bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.11.14.468504

Volumetric Tomographic 3D Bioprinting of Heterocellular Bone-like Tissues in Seconds

Gehlen, J.·Qiu, W.·Mueller, R.·Qin, X.-H.
View original source ↗Export metadata ↓

Abstract

Tomographic volumetric bioprinting (VBP) has recently emerged as a powerful tool for rapid solidification of cell-laden hydrogel constructs within seconds. However, its practical applications in tissue engineering requires a detailed understanding of how different printing parameters (concentration of resins, laser dose) affect cell activity and tissue formation. Herein, we explore a new application of VBP in bone tissue engineering by merging a soft gelatin methacryloyl (GelMA) bioresin (<5 kPa) with 3D endothelial co-culture to generate heterocellular bone-like constructs with enhanced functionality. To this, a series of bioresins with varying concentrations of GelMA and lithium Phenyl(2,4,6-trimethylbenzoyl)phosphinate (LAP) photoinitiator were formulated and characterized in terms of photo-reactivity, printability and cell-compatibility. A bioresin with 5% GelMA and 0.05% LAP was identified as the optimal formulation for VBP of complex perfusable constructs within 30 s at high cell viability (>90%). The fidelity was validated by micro-computed tomography and confocal microscopy. Compared to 10% GelMA, this bioresin provided a softer and more permissive environment for osteogenic differentiation of human mesenchymal stem cells (hMSCs). The expression of osteoblastic markers (collagen-I, ALP, osteocalcin) and osteocytic markers (podoplanin, Dmp1) was monitored for 42 days. After 21 days, early osteocytic markers were significantly increased in 3D co-cultures with human umbilical vein endothelial cells (HUVECs). Additionally, we demonstrate VBP of a perfusable, pre-vascularized model where HUVECs self-organized into an endothelium-lined channel. Altogether, this work leverages the benefits of VBP and 3D co-culture, offering a promising platform for fast scaled biofabrication of 3D bone-like tissues with unprecedented functionality. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/468504v4_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1294740org.highwire.dtl.DTLVardef@104d811org.highwire.dtl.DTLVardef@105677dorg.highwire.dtl.DTLVardef@1cd60e4_HPS_FORMAT_FIGEXP M_FIG C_FIG Statement of significanceThis study explores new strategies for ultrafast bio-manufacturing of bone tissue models by leveraging the advantages of tomographic volumetric bioprinting (VBP) and endothelial co-culture. After screening the properties of a series of photocurable gelatin methacryloyl (GelMA) bioresins, a formulation with 5% GelMA was identified with optimal printability and permissiveness for osteogenic differentiation of human mesenchymal stem cells (hMSC). We then established 3D endothelial co-cultures to test if the heterocellular interactions may enhance the osteogenic differentiation in the printed environments. This hypothesis was evidenced by increased gene expression of early osteocytic markers in 3D co-cultures after 21 days. Finally, VBP of a perfusable cell-laden tissue construct is demonstrated for future applications in vascularized tissue engineering.

Source connections

Explore related subjects

bioengineeringPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gehlen, J., Qiu, W., Mueller, R., Qin, X.-H.. 2021-11-15. Volumetric Tomographic 3D Bioprinting of Heterocellular Bone-like Tissues in Seconds. https://doi.org/10.1101/2021.11.14.468504

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Single-Component Photo-Crosslinked Lipoic Acid Functionalized Mucin Hydrogels: Dynamic Mucus Mimetics for Antiviral Protection

