Self-organized emergence of hyaline cartilage in hiPSC-derived multi-tissue organoids

Here, we developed a simple xeno- and feeder-free protocol for human hyaline cartilage production in vitro using hydrogel-cultured multi-tissue organoids (MTO). We investigate gene regulatory networks during spontaneous hiPSC-MTO differentiation using RNA sequencing and bioinformatic analyses. We find the interplays between BMPs and neural FGF pathways are associated with MTO phenotype transition. We recognize TGF-beta/BMP and Wnt signaling likely contribute to the long-term maintenance of cartilage growth and specific increase in articular cartilage development. By comparing MTO cartilage transcription signature with human lower limb chondrocytes, we observe MTO chondrocytes show strong correlation with fetal lower limb cartilage tissues. Collectively, our findings describe self-organized emergence of MTO hyaline cartilage, its associated molecular pathways, and spontaneous adoption of articular cartilage development trajectories by MTO. Multi-tissue organoids were induced from induced pluripotent stem cell line ATCC-BYS0110 (ATCC, Cat. #ACS-1024) and harvested at 8, 11, and 15 weeks in culture (n=2 biological replicates of each). MTOs were lysed in RLT buffer (Qiagen, Hilden, Germany) and RNA isolated from cell lysates using the RNeasy Plus mini kit (Cat. No. / ID: 74134Qiagen, Hilden, Germany) according to manufacturer’s instructions. Extracted RNA was then quantified by RiboGreen RNA assay (Thermo Fisher Scientific, Waltham, MA) and quality/size analyzed by Agilent BioAnalyzer (Agilent Technologies, Santa Clara, CA). 2 x 50bp FastQ paired-end reads for 6 samples (n=62.4 Million average per sample) were trimmed using Trimmomatic (v0.33) enabled with the optional “-q” option; 3bp sliding-window trimming from 3’ end requiring minimum Q30. Quality control on raw sequence data for each sample was performed with FastQC. Read mapping was performed via Hisat2 (v2.1.0) using the Human genome (GRCh38) as reference. Gene quantification was done via Feature Counts for raw read counts. Existing RNA-seq data were processed using the same pipeline.