Injectable Microporous Annealed Crescent-Shaped (MAC) Particle Hydrogel Scaffold for Enhanced Cell Infiltration

Abstract

Hydrogels are widely used for tissue engineering applications to support cellular growth, yet the tightly woven structure often restricts cell infiltration and expansion. Consequently, granular hydrogels with microporous architectures have emerged as a new class of biomaterial. Particularly, the development of microporous annealed particle (MAP) hydrogel scaffolds has shown improved stability and integration with host tissue. However, the predominant use of spherically shaped particles limits scaffold porosity, potentially limiting the level of cell infiltration. Here, a novel microporous annealed crescent-shaped particle (MAC) scaffold that is predicted to have improved porosity and pore interconnectivity in silico is presented. With microfluidic fabrication, tunable cavity sizes that optimize interstitial void space features are achieved. In vitro, cells incorporated into MAC scaffolds form extensive 3D multicellular networks. In vivo, the injectable MAC scaffold significantly enhances cell infiltration compared to spherical MAP scaffolds, resulting in increased numbers of myofibroblasts and leukocytes present within the gel without relying on external biomolecular chemoattractants. The results shed light on the critical role of particle shape in cell recruitment, laying the foundation for MAC scaffolds as a next-generation granular hydrogel for diverse tissue engineering applications.

Keywords: biomaterials; hydrogel; microfluidics; microparticles; tissue engineering.

Figure 1:. Computational Simulation of Granular Hydrogel Scaffolds and Particle Transport

(a) Design schematics for crescent-shaped and spherical building block particles with corresponding granular hydrogels. (b) Visualization of the void space within granular hydrogels (c) Quantitative analysis of porosity of spherical and crescent granular hydrogel formulations showing increased porosity with larger cavities. (d) Simulation of beads transport through granular hydrogels. Beads trickled down into the scaffolds while the trajectories were recorded and analyzed. (e) Illustrative schematic of the calculation of tortuosity, which is the ratio of the distance traveled along the entire path to the net displacement. (f) Tortuosity of spherical and crescent granular hydrogel formulations, where tortuosity decreased with enhanced cavity size percentage. Data presented as violin plots, n = 100. P-values are calculated using one-way ANOVA with Turkey’s multiple comparisons, *P < 0.05, **** P< 0.0001.

Figure 2:. Fabrication of Crescent-Shaped Microfluidic Hydrogel Particle (μGel) Building Blocks

(a) Illustrative schematic of microgel formation using a flow-focusing microfluidic device. A pre-gel and crosslinking solution were injected and segmented into monodisperse droplets, followed by aqueous two-phase separation and in situ crosslinking via Michael addition. (b) Representative images of spontaneous phase separation of droplets in emulsions comprising various concentration ratios. Scale bar, 25 μm. (c) Diameter distribution of spherical and crescent-shaped particles. (d) Representative images of washed crescent-shaped particles with different cavity sizes after polymerization. Scale bar, 40 μm. (e) Cavity size percentages increase with increasing gelatin concentrations in the emulsion and aqueous phase post-swelling. Data presented as mean ± SD, n = 6. P-values are calculated using two-way ANOVA with Šidák correction, *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. (f) Illustration of the annealing process for microgels via spontaneous Michael addition between PEG-dithiol and surface residual vinyl groups on the particles. (g) Representative 3D confocal fluorescence image of the microporous annealed crescent (MAC) scaffold. Scale bar, 100 μm.

Figure 3:. Geometrical Characterization of the Interstitial Void Space of MAP Scaffolds via Confocal Microscopy

(a) Representative 3D volumetric constructs of the interstitial void space within granular hydrogels made with spheres (S), crescent with 6% gelatin (C6), 8% gelatin (C8), and 10% gelatin (C10) formulations. Scale bar, 200 μm. (b) 3D scaffold porosity quantified by IMARIS software. Data presented as mean ± SD, n = 3. P-values are calculated using one-way ANOVA with Turkey’s multiple comparisons. (c) 2D Pore size distribution of granular hydrogels quantified by FIJI software. Data presented as mean ± SD, n = 12,000 to 14,000. P-values are calculated using one-way ANOVA with Turkey’s multiple comparisons, **P < 0.01; ***P < 0.001; ****P < 0.0001. (d) 2D Pore anisotropy represented by pore aspect ratio. Data presented as violin plots, n = 12,000 to 14,000. P-values are calculated using one-way ANOVA with Turkey’s multiple comparisons, ****P < 0.0001. (e) 2D Pore circularity. Data presented as violin plots, n = 12,000 to 14,000. P-values are calculated using one-way ANOVA with Turkey’s multiple comparisons, *P < 0.05; ***P < 0.001.

