Scientific publications

Tissue functions rely on complex structural, biochemical, and biomechanical cues that guide cellular behavior and organization. Synthetic cells, a promising new class of biomaterials, hold significant potential for mimicking these tissue properties using simplified, nonliving building blocks. Advanced synthetic cell models have already shown utility in biotechnology and immunology, including applications in cancer targeting and antigen presentation. Recent bottom-up approaches have also enabled synthetic cells to assemble into 3D structures with controlled intercellular interactions, creating tissue-like architectures. Despite these advancements, challenges remain in replicating multicellular behaviors and dynamic mechanical environments. Here, we review recent advancements in synthetic cell-based tissue formation and introduce a three-pillar framework to streamline the development of synthetic tissues. This approach, focusing on synthetic extracellular matrix integration, synthetic cell self-organization, and adaptive biomechanics, could enable scalable synthetic tissues engineering for regenerative medicine and drug development.

Lanthanide-doped upconversion microparticles (UCMP) enable composites for luminescence thermometry with long luminescence lifetime and narrowband absorption and emission spectra. Being non-toxic, easily synthesizable, and having a bright, stable emission makes them an attractive candidate for in-vivo monitoring of key environmental parameters such as temperature. We use them to create soft, biodegradable, miniaturized seed-like robots endowed with fluorescence tags for the sustainable environmental monitoring of topsoil and air above soil environments. Our aim is an airborne platform with a sufficient signal-to-noise ratio to identify the concentration of targeted soil parameters. Here, we study the photoluminescence of Er, Yb: NaYF4 UCMPs embedded in polylactic acid (PLA) polymeric matrix to assess their suitability for remote read-out. We assessed the signal-to-noise ratio in terms of excitation intensity, UCMP concentration, working distance, and sample orientation. We evaluated the signal stability over long exposure time as well as for amplitude-modulated excitation. Finally, we carried out ratiometric and lifetime measurements of luminescence emission in order to demonstrate the feasibility of such sensors in measuring the variation of temperature. Overall, the rare-earth doped UCMPs embedded in biodegradable polymer can be used for remote thermometry, displaying a significant signal-to-noise ratio for luminescence emission detection and subsequent derivation of temperature.

Long-lasting polypore fungi are significant producers of terpene cyclases of high interest for medicinal or biotechnological applications. Following the 1000 Fungal Genomes initiative launched by the Joint Genome Institute, the genome of Cubamyces (C.) menziesii and identified 18 genes encoding sesquiterpene cyclases (STCs) is explored. In a search for robust catalysts suitable for practical applications, the 18 codon-optimized open reading frames are cloned and overproduced the C. menziesii STCs in Escherichia coli. In ten cases, the catalytically active enzyme is purified and tested with three chemically synthesized linear diphosphates: geranyl diphosphate, farnesyl diphosphate (FDP), and geranylgeranyl diphosphate. Only FDP proved to be a substrate for these 10 enzymes. The product specificity of all these enzymes is determined by (GC-MS) gas chromatography mass spectrometry and (NMR) nuclear magnetic resonance analysis. Among the 10 enzymes, four produced a predominant compound, four yielded two main compounds, and the remaining two acted as a multiproduct catalysts. This work sheds light on the potential sesquiterpenes involved in the chemical ecology of the polypore C. menziesii and provides evidence for the potential of Polyporales fungi in the identification of new sesquiterpene cyclase activities.

Elastocaloric technology is a new way to heat and cool spaces by using stretchy metals, called shape-memory alloys, instead of harmful refrigerant gases. When these metals are squeezed or stretched, they heat up; and when they relax, they cool down. This process is called the elastocaloric effect and it is more energy efficient than traditional cooling systems, making it a cleaner, greener alternative. Elastocaloric systems could cool homes, schools, and workplaces, and they could refrigerate food and medicine in areas with limited electricity. Researchers are also testing this technology for cooling and heating of electric vehicles, where it could help conserve battery life, and for heating buildings in colder climates. Despite its promise, elastocaloric technology faces challenges, such as improving the durability of materials and making the shape-memory alloys more affordable. With continued research, this technology could someday help to reduce greenhouse gas emissions, lower energy costs, and bring life-saving cooling to more people all over the world.

Structurally heterogeneous materials present major challenges for characterization due to their complex nanoscale order. Sodium poly(heptazine imide) (NaPHI), a layered carbon nitride photocatalyst, exemplifies this complexity, with its precise structure remaining unresolved. Here, we uncover new structural insights into NaPHI using energy-filtered four-dimensional scanning transmission electron microscopy combined with machine-learning-based diffraction image segmentation, supported by transmission electron microscopy, atomic force microscopy, X-ray diffraction, and Raman spectroscopy. At the mesoscale, NaPHI flakes display bent morphologies, while nanodiffraction patterns reveal features characteristic of stacking disorder. Based on these insights, we modeled a NaPHI-layered structure incorporating out-of-plane undulations (waves) with amplitudes of ∼0.5 Å and wavelengths of 2–3 nm. This model reproduces the observed line features in nanodiffraction patterns and agrees with powder X-ray diffraction data, thereby bridging local and bulk structural information. The introduced approach uses data-driven machine learning to identify statistically significant features, offering a robust framework for structural analysis of semi-crystalline materials.

