige Aufnahmen mit variabler Auflösung, die mit keinem Mikroskop nach dem aktuellen Stand der Technik möglich sind. In dieser Arbeit präsentiere ich zwei neue Techniken zur adaptiven Abbildung biologischer Proben basierend auf der Lichtblatt-Fluoreszenzmikroskopie (LSFM). Zuerst stelle ich eine berührungslose Positionierungstechnik vor, die auf Magnetkräften basiert, um die Probe im Mikroskop zu orientieren. Bei der Abbildung biologischer Proben gibt es oft nur eine Probenorientierung, welche die beste Sicht auf die Region von Interesse bietet. Diese Vorzugsorientierung ändert sich typischerweise mit der Zeit, wenn die Probe wächst und sich entwickelt. Die Positionierungstechnik ermöglicht es, Proben immer aus dem optimalen Betrachtungswinkel abzubilden. Zweitens stelle ich einen neuen Typ von LSFM vor, der sein Detektionsschema autonom an den Probenzustand anpasst. Dieses Mikroskop enthält ein anpassbares Vergrößerungsmodul, um die Entwicklung des millimetergr

Gewebe sind nicht nur durch ihre biochemische Zusammensetzung definiert, sondern auch durch ihre individuellen mechanischen Eigenschaften. Inzwischen ist es weithin akzeptiert, dass Zellen ihre mechanische Umgebung spüren und darauf reagieren. Zum Beispiel werden Zellmigration und die Differenzierung von Stammzellen durch die Umgebungssteifigkeit beeinflusst. Um diese Effekte in vitro zu untersuchen, wurden viele Zellkulturstudien auf 2D Hydrogelsubstraten durchgeführt. Zusätzlich dazu steigt die Anzahl von Studien an, die hydrogelbasierte 3D-Scaffolds nutzen, um 2D Studien zu validieren und die experimentellen Bedingungen der Situation in vivo anzunähern. Jedoch erweist es sich weiterhin als schwierig den Effekt von Mechanik in 3D in vitro zu untersuchen, da in den gemeinhin genutzten 3D Hydrogelsystemen immer eine Kopplung zwischen Gelporosität und Steifigkeit besteht. Zusätzlich hängt die Konzentration der biologisch aktiven Bindungsstellen für Zellen oft ebenfalls von der Steifigkeit ab. Diese Arbeit präsentiert die Entwicklung und Optimierung neuer 3D Hydrogelkugel-Scaffolds, in denen die Steifigkeit von der Porosität schließlich entkoppelt wird. Mit Hydrogelkugeln als Scaffold-Bausteine ist es nun möglich 3D Scaffolds mit definierten mechanischen Eigenschaften und konstanter Porengröße zu generieren. Während der Methodenentwicklung wurden verschiedene Prinzipien und Kultivierungskammern konstruiert und überarbeitet, gefolgt von der theoretischen Betrachtung der Sauerstoffdiffusion, um die Eignung der gewählten Kammer hinsichtlich Zellvitalität und Zellwachstum zu überprüfen. Eine Kombination aus mehreren getesteten Filtern wurde ausgewählt um HydrogelkugelScaffolds erfolgreich in der ausgewählten Kammer zu generieren. Im Weiteren wurden verschiedene Hydrogelmaterialien untersucht hinsichtlich der erfolgreichen Produktion monodisperser Hydrogelkugeln und der Erzeugung stabiler Scaffolds. Hydrogelkugeln aus Polyacrylamid (PAAm) wurden als Scaffold-Bausteine ausg, Tissues are defined not only by their biochemical composition, but also by their distinct mechanical properties. It is now widely accepted that cells sense their mechanical environment and respond to it. For example, cell migration and stem cell differentiation is affected by stiffness. To study these effects in vitro, many cell culture studies have been performed on 2D hydrogel substrates. Additionally, the amount of 3D studies based on hydrogels as 3D scaffold is increasing to validate 2D in vitro studies and adjust experimental conditions closer to the situation in vivo. However, studying the effects of mechanics in vitro in 3D is still challenging as commonly used 3D hydrogel assays always link gel porosity with stiffness. Additionally, the concentration of biologically active adhesion sides often also depends on the stiffness. This work presents the development and optimization of novel 3D hydrogel bead scaffolds where the stiffness is finally decoupled from porosity. With hydrogel beads as scaffold building blocks it was possible to generate 3D scaffolds with defined mechanical properties and a constant pore size. During the method development, different culture devices were constructed and revised, followed by oxygen diffusion simulations to proof the suitability of the chosen device for cell survival and growth. A combination of different filter approaches was selected to generate hydrogel bead scaffolds in the culture device. Furthermore, different hydrogel materials were investigated regarding successful production of monodisperse beads and stable scaffold generation. Polyacrylamide (PAAm) hydrogel beads were chosen as scaffold building blocks to demonstrate live-cell imaging and successful cell survival over four days in differently compliant hydrogel bead scaffolds. Moreover, first cell migration experiments were performed by using plain PAAm hydrogel beads as well as PAAm hydrogel beads functionalized with adhesion molecules with differently stiff layer

