218 research outputs found
Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities
The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies.
Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes.
Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics.
Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities.
Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system
Stochastic dynamics of migrating cells
Cell migration is critical in many physiological phenomena, including embryogenesis, immune response, and cancer. In all these processes, cells face a common physical challenge: they navigate confining extra-cellular environments, in which they squeeze through thin constrictions. The motion of cells is powered by a complex machinery whose molecular basis is increasingly well understood. However, a quantitative understanding of the functional cell behaviours that emerge at the cellular scale remains elusive. This raises a central question, which acts as a common thread throughout the projects in this thesis: do migrating cells exhibit emergent dynamical 'laws' that describe their behavioural dynamics in confining environments?
To address this question, we develop data-driven approaches to infer the dynamics of migrating cells directly from experimental data. We study the migration of cells in artificial confinements featuring a thin constriction across which cells repeatedly squeeze. From the experimental cell trajectories, we infer an equation of cell motion, which decomposes the dynamics into deterministic and stochastic contributions. This approach reveals that cells deterministically drive themselves into the thin constriction, which is in contrast to the intuition that constrictions act as effective barriers. This active driving leads to intricate non-linear dynamics that are poised close to a bifurcation between a bistable system and a limit cycle oscillator. We further generalize this data-driven framework to detect and characterize the variance of migration behaviour within a cell population and to investigate how cells respond to varying confinement size, shape, and orientation.
We next investigate the mechanistic basis of these dynamics. Cell migration relies on the concerted dynamics of several cellular components, including cell protrusions and adhesive connections to the environment. Based on the experimental data, we systematically constrain a mechanistic model for confined cell migration. This model indicates that the observed deterministic driving is a consequence of the combined effects of the variable adhesiveness of the environment and a self-reinforcement of cell polarity in response to thin constrictions. These results suggest polarity feedback adaptation as a key mechanism in confined cell migration.
Finally, we investigate the dynamics of interacting cells. To enable inference of cell-cell interactions, we develop Underdamped Langevin Inference, an inference method for stochastic high-dimensional and interacting systems. We apply this method to experiments of confined pairs of cells, which repeatedly collide with one another. This reveals that non-cancerous (MCF10A) and cancerous (MDA-MB-231) cells exhibit distinct interactions: while the non-cancerous cells exhibit repulsion and effective friction, the cancerous cells exhibit attraction and a surprising 'anti-friction' interaction. These interactions lead to non-cancerous cells predominantly reversing upon collision, while the cancer cells are able to efficiently move past one another by relative sliding. Furthermore, we investigate the effects of cadherin-mediated molecular contacts on cell-cell interactions in collective migration.
Taken together, the data-driven approaches presented in this thesis may help to provide a new avenue to uncover the emergent laws governing the stochastic dynamics of migrating cells. We demonstrate how these approaches can provide key insights both into underlying mechanisms as well as emergent cell behaviours at larger scales.Zellmigration ist ein Kernelement vieler physiologischer Phänomene wie der Embryogenese, dem Immunsystem und der Krebsmetastase. In all diesen Prozessen stehen Zellen vor einer physikalischen Herausforderung: Sie bewegen sich in beengten Umgebungen, in denen sie Engstellen passieren müssen. Die Zellbewegung wird von einer komplexen Maschinerie an- getrieben, deren molekulare Komponenten immer besser verstanden werden. Demgegenüber fehlt ein quantitatives Verständnis des funktionalen Migrationsverhaltens der Zelle als Ganzes. Die verbindende Fragestellung der Projekte in dieser Arbeit lautet daher: gibt es emergente dynamische 'Gesetze', die die Verhaltensdynamik migrierender Zellen in beengten Umgebungen beschreiben?
Um dieser Frage nachzugehen, entwickeln wir datengetriebene Ansätze, die es uns erlauben, die Dynamik migrierender Zellen direkt aus experimentellen Daten zu inferieren. Wir untersuchen Zellmigration in künstlichen Systemen, in denen Zellen Engstellen wiederholt passieren müssen. Aus den experimentellen Zelltrajektorien inferieren wir eine Bewegungsgleichung, die die Dynamik in deterministische und stochastische Komponenten trennt. Diese Methode zeigt, dass sich Zellen deterministisch 'aktiv' in die Engstellen hineinbewegen, ganz entgegen der intuitiven Erwartung, dass Engstellen als Hindernis fungieren könnten. Dieser aktive Antrieb führt zu einer komplexen nichtlinearen Dynamik im Übergangsbereich zwischen einem bistabilen System und einem Grenzzyklus-Oszillator. Wir verallgemeinern diesen datenbasierten Ansatz, um die Varianz des Migrationsverhaltens innerhalb einer Zellpopulation zu quantifizieren, und analysieren, wie Zellen auf die Größe, Form und Orientierung ihrer Umgebung reagieren.
Darauf aufbauend untersuchen wir die zugrundeliegenden Mechanismen dieser Dynamik. Zellmigration basiert auf verschiedenen zellulären Komponenten, wie unter Anderem den Zellprotrusionen und der Adhäsion mit der Umgebung. Auf Basis der experimentellen Daten entwickeln wir ein mechanistisches Modell für Zellmigration in beengten Systemen, welches zeigt, dass der beobachtete aktive Antrieb eine Konsequenz zweier Effekte ist: Einer variierenden Adhäsion mit der Umgebung und einer Zellpolarität, die sich in Engstellen selbst verstärkt. Diese Ergebnisse deuten darauf hin, dass die Anpassung der Zellpolarität an die lokale Geometrie ein Schlüsselmechanismus in beengter Zellmigration ist.
Schließlich analysieren wir die Dynamik interagierender Zellen. Um Zell-Zell Interaktionen zu inferieren, entwickeln wir die Underdamped Langevin Inference, eine Inferenzmethode für stochastische hochdimensionale und interagierende Systeme. Wir wenden diese Methode auf Daten von eingeschlossenen Zellpaaren an, welche wiederholt miteinander kollidieren. Dies zeigt, dass gesunde (MCF10A) und krebsartige (MDA-MB-231) Zellen unterschiedliche Interaktionen aufweisen: Während gesunde Zellen mit Abstoßung und effektiver Reibung interagieren, zeigen Krebszellen Anziehung und eine überraschende 'Anti-Reibung'. Diese Interaktionen führen dazu, dass gesunde Zellen nach Kollisionen primär umkehren, während Krebszellen effizient aneinander vorbeigleiten. Darüberhinaus analysieren wir die Effekte von Cadherin-basierten Molekularkontakten auf Zell-Zell Interaktionen in kollektiver Migration.
Zusammenfassend könnten die in dieser Arbeit präsentierten datengetriebenen Ans ̈atze dabei helfen, ein besseres Verständnis der emergenten stochastischen Dynamik migrierender Zellen zu erlangen. Wir zeigen, wie diese Methoden wichtige Erkenntnisse sowohl über die zugrundeliegenden Mechanismen als auch über das emergente Zellverhalten liefern können
- …