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Single-cell RNA-seq analysis reveals the progression of human osteoarthritis
  1. Quanbo Ji1,2,
  2. Yuxuan Zheng2,3,4,
  3. Guoqiang Zhang1,
  4. Yuqiong Hu2,3,4,
  5. Xiaoying Fan2,3,
  6. Yu Hou2,3,
  7. Lu Wen2,3,
  8. Li Li2,3,
  9. Yameng Xu5,
  10. Yan Wang1,
  11. Fuchou Tang2,3,4
  1. 1 Department of Orthopaedics, General Hospital of Chinese People’s Liberation Army, Beijing, China
  2. 2 Biomedical Institute for Pioneering Investigation via Convergence and Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
  3. 3 Beijing Advanced Innovation Center for Genomics (ICG), College of Life Science, Peking University, Beijing, China
  4. 4 Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
  5. 5 Department of Traditional Chinese Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
  1. Correspondence to Prof. Yan Wang, Department of Orthopaedics, General Hospital of Chinese People’s Liberation Army, Beijing 100853, China; yanwang301{at} and Prof. Fuchou Tang, Beijing Advanced Innovation Center for Genomics (ICG), College of Life Science, Peking University, Beijing 100871, China; tangfuchou{at}


Objectives Understanding the molecular mechanisms underlying human cartilage degeneration and regeneration is helpful for improving therapeutic strategies for treating osteoarthritis (OA). Here, we report the molecular programmes and lineage progression patterns controlling human OA pathogenesis using single-cell RNA sequencing (scRNA-seq).

Methods We performed unbiased transcriptome-wide scRNA-seq analysis, computational analysis and histological assays on 1464 chondrocytes from 10 patients with OA undergoing knee arthroplasty surgery. We investigated the relationship between transcriptional programmes of the OA landscape and clinical outcome using severity index and correspondence analysis.

Results We identified seven molecularly defined populations of chondrocytes in the human OA cartilage, including three novel phenotypes with distinct functions. We presented gene expression profiles at different OA stages at single-cell resolution. We found a potential transition among proliferative chondrocytes, prehypertrophic chondrocytes and hypertrophic chondrocytes (HTCs) and defined a new subdivision within HTCs. We revealed novel markers for cartilage progenitor cells (CPCs) and demonstrated a relationship between CPCs and fibrocartilage chondrocytes using computational analysis. Notably, we derived predictive targets with respect to clinical outcomes and clarified the role of different cell types for the early diagnosis and treatment of OA.

Conclusions Our results provide new insights into chondrocyte taxonomy and present potential clues for effective and functional manipulation of human OA cartilage regeneration that could lead to improved health.

  • osteoarthritis
  • knee osteoarthritis
  • chondrocytes

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  • QJ and YZ contributed equally.

  • Handling editor Josef S Smolen

  • Contributors QJ, YZ, YW and FT conceived the project. QJ designed the experiments. YZ designed and performed bioinformatics analyses. QJ, LL and YX performed cell and molecular experiments. QJ performed single-cell RNA-seq and histological analysis. YQH, XF, YH and YX contributed reagents, materials and analysis tools. GZ recruited patients. LW provided comments.

  • Funding The work was financially supported through grants from the National Programs for High Technology Research and Development (2015AA033701) and the National Natural Science Foundation (81672195, 81630067, 81672602 and 81371976).

  • Competing interests None declared.

  • Patient consent Obtained.

  • Ethics approval General Hospital of the People’s Liberation Arm, Beijing, China.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement The single-cell RNA-seq data, quality control information and cluster information are available at the NCBI’s Gene Expression Omnibus (GEO) data repository with the accession ID GSE104782.

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