Publications

Building synthetic multicellular systems using non-living molecular components is a grand challenge in the field of bottom-up synthetic biology. Towards this goal, a diverse range of chemistries have been developed to provide mechanisms of intercellular communication and methods to assemble multicellular compartments. However, building bottom-up synthetic multicellular systems is still challenging because it requires the integration of intercellular reaction networks with compatible cellular compartment properties. In this study, we encapsulated cell-free expression systems (CFES) expressing two quorum sensing genetic circuits into droplet interface bilayer (DIB) synthetic cells to demonstrate bidirectional communication. We further develop a method of generating custom DIB multicellular structures by acoustic liquid handling to automatically dispense the CFES droplets and show the potential for multiplexing compartmentalized gene circuits for generating heterogeneous populations of cells. Our work provides a step towards building more complex multicellular systems with intercellular communication from the bottom-up to study and experimentally model biological multiscalar processes.

Basic helix-loop-helix genes, particularly proneural genes, are well-described triggers of cell differentiation, yet information on their dynamics is limited, notably in human development. Here, we focus on Neurogenin 3 (NEUROG3), which is crucial for pancreatic endocrine lineage initiation. By monitoring both NEUROG3 gene expression and protein in single cells using a knockin dual reporter in 2D and 3D models of human pancreas development, we show an approximately 2-fold slower expression of human NEUROG3 than that of the mouse. We observe heterogeneous peak levels of NEUROG3 expression and reveal through long-term live imaging that both low and high NEUROG3 peak levels can trigger differentiation into hormone-expressing cells. Based on fluorescence intensity, we statistically integrate single-cell transcriptome with dynamic behaviors of live cells and propose a data-mapping methodology applicable to other contexts. Using this methodology, we identify a role for KLK12 in motility at the onset of NEUROG3 expression.

The concomitant occurrence of tissue growth and organization is a hallmark of organismal development1-3. This often means that proliferating and differentiating cells are found at the same time in a continuously changing tissue environment. How cells adapt to architectural changes to prevent spatial interference remains unclear. Here, to understand how cell movements that are key for growth and organization are orchestrated, we study the emergence of photoreceptor neurons that occur during the peak of retinal growth, using zebrafish, human tissue and human organoids. Quantitative imaging reveals that successful retinal morphogenesis depends on the active bidirectional translocation of photoreceptors, leading to a transient transfer of the entire cell population away from the apical proliferative zone. This pattern of migration is driven by cytoskeletal machineries that differ depending on the direction: microtubules are exclusively required for basal translocation, whereas actomyosin is involved in apical movement. Blocking the basal translocation of photoreceptors induces apical congestion, which hampers the apical divisions of progenitor cells and leads to secondary defects in lamination. Thus, photoreceptor migration is crucial to prevent competition for space, and to allow concurrent tissue growth and lamination. This shows that neuronal migration, in addition to its canonical role in cell positioning4, can be involved in coordinating morphogenesis.

We present a meshfree collocation scheme to discretize intrinsic surface differential operators over scalar fields on smooth curved surfaces with given normal vectors and a non-intersecting tubular neighborhood. The method is based on discretization-corrected particle strength exchange (DC-PSE), which generalizes finite difference methods to meshfree point clouds. The proposed Surface DC-PSE method is derived from an embedding theorem, but we analytically reduce the operator kernels along surface normals to obtain a purely intrinsic computational scheme over surface point clouds. We benchmark Surface DC-PSE by discretizing the Laplace–Beltrami operator on a circle and a sphere, and we present convergence results for both explicit and implicit solvers. We then showcase the algorithm on the problem of computing Gauss and mean curvature of an ellipsoid and of the Stanford Bunny by approximating the intrinsic divergence of the normal vector field. Finally, we compare Surface DC-PSE with surface finite elements (SFEM) and diffuse-interface finite elements (DI FEM) in a validation case.

We present a versatile open-source pipeline for simulating inhomogeneous reaction–diffusion processes in highly resolved, image-based geometries of porous media with reactive boundaries. Resolving realistic pore-scale geometries in numerical models is challenging and computationally demanding, as the scale differences between the sizes of the interstitia and the whole system can lead to prohibitive memory requirements. The present pipeline combines a level-set method with geometry-adapted sparse block grids on GPUs to efficiently simulate reaction–diffusion processes in image-based geometries. We showcase the method by applying it to fertilizer diffusion in soil, heat transfer in porous ceramics, and determining effective diffusion coefficients and tortuosity. The present approach enables solving reaction–diffusion partial differential equations in real-world geometries applicable to porous media across fields such as engineering, environmental science, and biology.

