Single-cell analysis means to analyze cells on an individual level. This individual analysis enhances the investigation of the heterogeneity among and the classification of individual cells. Single-cell analysis is a broad term and can include various measurements. This thesis utilizes single-cell RNA sequence data that measures RNA sequences representing genes for individual cells. This data is often high-dimensional, with tens of thousands of RNA sequences measured for each cell. Dimension reduction is therefore necessary when analyzing the data. One proposed dimension reduction method is the unsupervised machine learning method variational autoencoders. The scVI framework has previously implemented a variational autoencoder for analyzing single-cell RNA sequence data. The variational autoencoder of the scVI has one latent space with a Gaussian distribution. Several extensions have been made to the scVI framework since its creation. This thesis proposes an additional extension consisting of a variational autoencoder with two latent spaces, called hybridVI. One of these latent spaces has a Gaussian distribution and the other a von Mises-Fisher distribution. The data is separated between these two latent spaces, meaning that some of the genes go through one latent space and the rest go through the other. In this thesis the cell cycle genes go through the von Mises-Fisher latent space and the rest of the genes go through the Gaussian latent space. The motivation behind the von Mises-Fisher latent space is that cell cycle genes are believed to follow a circular distribution. Putting these genes through a von Mises-Fisher latent space instead of a Gaussian latent space could provide additional insights into the data. The main focus of this thesis was to analyze the impact this separation. The analysis consisted of comparing the performance of the hybridVI model, to the original scVI variational autoencoder. The comparison utilized three annotated datasets, one peripheral blood mononuclear cell dataset, one cortex cell dataset, and one B cell dataset collected by the Henriksson lab at Umeå University. The evaluation metrics used were the adjusted rand index, normalized mutual information and a Wilcoxon signed ranks test was used to determine if the results had statistical significance. The results indicate that the size of the dataset was essential for achieving robust and statistically significant results. For the two datasets that yielded statistically significant results, the scVI model performed better than the hybridVI model. However, more research analyzing biological aspects is necessary to declare the hybridVI model’s effect on the biological interpretation of the results. / Individuell cellanalys är en relativt ny metod som möjliggör undersökning av celler på indivudiell nivå. Det här examensarbetet analyserar RNA sekvens data, där RNA sekvenser är specifierade för individuella celler. Den här sortens data är ofta högdimensionell med flera tusen gener noterade för varje cell. För att möjliggöra en analys av den här datan krävs någon form av dimensionreducering. En föreslagen metod är den ovövervakade maskininlärningsmetoden variational autoencoders. Ett ramverk, scVI, har framtagit en variational autoencoder designad för att hantera den här sortens data. Den här modellen har endast en latentrymd med en normalfördelning. Det här examensarbetet föreslår en utökning av det här ramverket med en variational autoencoder med två latentrymder,där den ena är normalfördelad och den andra följer en von Mises-Fisher fördelning. Motiveringen till en sådan fördelning är att cellcykelgener är antagna att tillhöra en cirkulär fördelning. Cellcykelgenerna i datan kan därmed hanteras av den cirkulära latentrymden. Huvudfokuset i den här studien är att undersöka om den här separationen av gener kan förbättra modellens förmåga att hitta korrekta kluster. Experimentet utfördes på tre annoterade dataset, ett som bestod av perifera mononukleära blodceller, ett som bestod av hjärnbarksceller och ett som bestod av B celler insamlat av Henrikssongruppen vid Umeå universitet. Modellen från scVI ramverket jämfördes med den nya metoden med två latentrymder, hybridVI. Måtten som användes för att bedöma de modellerna var adjusted rand index och normaliserad mutual information och ett Wilcoxon Signed-Ranks test användes för att bedöma resultatens statistiska signifikans. Resultaten påvisar att de båda modellerna preseterar bättre och mer konsekvent för större dataset. Två dataset gav statistiskt signifikanta resultat och visade att scVI modellen presterade bättre än hybridmodellen. Det behövs dock en biologisk analys av resultaten för att undersöka vilken modells resultat som har mest biologisk relevans.

