Search bioRxiv⌕ Search

Biology subjects

Chai, R. C.

Publications and source records attributed to Chai, R. C..

3 recordsLinked to original sources

Multiscale analysis and functional validation of the cellular and genetic determinants of skeletal disease

Musculoskeletal diseases are a major health burden. Development of bone-active therapies has been hindered by limited understanding of the cells and genes that regulate the skeleton. We exploited the value of cross-species analysis and developed single-cell methodologies in skeletal tissues to define the critical endosteal compartment that regulates bone turnover. Thirty-four distinct cell types were identified, and disease-relevant cells prioritised by enrichment for rare skeletal disorder genes and bone mineral density-associated genes in an extended UK Biobank GWAS. Functional validation was undertaken in over one thousand genetically modified mouse models. Endothelial and vascular smooth muscle cells were identified as novel skeletal disease-relevant cells alongside osteoblast, chondrocyte and osteoclast cell lineages. Hundreds of cell-specific genes with unappreciated roles in skeletal pathophysiology were identified. This comprehensive cellular and molecular framework underpins skeletal physiology and disease, and will help prioritise new therapeutic targets to accelerate development of novel therapies to treat musculoskeletal disease.

genetics↗

ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor-ligand interactions that specify cell fate

Cell fate is commonly studied by profiling the gene expression of single cells to infer developmental trajectories based on expression similarity, RNA velocity, or statistical mechanical properties. However, current approaches do not recover microenvironmental signals from the cellular niche that drive a differentiation trajectory. We resolve this with environment-aware trajectory inference (ENTRAIN), a computational method that integrates trajectory inference methods with ligand-receptor pair gene regulatory networks to identify extracellular signals and evaluate their relative contribution towards a differentiation trajectory. The output from ENTRAIN can be superimposed on spatial data to co-localize cells and molecules in space and time to map cell fate potentials to cell-cell interactions. We validate and benchmark our approach on single-cell bone marrow and spatially resolved embryonic neurogenesis datasets to identify known and novel environmental drivers of cellular differentiation. ENTRAIN is available as a public package at https://github.com/theimagelab/entrain and can be used on both single-cell and spatially resolved datasets.

bioinformatics↗

Osteocyte Transcriptome Mapping Identifies a Molecular Landscape Controlling Skeletal Homeostasis and Susceptibility to Skeletal Disease

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define an osteocyte transcriptome signature of 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this program is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human orthologs that cause monogenic skeletal disorders (P=2.4x10-22) and are associated with the polygenic diseases osteoporosis (P=1.8x10-13) and osteoarthritis (P=1.6x10-7). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance of osteocytes in human skeletal disease.

systems biology↗