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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 changes were reversed as stiffened cells softened and lipid droplets resumed long-range motion. Cell softening and lipid droplet motion recovery coincided with protein disaggregation. These processes could be delayed by impairing protein disaggregation through genetic perturbation of the molecular chaperone Hsp104, which functions as a protein disaggregase. In contrast, no influence on protein disaggregation or heat-induced cytoplasmic material property changes was detected for the small heat shock protein Hsp16. These results suggest that the cytoplasm gels upon severe heat shock due to protein aggregation and is refluidized during recovery with the help of Hsp104. Remarkably, cells resumed growth only after refluidization of the cytoplasm, suggesting that reversible cytoplasmic gelation may contribute to regulation of the heat-induced growth arrest. In addition, cytoplasmic gelation could potentially preserve cellular architecture during heat shock. Overall, the results from my thesis work indicate that reversible cytoplasmic gelation due to macromolecular protein assembly may be a universal cellular response to severe stress which is associated with a stress-protective growth arrest. A likely stress-specific part of this response is the chaperone-dependent refluidization of the cytoplasm, which might explain the prolonged growth arrest seen upon severe heat shock as compared to other stresses and might allow more time for the repair of heat-induced damage.:Abstract Zusammenfassung Table of contents Figure index List of abbreviations 1 Introduction 1.1 Heat shock affects cellular function and fitness 1.1.1 Cells respond to stress in phases 1.1.2 Heat shock threatens cellular homeostasis and structural integrity 1.1.3 Stress severity determines detrimental effects of heat shock 1.1.4 Heat stress causes protein aggregation 1.1.5 Heat shock granules are functional aggregates in yeast 1.2 The heat shock response protects cellular fitness 1.2.1 Cells change transcription to adapt to stress 1.2.2 Molecular chaperones are important in stress protection 1.2.3 Hsp104 is a protein disaggregase chaperone 1.2.4 Small heat shock proteins modulate protein aggregation 1.2.5 Stress severity determines modules of the heat shock response 1.3 Cytoplasmic material properties change during stress 1.3.1 Cells homeostatically adapt cytoplasmic material properties during stress 1.3.2 The cytoplasm is viscoelastic 1.3.3 Is the cytoplasm a gel? 1.3.4 Stress can induce cytoplasmic gelation 1.4 Research aims 2 Materials and Methods 2.1 S. pombe strains and growth conditions 2.1.1 Growth conditions 2.1.2 Construction of S. pombe strains 2.1.3 S. pombe transformation 2.1.4 S. pombe colony PCR 2.1.5 S. pombe strains used in this thesis 2.2 Plasmids and cloning 2.2.1 Plasmids used in this thesis 2.2.2 Construction of plasmid for fluorescent GEM nanoparticle expression 2.2.3 E. coli transformation 2.2.4 Plasmid purification from E. coli 2.3 S. pombe stress treatments 2.3.1 Heat shock treatment 2.3.2 Osmoadaptation 2.4 Cell biological methods 2.4.1 Viability assay 2.4.2 Growth assay 2.5 Cell bulk mechanical assays 2.5.1 Spheroplasting assay 2.5.2 Atomic force microscopy 2.5.3 Real-time deformability cytometry 2.5.4 RT-DC sample preparation 2.5.5 RT-DC setup and measurements 2.5.6 RT-DC data evaluation 2.6 Microscopy 2.6.1 Microscopy of GEM particles 2.6.2 Fluorescence microscopy of endogenously labeled Pabp-mCherry 2.6.3 Microscopy of µNS particles 2.7 Image analysis 2.7.1 Image analysis of Pabp-mCherry in vivo fluorescence microscopy 2.7.2 Differenced brightfield image analysis 2.7.3 Kymographs 2.8 Single-particle tracking analysis 2.8.1 Particle tracking 2.8.2 Mean squared displacement analysis 2.9 Optical diffraction tomography (ODT) 2.9.1 ODT sample preparation 2.9.2 ODT optical setup and measurements 2.9.3 ODT tomogram reconstruction and quantitative analysis 2.10 Lysis and sedimentation assay 2.10.1 Lysis buffer 2.10.2 S. pombe heat shock treatment and lysis 2.10.3 Sedimentation assay 2.10.4 Protein concentration measurement 2.10.5 SDS-PAGE 2.10.6 Coomassie staining 2.10.7 Western Blot 3 Results 3.1 Physical and chemical conditions affect heat shock survival and heat-induced growth arrest of S. pombe 3.1.1 S. pombe arrests growth during severe heat shock 3.1.2 Heat-induced growth arrest is dose-responsive 3.1.3 Heat-induced growth arrest depends on experimental conditions 3.1.4 Buffer pH and energy source have a strong impact on heat shock survival 3.1.5 Osmoadaptation protects cells during heat shock 3.2 Severe heat shock induces reversible cellular stiffening 3.2.1 Cellular rounding upon cell wall removal is delayed after heat shock 3.2.2 Elastic modulus of S. pombe cells is increased after heat shock 3.2.3 Recovery from heat-induced growth arrest is preceded by cell softening 3.3 Long-range particle dynamics in cytoplasm are abolished after heat shock 3.3.1 Small particle dynamics are largely independent of heat shock treatment 3.3.2 Lipid droplets are confined in space after heat shock 3.4 Cytoplasmic crowding increases during heat shock 3.5 Heat shock induces reversible protein aggregation 3.5.1 Insoluble protein fraction is increased after heat shock 3.5.2 Heat shock granules form reversibly during heat shock 3.5.3 HSG formation and dissolution are correlated with changes in cytoplasmic long-range dynamics 3.6 Molecular chaperones modulate cytoplasmic material property changes during heat stress recovery 3.6.1 Hsp104 but not Hsp16 is required for disaggregation of heat shock granules 3.6.2 Hsp104 but not Hsp16 is required for recovery from heat-induced growth arrest 3.6.3 Hsp104 but not Hsp16 is required for recovery of cytoplasmic long-range dynamics 3.6.4 Hsp104 but not Hsp16 is required for rapid reversal of cellular stiffening which coincides with growth recovery 4 Discussion 4.1 Summary and model 4.2 Which mechanism underlies cell stiffening upon heat shock? 