Multicellular life is formed by an orchestrated interplay of processes on different scales in space and time. Observing and quantitatively measuring these processes in an intact, living organism requires gentle and adaptive imaging. One example of such a process is the scaling of the mitotic spindle during early development. The spindle segregates the chromosomes during cell division and the spindle length determines the positioning of the chromosomes in the successive daughter cells. Thus, adaptation of spindle size to cell size is crucial for proper functioning. Early development is an excellent phase to study spindle scaling since cells rapidly divide in the absence of growth. In this phase, the spindle can be studied in cells of the same organism changing its volume orders of magnitude. During early zebrafish embryogenesis, the mitotic spindle only appears for three minutes out of the fifteen minutes cell cycle. Quantifying these short-lived events in a living embryo requires flexible and adaptive multi-resolution recordings, which are impossible with any state-of-the-art microscope. In this thesis, I present two new techniques to adaptively image biological samples based on light sheet fluorescence microscopy (LSFM). First, I present a remote, contact-free positioning technique based on magnetic forces to orient the sample in the microscope. When imaging biological samples, there is often only one sample orientation that offers the best view on the region of interest. This preferred orientation typically changes over time as the specimen grows and develops. The contact-free positioning technique allows to always image specimens from the optimal viewing angle. I demonstrate the functionality of this method by 3D orientation of zebrafish embryos and zebrafish larvae. Second, I present a new type of LSFM that autonomously adapts its detection scheme to the sample state. This microscope contains an adaptable magnification module to map the development of the millim, Multizelluläres Leben wird durch ein orchestriertes Zusammenspiel von Prozessen auf verschiedenen Skalen in Raum und Zeit gebildet. Beobachtung und quantitative Messungen dieser Vorgänge in einem intakten, lebenden Organismus erfordern schonende und adaptive Bildgebung. Ein Beispiel für einen solchen Prozess ist die Größenanpassung der mitotischen Spindel während der frühen Entwicklung. Die Spindel trennt die Chromosomen während der Zellteilung und die Spindellänge bestimmt die Positionierung der Chromosomen in den Tochterzellen. Daher ist die Anpassung der Spindelgröße an die Zellgröße entscheidend für die ordnungsgemäße Funktion. Die Phase der frühen Entwicklung eignet sich hervorragend zur Untersuchung der Spindel-Skalierung, da die Zellen sich schnell teilen ohne zu wachsen. Während der frühen Zebrafischembryogenese erscheint die Spindel nur drei Minuten innerhalb des fünfzehnminütigen Zellzyklus. Die Quantifizierung dieser kurzlebigen Ereignisse in einem lebenden Embryo erfordert flexible und anpassungsfähige Aufnahmen mit variabler Auflösung, die mit keinem Mikroskop nach dem aktuellen Stand der Technik möglich sind. In dieser Arbeit präsentiere ich zwei neue Techniken zur adaptiven Abbildung biologischer Proben basierend auf der Lichtblatt-Fluoreszenzmikroskopie (LSFM). Zuerst stelle ich eine berührungslose Positionierungstechnik vor, die auf Magnetkräften basiert, um die Probe im Mikroskop zu orientieren. Bei der Abbildung biologischer Proben gibt es oft nur eine Probenorientierung, welche die beste Sicht auf die Region von Interesse bietet. Diese Vorzugsorientierung ändert sich typischerweise mit der Zeit, wenn die Probe wächst und sich entwickelt. Die Positionierungstechnik ermöglicht es, Proben immer aus dem optimalen Betrachtungswinkel abzubilden. Zweitens stelle ich einen neuen Typ von LSFM vor, der sein Detektionsschema autonom an den Probenzustand anpasst. Dieses Mikroskop enthält ein anpassbares Vergrößerungsmodul, um die Entwicklung des millimetergr

