Biomaterials composed of synthetic cells have the potential to adapt and differentiate guided by physicochemical environmental cues. Inspired by biological systems in development, which extract positional information (PI) from morphogen gradients in the presence of uncertainties, we here investigate how well synthetic cells can determine their position within a multicellular structure. To calculate PI, we created and analyzed a large number of synthetic cellular assemblies composed of emulsion droplets connected via lipid bilayer membranes. These droplets contained cell-free feedback gene circuits that responded to gradients of a genetic inducer acting as a morphogen. PI is found to be limited by gene expression noise and affected by the temporal evolution of the morphogen gradient and the cell-free expression system itself. The generation of PI can be rationalized by computational modeling of the system. We scale our approach using three-dimensional printing and demonstrate morphogen-based differentiation in larger tissue-like assemblies.

3D bioprinting is a tissue engineering approach using additive manufacturing to fabricate tissue equivalents for regenerative medicine or medical drug testing. For this purpose, biomaterials that provide the essential microenvironment to support the viability of cells integrated directly or seeded after printing are processed into three-dimensional (3D) structures. Compared to extrusion-based 3D printing, which is most commonly used in bioprinting, stereolithography (SLA) offers a higher printing resolution and faster processing speeds with a wide range of cell-friendly materials such as gelatin- or collagen-based hydrogels and SLA is, therefore, well suited to generate 3D tissue constructs. While there have been numerous publications of conversions and upgrades for extrusion-based printers, this is not the case for state-of-the-art SLA technology in bioprinting. The high cost of proprietary printers severely limits teaching and research in SLA bioprinting. With mSLAb, we present a low-cost and open-source high-resolution 3D bioprinter based on masked SLA (mSLA). mSLAb is based on an entry-level (€350) desktop mSLA printer (Phrozen Sonic Mini 4 K), equipped with temperature control and humidification of the printing chamber to enable the processing of cell-friendly hydrogels. Additionally, the build platform was redesigned for easy sample handling and microscopic analysis of the printed constructs. All modifications were done with off-the-shelf hardware and in-house designed 3D printed components, printed with the same printer that was being modified. We validated the system by printing macroscopic porous scaffolds as well as hollow channels from gelatin-based hydrogels as representative structures needed in tissue engineering.

Established acoustofluidic lab-on-a-chip devices enable contactless, label-free trapping of cells, but do not provide a quantitative readout of a levitated sample’s orientation, restricting their analytical reach. We present a synthetic-to-real deep learning framework that addresses this gap by estimating the five-dimensional (5D) pose of a trident-shaped micro-calibrator co-trapped with the cells. Here, 5D pose denotes the two-dimensional (2D) bounding-box center in the image plane and the calibrator’s three-dimensional (3D) orientation. Printed by two-photon polymerization with 10, 15, and 20 $\mathsf{\mu }$m arms, the calibrator provides the rigid, asymmetric geometry that a translucent, deformable spheroid lacks. The framework cascades detection, foreground segmentation, and rotation regression, with the normalized bounding-box center as a positional auxiliary input, and optionally applies temporal smoothing; it is trained on domain-randomized renderings, eliminating manual 3D pose annotation. On 15,000 synthetic test images, it reached a mean geodesic rotation error of 0.82°. The ablation study showed that the positional input substantially improves rotation estimation and background removal further lowers the error, and identified segmentation, not detection, as the dominant upstream error source. Pseudo-label adaptation raised the expert-assessed correctness rate from 34.71% to 83.62% on 5281 real microscopy frames from which the pseudo-labels were drawn (adaptation performance) and from 2.48% to 29.34% on 242 held-out fluorescence frames with adherent cells (generalization performance). This demonstrates the feasibility of a calibrator-based orientation readout on an established acoustofluidic platform without hardware change.