Replica molding is a widely used technique for the fabrication of polymer microstructures. As structural dimensions decrease, anti-stick surface treatment of the mold becomes increasingly critical to ensure clean demolding and preserve structural integrity. We fabricated arrays of micropillars with 20 µm diameter and 60 µm height using medical-grade polydimethylsiloxane (PDMS), MDX4-4210, and observed a high fraction of collapsed pillars for the first molding after fluorosilanization of the mold to reduce sticking. To address this issue, we systematically investigated the surface treatment protocol for the molds, made from the PDMS Sylgard 184. We provide results from complementary measurement methods, to show that an additional vacuum step partially removes unbound fluorosilane, but does not improve pillar stability. In contrast, a method based on multiple replications, where the first replication effectively removes residual fluorosilane from the mold, significantly enhances structural stability. Mechanical testing further revealed that the presence of fluorosilane lowers the Young’s modulus of both PDMS materials, MDX4-4210 and Sylgard 184, suggesting interference with the curing process. Confocal Brillouin microscopy indicated an elongation of replicated pillars and revealed a softening close to the surfaces, as well as mechanical inhomogeneities in collapsed pillars. We discuss modifications to the molding protocol to improve the reproducibility and mechanical stability of the replicated microstructures, offering insights towards more reliable routes for the fabrication of residue-free, high-aspect ratio features with controlled surface chemistry.

There is a growing scientific interest in material unpleasantness, yet the role of distinct physical parameters in perceptual and affective haptic experiences with liquids remains to be fully understood. To address this, we investigated how perceptual qualities of liquids relate to measurable physical properties and unpleasantness during active touch. We prepared 15 custom liquid samples using everyday materials. Rheological measurements showed that samples varied between physical viscosity 1mPA s and 45 Pa s ⁠. Participants explored each sample using circular rubbing motions with their index fingers. A camera system tracked finger movements, and a force sensor revealed applied normal forces, pull-off force (PoF) and the coefficient of friction (CoF). We compared these physical properties with the perceptual dimensions from our earlier work: perceived viscosity and slipperiness. Perceived viscosity correlated strongly with both physical viscosity and PoF, but not with CoF. Conversely, perceived slipperiness was associated with CoF, but not PoF or physical viscosity, demonstrating distinct links between physics and perception of liquids. Interestingly, PoF but not CoF was significantly linked to unpleasantness, suggesting that PoF but not CoF is crucial for liquid unpleasantness. These findings advance our understanding of how distinct physical properties relate to perceptual and affective experiences of liquids.

Contact resonance atomic force microscopy (CR-AFM) has been used in many studies to characterize variations in the elastic and viscoelastic constants of materials along a heterogeneous surface. In almost all experimental work, the quantitative modulus of the surface is calculated in reference to a known reference material, rather than calculated directly from the dynamics models of the cantilever. We measured the cantilever displacement with very high sampling frequencies over the course of the experiment and captured its oscillations that result from thermal energy. Using short-term Fourier transformations, it was possible to fit the thermal resonance peak of the normal displacement to track the frequency and Q-factor of the cantilever during an experiment, using a similar process to that used to calibrate the normal bending stiffness of cantilevers. With this quantitative data, we have used the dynamic mechanics models relating the contact stiffness of the tip/cantilever pressing into a surface with the oscillation frequency of the cantilever and show that they did not accurately model the experiment. Several material combinations of tip and sample were examined; tip size and cantilever stiffness demonstrate that existing models cannot capture the physics of this problem. While concrete solutions to use analytical models to interpret CR-AFM data have not been found, a possible solution may include revisiting the analytical model to capture a potentially more complex system than the current model, improved matching the cantilever/sample stiffness to obtain a larger variation in contact stiffness with frequency, or investigating the use of higher-order modes that may achieve this improved match.

Methods for the precise temporal control of cell surface receptor activation are indispensable for the investigation of signaling processes in mammalian cells. Optogenetics enables such precise control, but its application in primary cells is limited by the imperative for genetic manipulation of target cells. We here describe a method that overcomes this obstacle and enables the precise activation of the T cell receptor in nongenetically engineered human T cells by light. Our optogenetic receptor activation system OptoREACT employs a TCR-specific scFv fused to PIF6 that interacts with tetramerized PhyB in a light-dependent manner and thereby clusters and activates the T cell receptor in response to red light. OptoREACT not only omits genetic manipulation of the target cell but, because of its modular nature, is likely applicable to a broad range of oligomerization-activated cell surface receptors.

The construction and assembly of information-processing biomaterials are limited by the need for laborious assembly of various circuits. A new framework to assemble protein-based elements encoding complex Boolean operations enables user-defined release of biomolecules from these materials.