Cancer progression is accompanied with alterations in the cell biomechanical phenotype, including changes in cell structure, morphology, and responses to microenvironmental stress. These alterations result in an increased deformability of transformed cells and reduced resistance to mechanical stimuli, enabling motility and invasion. Therefore, single cell biomechanical properties could be served as a powerful label-free biomarker for effective characterization and early detection of single cancer cells. Advances and innovations in microsystems and nanotechnology have facilitated interrogation of the biomechanical properties of single cells to predict their tumorigenicity, metastatic potential, and health state. This dissertation utilized Atomic Force Microscopy (AFM) for the cell biomechanical phenotyping for cancer diagnosis and early detection, efficacy screening of potential chemotherapeutic agents, and also cancer stem-like/tumor initiating cells (CSC/TICs) characterization as the critical topics received intensive attention in the search for effective cancer treatment. Our findings demonstrated the capability of exogenous sphingosine to revert the aberrant biomechanics of aggressive cells and showed a unique, mechanically homogeneous, and extremely soft characteristic of CSC/TICs, suitable for their targeted isolation. To make full use of cell biomechanical cues, this dissertation also considered the application of nonlinear viscoelastic models such as Fractional Zener and Generalized Maxwell models for the naturally complex, heterogeneous, and nonlinear structure of living cells. The emerging need for a high-throughput clinically relevant alternative for evaluating biomechanics of individual cells led us to the development of a microfluidic system. Therefore, a high-throughput, label-free, automated microfluidic chip was developed to investigate the biophysical (biomechanical-bioelectrical) markers of normal and malignant cells. Most importantly, this dissertation also explored the biomechanical response of cells upon a dynamic loading instead of a typical transient stress. Notably, metastatic and non-metastatic cells subjected to a pulsed stress regimen exerted by AFM exhibited distinct biomechanical responses. While non-metastatic cells showed an increase in their resistance against deformation and resulted in strain-stiffening behavior, metastatic cells responded by losing their resistance and yielded slight strain-softening. Ultimately, a second generation microfluidic chip called an iterative mechanical characteristics (iMECH) analyzer consisting of a series of constriction channels for simulating the dynamic stress paradigm was developed which could reproduce the same stiffening/softening trends of non-metastatic and metastatic cells, respectively. Therefore, for the first time, the use of dynamic loading paradigm to evaluate cell biomechanical responses was used as a new signature to predict malignancy or normalcy at a single-cell level with a high (~95%) confidence level. / Ph. D.

Bioimpedance presents a versatile, label-free means of monitoring biological cells and their responses to physical, chemical and biological stimuli. Breast cancer is the second most common type of cancer among women in the United States. Although significant progress has been made in diagnosis and treatment of this disease, there is a need for robust, easy-to-use technologies that can be used for the identification and discrimination of critical subtypes of breast cancer in biopsies obtained from patients. This dissertation makes contributions in three major areas towards addressing the goal. First, we developed miniaturized bioimpedance sensors using MEMS and microfluidics technology that have the requisite traits for clinical use including reliability, ease-of-use, low-cost and disposability. Here, we designed and fabricated two types of bioimpedance sensors. One was based on electric cell-substrate impedance sensing (ECIS) to monitor cell adhesion based events and the other was a microfluidic device with integrated microelectrodes to examine the biophysical properties of single cells. Second, we examined a panel of triple negative breast cancer (TNBC) cell lines and a hormone therapy resistant model of breast cancer in order to improve our understanding of the bioimpedance spectra of breast cancer subtypes. Third, we explored strategies to improve the sensitivity of the microelectrodes to bioimpedance measurements from breast cancer cells. We investigated nano-scale coatings on the surface of the electrode and geometrical variations in a branched electrode design to accomplish this. This work demonstrates the promise of bioimpedance technologies in monitoring diseased cells and their responses to pharmaceutical agents, and motivates further research in customization of this technique for use in personalized medicine. / Ph. D.