Gewebe sind nicht nur durch ihre biochemische Zusammensetzung definiert, sondern auch durch ihre individuellen mechanischen Eigenschaften. Inzwischen ist es weithin akzeptiert, dass Zellen ihre mechanische Umgebung spüren und darauf reagieren. Zum Beispiel werden Zellmigration und die Differenzierung von Stammzellen durch die Umgebungssteifigkeit beeinflusst. Um diese Effekte in vitro zu untersuchen, wurden viele Zellkulturstudien auf 2D Hydrogelsubstraten durchgeführt. Zusätzlich dazu steigt die Anzahl von Studien an, die hydrogelbasierte 3D-Scaffolds nutzen, um 2D Studien zu validieren und die experimentellen Bedingungen der Situation in vivo anzunähern. Jedoch erweist es sich weiterhin als schwierig den Effekt von Mechanik in 3D in vitro zu untersuchen, da in den gemeinhin genutzten 3D Hydrogelsystemen immer eine Kopplung zwischen Gelporosität und Steifigkeit besteht. Zusätzlich hängt die Konzentration der biologisch aktiven Bindungsstellen für Zellen oft ebenfalls von der Steifigkeit ab. Diese Arbeit präsentiert die Entwicklung und Optimierung neuer 3D Hydrogelkugel-Scaffolds, in denen die Steifigkeit von der Porosität schließlich entkoppelt wird. Mit Hydrogelkugeln als Scaffold-Bausteine ist es nun möglich 3D Scaffolds mit definierten mechanischen Eigenschaften und konstanter Porengröße zu generieren. Während der Methodenentwicklung wurden verschiedene Prinzipien und Kultivierungskammern konstruiert und überarbeitet, gefolgt von der theoretischen Betrachtung der Sauerstoffdiffusion, um die Eignung der gewählten Kammer hinsichtlich Zellvitalität und Zellwachstum zu überprüfen. Eine Kombination aus mehreren getesteten Filtern wurde ausgewählt um HydrogelkugelScaffolds erfolgreich in der ausgewählten Kammer zu generieren. Im Weiteren wurden verschiedene Hydrogelmaterialien untersucht hinsichtlich der erfolgreichen Produktion monodisperser Hydrogelkugeln und der Erzeugung stabiler Scaffolds. Hydrogelkugeln aus Polyacrylamid (PAAm) wurden als Scaffold-Bausteine ausg, Tissues are defined not only by their biochemical composition, but also by their distinct mechanical properties. It is now widely accepted that cells sense their mechanical environment and respond to it. For example, cell migration and stem cell differentiation is affected by stiffness. To study these effects in vitro, many cell culture studies have been performed on 2D hydrogel substrates. Additionally, the amount of 3D studies based on hydrogels as 3D scaffold is increasing to validate 2D in vitro studies and adjust experimental conditions closer to the situation in vivo. However, studying the effects of mechanics in vitro in 3D is still challenging as commonly used 3D hydrogel assays always link gel porosity with stiffness. Additionally, the concentration of biologically active adhesion sides often also depends on the stiffness. This work presents the development and optimization of novel 3D hydrogel bead scaffolds where the stiffness is finally decoupled from porosity. With hydrogel beads as scaffold building blocks it was possible to generate 3D scaffolds with defined mechanical properties and a constant pore size. During the method development, different culture devices were constructed and revised, followed by oxygen diffusion simulations to proof the suitability of the chosen device for cell survival and growth. A combination of different filter approaches was selected to generate hydrogel bead scaffolds in the culture device. Furthermore, different hydrogel materials were investigated regarding successful production of monodisperse beads and stable scaffold generation. Polyacrylamide (PAAm) hydrogel beads were chosen as scaffold building blocks to demonstrate live-cell imaging and successful cell survival over four days in differently compliant hydrogel bead scaffolds. Moreover, first cell migration experiments were performed by using plain PAAm hydrogel beads as well as PAAm hydrogel beads functionalized with adhesion molecules with differently stiff layer

The main goal of industrial networks is to transmit mission critical messages. Deterministic real-time QoS is a key requirement for critical messages. In this work, the realization of a centralized deterministic QoS framework is presented and analyzed. The evaluation shows that this framework provides high performance in terms of maximum traffic intensity at reasonable computation costs., Der Hauptzweck industrieller Kommunikationsnetze besteht darin, funktionskritische Nachrichten zu übertragen. Deterministische Echtzeit QoS ist eine Schlüsselanforderung für diese Nachrichten. In dieser Arbeit wird die Realisierung eines zentralisierten deterministischen QoS Frameworks vorgestellt. Die Evaluierung zeigt, dass dieses Framework eine hohe Leistungsfähigkeit hinsichtlich der maximalen Verkehrsintensität zu einem vertretbaren Berechnungsaufwands aufweist.