Native mucus protects epithelial surfaces through viscoelastic networks of disulfide-linked mucins. However, commercially available mucins often fail to reconstitute comparable mechanical and barrier properties, while synthetic and hybrid mucus mimetics require polymeric crosslinkers that may alter mucus-like composition. Inspired by the native architecture, we directly functionalized porcine gastric and bovine submaxillary mucins with -lipoic acid to develop a single-component, photo-crosslinkable hydrogels. Upon UV irradiation (365 nm), the modified mucins rapidly form disulfide-crosslinked networks without added photo-initiator/ polymeric crosslinkers, two orders of magnitude faster as compared with PEG-SH approaches. The resulting mucin-lipoic acid (MUC-LA) hydrogels exhibit mucus-relevant properties, including tunable viscoelasticity, redox responsiveness, self-healing, and size-dependent restrictions on nanoparticle transport. In transwell-based infection models, MUC-LA hydrogels (2-3%) mimicking the healthy airway mucus (G' {approx} 10-20 Pa) suppress epithelial infection by SARS-CoV-2 and influenza A virus. Crosslinked MUC-LA provides greater protection against SARS-CoV-2 than the non-crosslinked, whereas both formulations suppress influenza A infection, suggesting distinct contributions from network formation and mucin-associated interactions. These findings establish a dynamic, mucus-mimetic platform integrating rapid photo-gelation, controllable mechanics, selective transport, and antiviral barrier functionality. MUC-LA hydrogels offer opportunities to investigate mucosal transport and host-pathogen interactions, with potential applications in drug delivery, and as biomaterials.

bioengineering↗
bioRxiv2026

Population-level emulation enables fast Bayesian calibration of cardiac digital twins

Purpose: Patient-specific cardiac digital twins have the potential to guide individualized therapy, but generating these in a routine clinical workflow remains challenging. This is especially true when the twins must carry quantified uncertainty: Bayesian calibration requires thousands of simulations per patient, and the emulators that reduce this cost are typically trained one patient at a time. We aimed to train a single emulator across a patient cohort, so that a new patient can be calibrated in minutes without new simulations. Methods: We trained a population-level Gaussian process emulator whose inputs included a 12-dimensional latent encoding of the clinical features of each patient, so that a single emulator represents the whole cohort. We compared spending simulation budgets on resampling the real cohort with augmenting the cohort with digital chimeras, synthetic patients interpolated between real patients. Seven parameters of a closed-loop heart and circulation model were calibrated by Markov chain Monte Carlo to ten clinical biomarkers in 26 patients with left bundle branch block, each using an emulator trained without that patient, and the calibrated posteriors were compared against the full simulator. Results: A single emulator, trained without the held-out patient, calibrated the digital twin of each patient in 59.7-63.0 s per patient, versus 15 h with the simulator. Resampling the real cohort was the more efficient training strategy, bringing every output to R2 greater than or equal to 0.9, which augmentation did not achieve for the same simulation budget. For held-out patients, both strategies performed comparably (median leave-one-out normalized root-mean-square error 11.0% vs. 10.7%; p = 0.745), and error rose with distance from the cohort centroid (Spearman rho = +0.406). The calibrated models showed no cohort-wide bias (median standardized residual -0.12 against the clinical targets), and 95% predictive intervals contained 78.5% of targets, with the largest misfit in systolic blood pressure. Conclusion: Removing the need for new simulations for every new patient is a meaningful step toward uncertainty-aware cardiac digital twins that fit within a routine clinical workflow. Because the simulator, surrogate and sampler are interchangeable, the approach used here can be applied to other (cardiac) models and cohorts.

bioengineering↗
bioRxiv2026

Multi-Structure Segmentation of Type B Aortic Dissection from CT Images with U-Net

Type B aortic dissection (TBAD) is a life-threatening condition in which a tear in the intimal layer separates the aortic wall into a true and a false lumen. Assessment of the lumen, the intimal flap, and false-lumen thrombosis from CT images is important for risk analysis and treatment planning. This work applied U-Net-based deep neural networks to the automatic segmentation of these structures in TBAD. We collected 46 CT scans from 20 patients, with voxel-wise annotations of three structures: the aortic lumen, the intimal flap, and the thrombus. We explored four models: two convolutional networks, nnU-Net 2D and 3D, and two transformer-based architectures, TransUNet 2D and 3D. We observed that (1) for both nnU-Net and TransUNet, the 3D models outperformed their 2D counterparts on all three structures. The difference was small for the aortic lumen but large for the thin flap and the thrombus. The best model, nnU-Net 3D, reached a DSC of 93.05% for the lumen, 50.22% for the flap, and 69.94% for the thrombus. (2) The intimal flap was the most difficult structure for all models. Even the best 3D models showed large thrombus boundary errors in a few scans. These results provide a baseline for developing more advanced models for accurate segmentation of these structures in TBAD.

bioengineering↗