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Background: Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration, and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangiocarcinoma organoids in parallel using light sheet and bright-field microscopy for up to 7 days.
Results: We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation, and degeneration and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation, and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright-field microscopy analysis of entire cultures.
Conclusion: Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.
Cell fate clusters in ICM organoids arise from cell fate heredity and division: a modelling approach
(2020)
During the mammalian preimplantation phase, cells undergo two subsequent cell fate decisions. During the first decision, the trophectoderm and the inner cell mass are formed. Subsequently, the inner cell mass segregates into the epiblast and the primitive endoderm. Inner cell mass organoids represent an experimental model system, mimicking the second cell fate decision. It has been shown that cells of the same fate tend to cluster stronger than expected for random cell fate decisions. Three major processes are hypothesised to contribute to the cell fate arrangements: (1) chemical signalling; (2) cell sorting; and (3) cell proliferation. In order to quantify the influence of cell proliferation on the observed cell lineage type clustering, we developed an agent-based model accounting for mechanical cell–cell interaction, i.e. adhesion and repulsion, cell division, stochastic cell fate decision and cell fate heredity. The model supports the hypothesis that initial cell fate acquisition is a stochastically driven process, taking place in the early development of inner cell mass organoids. Further, we show that the observed neighbourhood structures can emerge solely due to cell fate heredity during cell division.
In China and other countries of East Asia, so-called Ling-zhi or Reishi mushrooms are used in traditional medicine since several centuries. Although the common practice to apply the originally European name ‘Ganoderma lucidum’ to these fungi has been questioned by several taxonomists, this is still generally done in recent publications and with commercially cultivated strains. In the present study, two commercially sold strains of ‘G. lucidum’, M9720 and M9724 from the company Mycelia bvba (Belgium), are compared for their fruiting body (basidiocarp) morphology combined with molecular phylogenetic analyses, and for their secondary metabolite profile employing an ultra-performance liquid chromatography–electrospray ionization mass spectrometry (UPLC–ESIMS) in combination with a high resolution electrospray ionization mass spectrometry (HR-ESI-MS). According to basidiocarp morphology, the strain M9720 was identified as G. lucidum s.str. whereas M9724 was determined as Ganoderma lingzhi. In molecular phylogenetic analyses, the M9720 ITS and beta-tubulin sequences grouped with sequences of G. lucidum s.str. from Europe whereas those from M9724 clustered with sequences of G. lingzhi from East Asia. We show that an ethanol extract of ground basidiocarps from G. lucidum (M9720) contains much less triterpenic acids than found in the extract of G. lingzhi (M9724). The high amount of triterpenic acids accounts for the bitter taste of the basidiocarps of G. lingzhi (M9724) and of its ethanol extract. Apparently, triterpenic acids of G. lucidum s.str. are analyzed here for the first time. These results demonstrate the importance of taxonomy for commercial use of fungi.
Generation of an efficient agent-based framework for the simulation of 3D multicellular systems
(2024)
In developmental biology, the focus has shifted from mainly considering genetic and molecular aspects to considering mechanical aspects, as it has become evident in recent years that mechanical forces, tensions, and physical interactions play a significant role alongside molecular mechanisms in developmental biology. Computational models provide a useful tool for the investigation of the complex cell choreography in tissue and organ development. In particular, they allow the identification of principles governing complex behaviours and greatly contribute to understanding self-organising systems. Agent-based models act as a ”virtual laboratory”, facilitating the formulation and testing of biological hypotheses.
In this work, a mathematical model is formulated to describe the dynamics and interactions of multicellular systems. This model formulation results in a large system of coupled stochastic differential equations. Furthermore, a simulation framework is introduced to solve the system of coupled stochastic differential equations numerically. In particular, mechanical processes such as cell-cell interactions, cell growth and division, cell polarity, and active migration are considered. Firstly, a CPU-based version of the simulation framework, implemented in Python and MATLAB, is presented. This version also provides scientists with limited programming experience the abil- ity to simulate systems involving several thousand cells. Additionally, a GPU-based framework version, implemented in CUDA and C++, is introduced. This version primarily targets modellers with advanced programming knowledge. It significantly reduces simulation runtime, allows for the simulation of very large systems, and incorporates additional extensions.
The implemented CPU-based simulation framework was applied to two different biological systems. The first application concerned inner cell mass organoids (ICM organoids), which serve as an experimental model system to study early embryogenesis. In particular, ICM organoids reflect the second cell fate decision, i.e., the differentiation into embryonic tissue and yolk sac, as well as subsequent cell sorting. Using the presented simulation framework, it was demonstrated that the experimentally observed local clustering of cell types can be attributed to mechanical processes, specifically cell growth, cell division, and cell fate inheritance. These results provide evidence that molecular cell fate determination occurs within a short period during the early development of ICM organoids, and that mechanical processes and interactions predominantly characterise subsequent processes. Furthermore, it was shown that differential adhesion and undirected cell movement in a three-dimensional system are sufficient to drive the segregation of different cell types.
The second biological application focused on pancreas-derived organoids, which simulate organ development, in this case, pancreas development. These organoids exhibit high variability in their qualitative behaviour, including volume oscillations, rotation and migration, fusions, and the formation of internal structures. The presented simulation framework was applied to the volume oscillations of the organoids. It was demonstrated that these oscillations depend significantly on the cell division dynamics and size of the organoids, as well as the elasticity and adhesion strength of the cells.
Both biological applications of the framework illustrate its modular structure and, thus, its adaptability to various biological systems. They also emphasise that mechanical processes play a fundamental role in the self-organisation of complex systems. The presented framework en- ables the efficient modelling of multicellular systems and serves as an effective tool for explaining complex behaviour by coupling simple underlying mechanisms.