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Flow Cytometry Analysis of Immune Cell Subsets within the Murine Spleen, Bone Marrow, Lymph Nodes and Synovial Tissue in an Osteoarthritis Model
Published on: April 24, 2020
Molecular clustering in osteoarthritis primary tissues identifies shared inflammatory and tissue-specific pathway
Odysseas Sotirios Stergiou1, Norbert Bittner2, Georgia Katsoula3
1Graduate School of Experimental Medicine, Technical University of Munich, München, Germany; Institute of Translational Genomics, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt, Neuherberg, Germany; Technical University of Munich (TUM), TUM University Hospital, TUM School of Medicine and Health, Munich, Germany.
Objectives:
To disentangle the molecular heterogeneity of knee osteoarthritis (OA) through the classification and characterization of transcriptomic clusters in multiple joint tissues, and to uncover distinct biological pathways that will facilitate improved patient stratification.
Methods:
We analyzed RNA sequencing data from 330 knee OA patients across low- and high-grade OA knee cartilage, synovium and infrapatellar fat pad tissues. We used unsupervised machine learning to identify distinct transcriptomic clusters and subsequently performed cluster-specific differential expression and pathway enrichment analyses. We applied multi-omics factor analysis in low-grade cartilage to construct a gene expression-based classifier for subtype prediction, which we validated in an independent knee OA RNA sequencing dataset.
Results:
We identified robust clusters across all four joint tissues. In low-grade cartilage, we identified two patient groups separated by differences in inflammation and transcriptional regulation. A gene classifier distinguished these groups with a cross-validated accuracy of 94.5% (95% CI 91.1-96.6%). We also reproduced these subtypes in an external cohort in which the same axis similarly separated the subgroups. In high-grade cartilage there were three distinct clusters, characterized by inflammatory, neuroactive receptor-signaling, and housekeeping-transcriptional programs. Both synovium and infrapatellar fat pad showed two distinct subgroups. Despite the histological differences between these two tissues, subgrouping was based on shared biological functions related to immune activation, alongside disease tissue-specific ones.
Conclusions:
Our findings identify gene expression-based patient clusters in different primary joint tissues and point to shared and disease tissue-specific molecular programs in OA, thus setting the foundation for transcription signature-based patient stratification.
Insights
This study classified knee osteoarthritis (OA) molecular subtypes in cartilage and other joint tissues using gene expression. Findings reveal shared and tissue-specific pathways for better patient stratification in OA.
Area of Science:
- Genomics
- Molecular Biology
- Biomedical Engineering
Background:
- Knee osteoarthritis (OA) exhibits significant molecular heterogeneity.
- Understanding this heterogeneity is crucial for developing targeted therapies and improving patient stratification.
- Current classifications often lack molecular granularity across different joint tissues.
Purpose of the Study:
- To classify and characterize transcriptomic clusters in multiple knee joint tissues from osteoarthritis patients.
- To identify distinct biological pathways driving OA heterogeneity.
- To establish a foundation for gene expression-based patient stratification in OA.
Main Methods:
- RNA sequencing data from 330 knee OA patients across cartilage, synovium, and infrapatellar fat pad tissues were analyzed.
- Unsupervised machine learning identified transcriptomic clusters, followed by differential expression and pathway enrichment analyses.
- A gene expression-based classifier was developed and validated for subtype prediction.
Main Results:
- Robust transcriptomic clusters were identified in all analyzed joint tissues.
- Low-grade cartilage showed two patient groups distinguished by inflammation and transcriptional regulation (94.5% classifier accuracy).
- High-grade cartilage revealed three clusters (inflammatory, neuroactive, housekeeping); synovium and fat pad showed two clusters each, with shared immune activation pathways.
Conclusions:
- Gene expression-based patient clusters were identified across different primary joint tissues in knee osteoarthritis.
- Shared and tissue-specific molecular programs underlying OA pathogenesis were uncovered.
- These findings provide a basis for developing transcription signature-based patient stratification strategies for OA.
