Variations in mechanical stiffness alter microvascular sprouting and stability in a PEG hydrogel model of idiopathic pulmonary fibrosis

Abstract

Objective: Microvascular remodeling is governed by biomechanical and biochemical cues which are dysregulated in idiopathic pulmonary fibrosis. Understanding how these cues impact endothelial cell-pericyte interactions necessitates a model system in which both variables can be independently and reproducibly modulated. In this study we develop a tunable hydrogel-based angiogenesis assay to study how varying angiogenic growth factors and environmental stiffness affect sprouting and vessel organization.

Methods: Lungs harvested from mice were cut into 1 mm long segments then cultured on hydrogels having one of seven possible stiffness and growth factor combinations. Time course, brightfield, and immunofluorescence imaging were used to observe and quantify sprout formation.

Results: Our assay was able to support angiogenesis in a comparable manner to Matrigel in soft 2 kPa gels while enabling tunability to study the effects of stiffness on sprout formation. Matrigel and 2 kPa groups contained significantly more samples with sprouts when compared to the stiffer 10 and 20 kPa gels. Growth factor treatment did not have as obvious an effect, although the 20 kPa PDGF + FGF-treated group had significantly longer vessels than the vascular endothelial growth factor-treated group.

Conclusions: We have developed a novel, tunable hydrogel assay for the creation of lung explant vessel organoids which can be modulated to study the impact of specific environmental cues on vessel formation and maturation.

Keywords: angiogenesis; endothelial cells; fibrosis; hydrogel; microvascular remodeling.

Figure 1.

Overview of angiogenesis assay protocol.

Figure 2.

Sample images of vessel formation categories.

Figure 3.

(A) Representative brightfield image of microvessel sprout (black arrow) with (B) ECs (white arrow) identity confirmed with CD31 immunostaining and pericyte (yellow arrow) identity confirmed with TdTomato expression.

Figure 4.

Snapshots from a video recording of cell sprouting dynamics in Matrigel and 2kPa VEGF-treated gel over 16 hours (hour 77–93 after implantation). In the Matrigel, sprout formation between hours 77 and 87 is observed (blue arrow) but this sprout has shortened or regressed by hour 93 (blue arrow). Network growth and competitive branch formation (red arrow) is also observed. In the 2kPa VEGF-treated gel, bridging between one multicellular sprout and another is observed (yellow arrow).

Figure 5.

(A)The percentage of samples containing sprouts in each condition. (B)The average number of sprouts per sample in each group. Statistical analysis was conducted using a two-way ANOVA with Tukey’s post-hoc test to quantify differences when compared to Matrigel Control group. N = 3 per condition, ns = not significant, *p<0.05, **p<0.01, ***p<0.001.

Figure 6.

(A) The percentage of samples containing sprouts in each VEGF-treated gel condition. (B) The average number of sprouts per sample in each group. (C) The average longest sprout and (D) average shortest sprout. Statistical analysis was done using a one-way ANOVA with Tukey’s post-hoc test to quantify differences between groups. N = 3 per condition, *p<0.05, **p<0.01.

Figure 7.

(A) The percentage of samples containing sprouts in each PDGF+FGF-treated gel condition. (B) The average number of sprouts per sample in each group. (C)The average longest sprout and (D) average shortest sprout. Statistical analysis was done using a one-way ANOVA with Tukey’s post-hoc test to quantify differences between groups. N = 3 per condition, *p<0.05, **p<0.01.

Figure 8.

Analysis of effects of gel treatment on the percentage of samples containing sprouts in each condition. The average number of sprouts per sample in each group. The average longest sprout and average shortest sprout. Statistical analysis was done using a Student’s T-test to quantify differences between treatments. N = 3 per condition, *p<0.05, **p<0.01.

Figure 9.

MMP-9 ELISA analysis of cell culture media collected at days 2, 4, and 6 from PDGF+FGF containing gels with stiffnesses of (A) 2 kPa, (B) 10 kPa, and (C) 20kPa. Statistical analysis was done using a one-way ANOVA with Tukey’s post-hoc test to quantify differences between groups. N = 3 per condition, *p<0.05.

Figure 10.

(A) The percentage of samples containing sprouts in each PDGF+FGF-treated gel condition according to IF analysis. (B) Overview of the workflow and challenges of immunofluorescent image analysis using AngioTool, which quantified: (C) average vessel length, (D) average total vessel length, (E) average number of junctions, and (F) average total number of end points. Statistical analysis was done using a one-way ANOVA with Tukey’s post-hoc test to quantify differences between groups. N = 4 per condition, *p<0.05, **p<0.01.