AccScience Publishing / CAR / Online First / DOI: 10.36922/CAR026210002
Submit to CAR
Cite this article
60
Views
Related Info Links
Google Scholar
More by Authors Links
Qiu Xie
Ren Xu
Lin Meng
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
REVIEW ARTICLE

Applications of single-cell and spatial transcriptomics in osteoarthritis

Xin Wang1 ,  Jiale Ren1 ,  Jiasheng Yang1 ,  Xinzhe Yue1 ,  Qiu Xie2* ,  Jie Han3 ,  Ren Xu3* ,  Lin Meng4*
Show Less
1 Major in Advanced Electrical, Electronic and Computer Systems, Graduate School of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga , Japan
2 State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing , China
3 State Key Laboratory of Cellular Stress Biology, School of Medicine, Xiamen University, Xiamen, Fujian , China
4 Department of Electronic and Computer Engineering, College of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga , Japan
CAR, 026210002 https://doi.org/10.36922/CAR026210002
Received: 19 May 2026 | Revised: 26 July 2026 | Accepted: 4 August 2026 | Published online: 8 October 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

Osteoarthritis (OA) is no longer regarded simply as a disease of cartilage wear but as a disorder affecting the entire joint. Disease progression involves a variety of cell types and local tissue environments, and traditional bulk transcriptomic analysis cannot fully capture these changes. This article summarizes recent work using single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to study OA pathogenesis. Through scRNA-seq, researchers found disease-related and potentially protective cell groups in joint cartilage, synovial membrane, meniscus, and subchondral bone. The technology can also track changes in cell states and analyze communication among different cell types as the disease progresses. ST retains organizational structure information, so gene expression patterns can correspond to specific tissue areas and local immune microenvironments. Together, these methods have gradually shifted OA research from analyzing a single pathway to focusing on interactions among different tissues and cell groups in lesioned joints. This review also discusses current technical limitations and future directions, especially further improvements in spatiotemporal atlas construction and multi-omics integration. These advances may help to discover new biomarkers and promote the development of more accurate OA treatment strategies.

Graphical abstract
Keywords
Osteoarthritis
Single-cell RNA sequencing
Spatial transcriptomics
Cellular heterogeneity
Joint microenvironment
Precision medicine
Funding
This work was supported by the Bilateral Joint Research Project (Grant No. JPJSBP120257418) between the Japan Society for the Promotion of Science (JSPS) and the National Natural Science Foundation of China (NSFC); and the National High Level Hospital Clinical Research Funding (grant no. 2025-PUMCH-A-108).
Conflict of interest
Ren Xu serves as one of the Associate Editors of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. The authors declare no conflicts of interest.
References
  1. Steinmetz JD, Culbreth GT, Haile LM, et al. Global, regional, and national burden of osteoarthritis, 1990–2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5(9):e508-e522. doi: 10.1016/s2665-9913(23)00163-7
  2. Allen KD, Thoma LM, Golightly YM. Epidemiology of osteoarthritis. Osteoarthr Cartil. 2022;30(2):184-195. doi: 10.1016/j.joca.2021.04.020
  3. Loeser RF, Goldring SR, Scanzello CR, Goldring MB. Osteoarthritis: a disease of the joint as an organ. Arthritis Rheum. 2012;64(6):1697-1707. doi: 10.1002/art.34453
  4. Yao Q, Wu X, Tao C, et al. Osteoarthritis: pathogenic signaling pathways and therapeutic targets. Signal Transduct Target Ther. 2023;8(1):56. doi: 10.1038/s41392-023-01330-w
  5. Hunter DJ. Pharmacologic therapy for osteoarthritis—the era of disease modification. Nat Rev Rheumatol. 2011;7(1):13-22. doi: 10.1038/nrrheum.2010.178
  6. Tuerlings M, van Hoolwerff M, Houtman E, et al. RNA sequencing reveals interacting key determinants of osteoarthritis acting in subchondral bone and articular cartilage: identification of IL11 and CHADL as attractive treatment targets. Arthritis Rheumatol. 2021;73(5):789-799.doi: 10.1002/art.41600
