GSE193102
Orthogonally induced differentiation of stem cells for the pattering of vascularized organoids and bioprinted tissues [scRNA-seq]
Simultaneous differentiation of human induced pluripotent stem cells (hiPSCs) into divergent cell types offers a pathway to achieving tailorable cellular complexity, patterned architecture, and function in engineered human organoids and tissues. Recent transcription factor (TF) overexpression protocols typically produce a single cell type of interest rather than the multitude of cell types and structural organization found in native human tissues. Here, we report an orthogonal induced differentiation platform, wherein pluripotent cells are simultaneously co-differentiated into distinct cell types to generate organoids and bioprinted tissues with controlled composition and organization. To demonstrate this platform, we differentiated endothelial cells and neurons from hiPSCs in a one-pot system containing either neural or endothelial stem cell-specifying media. By aggregating inducible-TF and wild type hiPSCs into pooled and multicore-shell embryoid bodies, we produced vascularized and patterned cortical organoids within days. Using multimaterial 3D bioprinting, we patterned 3D neural tissues from densely cellular, matrix-free stem cell inks that underwent orthogonal induced differentiation to generate distinct layered regions composed of neural stem cells, endothelium, and neurons, respectively. Given the high proliferative capacity and patient-specificity of hiPSCs, our platform provides a facile route for programming cells and multicellular tissues for drug screening and therapeutic applications. Overall design: WT hiPSC, randomly pooled, and multicore-shell cortical organoids were cultured using the methods described in the paper until day 25. Technical duplicates with two batches were created for each of 3 conditions comprising 12 total sequenced organoids. Mixed and Multicore-shell organoids were comprised of WT PGP1s, iEndo PGP1s, and iNeuron PGP1s in different seeding schema.