Molecular Therapy — Nucleic AcidsGenetic & Genomic MedicinePeer-reviewed journal article
Single-Cell Transcriptomics of Multi-Site Cell Therapy in Osteoarthritis: Tissue-Specific Traits and Treatment Correlations
Paramita ChatterjeeiD, Hazel Y. StevensiD, Linda E. KippneriD, Carolyn Yeago, Hicham DrissiiD, Kenneth MautneriD, Scott D. BodeniD, Greg C. GibsoniD, Krishnendu RoyiD
Abstract
Knee-Osteoarthritis (Knee-OA) is a prevalent joint disorder lacking FDA-approved cell-therapies to halt its progression. This study uses single-cell RNA sequencing to analyze bone marrow aspirate concentrate (BMAC) and stromal vascular fraction (SVF) samples in a clinical trial of autologous cell therapies. Trial site-specific variability was significant in BMAC, necessitating tailored normalization, whereas SVF was less affected, likely due to uniform subcutaneous fat sampling. Variance partitioning and tensor decomposition identified site effects in BMAC but revealed shared pathways across cell types in both tissues. Differential gene expression (DEG) analysis between responders and non-responders yielded no significant findings, though likelihood ratio testing (LRT) revealed enrichment for DEG patterns linked to disease severity, potentially masked by patient heterogeneity. Key BMAC pathways included oxidative phosphorylation, unfolded protein response, and TNF signaling. Cell-cell communication analysis suggested enhanced HLA signaling in non-responder MSCs, consistent with inflammation, while responders showed more coordinated immune interactions. BMAC-MSCs promoted chondrocyte proliferation, whereas SVF-MSCs emphasized immune regulation. Variability in therapy outcomes reflects patient heterogeneity beyond genomic factors, complicating the immediate use of genomic profiling to guide treatment. Nonetheless, as molecular pathways are better understood, integrating genomic insights into personalized strategies may become feasible.
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: cell-cell communication
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 2
- FWCI
- 8.99
- Field percentile
- top 4%
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