The growth plate (GP) comprising sequentially differentiated cell layers is a critical structure for bone elongation and regeneration. Although several key regulators in GP development have been identified using genetic perturbation, systematic understanding is still limited. Here, researchers from Tsinghua University used single-cell RNA-sequencing (RNA-seq) to determine the gene expression profiles of 217 single cells from GPs and developed a bioinformatics pipeline named Sinova to de novo reconstruct physiological GP development in both temporal and spatial high resolution. Our unsupervised model not only confirmed prior knowledge, but also enabled the systematic discovery of genes, potential signal pathways, and surface markers CD9/CD200 to precisely depict development. Sinova further identified the effective combination of transcriptional factors (TFs) that regulates GP maturation, and the result was validated using an in vitro EGFP-Col10a screening system. Our case systematically reconstructed molecular cascades in GP development through single-cell profiling, and the bioinformatics pipeline is applicable to other developmental processes.
Single-Cell Profiling Reveals Cell Heterogeneity throughout the GP
The distal cartilage structure of the tibia at postnatal day 7 (P7) consisted of four zones that were sequentially differentiated: the RZ, the PZ, the PHZ, and the HZ. The PZ, PHZ, and HZ generally compose the GP structure.
(A) Schematic diagrams of the single-cell analysis of cell populations from the GP. The H&E staining graph and diagram illustrate four cell types comprising tibia distal cartilage free from invasion by circulating cell types in the C57BL/6 mouse at P7. The dashed-line box indicates the microdissected structure of the GP across the three zones that were used for the single-cell analysis. These microstructures were trypsinized to yield a single-cell suspension and trapped using the Fluidigm C1 auto prepare system for the downstream single-cell mRNA-seq experiment (for details, see Movie S1 protocols).
(B) CC of 217 single cells by transcriptome analysis revealed at least three populations.
(B′) Optimal cluster numbers were determined in the delta plot to reveal the relative changes in the cumulative density function (CDF) in the CC.
(C) In parallel, PCA indicated the relative similarity across the 217 cells in three-dimensional space. K-means clustering further grouped the cells into three clusters marked with different colors in the PCA plot. A dissociated cluster (blue), contained outlier cells that exactly matched a distal cluster (purple dash line box) in the CC, as indicated by the arrow (B and C).
(D–F) Matrix protein gene expression distributions of Col10a (D), Col9a (E), and Comp (F) in the 217 single cells. The gene expression levels are indicated by the color of the bar.