Figure 4:. Influence of Particle Shape on Rheometry Properties and Injectability of Granular Hydrogel

(a) Representative images of microporous annealed particles with the sphere or crescent formulations. Scale bar, 5 mm. (b) Storage modulus of un-annealed and pre-annealed granular hydrogel formulations, showing a similar trend of increased modulus after annealing. Data presented as mean ± SD, n = 3. P-values are calculated using two-way ANOVA with Šidák correction, ****P < 0.0001. (c) Viscosity of un-annealed and pre-annealed granular hydrogel formulations, where the overall viscosity was higher for crescent-shaped particle formulation. Data presented as mean ± SD, n = 3. P-values are calculated using two-way ANOVA with Šidák correction, **P < 0.01; ***P < 0.001; ****P < 0.0001. (d) Flow curve indicating yield stress for both granular hydrogel formulations. Data presented as mean ± SD, n = 3. (e) Dynamic viscosity curve showcasing shear-thinning properties. Data presented as mean ± SD, n = 3. (f) Representative image of extrusion of granular hydrogels through 25G syringe needles. (g) Extrusion force of crescent-shaped and spherical granular hydrogels. Data presented as mean ± SD, n = 3. P-values are calculated using two-way ANOVA with Šidák correction, **P < 0.01; ***P < 0.001.

Figure 5:. Mesenchymal Stem Cell Seeding and Expansion in Spherical and Crescent MAP Scaffolds in vitro

(a) Illustration of the 3D encapsulation where the MSCs were dynamically incorporated into the void spaces of the granular hydrogel scaffold. (b) Top view of a confocal Z stack series of the spherical formulation, along with (c) top view of the MSCs encapsulated within. Scale bar, 100 μm. (d) Top view of the confocal Z stack of the crescent formulation, along with (e) top view of the MSCs encapsulated within, showcasing cells wrapping around the surfaces of crescent-shaped particles. Scale bar, 100 μm. (f) Representative max projection fluorescence imaging demonstrating the formation of 3D cellular networks during 14 days of culture in MAP scaffolds in vitro, exhibiting distinct morphology differences between the two formulations. Scale bar, 200 μm. (g) Cell count at day 14 within the volumetric confocal Z stack series. Data presented as mean ± SD, n = 3. P-values are calculated using unpaired two-tailed t test. (h) Total 3D cell volume within the confocal Z stack series. Data presented as mean ± SD, n = 3. P-values are calculated using unpaired two-tailed t test, *P < 0.05. (i) Representative 3D constructs of cell clusters within the MAP scaffolds using IMARIS software. Scale bar, 200 μm. (j) Cluster count. Data presented as mean ± SD, n = 3. P-values are calculated using unpaired two-tailed t test, *P < 0.05. (k) Cell-to-cluster ratio. Data presented as mean ± SD, n = 3. P-values are calculated using unpaired two-tailed t test, *P < 0.05. (l) Individual cluster volume occupied. Data presented as mean, n = 3. P-values are calculated using unpaired two-tailed t test. (m) Cluster sphericity. Data presented as mean, n = 3. P-values are calculated using unpaired two-tailed t test.

Figure 6:. Quantification of Cell Infiltration using Flow Cytometry

(a) Schematic illustration of the quantification procedure via flow cytometry. (b) Cell density within MAP gel scaffolds post-injection quantified by flow cytometry. (c) CD11b+ leukocytes, (d) total macrophages, (e) LyC6^hiCD206-CD163- M1 macrophages, and (f) LyC6^lowCD206+CD163+ M2 macrophages density quantified over seven days. All the data above was presented as mean ± SD, n = 6. P-values are calculated using two-way ANOVA with Šidák correction, *P < 0.05; ***P < 0.001; ****P < 0.0001.(g) Percentage of total macrophages that are M1 and M2 macrophages within MAP gel scaffolds at day 7.

Figure 7:. Subcutaneous Delivery of MAP Scaffolds to Evaluate Cell Infiltration In Vivo

(a) Illustrative schematic of the subcutaneous delivery model to analyze cell infiltration from the tissue-scaffold interface. (b) Representative images of MAP gel scaffolds revealed differences in cell infiltration and distribution after three days. Scale bar, 200 μm. (c) Cell density fraction from the scaffold-tissue interface across 800 µm in depth, where a higher overall cell density was observed in the crescent-shaped particle scaffold. Data presented as mean, n = 12. P-values are calculated using two-way ANOVA with Šidák correction, *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. (d) Representative images of infiltrating α-SMA positive cells. Scale bar, 100 μm; inset scale bar, 25 μm. (e) α-SMA fluorescent area coverage revealed a 3-fold increase for MAC scaffolds. Data presented as mean ± SD, n = 3. P-values are calculated using unpaired two-tailed t test, **P < 0.01. (f) Representative images of infiltrating CD11b positive leukocytes. Scale bar, 100 μm; inset scale bar, 25 μm. (g) CD11b fluorescent area coverage showcased a 4 fold increase for MAC. Data presented as mean ± SD, n = 3. P-values are calculated using unpaired two-tailed t test, *P < 0.05.