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Formation of segments during vertebrate embryogenesis is regulated by a biological clock. Models and experimental data indicate that the core of this clock consists of a cell- autonomous single cell oscillator. This oscillator likely involves a genetic feedback loop of transcriptional repressors belonging to the hairy gene family. In zebrafish, three her genes, her1, hes6 and her7, have been identified as core oscillator components. The main purpose of this project was to study the molecular mechanism of the hairy gene negative feedback oscillator in single cells. To determine whether a single cell oscillator is part of the zebrafish segmentation clock, a cell dissociation protocol was established to track the expression of Her1 ex vivo. Upon dissociation, Her1 expression continued to oscillate for up to three cycles. The period of oscillations was significantly slower than that of the segmentation clock, but appears to speed up in the presence of serum. To test whether the hairy gene interactions are sufficient to generate oscillations in single cells, a protocol was established that uses synthetic biology principles to design, construct and characterize hairy gene networks in yeast. First a library of network parts, containing hairy genes, promoters and Her binding sites was generated and subsequently assembled into simple devices to test their functionality in yeast. The three core oscillator components, Her1, Hes6 and Her7, were characterized and optimized for expression in yeast. In the SWITCH-OFF assay, the Her1 protein, modified with a MigED yeast repressor domain, was found to function as a transcriptional repressor in yeast, while Hes6 with the same modification can not. The dissociation of segmentation clock cells provides the first direct evidence that single cell oscillators exist in zebrafish. In this system, oscillator dynamics can be studied without the interactions of higher level clock components. In parallel, establishing a yeast chassis for hairy gene networks provides a novel technique to directly test predicted oscillator mechanisms by constructing them ’bottom up’.

Diese Arbeit entstand im Rahmen des Projektes „Molekulare Endospektroskopie“ an der Technischen Universität Dresden. Der Titel drückt aus, dass durch Kopplung von Endoskopie und Spektroskopie Gewebe auf molekularer Ebene charakterisiert wird. Infrarot- (IR-) und Raman-Spektroskopie bieten dabei besondere Vorteile, da sie zu den molekülspektroskopischen Verfahren mit dem höchsten Informationsgehalt gehören. Beide Methoden beruhen auf Molekülschwingungen, deren Spektren einen chemischen Fingerabdruck über die Zusammensetzung und Struktur der Proben liefern. Der Autor leitete eine wissenschaftliche Nachwuchsgruppe, die die Grundlagen der schwingungs-spektroskopischen Methoden zur Bildgebung von Gewebe und Zellen entwickelte und auf klinische Probleme – vor allem aus dem neuroonkologischen Bereich – anwendete. Diese kumulative Habilitationsschrift fasst vierzehn Veröffentlichungen zusammen, wobei in der letzten die Untersuchung eines Hirntumormodells von Mäusen mit einer faseroptischen Sonde beschrieben wurde. Zunächst werden verschiedene Methoden der Biophotonik verglichen, um die hier eingesetzten Techniken in diesen Kontext zu stellen. Danach werden biomedizinische Anwendungen von Fourier-Transform-Infrarot- (FTIR-) und Raman-Imaging beschrieben. Die eigenen Beiträge sind untergliedert in (i) Raman- und FTIR-Imaging in der Neuroonkologie, (ii) FTIR-mikroskopisches Imaging von Gewebedünnschnitten und (iii) Raman- und FTIR-mikroskopisches Imaging von einzelnen Zellen. Abschließend wird in den Schlussfolgerungen und dem Ausblick diskutiert, welche Rolle die molekulare Endospektroskopie als neue instrumentell-analytische Methode in der medizinischen Diagnostik übernehmen kann. / This work summarizes the results of the project “Molecular Endospectroscopy” at the Dresden University of Technology. The title expresses that tissue is characterized on the molecular level by coupling endoscopy and spectroscopy. Infrared (IR) and Raman spectroscopy offer advantages for these applications because they belong to the methods of molecular spectroscopy with the highest information content. Both methods probe molecular vibrations that provide a chemical fingerprint for the composition and structure of samples. The author was leader of a junior research group which developed vibrational spectroscopic methods for imaging of cells and tissues and applied them to clinical problems, in particular from the field of neuro-oncology. The current cumulative habilitation thesis is based on fourteen publications. The last one describes studies of a murine brain tumor model using a fiber-optic probe. In the first part various biophotonic methods are compared. Then biomedical applications of Fourier transform infrared (FTIR) and Raman imaging are reported. The papers are grouped into the chapters (i) Raman and FTIR imaging in neuro-oncology, (ii) FTIR microscopic imaging of tissue sections and (iii) Raman and FTIR microscopic imaging of single cells. It is discussed in the conclusions and outlook how molecular endospectroscopy as a new analytical tool can complement the standard diagnostic methods.

