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Three-dimensional (3D) printing can be beneficial to tissue engineers and the regenerative medicine community because of its potential to rapidly build elaborate 3D structures from cellular and material inks. However, predicting changes to the structure and pattern of printed tissues arising from the mechanical activity of constituent cells is technically and conceptually challenging. This perspective is targeted to scientists and engineers interested in 3D bioprinting, but from the point of view of cells and tissues as mechanically active living materials. The dynamic forces generated by cells present unique challenges compared to conventional manufacturing modalities but also offer profound opportunities through their capacity to self-organize. Consideration of self-organization following 3D printing takes the design and execution of bioprinting into the fourth dimension of cellular activity. We therefore propose a framework for dynamic bioprinting that spatiotemporally guides the underlying biology through reconfigurable material interfaces controlled by 3D printers. 

Abstract IntroductionTraction force microscopy (TFM) is a widely used technique to measure cell contractility on compliant substrates that mimic the stiffness of human tissues. For every step in a TFM workflow, users make choices which impact the quantitative results, yet many times the rationales and consequences for making these decisions are unclear. We have found few papers which show the complete experimental and mathematical steps of TFM, thus obfuscating the full effects of these decisions on the final output. MethodsTherefore, we present this “Field Guide” with the goal to explain the mathematical basis of common TFM methods to practitioners in an accessible way. We specifically focus on how errors propagate in TFM workflows given specific experimental design and analytical choices. ResultsWe cover important assumptions and considerations in TFM substrate manufacturing, substrate mechanical properties, imaging techniques, image processing methods, approaches and parameters used in calculating traction stress, and data-reporting strategies. ConclusionsBy presenting a conceptual review and analysis of TFM-focused research articles published over the last two decades, we provide researchers in the field with a better understanding of their options to make more informed choices when creating TFM workflows depending on the type of cell being studied. With this review, we aim to empower experimentalists to quantify cell contractility with confidence. 

Abstract Multicellular systems, from bacterial biofilms to human organs, form interfaces (or boundaries) between different cell collectives to spatially organize versatile functions 1,2 . The evolution of sufficiently descriptive genetic toolkits probably triggered the explosion of complex multicellular life and patterning 3,4 . Synthetic biology aims to engineer multicellular systems for practical applications and to serve as a build-to-understand methodology for natural systems 5–8 . However, our ability to engineer multicellular interface patterns 2,9 is still very limited, as synthetic cell–cell adhesion toolkits and suitable patterning algorithms are underdeveloped 5,7,10–13 . Here we introduce a synthetic cell–cell adhesin logic with swarming bacteria and establish the precise engineering, predictive modelling and algorithmic programming of multicellular interface patterns. We demonstrate interface generation through a swarming adhesion mechanism, quantitative control over interface geometry and adhesion-mediated analogues of developmental organizers and morphogen fields. Using tiling and four-colour-mapping concepts, we identify algorithms for creating universal target patterns. This synthetic 4-bit adhesion logic advances practical applications such as human-readable molecular diagnostics, spatial fluid control on biological surfaces and programmable self-growing materials 5–8,14 . Notably, a minimal set of just four adhesins represents 4 bits of information that suffice to program universal tessellation patterns, implying a low critical threshold for the evolution and engineering of complex multicellular systems 3,5 .