Multicellular life is formed by an orchestrated interplay of processes on different scales in space and time. Observing and quantitatively measuring these processes in an intact, living organism requires gentle and adaptive imaging. One example of such a process is the scaling of the mitotic spindle during early development. The spindle segregates the chromosomes during cell division and the spindle length determines the positioning of the chromosomes in the successive daughter cells. Thus, adaptation of spindle size to cell size is crucial for proper functioning. Early development is an excellent phase to study spindle scaling since cells rapidly divide in the absence of growth. In this phase, the spindle can be studied in cells of the same organism changing its volume orders of magnitude. During early zebrafish embryogenesis, the mitotic spindle only appears for three minutes out of the fifteen minutes cell cycle. Quantifying these short-lived events in a living embryo requires flexible and adaptive multi-resolution recordings, which are impossible with any state-of-the-art microscope. In this thesis, I present two new techniques to adaptively image biological samples based on light sheet fluorescence microscopy (LSFM). First, I present a remote, contact-free positioning technique based on magnetic forces to orient the sample in the microscope. When imaging biological samples, there is often only one sample orientation that offers the best view on the region of interest. This preferred orientation typically changes over time as the specimen grows and develops. The contact-free positioning technique allows to always image specimens from the optimal viewing angle. I demonstrate the functionality of this method by 3D orientation of zebrafish embryos and zebrafish larvae. Second, I present a new type of LSFM that autonomously adapts its detection scheme to the sample state. This microscope contains an adaptable magnification module to map the development of the millim, Multizelluläres Leben wird durch ein orchestriertes Zusammenspiel von Prozessen auf verschiedenen Skalen in Raum und Zeit gebildet. Beobachtung und quantitative Messungen dieser Vorgänge in einem intakten, lebenden Organismus erfordern schonende und adaptive Bildgebung. Ein Beispiel für einen solchen Prozess ist die Größenanpassung der mitotischen Spindel während der frühen Entwicklung. Die Spindel trennt die Chromosomen während der Zellteilung und die Spindellänge bestimmt die Positionierung der Chromosomen in den Tochterzellen. Daher ist die Anpassung der Spindelgröße an die Zellgröße entscheidend für die ordnungsgemäße Funktion. Die Phase der frühen Entwicklung eignet sich hervorragend zur Untersuchung der Spindel-Skalierung, da die Zellen sich schnell teilen ohne zu wachsen. Während der frühen Zebrafischembryogenese erscheint die Spindel nur drei Minuten innerhalb des fünfzehnminütigen Zellzyklus. Die Quantifizierung dieser kurzlebigen Ereignisse in einem lebenden Embryo erfordert flexible und anpassungsfähige Aufnahmen mit variabler Auflösung, die mit keinem Mikroskop nach dem aktuellen Stand der Technik möglich sind. In dieser Arbeit präsentiere ich zwei neue Techniken zur adaptiven Abbildung biologischer Proben basierend auf der Lichtblatt-Fluoreszenzmikroskopie (LSFM). Zuerst stelle ich eine berührungslose Positionierungstechnik vor, die auf Magnetkräften basiert, um die Probe im Mikroskop zu orientieren. Bei der Abbildung biologischer Proben gibt es oft nur eine Probenorientierung, welche die beste Sicht auf die Region von Interesse bietet. Diese Vorzugsorientierung ändert sich typischerweise mit der Zeit, wenn die Probe wächst und sich entwickelt. Die Positionierungstechnik ermöglicht es, Proben immer aus dem optimalen Betrachtungswinkel abzubilden. Zweitens stelle ich einen neuen Typ von LSFM vor, der sein Detektionsschema autonom an den Probenzustand anpasst. Dieses Mikroskop enthält ein anpassbares Vergrößerungsmodul, um die Entwicklung des millimetergr