4.2.1 Heat-induced protein aggregation might cause cell stiffening 4.2.2 Heat-induced protein aggregation might lead to cytoplasmic gelation 4.2.3 Many factors could contribute to protein aggregation and cytoplasmic gelation 4.3 The heat-induced growth arrest state is associated with reversible cytoplasmic gelation 4.3.1 Cytoplasmic material property changes mark the severe heat-induced growth arrest state 4.3.2 Is cytoplasmic gelation a common response to severe stress? 4.4 What are the biological consequences of cytoplasmic gelation? 4.4.1 Cytoplasmic gelation might obstruct processes that require motion of large structures 4.4.2 Is cytoplasmic gelation upon heat shock protective? 4.5 Heat shock recovery involves the chaperone-mediated refluidization of the cytoplasm 4.5.1 Cytoplasmic refluidization is required for growth recovery 4.5.2 Stress tolerance is marked by enhanced reversibility of cytoplasmic gelation 4.5.3 The protein disaggregase chaperone Hsp104 regulates the reversal of heat-induced cytoplasmic material property changes 4.6 Conclusion References Acknowledgements Publications and Contributions 5 Erklärung entsprechend §5.5 der Promotionsordnung

A new method, probability distribution of diffusivities (time scaled square displacements between succeeding video frames), was developed to analyze single molecule tracking (SMT) experiments. This method was then applied to SMT experiments on ultrathin liquid tetrakis(2-ethylhexoxy)silane (TEHOS) films on Si wafer with 100 nm thermally grown oxide, and on thin semectic liquid crystal films. Spatial maps of diffusivities from SMT experiments on 220 nm thick semectic liquid crystal films reveal structure related dynamics. The SMT experiments on ultrathin TEHOS films were complemented by fluorescence correlation spectroscopy (FCS). The observed strongly heterogeneous single molecule dynamics within those films can be explained by a three-layer model consisting of (i) dye molecules adsorbed to the substrate, (ii) slowly diffusing molecules in the laterally heterogeneous near-surface region of 1 - 2 molecular diameters, and (iii) freely diffusing dye molecules in the upper region of the film. FCS and SMT experiments reveal a strong influence of substrate heterogeneity on SM dynamics. Thereby chemisorption to substrate surface silanols plays an important role. Vertical mean first passage times (mfpt) in those films are below 1 µs. This appears as fast component in FCS autocorrelation curves, which further contain a contribution from lateral diffusion and from adsorption events. Therefore, the FCS curves are approximated by a tri-component function, which contains an exponential term related to the mfpt, the correlation function for translational diffusion and a stretched exponential term for the broad distribution of adsorption events. Lateral diffusion coefficients obtained by FCS on 10 nm thick TEHOS films, thereby, are effective diffusion coefficients from dye transients in the focal area. They strongly depend on the substrate heterogeneity. Variation of the frame times for the acquisition of SMT experiments in steps of 20 ms from 20 ms to 200 ms revealed a strong dependence of the corresponding probability distributions of diffusivities on time, in particular in the range between 20 ms and 100 ms. This points to average dwell times of the dye molecules in at least one type of the heterogeneous regions (e.g. on and above silanol clusters) in the range of few tens of milliseconds. Furthermore, time series of SM spectra from Nile Red in 25 nm thick poly-n-alkyl-methacrylate (PnAMA) films were studied. In analogy to translational diffusion, spectral diffusion (shifts in energetic positions of SM spectra) can be studied by probability distributions of spectral diffusivities, i.e. time scaled square energetic displacements. Simulations were run and analyzed to study contributions from noise and fitting uncertainty to spectral diffusion. Furthermore the effect of spectral jumps during acquisition of a SM spectrum was investigated. Probability distributions of spectral diffusivites of Nile Red probing vitreous PnAMA films reveal a two-level system. In contrast, such probability distributions obtained from Nile Red within a 25 nm thick poly-n-butylmethacrylate film around glass transition and in the melt state, display larger spectral jumps. Moreover, for longer alkyl side chains a solvent shift to higher energies is observed, which supports the idea of nanophase separation within those polymers.

Modern fluorescence microscopy modalities, such as light-sheet microscopy, are capable of acquiring large three-dimensional images at high data rate. This creates a bottleneck in computational processing and analysis of the acquired images, as the rate of acquisition outpaces the speed of processing. Moreover, images can be so large that they do not fit the main memory of a single computer. Another issue is the information loss during image acquisition due to limitations of the optical imaging systems. Analysis of the acquired images may, therefore, find multiple solutions (or no solution) due to imaging noise, blurring, and other uncertainties introduced during image acquisition. In this thesis, we address the computational processing time and memory issues by developing a distributed parallel algorithm for segmentation of large fluorescence-microscopy images. The method is based on the versatile Discrete Region Competition (Cardinale et al., 2012) algorithm, which has previously proven useful in microscopy image segmentation. The present distributed implementation decomposes the input image into smaller sub-images that are distributed across multiple computers. Using network communication, the computers orchestrate the collective solving of the global segmentation problem. This not only enables segmentation of large images (we test images of up to 10^10 pixels) but also accelerates segmentation to match the time scale of image acquisition. Such acquisition-rate image segmentation is a prerequisite for the smart microscopes of the future and enables online data inspection and interactive experiments. Second, we estimate the segmentation uncertainty on large images that do not fit the main memory of a single computer. We there- fore develop a distributed parallel algorithm for efficient Markov- chain Monte Carlo Discrete Region Sampling (Cardinale, 2013). The parallel algorithm provides a measure of segmentation uncertainty in a statistically unbiased way. It approximates the posterior probability densities over the high-dimensional space of segmentations around the previously found segmentation. / Moderne Fluoreszenzmikroskopie, wie zum Beispiel Lichtblattmikroskopie, erlauben die Aufnahme hochaufgelöster, 3-dimensionaler Bilder. Dies führt zu einen Engpass bei der Bearbeitung und Analyse der aufgenommenen Bilder, da die Aufnahmerate die Datenverarbeitungsrate übersteigt. Zusätzlich können diese Bilder so groß sein, dass sie die Speicherkapazität eines einzelnen Computers überschreiten. Hinzu kommt der aus Limitierungen des optischen Abbildungssystems resultierende Informationsverlust während der Bildaufnahme. Bildrauschen, Unschärfe und andere Messunsicherheiten können dazu führen, dass Analysealgorithmen möglicherweise mehrere oder keine Lösung für Bildverarbeitungsaufgaben finden. Im Rahmen der vorliegenden Arbeit entwickeln wir einen verteilten, parallelen Algorithmus für die Segmentierung von speicherintensiven Fluoreszenzmikroskopie-Bildern. Diese Methode basiert auf dem vielseitigen "Discrete Region Competition" Algorithmus (Cardinale et al., 2012), der sich bereits in anderen Anwendungen als nützlich für die Segmentierung von Mikroskopie-Bildern erwiesen hat. Das hier präsentierte Verfahren unterteilt das Eingangsbild in kleinere Unterbilder, welche auf die Speicher mehrerer Computer verteilt werden. Die Koordinierung des globalen Segmentierungsproblems wird durch die Benutzung von Netzwerkkommunikation erreicht. Dies erlaubt die Segmentierung von sehr großen Bildern, wobei wir die Anwendung des Algorithmus auf Bildern mit bis zu 10^10 Pixeln demonstrieren. Zusätzlich wird die Segmentierungsgeschwindigkeit erhöht und damit vergleichbar mit der Aufnahmerate des Mikroskops. Dies ist eine Grundvoraussetzung für die intelligenten Mikroskope der Zukunft, und es erlaubt die Online-Betrachtung der aufgenommenen Daten, sowie interaktive Experimente. Wir bestimmen die Unsicherheit des Segmentierungsalgorithmus bei der Anwendung auf Bilder, deren Größe den Speicher eines einzelnen Computers übersteigen. Dazu entwickeln wir einen verteilten, parallelen Algorithmus für effizientes Markov-chain Monte Carlo "Discrete Region Sampling" (Cardinale, 2013). Dieser Algorithmus quantifiziert die Segmentierungsunsicherheit statistisch erwartungstreu. Dazu wird die A-posteriori-Wahrscheinlichkeitsdichte über den hochdimensionalen Raum der Segmentierungen in der Umgebung der zuvor gefundenen Segmentierung approximiert.

Modern fluorescence microscopy modalities, such as light-sheet microscopy, are capable of acquiring large three-dimensional images at high data rate. This creates a bottleneck in computational processing and analysis of the acquired images, as the rate of acquisition outpaces the speed of processing. Moreover, images can be so large that they do not fit the main memory of a single computer. Another issue is the information loss during image acquisition due to limitations of the optical imaging systems. Analysis of the acquired images may, therefore, find multiple solutions (or no solution) due to imaging noise, blurring, and other uncertainties introduced during image acquisition. In this thesis, we address the computational processing time and memory issues by developing a distributed parallel algorithm for segmentation of large fluorescence-microscopy images. The method is based on the versatile Discrete Region Competition (Cardinale et al., 2012) algorithm, which has previously proven useful in microscopy image segmentation. The present distributed implementation decomposes the input image into smaller sub-images that are distributed across multiple computers. Using network communication, the computers orchestrate the collective solving of the global segmentation problem. This not only enables segmentation of large images (we test images of up to 10^10 pixels) but also accelerates segmentation to match the time scale of image acquisition. Such acquisition-rate image segmentation is a prerequisite for the smart microscopes of the future and enables online data inspection and interactive experiments. Second, we estimate the segmentation uncertainty on large images that do not fit the main memory of a single computer. We there- fore develop a distributed parallel algorithm for efficient Markov- chain Monte Carlo Discrete Region Sampling (Cardinale, 2013). The parallel algorithm provides a measure of segmentation uncertainty in a statistically unbiased way. It approximates the posterior probability densities over the high-dimensional space of segmentations around the previously found segmentation. / Moderne Fluoreszenzmikroskopie, wie zum Beispiel Lichtblattmikroskopie, erlauben die Aufnahme hochaufgelöster, 3-dimensionaler Bilder. Dies führt zu einen Engpass bei der Bearbeitung und Analyse der aufgenommenen Bilder, da die Aufnahmerate die Datenverarbeitungsrate übersteigt. Zusätzlich können diese Bilder so groß sein, dass sie die Speicherkapazität eines einzelnen Computers überschreiten. Hinzu kommt der aus Limitierungen des optischen Abbildungssystems resultierende Informationsverlust während der Bildaufnahme. Bildrauschen, Unschärfe und andere Messunsicherheiten können dazu führen, dass Analysealgorithmen möglicherweise mehrere oder keine Lösung für Bildverarbeitungsaufgaben finden. Im Rahmen der vorliegenden Arbeit entwickeln wir einen verteilten, parallelen Algorithmus für die Segmentierung von speicherintensiven Fluoreszenzmikroskopie-Bildern. Diese Methode basiert auf dem vielseitigen "Discrete Region Competition" Algorithmus (Cardinale et al., 2012), der sich bereits in anderen Anwendungen als nützlich für die Segmentierung von Mikroskopie-Bildern erwiesen hat. Das hier präsentierte Verfahren unterteilt das Eingangsbild in kleinere Unterbilder, welche auf die Speicher mehrerer Computer verteilt werden. Die Koordinierung des globalen Segmentierungsproblems wird durch die Benutzung von Netzwerkkommunikation erreicht. Dies erlaubt die Segmentierung von sehr großen Bildern, wobei wir die Anwendung des Algorithmus auf Bildern mit bis zu 10^10 Pixeln demonstrieren. Zusätzlich wird die Segmentierungsgeschwindigkeit erhöht und damit vergleichbar mit der Aufnahmerate des Mikroskops. Dies ist eine Grundvoraussetzung für die intelligenten Mikroskope der Zukunft, und es erlaubt die Online-Betrachtung der aufgenommenen Daten, sowie interaktive Experimente. Wir bestimmen die Unsicherheit des Segmentierungsalgorithmus bei der Anwendung auf Bilder, deren Größe den Speicher eines einzelnen Computers übersteigen. Dazu entwickeln wir einen verteilten, parallelen Algorithmus für effizientes Markov-chain Monte Carlo "Discrete Region Sampling" (Cardinale, 2013). Dieser Algorithmus quantifiziert die Segmentierungsunsicherheit statistisch erwartungstreu. Dazu wird die A-posteriori-Wahrscheinlichkeitsdichte über den hochdimensionalen Raum der Segmentierungen in der Umgebung der zuvor gefundenen Segmentierung approximiert.