Periodic Reporting for period 2 - Demos (Design Principles of Branching Morphogenesis)
Período documentado: 2022-01-01 hasta 2023-06-30
1. Understanding how stochastic rules lead to robust morphogenetic outputs at the organ scale, and which constraints and optimal design principles they impose on physiological function.
2. Characterizing at the cellular scale the bi-directional feedbacks coordinating fate choices of stem/progenitor cells and niche signals during the extensive remodelling events that branching morphogenesis entails.
3. Developing at the subcellular and cellular scale an integrated mechanochemical theory of pattern formation in branched organs, to understand the coordination of mechanical forces and chemical signals defining their global structure.
Towards these goals, we are combining analytical and numerical tools with data analysis methods, to reach a quantitative understanding of the emergent mechanisms driving branching morphogenesis. We work on challenging our theoretical predictions with published datasets available for different organs, as well as design specific experimental tests in collaboration with experimental biology groups. This allows us to compare and contrast different systems, and extract generic classes of design principles of organogenesis across length scales. With this, we expect to generate novel insights of broad relevance for the fields of systems, computational and developmental biology.
Following the plans of Aim 1, we have in particular worked on a number of collaborations with cell and developmental biologists to compare and contrast the branching strategies of a number of different organs and cells. As planned in Aim 1.1 we have in particular shown that a number of quantitative features of liver branching architecture in vivo (Hankeova et al, eLife, 2021) and pancreatic cancer organoids in vitro (Randriamanantsoa et al, Nat Comm, 2022) were highly stochastic and heterogeneous both within a given tree and among different experiments/animals. Beyond epithelial branched trees, we have also investigated the branching structure of neurons. In particular, Mehmet Can Ucar (a postdoc in the lab) developed a theoretical framework for a stochastic self-organized branching process in the presence of external cues (Can Ucar et al, Nat Comm, 2022). We have also made significant progresses towards the aim 1.2 by studying the principles of optimal coverage during lymphatic branching morphogenesis – this work by Mehmet Can Ucar as first author is currently under revision in Nature Communication.
We have also made a number of progresses towards the completion of Aim 2, in particular Aim 2.1. In particular, Bernat Corominas-Murtra – a postdoc in the lab now assistant professor in the University of Graz, worked on a theory of stem cell dynamics as a stochastic competition for access to a spatially localized niche. In this model (Corominas-Murtra*, Scheele* et al, PNAS, 2020), cell divisions produce a steady cellular stream which advects cells away from the niche, while random rearrangements enable cells away from the niche to be favorably repositioned. Importantly, even when assuming that all cells in a tissue are molecularly equivalent, we predict a common (“universal”) functional dependence of the long-term clonal survival probability on distance from the niche, as well as the emergence of a well-defined number of functional stem cells, dependent only on the rate of random movements vs. mitosis-driven advection. We test the predictions of this theory on datasets of pubertal mammary gland tips and embryonic kidney tips, finding good agreement for the predicted functional dependency of the competition as a function of position, and thus functional stem cell number in each organ. This argues for a key role of positional fluctuations in dictating stem cell number and dynamics. Interesting, we also recently showed that the same model could apply beyond the setting of branching morphogenesis – being relevant to understand the homeostasis of the mouse intestinal epithelium (Azkanaz, Corominas-Murtra* et al, Nature, 2022). In particular, experiments revealed highly different dynamics and functional stem cell numbers in small vs large intestine results. We show theoretically that the number of effective stem cells is determined by different amounts of Wnt-dependent active retrograde movement, revealing a new channel of stem cell regulation that can be experimentally and pharmacologically manipulated.
Finally, we have also advanced towards both subaims of Aim 3 by looking at the mechano-chemical principles of 3D epithelium shape deformations. As planned in aim 3.1 we have developed analytical theories of 3D vertex model models with changing fluid pressure. As a proof of concept for experimental applications, we have worked on intestinal organoid morphogenesis, which undergo a budding transition (which can be seen as a simple elemental deformation and a first step to understand more complex phenomena such as branching), showing how mechano-osmotic forces coordinate this process (Yang*, Xue* et al, NCB, 2021). As planned in aim 3.2 we have also started to investigate the interplay between curvature and mechanics as a patterning mechanism. Again, we have first collaborated with experimentalists working on simpler systems such as MDCK monolayers to validate the ideas – including mechano-chemical ERK waves arising from coupling between cell shape and ERK signalling (Boocock*, Hino* et al, Nat Phys, 2021) as well as curvature sensing via active nuclear mechanosensation (Luciano*, Hannezo* et al, Nat Phys, 2021).
In particular, we are working actively on understanding the coupling between branching of different organs/cells (aim 1.3) taking datasets from neuronal morphologies as experimental basis, as well as working on understanding the relationship between function (flow transport) and branching morphology based on published datasets in mammary gland (aim 1.4). We are also generalizing the stem cell dynamics model that we published recently (see above) to understand the feedback between niche growth/dynamics and stem cells (aim 2.2). Finally, we are building on our findings using 3D vertex models with mechanochemical interactions (curvature-sensing and stress-sensing in particular) to understand the mechanical basis of the rules of branching that we studied already in aim 1.