Gewebe sind nicht nur durch ihre biochemische Zusammensetzung definiert, sondern auch durch ihre individuellen mechanischen Eigenschaften. Inzwischen ist es weithin akzeptiert, dass Zellen ihre mechanische Umgebung spüren und darauf reagieren. Zum Beispiel werden Zellmigration und die Differenzierung von Stammzellen durch die Umgebungssteifigkeit beeinflusst. Um diese Effekte in vitro zu untersuchen, wurden viele Zellkulturstudien auf 2D Hydrogelsubstraten durchgeführt. Zusätzlich dazu steigt die Anzahl von Studien an, die hydrogelbasierte 3D-Scaffolds nutzen, um 2D Studien zu validieren und die experimentellen Bedingungen der Situation in vivo anzunähern. Jedoch erweist es sich weiterhin als schwierig den Effekt von Mechanik in 3D in vitro zu untersuchen, da in den gemeinhin genutzten 3D Hydrogelsystemen immer eine Kopplung zwischen Gelporosität und Steifigkeit besteht. Zusätzlich hängt die Konzentration der biologisch aktiven Bindungsstellen für Zellen oft ebenfalls von der Steifigkeit ab. Diese Arbeit präsentiert die Entwicklung und Optimierung neuer 3D Hydrogelkugel-Scaffolds, in denen die Steifigkeit von der Porosität schließlich entkoppelt wird. Mit Hydrogelkugeln als Scaffold-Bausteine ist es nun möglich 3D Scaffolds mit definierten mechanischen Eigenschaften und konstanter Porengröße zu generieren. Während der Methodenentwicklung wurden verschiedene Prinzipien und Kultivierungskammern konstruiert und überarbeitet, gefolgt von der theoretischen Betrachtung der Sauerstoffdiffusion, um die Eignung der gewählten Kammer hinsichtlich Zellvitalität und Zellwachstum zu überprüfen. Eine Kombination aus mehreren getesteten Filtern wurde ausgewählt um HydrogelkugelScaffolds erfolgreich in der ausgewählten Kammer zu generieren. Im Weiteren wurden verschiedene Hydrogelmaterialien untersucht hinsichtlich der erfolgreichen Produktion monodisperser Hydrogelkugeln und der Erzeugung stabiler Scaffolds. Hydrogelkugeln aus Polyacrylamid (PAAm) wurden als Scaffold-Bausteine ausg, Tissues are defined not only by their biochemical composition, but also by their distinct mechanical properties. It is now widely accepted that cells sense their mechanical environment and respond to it. For example, cell migration and stem cell differentiation is affected by stiffness. To study these effects in vitro, many cell culture studies have been performed on 2D hydrogel substrates. Additionally, the amount of 3D studies based on hydrogels as 3D scaffold is increasing to validate 2D in vitro studies and adjust experimental conditions closer to the situation in vivo. However, studying the effects of mechanics in vitro in 3D is still challenging as commonly used 3D hydrogel assays always link gel porosity with stiffness. Additionally, the concentration of biologically active adhesion sides often also depends on the stiffness. This work presents the development and optimization of novel 3D hydrogel bead scaffolds where the stiffness is finally decoupled from porosity. With hydrogel beads as scaffold building blocks it was possible to generate 3D scaffolds with defined mechanical properties and a constant pore size. During the method development, different culture devices were constructed and revised, followed by oxygen diffusion simulations to proof the suitability of the chosen device for cell survival and growth. A combination of different filter approaches was selected to generate hydrogel bead scaffolds in the culture device. Furthermore, different hydrogel materials were investigated regarding successful production of monodisperse beads and stable scaffold generation. Polyacrylamide (PAAm) hydrogel beads were chosen as scaffold building blocks to demonstrate live-cell imaging and successful cell survival over four days in differently compliant hydrogel bead scaffolds. Moreover, first cell migration experiments were performed by using plain PAAm hydrogel beads as well as PAAm hydrogel beads functionalized with adhesion molecules with differently stiff layer

The routing algorithms used by current operators aim at coping with the demanded QoS requirements while optimizing the use of their network resources. These algorithms rely on the optimal substructure property (OSP), which states that an optimal path contains other optimal paths within it. However, we show that QoS metrics such as queuing delay and buffer consumption do not satisfy this property, which implies that the used algorithms lose their optimality and/or completeness. This negatively impacts the operator economy by causing a waste of network resources and/or violating Service Level Agreements (SLAs). In this paper, we propose a new so-called Mn taxonomy defining new metric classes. An Mn metric corresponds to a metric which requires the knowledge of the n previously traversed edges to compute its value at a given edge. Based on this taxonomy, we present three solutions for solving routing problems with the newly defined classes of metrics. We show that state-of-the-art algorithms based on the OSP indeed lose their original optimality and/or completeness properties while our proposed solutions do not, at the price of an increased computation time., Comment: 2018 International Conference on Communications (ICC 2018), Kansas City, MO (USA)