  7. Ji Q, Zheng Y, Zhang G, et al. Single-cell RNA-seq analysis reveals the progression of human osteoarthritis. Ann Rheum Dis. 2019;78(1):100-110. doi: 10.1136/annrheumdis-2017-212863
  8. Huang ZY, Luo ZY, Cai YR, et al. Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing. Osteoarthr Cartil. 2022;30(3):475-480. doi: 10.1016/j.joca.2021.12.007
  9. Longo SK, Guo MG, Ji AL, Khavari PA. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet. 2021;22(10):627-644. doi: 10.1038/s41576-021-00370-8
  10. Ståhl PL, Salmén F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78-82. doi: 10.1126/science.aaf2403
  11. Vickovic S, Eraslan G, Salmén F, et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat Methods. 2019;16(10):987-990. doi: 10.1038/s41592-019-0548-y
  12. Lou P, Lu X, Li M, et al. Single-cell sequencing reveals a senescent immune landscape in bone marrow lesions inducing articular cartilage damage in osteoarthritis. Bone Res. 2025;13(1):94. doi: 10.1038/s41413-025-00467-4
  13. Wang C, Zeng Z, Wang T, et al. Unraveling the spatial and signaling dynamics and splicing kinetics of immune infiltration in osteoarthritis synovium. Front Immunol. 2025;16:1521038. doi: 10.3389/fimmu.2025.1521038
  14. Wang H, Cheng P, Wang J, et al. Advances in spatial transcriptomics and its application in the musculoskeletal system. Bone Res. 2025;13(1):54. doi: 10.1038/s41413-025-00429-w
  15. Li X, Fang L, Zhou R, et al. Current cutting-edge omics techniques on musculoskeletal tissues and diseases. Bone Res. 2025;13(1):59. doi: 10.1038/s41413-025-00442-z
  16. Rai MF, Collins KH, Lang A, et al. Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies. Osteoarthr Cartil. 2024;32(4):385-397. doi: 10.1016/j.joca.2023.11.019
  17. Fan X, Sun AR, Young RSE, et al. Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications. Bone Res. 2024;12(1):7. doi: 10.1038/s41413-023-00304-6
  18. Xie P, Sun AR, Gao F, Li L, Crawford R, Prasadam I. Resolving microenvironment complexity and cellular heterogeneity in osteoarthritis via spatial transcriptomics. Osteoarthr Cartil. 2026;34(3):332-342. doi: 10.1016/j.joca.2025.07.007
  19. Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6(5):377-382. doi: 10.1038/nmeth.1315
  20. Ramsköld D, Luo S, Wang YC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30(8):777-782. doi: 10.1038/nbt.2282
  21. Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2(3):666-673. doi: 10.1016/j.celrep.2012.08.003
  22. Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096-1098. doi: 10.1038/nmeth.2639
  23. Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202-1214. doi: 10.1016/j.cell.2015.05.002
  24. Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161(5):1187-1201. doi: 10.1016/j.cell.2015.04.044
  25. Zheng GXY, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8(1):14049. doi: 10.1038/ncomms14049
  26. Stoeckius M, Hafemeister C, Stephenson W, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14(9):865-868. doi: 10.1038/nmeth.4380
  27. Liu Y, Da W, Xu MJ, et al. Single-cell transcriptomics reveals novel chondrocyte and osteoblast subtypes and their role in knee osteoarthritis pathogenesis. Signal Transduct Target Ther. 2025;10(1):40. doi: 10.1038/s41392-025-02136-8
  28. To K, Fei L, Pett JP, et al. A multi-omic atlas of human embryonic skeletal development. Nature. 2024;635(8039):657-667. doi: 10.1038/s41586-024-08189-z
  29. Fernandes LM, Khan NM, Trochez CM, et al. Single-cell RNA-seq identifies unique transcriptional landscapes of human nucleus pulposus and annulus fibrosus cells. Sci Rep. 2020;10(1):15263. doi: 10.1038/s41598-020-72261-7
  30. Moore L, Pan Z, Brotto M. RNAseq of osteoarthritic synovial tissues: systematic literary review. Front Aging. 2022;3:836791. doi: 10.3389/fragi.2022.836791
  31. Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet. 2023;24(8):494-515. doi: 10.1038/s41576-023-00580-2
  32. Delos Santos K, Rockel JS, Kapoor M. Spatial transcriptomics: challenges and future directions in musculoskeletal diseases. Curr Opin Rheumatol. 2026;38(2):143-153. doi: 10.1097/BOR.0000000000001140
  33. Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M, Cai L. Single-cell in situ RNA profiling by sequential hybridization. Nat Methods. 2014;11(4):360-361. doi: 10.1038/nmeth.2892
  34. Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348(6233):aaa6090. doi: 10.1126/science.aaa6090