Dans les cellules des eucaryotes, la transcription des gènes est contrôlée par une pléthore de complexes protéiniques. Cependant, la plupart de nos connaissances fondamentales sur la régulation de la transcription viennent des expériences biochimiques ou des expériences d’immunofluorescences utilisant des cellules fixées. Par conséquent, beaucoup d’efforts ont été consacré récemment pour obtenir des informations sur les mouvements dynamiques ou sur l’assemblage des facteurs de transcription directement dans des cellules vivantes. Nous avons développé une stratégie de marquage, appelé « versatile antibody-based imaging approach » (VANIMA), dans laquelle des anticorps marqués avec un fluorochrome sont introduit dans des cellules vivantes pour visualiser spécifiquement des protéines endogènes ou des modifications post-traductionnelle. Nous avons pu montrer que VANIMA peut être utilisé pour étudier des processus dynamique des mécanismes fondamental de la biologie y compris les facteurs de la machinerie de transcription ainsi que les modifications des histones dans des cellules vivantes de cancer humaine en utilisant la microscopie conventionnelle ou à super-résolution. Dans l’avenir VANIMA va servir comme un outil valable pour révéler les dynamiques des processus endogènes en biologie y compris la transcription directement dans des cellules vivantes individuelles. / In eukaryotic cells, gene transcription is controlled by a plethora of protein complexes. However, most of our basic knowledge about transcription regulation originate from biochemical experiments or immunofluorescence experiments using fixed cells. Consequently, many efforts have been devoted recently to obtain information about the dynamic movements or assembly of transcription factors directly from living cells. Therefore, we developed a labeling strategy, named versatile antibody-based imaging approach (VANIMA), in which fluorescently labeled antibodies are introduced into living cells to image specific endogenous proteins or posttranslational modifications. We were able to show that VANIMA can be used to study dynamical processes of fundamental biological mechanisms including factors of the transcription machinery as well as histone modifications in living human cancer cells using conventional or super-resolution microscopy. Hence, in the future VANIMA will serve as a valuable tool to uncover the dynamics of endogenous biological processes including transcription directly in single living cells.