The loss of efficiency and performance of bioprocesses on scale-up is well known, but not fully understood. This work addresses this problem, by studying the effect of some fermentation gradients (pH, glucose and oxygen) at a larger scale in a bench-scale two compartment reactor (PFR + STR) using the cadaverine-producing recombinant bacterium, Corynebacterium glutamicum DM1945 Δact3 Ptuf-ldcC_OPT. The initial scale down strategy increased the magnitude of these gradients by only increasing the mean cell residence time in the plug flow reactor (τ_PFR). The cell growth and product related rate constants were compared as the τ_PFR was increased; differences were significant in some cases, but only up to 2 min residence time. For example, losses in cadaverine productivity when compared to the control fed-batch fermentation on average for the τ_PFR of 1 min, 2 min and 5 min were 25 %, 42 % and 46 % respectively. This indicated that the increasing the τ_PFR alone does not necessarily increase the magnitude of fermentation gradients. The new scale-down strategy developed here, increased the magnitude of fermentation gradients by not only increasing the τ_PFR, but also considering the mean frequency at which the bacterial cells entered the PFR section (f_m). The f_m was kept constant by reducing the broth volume in the STR. Hence, the bacterial cells also spent shorter times in the well mixed STR, as the τ_PFR was increased (hypothesised as giving the bacterial cells less time to recover the non-ideal PFR section of the SDR). On adoption of this strategy cadaverine productivity decreases for the τ_PFR of 1 min, 2 min and 5 min were 25 %, 32 % and 53 % respectively. Thus, highlighting that loss in performance is most likely to occur as the magnitude of heterogeneity within the fermentation environment increases. However, Corynebacterium glutamicum DM1945 Δact3 Ptuf-ldcC_OPT did show some resilience in its biomass productivity. It was only marginally affected in the harshest of conditions simulated here.

Im ersten Teil der Arbeit wurden Strategien zur Analyse von Transkripten erarbeitet. Die ersten Versuche zielten darauf ab, in mit Glaskapillaren genommenen Einzelzellproben verschiedener Gewebeschichten RT-PCR durchzuführen, um spezifische Transkripte nachweisen zu können. Dies gelang für eine Reihe von Genen aus verschiedenen Pflanzenspezies. Dabei konnten sowohl Transkripte stark wie auch schwach exprimierter Gene nachgewiesen werden. <br /> Für die Erstellung von Gewebe-spezifischen Expressionsprofilen war es notwendig, die in vereinigten Zellproben enthaltene mRNA zunächst zu amplifizieren, um eine ausreichende Menge für Arrayhybridisierungen zu erhalten. Vor der Vermehrung wurde die mRNA revers transkribiert. Es wurden daran anschließend verschiedene Amplifikationsstrategien getestet: Die neben Tailing, Adapterligation und anderen PCR-basierenden Protokollen getestete Arbitrary-PCR hat sich in dieser Arbeit als einfache und einzige Methode herausgestellt, die mit so geringen cDNA-Mengen reproduzierbar arbeitet. Durch Gewebe-spezifische Array-hybridisierungen mit der so amplifizierten RNA konnten schon bekannte Expressionsmuster verschiedener Gene, vornehmlich solcher, die an der Photosynthese beteiligt sind, beobachtet werden. Es wurden aber auch eine ganze Reihe neuer offensichtlich Gewebe-spezifisch exprimierter Gene gefunden. Exemplarisch für die differentiell exprimierten Gene konnte das durch Arrayhybridisierungen gefundene Expressionsmuster der kleinen Untereinheit von Rubisco verifiziert werden. Hierzu wurden Methoden zum Gewebe-spezifischen Northernblot sowie semiquantitativer und Echtzeit-Einzelzell-RT-PCR entwickelt.