Comparative transcriptomic analysis identifies distinct molecular signatures and regulatory networks of chondroclasts and osteoclasts

Abstract

Abstract Background Chondroclasts and osteoclasts have been previously identified as the cells capable of resorbing mineralized cartilage and bone matrices, respectively. While both cell types appear morphologically similar, contain comparable ultrastructural features, and express tartrate-resistant acid phosphatase (TRAP), however, no information is available about the genomic similarities and differences between osteoclasts and chondroclasts. Methods To address this question, we laser captured homogeneous populations of TRAP-positive cells that interact with bone (osteoclasts) and TRAP-positive cells that interact with mineralized cartilage (chondroclasts) on the same plane from murine femoral fracture callus sections. We then performed a global transcriptome profiling of chondroclasts and osteoclasts by utilizing a mouse genome Agilent GE 4X44K V2 microarray platform. Multiple computational approaches and interaction networks were used to analyze the transcriptomic landscape of osteoclasts and chondroclasts. Results Our systematic and comprehensive analyses using hierarchical clustering and principal component analysis (PCA) demonstrate that chondroclasts and osteoclasts are transcriptionally distinct cell populations and exhibit discrete transcriptomic signatures as revealed by multivariate analysis involving scatter plot, volcano plot, and heatmap analysis. TaqMan qPCR was used to validate the microarray results. Intriguingly, the functional enrichment and integrated network analyses revealed distinct Gene Ontology terms and molecular pathways specific to chondroclasts and osteoclasts and further suggest that subsets of metabolic genes were specific to chondroclasts. Protein-protein interaction (PPI) network analysis showed an abundance of structured networks of metabolic pathways, ATP synthesis, and proteasome pathways in chondroclasts. The regulatory network analysis using transcription factor-target gene network predicted a pool of genes including ETV6, SIRT1, and ATF1 as chondroclast-specific gene signature. Conclusions Our study provides an important genetic resource for further exploration of chondroclast function in vivo. To our knowledge, this is the first demonstration of genetic landscape of osteoclasts from chondroclasts identifying unique molecular signatures, functional clustering, and interaction network.