Biomaterials · 2022 · 32 citations · 47 references
EngineeringBioengineeringBiomedical EngineeringRegenerative MedicineStem Cell BiologyTissue Engineering3D BioprintingCell-scaffold InteractionsRegenerative ScaffoldsScaffold FabricationTissue Engineering ScaffoldsTissue Engineering With Stem Cells3D Cell Culture3D Cell Culture SystemsConjunctival Stem CellsDifferentiation PotencyEpithelial-mesenchymal Transition MarkersHydrogel ScaffoldsHydrogel-based ScaffoldsOcular Tissue Engineering Scaffolds
Pterygium is an ocular surface disorder with high prevalence that can lead to vision impairment. As a pathological outgrowth of conjunctiva, pterygium involves neovascularization and chronic inflammation. Here, we developed a 3D multicellular in vitro pterygium model using a digital light processing (DLP)-based 3D bioprinting platform with human conjunctival stem cells (hCjSCs). A novel feeder-free culture system was adopted and efficiently expanded the primary hCjSCs with homogeneity, stemness and differentiation potency. The DLP-based 3D bioprinting method was able to fabricate hydrogel scaffolds that support the viability and biological integrity of the encapsulated hCjSCs. The bioprinted 3D pterygium model consisted of hCjSCs, immune cells, and vascular cells to recapitulate the disease microenvironment. Transcriptomic analysis using RNA sequencing (RNA-seq) identified a distinct profile correlated to inflammation response, angiogenesis, and epithelial mesenchymal transition in the bioprinted 3D pterygium model. In addition, the pterygium signatures and disease relevance of the bioprinted model were validated with the public RNA-seq data from patient-derived pterygium tissues. By integrating the stem cell technology with 3D bioprinting, this is the first reported 3D in vitro disease model for pterygium that can be utilized for future studies towards personalized medicine and drug screening.
47
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
Paul Shannon, Andrew Markiel, Owen Ozier et al. · Genome Research · 2003 · 54.3K citations · Full text
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
Likun Wang, Zhixing Feng, Xi Wang et al. · Bioinformatics · 2009 · 4.9K citations · Full text