![]() ![]() Studying 3D tessellation of printed droplets required advanced imaging and image analysis techniques. With this new level of control, we demonstrated 3D-printing of functional synthetic tissues mimics with precision never achieved before. In between these extremes, we found an optimal contact angle at which droplets tessellated regularly into a hexagonal closed-packed lattice. Strongly adhesive bilayers with large contact angles led to lattice distortions, while weakly adhesive bilayers with small contact angles resulted in loosely packed structures. Importantly, we found that the contact angle, while reflecting the balance of forces in the system, also represented a key geometrical parameter that strongly affected the droplet tessellation in 3D-printed synthetic tissue mimics. 11 We found that by varying the combination of oils and lipids used, we could control the adhesiveness of the lipid bilayers, which in turn determined the contact angle formed between two aqueous droplets. Our 3D-printed synthetic tissue mimics consist of hundreds of aqueous droplets in oil interfaced by lipid bilayers. 3D reconstruction from confocal images of 3D-printed droplets arranged in a hexagonal close-packed lattice. In our new paper “ Controlled packing and single-droplet resolution of 3D-printed functional synthetic tissues” in Nature Communications we explore the driving forces that dictate the regular tessellation of cell-like compartments in synthetic tissue mimics generated by 3D-printing. 10 A main limitation is the inability to assemble constructs of desired cellular composition and architectures in a controllable and reliable way. In recent years, advancements in building single cell-like structures have demonstrated promising results, 9 but current research developing and studying synthetic multicellular systems is sparse. 7 In multicellular organisms, sophisticated functions emerge from the coordinated interactions of specialised cells organised in specific architectures and patterns. In biological systems, the tessellation of cells within tissues is strongly linked to the evolution of multicellularity. Atoms and molecules tightly pack in dense crystal structures, 4 living cells tessellate within tissues, 5 and bees laboriously shape wax into hexagonal combs to efficiently store honey and pollen. ![]() In Nature, tessellations and repeated patterns are ubiquitous and found at a vast range of length scales. Since our early origins on this planet, we have decorated our surroundings with regular motifs and patterns, from the clay tilings in Sumerian temples 1 to modern day architecture 2 and interior design. We have tested the proposed 3D reconstruction method on time-lapse CLSM image stacks of the Arabidopsis Shoot Apical Meristem (SAM) and have shown that the AQVT based reconstruction method can correctly estimate the 3D shapes of a large number of SAM cells.Tessellation, or the filling of space with repeating geometric patterns, has fascinated humans for millennia. The proposed method, named as the `Adaptive Quadratic Voronoi Tessellation' (AQVT), is capable of handling both the sparsity problem and the non-uniformity in cell shapes by estimating the tessellation parameters for each cell from the sparse data-points on its boundaries. In the present work, we have proposed an anisotropic Voronoi tessellation based 3D reconstruction framework for a tightly packed multilayer tissue with extreme z-sparsity (2-4 slices/cell) and wide range of cell shapes and sizes. But, in case of Live Cell Imaging of an actively developing tissue, large depth resolution is not feasible in order to avoid damage to cells from prolonged exposure to laser radiation. However, the current methods of 3D reconstruction using CLSM imaging require large number of image slices per cell. The need for quantification of cell growth patterns in a multilayer, multi-cellular tissue necessitates the development of a 3D reconstruction technique that can estimate 3D shapes and sizes of individual cells from Confocal Microscopy (CLSM) image slices. ![]()
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