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Multiscale analysis and functional validation of the cellular and genetic determinants of skeletal disease [Mouse scRNA-seq]

ABSTRACT: Musculoskeletal diseases are a major global health burden. Development of new bone-active therapies is hindered by limited understanding of the complex interactions between the cells and genes that regulate the skeleton. To unravel this complexity, we systematically annotated all cells in bone and defined the genes that control their function at single-cell resolution. Integration with data from human gene-mapping studies of rare skeletal disorders and common skeletal disease traits identified novel disease genes, which we validated by functional analysis in more than one thousand genetic mouse models. This multiscale approach expands the repertoire of cells that regulate bone to include endothelial and vascular smooth muscle cells. This also revealed hundreds of skeletal disease-associated genes in this landscape of cells as potential drug targets. The cellular and genetic mechanisms revealed by this approach overcomes knowledge gaps and helps to accelerate development of next generation therapies to treat skeletal diseases.

ORGANISM(S): Mus musculus

PROVIDER: GSE212817 | GEO | 2026/02/04

REPOSITORIES: GEO

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Project description:This a model from the article: Modeling the interactions between osteoblast and osteoclast activities in bone remodeling. Lemaire V, Tobin FL, Greller LD, Cho CR, Suva LJ. J Theor Biol.2004 Aug 7;229(3):293-309. 15234198, Abstract: We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal remodeling. This model corresponds to the core model published in the paper. There is no corresponding plot to reproduce for this model. To obtain each of the 9 plots in the Figure 2 of the reference publication, there are some changes to be made to the core model. The curation figure reproduces figure 2 of the reference publication. There is a corresponding SBML and Copasi files for each of the plot. See curation tab for more details. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team. For more information see the terms of use. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Multiscale analysis and functional validation of the cellular and genetic determinants of skeletal disease [Mouse scRNA-seq]

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