<br /> Im zweiten Teil der Arbeit wurden Methoden zur Analyse von Metaboliten einschließlich anorganischer Ionen verwendet. Es stellte sich heraus, daß die multiparallele Methode der Gaschromatographie-Massenspektrometrie keine geeignete Methode für die Analyse selbst vieler vereinigter Zellinhalte ist. Daher wurde auf Kapillarelektrophorese zurückgegriffen. Eine Methode, die mit sehr kleinen Probenvolumina auskommt, eine hohe Trennung erzielt und zudem extrem geringe Detektionslimits besitzt. Die Analyse von Kohlenhydraten und Anionen erfordert eine weitere Optimierung. Über UV-Detektion konnte die K+-Konzentration in verschiedenen Geweben von A. thaliana bestimmt werden. Sie lag in Epidermis und Mesophyll mit ca. 25 mM unterhalb der für andere Pflanzenspezies (Solanum tuberosum und Hordeum vulgare) publizierten Konzentration. Weiter konnte gezeigt werden, daß zwölf freie Aminosäuren mittels einer auf Kapillarelektrophorese basierenden Methode in vereinigten Zellproben von Cucurbita maxima identifiziert werden konnten. Die Übertragung der Methode auf A. thaliana-Proben muß jedoch weiter optimiert werden, da die Sensitivität selbst bei Laser induzierter Fluoreszenz-Detektion nicht ausreichte.<br /> Im dritten und letzten Teil der Arbeit wurde eine Methode entwickelt, die die Analyse bekannter wie unbekannter Proteine in Gewebe-spezifischen Proben ermöglicht. Hierzu wurde zur Probennahme mittels mechanischer Mikrodissektion eine alternative Methode zur Laser Capture Microdissection verwendet, um aus eingebetteten Gewebeschnitten distinkte Bereiche herauszuschneiden und somit homogenes Gewebe anzureichern. Aus diesem konnten die Proteine extrahiert und über Polyacrylamidgelelektrophorese separariert werden. Banden konnten ausgeschnitten, tryptisch verdaut und massenspektrometrisch die Primärsequenz der Peptidfragmente bestimmt werden. So konnten als Hauptproteine im Mesophyll die große Untereinheit von Rubisco sowie ein Chlorophyll bindendes Protein gefunden werden.<br /> Die in dieser Arbeit entwickelten und auf die Modellpflanze Arabidopsis thaliana angewandten Einzelzellanalysetechniken erlauben es in Zukunft, physiologische Prozesse besser sowohl räumlich als auch zeitlich aufzulösen. Dies wird zu einem detaillierteren Verständnis mannigfaltiger Vorgänge wie Zell-Zell-Kommunikation, Signalweiterleitung oder Pflanzen-Pathogen-Interaktionen führen. / The subject of this thesis was the analysis of single plant cells in respect to their contents of i) transcripts, ii) inorganic cations and anions, iii) metabolites like amino acids and carbohydrates as well as iv) proteins. One task was the transfer of existing methods to single cell analysis on leaf tissues of the model plant Arabisopsis thaliana L., the second one was the refinement and the development, respectively, of new protocols for the analysis of such picoliter samples. For cell type specific sampling two different complimentary methods were applied: Using micro glass capillaries specific single cell contents could be harvested from intact plants, whereas typical sample volumes were in the picoliter range. Even the sampling of inner cell types such as companion cells could be demonstrated. Using mechanical micro dissection of embedded tissue a larger amount of homogenous tissue could be collected.<br /> Because single cell samples contain only femtogram amounts of mRNA, direct detection of transcripts is impossible. Therefore, two amplification protocols were applied to the cell samples: The first procedure makes use of specifically primed RT-PCR for amplification. Several genes derived from different plants and tissues could be detected after successful RT-PCR, including high as well as low expressed genes. The second method was developed to monitor the activity of many genes in parallel using array hybridisation with filters containing the cDNA of as many as 16.000 ESTs. For this purpose, unspecific RT-PCR as it is applied in the differential display was used to amplify different transcripts in just one reaction. However, in these tissue specific array hybridisations the expression patterns of several hundreds genes could be monitored. These included known tissue specific expression patterns (of mainly photosynthesis related genes) as well as a couple of unknown expression patterns. To verify the tissue specificity of gene activity some results were reconsidered using tissue specific northern blot hybridisations and real time RT-PCR, respectively. <br /> Secondly, metabolites (including inorganic ions) were investigated: Because gas chromatography-mass spectrometry does not reveal the sensitivity which in necessary for the analysis of even multiple pooled single cell samples capillary electrophoresis was applied for these studies. This method has a high potential as it needs only small amounts of starting material, has uncomparable low detection limits and exhibits a high number of theoretical plates.