  35. Wang X, Allen WE, Wright MA, et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018;361(6400):eaat5691. doi: 10.1126/science.aat5691
  36. Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363(6434):1463-1467. doi: 10.1126/science.aaw1219
  37. Eng CL, Lawson M, Zhu Q, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature. 2019;568(7751):235-239. doi: 10.1038/s41586-019-1049-y
  38. Stickels RR, Murray E, Kumar P, et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol. 2021;39(3):313-319. doi: 10.1038/s41587-020-0739-1
  39. Chen A, Liao S, Cheng M, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022;185(10):1777-1792.e21. doi: 10.1016/j.cell.2022.04.003
  40. Ren P, Zhang R, Wang Y, et al. Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors. Nat Commun. 2025;16(1):9232. doi: 10.1038/s41467-025-64292-3
  41. Biancalani T, Scalia G, Buffoni L, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods. 2021;18(11):1352-1362. doi: 10.1038/s41592-021-01264-7
  42. Kleshchevnikov V, Shmatko A, Dann E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol. 2022;40(5):661-671. doi: 10.1038/s41587-021-01139-4
  43. Fan Y, Bian X, Meng X, et al. Unveiling inflammatory and prehypertrophic cell populations as key contributors to knee cartilage degeneration in osteoarthritis using multi-omics data integration. Ann Rheum Dis. 2024;83(7):926-944. doi: 10.1136/ard-2023-224420
  44. Huang C, Zeng B, Zhou B, et al. Single-cell transcriptomic analysis of chondrocytes in cartilage and pathogenesis of osteoarthritis. Genes Dis. 2025;12(2):101241. doi: 10.1016/j.gendis.2024.101241
  45. Hao T, Pei Z, Hu S, et al. Identification of osteoarthritis-associated chondrocyte subpopulations and key gene-regulating drugs based on multi-omics analysis. Sci Rep. 2025;15(1):12448. doi: 10.1038/s41598-025-90694-w
  46. Sun Z, Yan M, Wang J, et al. Single-cell RNA sequencing reveals different chondrocyte states in femoral cartilage between osteoarthritis and healthy individuals. Front Immunol. 2024;15:1407679. doi: 10.3389/fimmu.2024.1407679
  47. Sun H, Wen X, Li H, et al. Single-cell RNA-seq analysis identifies meniscus progenitors and reveals the progression of meniscus degeneration. Ann Rheum Dis. 2020;79(3):408-417. doi: 10.1136/annrheumdis-2019-215926
  48. Tang S, Yao L, Ruan J, et al. Single-cell atlas of human infrapatellar fat pad and synovium implicates APOE signaling in osteoarthritis pathology. Sci Transl Med. 2024;16(731):eadf4590. doi: 10.1126/scitranslmed.adf4590
  49. Li J, Gui T, Yao L, et al. Synovium and infrapatellar fat pad share common mesenchymal progenitors and undergo coordinated changes in osteoarthritis. J Bone Miner Res. 2024;39(2):161-176. doi: 10.1093/jbmr/zjad009
  50. Elingaard-Larsen LO, Whytock KL, Divoux A, et al. Single-nuclei resolution of intermuscular adipose tissue indicates an inflammation-associated cellular profile in individuals with knee osteoarthritis: findings from the SOMMA KOA ancillary study. Aging Cell. 2026;25(1):e70348. doi: 10.1111/acel.70348
  51. Liao S, Yang M, Li D, et al. Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages. Front Immunol. 2023;13:1078414. doi: 10.3389/fimmu.2022.1078414
  52. Sebastian A, Hum NR, McCool JL, et al. Single-cell RNA-Seq reveals changes in immune landscape in post-traumatic osteoarthritis. Front Immunol. 2022;13:938075. doi: 10.3389/fimmu.2022.938075
  53. Wu Y, Liu J, Yu W, Wang X, Li J, Zeng W. Single-cell transcriptome and multi-omics integration reveal ferroptosis-driven immune microenvironment remodeling in knee osteoarthritis. Front Immunol. 2025;16:1608378. doi: 10.3389/fimmu.2025.1608378
  54. Hu Y, Chen W, Lan S, et al. Denosumab attenuates knee osteoarthritis progression by inhibiting synovial inflammation via the RANK/TRAF6/FSTL1 signalling. Nat Commun. 2025;16(1):11394. doi: 10.1038/s41467-025-66202-z
  55. Lin Z, Jariyasakulroj S, Shu Y, et al. Spatial transcriptomics of TMJ reveals a remodeling fibroblast-immune microenvironment driving arthritis pain. Adv Sci. 2026;13(18):e19816. doi: 10.1002/advs.202519816
  56. Nanus DE, Badoume A, Wijesinghe SN, et al. Synovial tissue from sites of joint pain in knee osteoarthritis patients exhibits a differential phenotype with distinct fibroblast subsets. eBioMedicine. 2021;72:103618. doi: 10.1016/j.ebiom.2021.103618