We present an artificial neural network architecture, termed STENCIL-NET, for equation-free forecasting of spatiotemporal dynamics from data. STENCIL-NET works by learning a discrete propagator that is able to reproduce the spatiotemporal dynamics of the training data. This data-driven propagator can then be used to forecast or extrapolate dynamics without needing to know a governing equation. STENCIL-NET does not learn a governing equation, nor an approximation to the data themselves. It instead learns a discrete propagator that reproduces the data. It therefore generalizes well to different dynamics and different grid resolutions. By analogy with classic numerical methods, we show that the discrete forecasting operators learned by STENCIL-NET are numerically stable and accurate for data represented on regular Cartesian grids. A once-trained STENCIL-NET model can be used for equation-free forecasting on larger spatial domains and for longer times than it was trained for, as an autonomous predictor of chaotic dynamics, as a coarse-graining method, and as a data-adaptive de-noising method, as we illustrate in numerical experiments. In all tests, STENCIL-NET generalizes better and is computationally more efficient, both in training and inference, than neural network architectures based on local (CNN) or global (FNO) nonlinear convolutions.

Biofilm-forming benthic diatoms are key primary producers in coastal habitats, where they frequently dominate sunlit intertidal substrata. The development of gliding motility in raphid diatoms was a key molecular adaptation that contributed to their evolutionary success. However, the structure-function correlation between diatom adhesives utilized for gliding and their relationship to the extracellular matrix that constitutes the diatom biofilm is unknown. Here, we have used proteomics, immunolocalization, comparative genomics, phylogenetics and structural homology analysis to investigate the evolutionary history and function of diatom adhesive proteins. Our study identified eight proteins from the adhesive trails of Craspedostauros australis, of which four form a new protein family called Trailins that contain an enigmatic Choice-of-Anchor A (CAA) domain, which was acquired through horizontal gene transfer from bacteria. Notably, the CAA-domain shares a striking structural similarity with one of the most widespread domains found in ice-binding proteins (IPR021884). Our work offers new insights into the molecular basis for diatom biofilm formation, shedding light on the function and evolution of diatom adhesive proteins. This discovery suggests that there is a transition in the composition of biomolecules required for initial surface colonization and those utilized for 3D biofilm matrix formation.

Three-dimensional cell and organoid cultures rely on the mechanical support of viscoelastic matrices. However, commonly used matrix materials lack control over key cell-instructive properties. Here we report on fully synthetic hydrogels based on DNA libraries that self-assemble with ultrahigh-molecular-weight polymers, forming a dynamic DNA-crosslinked matrix (DyNAtrix). DyNAtrix enables computationally predictable and systematic control over its viscoelasticity, thermodynamic and kinetic parameters by changing DNA sequence information. Adjustable heat activation allows homogeneous embedding of mammalian cells. Intriguingly, stress-relaxation times can be tuned over four orders of magnitude, recapitulating mechanical characteristics of living tissues. DyNAtrix is self-healing, printable, exhibits high stability, cyto- and haemocompatibility, and controllable degradation. DyNAtrix-based cultures of human mesenchymal stromal cells, pluripotent stem cells, canine kidney cysts and human trophoblast organoids show high viability, proliferation and morphogenesis. DyNAtrix thus represents a programmable and versatile precision matrix for advanced approaches to biomechanics, biophysics and tissue engineering.

Morphological and traditional genetic studies of the young Pliocene genus Hyles have led to the understanding that despite its importance for taxonomy, phenotypic similarity of wing patterns does not correlate with phylogenetic relationship. To gain insights into various aspects of speciation in the Spurge Hawkmoth (Hyles euphorbiae), we assembled a chromosome-level genome and investigated some of its characteristics.

Photoreceptor cells generate neuronal signals in response to capturing light. This process, called phototransduction, takes place in a highly specialized outer segment organelle. There are significant discrepancies in the reported amounts of many proteins supporting this process, particularly those of low abundance, which limits our understanding of their molecular organization and function. In this study, we used quantitative mass spectrometry to simultaneously determine the abundances of 20 key structural and functional proteins residing in mouse rod outer segments. We computed the absolute number of molecules of each protein residing within an individual outer segment and the molar ratio among all 20 proteins. The molar ratios of proteins comprising three well-characterized constitutive complexes in outer segments differed from the established subunit stoichiometries of these complexes by less than 7%, highlighting the exceptional precision of our quantification. Overall, this study resolves multiple existing discrepancies regarding the outer segment abundances of these proteins, thereby advancing our understanding of how the phototransduction pathway functions as a single, well-coordinated molecular ensemble.