<br /> The analysis of inorganic anions and carbohydrates needs further optimisations. Using UV absorption-detection potassium could be detected in different cell types whereas the concentrations in mesophyll and epidermis were found around 25 mM each. These concentrations are lower than in other species as Solanum tuberosum or Hordeum vulgare. For investigations of amino acids the cell samples were derivatized to make the use of laser induced fluorescence-detection capable. In samples derived from pumpkin (Cucurbita maxima) mesophyll twelve amino acids could be detected and identified. The transfer of this method to A. thaliana derived samples exhibited no results which may be due to the low concentration of free amino acids in these plants.<br /> Finally, a method was developed with which the existence of known and unknown proteins in tissue specific samples could be monitored. For this, mechanical micro dissection was used to: After embedding and sectioning the tissue of interest was cut out by an vibrating steel chisel to get homogenous samples. The proteins contained in these tissue pieces were extracted and separated by one dimensional SDS polyacrylamid gel electrophoresis. Several protein bands could be detected after staining with either silver or coomassie blue stain. These bands were cut out and sequenced by mass spectrometry. The large subunit of rubisco as well as one chlorophyll binding protein could be identified as the major proteins within the mesophyll.<br /> The single cell analysis methods which were developed and applied to the model plant A. thaliana in this thesis allow a better spatial as well as temporal resolution of analysis. This will lead to a more detailed understanding of physiological processes like cell to cell communication, signalling or plant-pathogen interactions.

Today,   most traditional method used in the research of immune cells, such as flow   cytometry and microscopy, are based on average values of cell responses.   However, immune cells are heterogeneous and respond differently to a given   stimuli. There is also a risk that important, but rare, behaviors of   individual cells are missed when a larger population of immune cells is   analyzed. Also, flow cytometry and microscopy do not allow long-term survival   of cells; these methods lack the ability to do dynamic long-term analysis of   motile immune cells, i.e. studies of cell-cell interactions, morphology and proliferation.   In a   patient who is affected by cancer, the cell heterogeneity contributes to the   ability to battle various types of cancer or virus infections. In an   outbreak, immune cells recognize and kill tumor cells. However, the number of   specific immune cells is sometimes too few to kill all the tumor cells in a   successful way. One way to help these patients is to isolate, select out and   cultivate the active immune cells with capacity to kill tumor cells.   The   Cell Physic Laboratory (a part of the department of Applied Physics) at the   Royal Institute of Technology (KTH) has developed a method for single-cell   analysis where the immune cells are trapped in microwells in a silicon chip.   The immune cells are then studied by using fluorescence microscopy in an   inverted setup. The method enables high-throughput experiments due to the   parallelization. Furthermore, since the immune cells survive long periods in   the chip, the cells can be analyzed over several days up to weeks. The   research group has also developed a semi-automatic ‘cell-picker’. The   cell-picker will be used in combination with the developed method for   single-cell analysis, which enables picking of cells of interest. In this report, experiments for the characterization and evaluation of the biocompatibility of two generations of the cell-picker will be presented. The experiments include development of a protocol for the cell-picking process, studies of the survival time of transferred cells for both generation of the cell-picker and studies of surface coating in the chip in order to increase the biocompatibility. The preliminary results indicate that the cell-picker has potential to be used as a selection tool for immune cells of interest.