  57. Peng J, Chen J, Gao D, et al. Integrative bulk and single-cell transcriptomic profiling reveals oxidative stress-related genes and potential therapeutic targets in osteoarthritis. Mediators Inflamm. 2025;2025:1240226. doi: 10.1155/mi/1240226
  58. Wu K, Zhong Z, Chen L, et al. Integrative analysis of bulk and single-cell RNA sequencing data reveals increased arachidonic acid metabolism in osteoarthritic chondrocytes. Front Med. 2025;12:1552029. doi: 10.3389/fmed.2025.1552029
  59. Zhang Z, Cheng D, Dang J, Wang X, Fan H, Liu D. Decoding endoplasmic reticulum stress on chondrocyte driving osteoarthritis development through integrating single-cell and transcriptomic profiling. Int J Med Sci. 2025;22(15):3906-3923. doi: 10.7150/ijms.119573
  60. Liu B, Liang Y, Wang C, et al. Dimeric PKM2 in chondrocytes impairs mitochondrial homeostasis in osteoarthritis. Cell Death Dis. 2026;17(1):370. doi: 10.1038/s41419-026-08621-4
  61. Fang C, Zhu S, Zhong R, et al. CDKN1A regulation on chondrogenic differentiation of human chondrocytes in osteoarthritis through single-cell and bulk sequencing analysis. Heliyon. 2024;10(5):e27466. doi: 10.1016/j.heliyon.2024.e27466
  62. Liu S, Chen H, Yang X, Wen Y, Chen L. Identification and validation of up-regulated TNFAIP6 in osteoarthritis with type 2 diabetes mellitus. Sci Rep. 2024;14(1):31450. doi: 10.1038/s41598-024-82985-5
  63. Liu B, Wang C, Weng Z, Yang Y, Shi Y, Zhang C. The glycolytic enzyme PFKFB3 alleviates DNA damage and chondrocyte senescence in osteoarthritis. Cell Death Discov. 2026;12(1):70. doi: 10.1038/s41420-025-02903-0
  64. Jachim SK, Venkatasubramanian D, Senevirathne G, et al. Leveraging single cell multiomic analyses to identify gene regulatory networks that drive human articular cartilage cell fate. Osteoarthr Cartil. 2026;34(3):358-371. doi: 10.1016/j.joca.2025.12.025
  65. Wang N, Liu K, Li JL, et al. Identification of osteoarthritis-related genes and potential drugs based on single cell RNA-seq data. Mol Med. 2026;32(1):1. doi: 10.1186/s10020-025-01379-z
  66. Zhao Z, Wu X, Wang D, Luo W, Ma JX, Ma XL. Integrated multiomics analysis identifies CDO1 as a novel therapeutic target for osteoarthritis. Endocr Metab Immune Disord Drug Targets. 2025;25. doi: 10.2174/0118715303387442250918111006
  67. Zhang D, Liu X, Zhang W, et al. Identification and validation of palmitoylation metabolism related biomarkers in osteoarthritis using transcriptomic and single cell RNA sequencing analyses. Sci Rep. 2026;16(1):1678. doi: 10.1038/s41598-025-31218-4
  68. Zeng M, Wang X, Chen T, et al. Comprehensive analysis on subchondral bone marrow lesions of human osteoarthritis by integrating bulk and single-cell transcriptomes. BMC Musculoskelet Disord. 2023;24(1):677. doi: 10.1186/s12891-023-06676-4
  69. Shibusaka K, Negishi S, Terashima A, et al. Defining subcellular synovial responses in TMJ osteoarthritis onset via mechanical stress and articular disk derangement models. Int J Oral Sci. 2026;18(1):28. doi: 10.1038/s41368-025-00411-6
  70. Negishi S, Shibusaka K, Maemura M, et al. Lubricin maintains temporomandibular joint homeostasis by regulating synovial inflammation. Regen Ther. 2026;31:101051. doi: 10.1016/j.reth.2025.101051
  71. Vincent TL, McLean CJ, Full LE, Peston D, Saklatvala J. FGF-2 is bound to perlecan in the pericellular matrix of articular cartilage, where it acts as a chondrocyte mechanotransducer. Osteoarthr Cartil. 2007;15(7):752-763. doi: 10.1016/j.joca.2007.01.021
  72. Chia SL, Sawaji Y, Burleigh A, et al. Fibroblast growth factor 2 is an intrinsic chondroprotective agent that suppresses ADAMTS-5 and delays cartilage degradation in murine osteoarthritis. Arthritis Rheum. 2009;60(7):2019-2027. doi: 10.1002/art.24654
  73. Wei F, Zhou J, Wei X, et al. Activation of Indian Hedgehog promotes chondrocyte hypertrophy and upregulation of MMP-13 in human osteoarthritic cartilage. Osteoarthr Cartil. 2012;20(7):755-763. doi: 10.1016/j.joca.2012.03.010
  74. Philpott HT, Birmingham TB, Blackler G, et al. Association of synovial innate immune exhaustion with worse pain in knee osteoarthritis. Arthritis Rheumatol. 2025;77(6):664-676. doi: 10.1002/art.43089
Next article in this issue
Share
Back to top
Cell Aging & Regeneration, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept