Osteoarthritis and Cartilage OpenPhysiologyPeer-reviewed journal article
A standardized, open-source, portable model for noninvasive joint injury in mice
Michael D. NewtoniD, Lindsey LammliniD, Sofia Gonzalez-NoldeiD, Scarlet C. Howser, Isabelle SmithiD, Luke StasikelisiD, Alexander J. KnightsiD, Armen N. AkopianiD, Kyle Allen, Alejandro Almarza, Benjamin Russell ArenkieliD, Maryam Aslam, Başak Dönertaş AyaziD, Yangjin BaeiD, Bruna Balbino de PaulaiD, Anita Bandrowski, Mario Danilo BoadaiD, Jacqueline Boccanfuso, Jyl BolineiD, Dawen CaiiD, Dellina Lane Carpio, Robert Caudle, Racel Cela, Yong Chen, Rui CheniD, , Julia Younis
Abstract
Preclinical models of osteoarthritis (OA) are crucial for the study of disease mechanisms and for the development of critically-needed disease-modifying therapeutics. While surgical OA models, such as the destabilization of the medial meniscus (DMM), have been the gold standard in the field for decades, noninvasive joint loading-based models have increased in popularity and utility. To facilitate standardization of the noninvasive anterior cruciate ligament rupture (ACLR) model in mice, we present the Mobile Joint-Injury Operator (MoJO) - an open-source protocol with accompanying fixtures and data, designed for a low-cost, commercially-available, portable uniaxial testing system with a small footprint. We provide 3d-printable fixture designs and a rapid, highly-repeatable ACLR-mediated joint injury protocol that results in the expected post-traumatic osteoarthritis phenotype in male and female C56Bl/6 mice. We then describe the expected mechanical data from the injury procedure and offer various troubleshooting strategies. Finally, we summarize the resultant PTOA phenotype by knee hyperalgesia testing, {micro}CT imaging, flow cytometry, and histological assessment. Increased standardization of this model is a critical aspect of the overall refinement of animal models of OA.
- organism: mouse
- assay: imaging
- assay: flow cytometry
- research focus: cell type annotation
- research focus: differential expression
- research focus: spatial domain detection
- approach: experimental assay
- Citations
- 3
- FWCI
- 2.20
- Field percentile
- top 12%
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