Optofluidics, nominally the research area where optics and fluidics merge, is a relatively new research field and it is only in the last decade that there has been a large increase in the number of optofluidic applications, as well as in the number of research groups, devoted to the topic. Nowadays optofluidics applications include, without being limited to, lab-on-a-chip devices, fluid-based and controlled lenses, optical sensors for fluids and for suspended particles, biosensors, imaging tools, etc. The long list of potential optofluidics applications, which have been recently demonstrated, suggests that optofluidic technologies will become more and more common in everyday life in the future, causing a significant impact on many aspects of our society. A characteristic of this research field, deriving from both its interdisciplinary origin and applications, is that in order to develop suitable solutions a combination of a deep knowledge in different fields, ranging from materials science to photonics, from microfluidics to molecular biology and biophysics, is often required. As a direct consequence, also being able to understand the long-term evolution of optofluidics research is not easy. In this article, we report several expert contributions on different topics so as to provide guidance for young scientists. At the same time, we hope that this document will also prove useful for funding institutions and stakeholders to better understand the perspectives and opportunities offered by this research field., QC 20170822, CellRing

A major goal in biological imaging is to visualize interactions of different tissues, often fluorescently labeled, during dynamic processes. Only a few of these labels fit into the available spectral range without overlap, but can be separated computationally if the full spectrum of every single pixel is known. In medical imaging, hyperspectral techniques show promise to identify different tissue types without any staining. Yet, microscopists still commonly acquire spectral information either with filters, thus integrating over a few broad bands only, or point-wise, dispersing the spectra onto a multichannel detector, which is inherently slow. Light sheet fluorescence microscopy (LSFM) and optical projection tomography (OPT) are two techniques to acquire 3D microscopic data fast, photon-efficiently and gently on the specimen. LSFM works in fluorescence mode and OPT in transmission. Both are based on a fast widefield detection scheme where a 2D detector records the spatial information but leaves no room to acquire dispersed spectra. Hyperspectral imaging had not yet been demonstrated for either technique. In this work, I developed a line-scanning hyperspectral LSFM and an excitation scanning OPT to acquire 5D data (3D spatial, 1D temporal, 1D spectral) and optimized the performance of both setups to minimize acquisition times without sacrificing image contrast, spatial or spectral information. I implemented and assessed different evaluation pipelines to classify and unmix relevant features. I demonstrate the efficiency of my workflow by acquiring up to five fluorescent markers and the autofluorescence in \\zf and fruit fly embryos on my hyperspectral LSFM. I extracted both concentration maps and spectra for each of these fluorophores from the multidimensional data. The same methods were applied to investigate the transmission data from my spectral OPT, where I found evidence that OPT image formation is governed by refraction, whereas scattering and absorption only pl, Ein wichtiges Ziel biologischer Bildgebung ist die Visualisierung des Zusammenspiels von verschiedenen, meist fluoreszent markierten, Geweben bei dynamischen Prozessen. Nur wenige dieser Farbstoffe passen ohne Überlapp in das zur Verfügung stehende Spektrum. Sie können jedoch rechnerisch getrennt werden, wenn das gesamte Spektrum jedes Pixels bekannt ist. In medizinischen Anwendungen versprechen hyperspektrale Techniken, verschiedene Gewebetypen markierungsfrei zu identifizieren. Dennoch ist es in der Mikroskopie noch immer üblich, spektrale Information entweder mit Filtern über breiten Bändern zu integrieren, oder Punktspektren mithilfe von Dispersion zu trennen und auf einem Multikanaldetektor aufzunehmen, was inhärent langsam ist. Light Sheet Fluorescence Microscopy (LSFM) und Optical Projection Tomography (OPT) nehmen 3D Mikroskopiedaten schnell, photoneneffizient und sanft für die Probe auf. LSFM arbeitet mit Fluoreszenz, OPT