Today many cell biological techniques study large cell populations where an average estimate of individual cells’ behavior is observed. On the other hand, single cell analysis is required for studying functional heterogeneities between cells within populations. This thesis presents work that combines the use of microfluidic devices, optical microscopy and automated image analysis to design various cell biological assays with single cell resolution including cell proliferation, clonal expansion, cell migration, cell-cell interaction and cell viability tracking. In fact, automated high throughput single cell techniques enable new studies in cell biology which are not possible with conventional techniques. In order to automatically track dynamic behavior of single cells, we developed a microwell based device as well as a droplet microfluidic platform. These high throughput microfluidic assays allow automated time-lapse imaging of encapsulated single cells in micro droplets or confined cells inside microwells. Algorithms for automatic quantification of cells in individual microwells and micro droplets are developed and used for the analysis of cell viability and clonal expansion. The automatic counting protocols include several image analysis steps, e.g. segmentation, feature extraction and classification. The automatic quantification results were evaluated by comparing with manual counting and revealed a high success rate. In combination these automatic cell counting protocols and our microfluidic platforms can provide statistical information to better understand behavior of cells at the individual level under various conditions or treatments in vitro exemplified by the analysis of function and regulation of immune cells. Thus, together these tools can be used for developing new cellular imaging assays with resolution at the single cell level. To automatically characterize transient migration behavior of natural killer (NK) cells compartmentalized in microwells, we developed a method for single cell tracking. Time-lapse imaging showed that the NK cells often exhibited periods of high motility, interrupted with periods of slow migration or complete arrest. These transient migration arrest periods (TMAPs) often overlapped with periods of conjugations between NK cells and target cells. Such conjugation periods sometimes led to cell-mediated killing of target cells. Analysis of cytotoxic response of NK cells revealed that a small sub-class of NK cells called serial killers was able to kill several target cells. In order to determine a starting time point for cell-cell interaction, a novel technique based on ultrasound was developed to aggregate NK and target cells into the center of the microwells. Therefore, these assays can be used to automatically and rapidly assess functional and migration behavior of cells to detect differences between health and disease or the influence of drugs. The work presented in this thesis gives good examples of how microfluidic devices combined with automated imaging and image analysis can be helpful to address cell biological questions where single cell resolution is necessary. / <p>QC 20130927</p>

Metabolic heterogeneity (MH) occurs when isogenic microbial populations display cell-to-cell differences in metabolic traits, albeit exposed to homogeneous conditions. Despite the increasing focus on MH, its triggering factors remain largely unknown. In the present thesis, I used stable isotope probing and chemical imaging with nanoscale Secondary Ion Mass Spectrometry (nanoSIMS) to study MH at single-cell level, in model organisms, synthetic and natural communities, to understand i) how abiotic factors, biotic interactions and antibiotics exposure influence MH and ii) its potential ecological role. Moreover, I optimized sample preparation for chemical and high-resolution imaging and suggested two different indices as ‘unit measure’ of MH. As results, I have shown for the first time that MH is displayed by microorganisms under favorable growth conditions, although none of the tested abiotic factors prevailed as the main trigger of MH. I brought insights on how biotic interactions play a role in the functional heterogeneity using bacteria pseudo-fungi co-cultures. I found that antibiotics reduce Carbon and Nitrogen assimilation rates of targeted phylogenetic groups in river-water communities, while increasing their MH, pointing to its ecological importance in natural environments. To conclude, I provided novel insights on the phenomenon of MH and its dynamics at different ecological scales.:Abbreviation list Summary Introduction Knowledge gaps Results and discussion - Optimization of sample preparation - Validation of quantitation methods - Abiotic factors shaping metabolic heterogeneity in bacterial populations - Influence of biotic factors in shaping heterogeneity - Metabolic Heterogeneity and ecophysiology of natural microbial populations influenced by emerging contaminants Conclusions Outlook Bibliography Appendix Acknowledgments Curriculum Vitae List of publications

