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Advancing skeletal health and disease research with single-cell RNA sequencing

Peng Lin, Yibo Gan, Jian He, Sien Lin, Jiankun Xu, Liang Chang, Liming Zhao, Jun Zhu, Liang Zhang, Sha Huang, Ou Hu, Yingbo Wang, Huaijian Jin, Yangyang Li, Pulin Yan, Lin Chen, Jianxin Jiang, Peng Liu

2024Military Medical Research21 citationsDOIOpen Access PDF

Abstract

Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.

Topics & Concepts

MedicineDiseaseBioinformaticsOsteoarthritisComputational biologyNeurosciencePathologyBiologyAlternative medicineMusculoskeletal Disorders and RehabilitationOsteoarthritis Treatment and MechanismsSingle-cell and spatial transcriptomics