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Updated: Jul 2, 2026

Creation of a Knee Joint-on-a-Chip for Modeling Joint Diseases and Testing Drugs

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Single-Cell Transcriptomics Reveals Riluzole as an Osteoarthritis Candidate Drug via OB-NE Signaling Modulation and

Kun Liu¹, Jia-Li Li¹, Yan Chen²

¹Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, 410081, People's Republic of China.

Drug Design, Development and Therapy

|April 17, 2026

Summary

This summary is machine-generated.

Osteoarthritis involves osteoblast and immune cell interactions. Riluzole shows promise in reducing joint degeneration by targeting specific pathways, offering a potential new treatment for OA.

Keywords:

drug repositioning osteoarthritis osteoblasts riluzole single-cell transcriptome sequencing

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Area of Science:

Orthopedics and Immunology
Molecular Biology
Pharmacology

Background:

Osteoarthritis (OA) pathogenesis involves complex cellular interactions, with osteoblast (OB) and immune cell roles poorly understood.
Understanding OB-immune cell crosstalk is crucial for developing novel OA therapeutics.

Purpose of the Study:

To investigate OB-immune cell communication in OA.
To identify potential therapeutic targets and repositioned drugs for OA treatment.

Main Methods:

Single-cell RNA sequencing of human femoral head tissue.
Bioinformatic analyses including differential expression, enrichment, and cell communication analysis.
Network proximity-based drug repositioning, Mendelian randomization, and zebrafish OA models.

Main Results:

Osteoblast populations identified as key OA-associated cells with enriched neutrophil-mediated immune pathways.
Enhanced bidirectional osteoblast-neutrophil signaling via CXCL12-CXCR4 and RETN-CAP1 pathways in OA.
Riluzole identified as a therapeutic candidate, showing reduced joint degeneration and pathway modulation in zebrafish models.

Conclusions:

Riluzole demonstrates potential as a repositioned drug for osteoarthritis.
Further translational validation is required due to study limitations such as sample size.