Der TGFβ-Signalweg ist ein multifunktionales System, das zelluläre Prozesse reguliert, die von Proliferation und Migration bis zu Differenzierung und Zelltod reichen. Nach Ligandenbindung und Rezeptoraktivierung translozieren SMAD-Proteine zum Zellkern und induzieren die Expression zahlreicher Zielgene. Während viele Komponenten des TGFβ-Signalweges identifiziert wurden, verstehen wir noch nicht genau, wie die Aktivierung des Signalwegs in verschiedene zelluläre Antworten übersetzt wird. Da die zelluläre Antwort auf einen gegebenen Stimulus oft sogar in genetisch identischen Zellen variiert, konzentrierte ich mich auf die Messung der Signalwegaktivität auf der Einzelzellebene. Durch die Kombination fluoreszierender Reporterzelllinien mit Zeitraffer-Lebendzellmikroskopie und automatisierter Bildanalyse beobachtete ich die zytoplasmatische und nukleäre Translokation von SMADs mit hoher zeitlicher und räumlicher Auflösung in Hunderten einzelner Zellen. Unsere Experimente zeigten, dass die Signalwegaktivität in eine erste synchrone Phase der SMAD-Translokation, gefolgt von einer Adaption und einer zweiten Signalphase mit hoher Variabilität in Stärke und Dauer der nuklearen Akkumulation unterteilt werden kann. Darüber hinaus beobachtete ich, dass Zellen, die aufgrund ihrer dynamischen Eigenschaften in Subpopulationen gruppiert sind, unterschiedliche phänotypische Reaktionen zeigen. Ich war nun daran interessiert, die Netzwerkinteraktionen zu identifizieren, die diese Dynamiken formen und fokussierte mich auf den Crosstalk mit nicht-kanonischen Komponenten des TGFβ-Signalweges. Ich konnte zeigen, dass die Hemmung der MAP Kinasen p38 und ERK die zweite Signalphase spezifisch aufhebt. Diese dynamische Remodellierung führt zu Veränderungen in der Zielgenexpression und den Zellschicksalen. Dies wird zu einem tieferen Verständnis der molekularen Netzwerke führen, die die TGFβ-Signaltransduktion regulieren und Möglichkeiten eröffnen, es in erkrankten Zellen zu modulieren. / The TGFβ pathway is a multi-functional signaling system regulating cellular processes ranging from proliferation and migration to differentiation and cell death. Upon ligand binding and receptor activation, SMAD proteins translocate to the nucleus and induce expression of numerous target genes. While many components of the TGFβ pathway have been identified, we are still challenged to understand how pathway activation is translated into distinct cellular responses. As the cellular response to a given stimulus often varies even in genetically identical cells, I focused on measuring pathway activity on the single cell level. By combining fluorescent reporter cell lines with time-lapse live-cell microscopy and automated image analysis, I monitored the cytoplasmic to nuclear translocation of SMADs with high temporal and spatial resolution in hundreds of individual cells. Our experiments demonstrated that pathway activity can be divided into a first synchronous phase of SMAD translocation, followed by adaptation and a second signaling phase with high variability in the extent and duration of nuclear accumulation. Furthermore, I observed that cells clustered into subpopulations according to their dynamic features show different phenotypic responses. I was interested in identifying the network interactions that shape these dynamics and focus on crosstalk with non-canonical components of the TGFβ pathway. I could show that inhibition of the MAP kinases p38 and ERK specifically abrogates the second signaling phase. This dynamic remodeling led to changes in target gene expression and cell fate decisions. I explored the molecular mechanisms underlying this interaction of the canonical and non-canonical pathways. This will provide a deeper understanding of the molecular networks regulating TGFβ signaling and open opportunities to modulate it in diseased cells.

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