in Transmission. Beide basieren auf schneller Weitfelddetektion, wobei die räumliche Information mit einem 2D Detektor aufgenommen wird, der keinen Raum lässt, um die getrennten Spektren zu messen. Hyperspektrale Bildgebung wurde bis jetzt für keine der zwei Techniken gezeigt. Ich habe ein hyperspektrales LSFM mit Linienabtastung und ein OPT mit Wellenlängenabtastung entwickelt, um 5D Daten (3D räumlich, 1D zeitlich, 1D spektral) aufzunehmen. Beide Aufbauten wurden hinsichtlich minimaler Aufnahmezeit optimiert, ohne dabei Kontrast, räumliche oder spektrale Auflösung zu opfern. Ich habe verschiedene Abläufe zum Klassifizieren und Trennen der Hauptkomponenten implementiert. Ich nehme bis zu fünf Fluorophore und Autofluoreszenz in Zebrafisch- und Fruchtfliegenembryos mit dem hyperspektralen LSFM auf und zeige die Effizienz des gesamten Ablaufes, indem ich Spektren und räumliche Verteilung aller Marker extrahiere. Die Transmissionsdaten des spektralen OPT werden mit denselben Methoden untersucht. Ich konnte belegen, dass die Bildformation im O

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

A major goal in biological imaging is to visualize interactions of different tissues, often fluorescently labeled, during dynamic processes. Only a few of these labels fit into the available spectral range without overlap, but can be separated computationally if the full spectrum of every single pixel is known. In medical imaging, hyperspectral techniques show promise to identify different tissue types without any staining. Yet, microscopists still commonly acquire spectral information either with filters, thus integrating over a few broad bands only, or point-wise, dispersing the spectra onto a multichannel detector, which is inherently slow. Light sheet fluorescence microscopy (LSFM) and optical projection tomography (OPT) are two techniques to acquire 3D microscopic data fast, photon-efficiently and gently on the specimen. LSFM works in fluorescence mode and OPT in transmission. Both are based on a fast widefield detection scheme where a 2D detector records the spatial information but leaves no room to acquire dispersed spectra. Hyperspectral imaging had not yet been demonstrated for either technique. In this work, I developed a line-scanning hyperspectral LSFM and an excitation scanning OPT to acquire 5D data (3D spatial, 1D temporal, 1D spectral) and optimized the performance of both setups to minimize acquisition times without sacrificing image contrast, spatial or spectral information. I implemented and assessed different evaluation pipelines to classify and unmix relevant features. I demonstrate the efficiency of my workflow by acquiring up to five fluorescent markers and the autofluorescence in \\zf and fruit fly embryos on my hyperspectral LSFM. I extracted both concentration maps and spectra for each of these fluorophores from the multidimensional data. The same methods were applied to investigate the transmission data from my spectral OPT, where I found evidence that OPT image formation is governed by refraction, whereas scattering and absorption only pl, Ein wichtiges Ziel biologischer Bildgebung ist die Visualisierung des Zusammenspiels von verschiedenen, meist fluoreszent markierten, Geweben bei dynamischen Prozessen. Nur wenige dieser Farbstoffe passen ohne Überlapp in das zur Verfügung stehende Spektrum. Sie können jedoch rechnerisch getrennt werden, wenn das gesamte Spektrum jedes Pixels bekannt ist. In medizinischen Anwendungen versprechen hyperspektrale Techniken, verschiedene Gewebetypen markierungsfrei zu identifizieren. Dennoch ist es in der Mikroskopie noch immer üblich, spektrale Information entweder mit Filtern über breiten Bändern zu integrieren, oder Punktspektren mithilfe von Dispersion zu trennen und auf einem Multikanaldetektor aufzunehmen, was inhärent langsam ist. Light Sheet Fluorescence Microscopy (LSFM) und Optical Projection Tomography (OPT) nehmen 3D Mikroskopiedaten schnell, photoneneffizient und sanft für die Probe auf. LSFM arbeitet mit Fluoreszenz, OPT in Transmission. Beide basieren auf schneller Weitfelddetektion, wobei die räumliche Information mit einem 2D Detektor aufgenommen wird, der keinen Raum lässt, um die getrennten Spektren zu messen. Hyperspektrale Bildgebung wurde bis jetzt für keine der zwei Techniken gezeigt. Ich habe ein hyperspektrales LSFM mit Linienabtastung und ein OPT mit Wellenlängenabtastung entwickelt, um 5D Daten (3D räumlich, 1D zeitlich, 1D spektral) aufzunehmen. Beide Aufbauten wurden hinsichtlich minimaler Aufnahmezeit optimiert, ohne dabei Kontrast, räumliche oder spektrale Auflösung zu opfern. Ich habe verschiedene Abläufe zum Klassifizieren und Trennen der Hauptkomponenten implementiert. Ich nehme bis zu fünf Fluorophore und Autofluoreszenz in Zebrafisch- und Fruchtfliegenembryos mit dem hyperspektralen LSFM auf und zeige die Effizienz des gesamten Ablaufes, indem ich Spektren und räumliche Verteilung aller Marker extrahiere. Die Transmissionsdaten des spektralen OPT werden mit denselben Methoden untersucht. Ich konnte belegen, dass die Bildformation im O