Saccharomyces cerevisiae ist ein bekanntester Modellorganismen in der Systembiologie, der häufig zur Untersuchung des mitotischen Zellzyklus eukaryotischer Zellen verwendet wird. Des Zellzyklus wird durch Cycline, Cyclin-abhängige Kinasen (CDK) und CDK-Inhibitoren (CKI) reguliert. Der wichtigste Kontrollpunkt innerhalb des Zellzyklus reguliert den Übergang von der G1 in die S Phase und wird START genannt. Im dieser Arbeit verwenden wir einen stochastischen Modellierungsansatz, um die Auswirkungen verschiedener Synchronisationsmethoden auf den Zellzyklus zu untersuchen. Um Modellparameter zu schätzen, kombinieren wir Phasen aufgelöste mRNA-Verteilungen unsynchronisierter Einzelzellen und Protein-Zeitreihen synchronisierter Zellpopulationen. Somit können wir mRNA-Dynamiken für ausgewählte Synchronisationsmethoden vorhersagen. In einem zweistufigen Optimierungsansatz unterscheiden wir zwischen mRNA- und Protein-Ebene. Die Parameterschätzung basiert auf der Maximum-Likelihood-Methode. Die Phasen aufgelösten mRNA-Verteilungen wurden mithilfe der smFISH-Technik für SIC1, CLN2 und CLB5 gemessen. Die Protein-Zeitreihen wurden mithilfe von Western Blots für entsprechenden Proteine gemessen. Die gemessenen Moleküle sind die Hauptregulatoren des G1-S Phasenübergangs, welche die Komponenten unseres Zellzyklusmodells darstellen. Durch die erfolgreiche Integration von qualitativ unterschiedlichen Datentypen in der Parameterschätzung konnten wir eine systematische Analyse von Synchronisationseffekten auf den Zellzyklus durchführen. Der zeitlicher Ablauf des Zellzyklus ist dabei maßgeblich beeinflusst. Die stärksten zeitlichen Veränderungen weist die Synchronisation mit alpha-Faktor auf. Elutrierte Zellen sind den unsynchronisierten Zellen trotz verlängerter G1 Phase am ähnlichsten. Wir zeigen in dieser Arbeit, dass synchronisierte Zellpopulationen unzureichend sind, um Rückschlüsse auf den Zellzyklus unsynchronisierter Zellen zu ziehen. / cell cycle, G1/S transition, stochastic modeling, parameter estimation, smFISH, singel cells, Western blotting, cell populations Saccharomyces cerevisiae is a famous model organism in systems biology to study the mitotic cell cycle in eukaryotic cells. The cell cycle is a highly controlled process which is regulated by cyclins, cycline-dependent kinases (CDK) and cyclin-dependent kinase inhibitors (CKI). The main kinase involved in cell cycle regulation is Cdc28. START is the most important check point and controls the G1 to S phase transition. At this point, cells decide if they enter a new cell division cycle or not. In this study, we analyze influences of different synchronization methods on the cell cycle and differences between unsynchronized and synchronized cells by using a stochastic modeling approach. We combine phase-resolved mRNA distributions of unsynchronized single cells and protein time courses of synchronized cell populations to estimate model parameters and to predict synchronization specific mRNA dynamics. Parameter estimation is based on a maximum likelihood approach and performed in a 2-step-optimization in which we differentiate between mRNA and protein level. We measured phase-resolved mRNA distributions of mRNA species SIC1, CLN2 and CLB5 by smFISH and protein time courses of protein species Sic1, Cln2 and Clb5 by Western blotting. These molecules are key regulators of the G1 to S phase transition and represent components of our cell cycle model. By integrating qualitatively different data types in parameter estimation, we come up with a systematic analysis of synchronization effects on the cell cycle. Cell cycle timing is mainly responsible for differences between unsynchronized and synchronized cells and is mostly affected in alpha-factor synchronized cells. Ignoring the prolongation of the G1 phase, elutriated cells are most similar to unsynchronized cells. We show that synchronized cell populations are insufficient to derive general cell cycle behavior of unsynchronized cells.

The analysis of wide-angle cellular light scattering patterns is a challenging problem. Small changes to the organization, orientation, shape, and optical properties of scatterers and scattering populations can significantly alter their complex two-dimensional scattering signatures. Because of this, it is difficult to find methods that can identify medically relevant cellular properties while remaining robust to experimental noise and sample-to-sample differences. It is an important problem. Recent work has shown that changes to the internal structure of cells---specifically, the distribution and aggregation of organelles---can indicate the progression of a number of common disorders, ranging from cancer to neurodegenerative disease, and can also predict a patient's response to treatments like chemotherapy. However, there is no direct analytical solution to the inverse wide-angle cellular light scattering problem, and available simulation and interpretation methods either rely on restrictive cell models, or are too computationally demanding for routine use. This dissertation addresses these challenges from a computational vantage point. First, it explores the theoretical limits and optical basis for wide-angle scattering pattern analysis. The result is a rapid new simulation method to generate realistic organelle scattering patterns without the need for computationally challenging or restrictive routines. Pattern analysis, image segmentation, machine learning, and iterative pattern classification methods are then used to identify novel relationships between wide-angle scattering patterns and the distribution of organelles (in this case mitochondria) within a cell. Importantly, this work shows that by parameterizing a scattering image it is possible to extract vital information about cell structure while remaining robust to changes in organelle concentration, effective size, and random placement. The result is a powerful collection of methods to simulate and interpret experimental light scattering signatures. This gives new insight into the theoretical basis for wide-angle cellular light scattering, and facilitates advances in real-time patient care, cell structure prediction, and cell morphology research.

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