Member (Student), Cardiovascular Institute
Education & Certifications
BASc, University of British Columbia, Materials Engineering (2021)
Diffusion-Based 3D Bioprinting Strategies.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
3D bioprinting has enabled the fabrication of tissue-mimetic constructs with freeform designs that include living cells. In the development of new bioprinting techniques, the controlled use of diffusion has become an emerging strategy to tailor the properties and geometry of printed constructs. Specifically, the diffusion of molecules with specialized functions, including crosslinkers, catalysts, growth factors, or viscosity-modulating agents, across the interface of printed constructs will directly affect material properties such as microstructure, stiffness, and biochemistry, all of which can impact cell phenotype. For example, diffusion-induced gelation is employed to generate constructs with multiple materials, dynamic mechanical properties, and perfusable geometries. In general, these diffusion-based bioprinting strategies can be categorized into those based on inward diffusion (i.e., into the printed ink from the surrounding air, solution, or support bath), outward diffusion (i.e., from the printed ink into the surroundings), or diffusion within the printed construct (i.e., from one zone to another). This review provides an overview of recent advances in diffusion-based bioprinting strategies, discusses emerging methods to characterize and predict diffusion in bioprinting, and highlights promising next steps in applying diffusion-based strategies to overcome current limitations in biofabrication.
View details for DOI 10.1002/advs.202306470
View details for PubMedID 38145962
Embedded 3d Bioprinting of Collagen Inks into Microgel Baths to control hydrogel Microstructure and Cell Spreading.
Advanced healthcare materials
Microextrusion-based 3D bioprinting into support baths has emerged as a promising technique to pattern soft biomaterials into complex, macroscopic structures. We hypothesized that interactions between inks and support baths, which are often composed of granular microgels, could be modulated to control the microscopic structure within these macroscopic-printed constructs. Using printed collagen bioinks crosslinked either through physical self-assembly or bioorthogonal covalent chemistry, we demonstrate that microscopic porosity is introduced into collagen inks printed into microgel support baths but not bulk gel support baths. The overall porosity is governed by the ratio between the ink's shear viscosity and the microgel support bath's zero-shear viscosity. By adjusting the flow rate during extrusion, the ink's shear viscosity was modulated, thus controlling the extent of microscopic porosity independent of the ink composition. For covalently crosslinked collagen, printing into support baths comprised of gelatin microgels (15-50 µm) resulted in large pores (∼40 µm) that allowed human corneal mesenchymal stromal cells to readily spread, while control samples of cast collagen or collagen printed in non-granular support baths did not allow cell spreading. Taken together, these data demonstrate a new method to impart controlled microscale porosity into 3D printed hydrogels using granular microgel support baths. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/adhm.202303325
View details for PubMedID 38134346
- Gelation of Uniform Interfacial Diffusant in Embedded 3D Printing ADVANCED FUNCTIONAL MATERIALS 2023
Spatially controlled construction of assembloids using bioprinting.
2023; 14 (1): 4346
The biofabrication of three-dimensional (3D) tissues that recapitulate organ-specific architecture and function would benefit from temporal and spatial control of cell-cell interactions. Bioprinting, while potentially capable of achieving such control, is poorly suited to organoids with conserved cytoarchitectures that are susceptible to plastic deformation. Here, we develop a platform, termed Spatially Patterned Organoid Transfer (SPOT), consisting of an iron-oxide nanoparticle laden hydrogel and magnetized 3D printer to enable the controlled lifting, transport, and deposition of organoids. We identify cellulose nanofibers as both an ideal biomaterial for encasing organoids with magnetic nanoparticles and a shear-thinning, self-healing support hydrogel for maintaining the spatial positioning of organoids to facilitate the generation of assembloids. We leverage SPOT to create precisely arranged assembloids composed of human pluripotent stem cell-derived neural organoids and patient-derived glioma organoids. In doing so, we demonstrate the potential for the SPOT platform to construct assembloids which recapitulate key developmental processes and disease etiologies.
View details for DOI 10.1038/s41467-023-40006-5
View details for PubMedID 37468483
View details for PubMedCentralID PMC10356773
3D bioprinting of dynamic hydrogel bioinks enabled by small molecule modulators.
2023; 9 (13): eade7880
Three-dimensional bioprinting has emerged as a promising tool for spatially patterning cells to fabricate models of human tissue. Here, we present an engineered bioink material designed to have viscoelastic mechanical behavior, similar to that of living tissue. This viscoelastic bioink is cross-linked through dynamic covalent bonds, a reversible bond type that allows for cellular remodeling over time. Viscoelastic materials are challenging to use as inks, as one must tune the kinetics of the dynamic cross-links to allow for both extrudability and long-term stability. We overcome this challenge through the use of small molecule catalysts and competitors that temporarily modulate the cross-linking kinetics and degree of network formation. These inks were then used to print a model of breast cancer cell invasion, where the inclusion of dynamic cross-links was found to be required for the formation of invasive protrusions. Together, we demonstrate the power of engineered, dynamic bioinks to recapitulate the native cellular microenvironment for disease modeling.
View details for DOI 10.1126/sciadv.ade7880
View details for PubMedID 37000873
Enhancing Metabolome Coverage in Data-Dependent LC-MS/MS Analysis through an Integrated Feature Extraction Strategy
2019; 91 (22): 14433-14441
In untargeted metabolomics, conventional data preprocessing software (e.g., XCMS, MZmine 2, MS-DIAL) are used extensively due to their high efficiency in metabolic feature extraction. However, these programs present limitations in recognizing low-abundance metabolic features, thus hindering complete metabolome coverage from the analysis. In this work, we explored the possibility of enhancing the metabolome coverage of data-dependent liquid chromatography-tandem mass spectrometry (LC-MS/MS) results by rescuing metabolic features that are missed by conventional software. To achieve this goal, we first categorized the metabolic features into four confidence levels based on their chromatographic peak shapes and the presence of corresponding MS/MS spectra. We then assessed the false positives and quantitative accuracy of the metabolic features that contain MS/MS spectra but are not recognized by conventional software. Our results indicate that these missed features contain valid and important metabolic information and should be integrated into the conventional metabolomics results. Thus, we developed a data-preprocessing pipeline to extract low-abundance metabolic features and integrate them with the results from conventional programs. This integrated feature extraction strategy was tested on a set of fecal metabolomic data retrieved from mice who have undergone normal diet vs high-fat diet treatments. In our test data set, the integrated feature extraction approach increased the number of significant features being extracted by 24.4% and identified five additional metabolites bearing critical biological meanings. Our results show that this integrated feature extraction strategy remarkably improves the metabolome coverage beyond that of conventional data preprocessing, therefore facilitating the confirmation of metabolites of interest and accomplishment of a higher success rate in de novo metabolite identification.
View details for DOI 10.1021/acs.analchem.9b02980
View details for Web of Science ID 000498280100038
View details for PubMedID 31626534