A major goal in biological imaging is to visualize interactions of different tissues, often fluorescently labeled, during dynamic processes. Only a few of these labels fit into the available spectral range without overlap, but can be separated computationally if the full spectrum of every single pixel is known. In medical imaging, hyperspectral techniques show promise to identify different tissue types without any staining. Yet, microscopists still commonly acquire spectral information either with filters, thus integrating over a few broad bands only, or point-wise, dispersing the spectra onto a multichannel detector, which is inherently slow. Light sheet fluorescence microscopy (LSFM) and optical projection tomography (OPT) are two techniques to acquire 3D microscopic data fast, photon-efficiently and gently on the specimen. LSFM works in fluorescence mode and OPT in transmission. Both are based on a fast widefield detection scheme where a 2D detector records the spatial information but leaves no room to acquire dispersed spectra. Hyperspectral imaging had not yet been demonstrated for either technique. In this work, I developed a line-scanning hyperspectral LSFM and an excitation scanning OPT to acquire 5D data (3D spatial, 1D temporal, 1D spectral) and optimized the performance of both setups to minimize acquisition times without sacrificing image contrast, spatial or spectral information. I implemented and assessed different evaluation pipelines to classify and unmix relevant features. I demonstrate the efficiency of my workflow by acquiring up to five fluorescent markers and the autofluorescence in \\zf and fruit fly embryos on my hyperspectral LSFM. I extracted both concentration maps and spectra for each of these fluorophores from the multidimensional data. The same methods were applied to investigate the transmission data from my spectral OPT, where I found evidence that OPT image formation is governed by refraction, whereas scattering and absorption only pl, Ein wichtiges Ziel biologischer Bildgebung ist die Visualisierung des Zusammenspiels von verschiedenen, meist fluoreszent markierten, Geweben bei dynamischen Prozessen. Nur wenige dieser Farbstoffe passen ohne Überlapp in das zur Verfügung stehende Spektrum. Sie können jedoch rechnerisch getrennt werden, wenn das gesamte Spektrum jedes Pixels bekannt ist. In medizinischen Anwendungen versprechen hyperspektrale Techniken, verschiedene Gewebetypen markierungsfrei zu identifizieren. Dennoch ist es in der Mikroskopie noch immer üblich, spektrale Information entweder mit Filtern über breiten Bändern zu integrieren, oder Punktspektren mithilfe von Dispersion zu trennen und auf einem Multikanaldetektor aufzunehmen, was inhärent langsam ist. Light Sheet Fluorescence Microscopy (LSFM) und Optical Projection Tomography (OPT) nehmen 3D Mikroskopiedaten schnell, photoneneffizient und sanft für die Probe auf. LSFM arbeitet mit Fluoreszenz, OPT in Transmission. Beide basieren auf schneller Weitfelddetektion, wobei die räumliche Information mit einem 2D Detektor aufgenommen wird, der keinen Raum lässt, um die getrennten Spektren zu messen. Hyperspektrale Bildgebung wurde bis jetzt für keine der zwei Techniken gezeigt. Ich habe ein hyperspektrales LSFM mit Linienabtastung und ein OPT mit Wellenlängenabtastung entwickelt, um 5D Daten (3D räumlich, 1D zeitlich, 1D spektral) aufzunehmen. Beide Aufbauten wurden hinsichtlich minimaler Aufnahmezeit optimiert, ohne dabei Kontrast, räumliche oder spektrale Auflösung zu opfern. Ich habe verschiedene Abläufe zum Klassifizieren und Trennen der Hauptkomponenten implementiert. Ich nehme bis zu fünf Fluorophore und Autofluoreszenz in Zebrafisch- und Fruchtfliegenembryos mit dem hyperspektralen LSFM auf und zeige die Effizienz des gesamten Ablaufes, indem ich Spektren und räumliche Verteilung aller Marker extrahiere. Die Transmissionsdaten des spektralen OPT werden mit denselben Methoden untersucht. Ich konnte belegen, dass die Bildformation im O

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.