Single-cell proteomics resolves major cell populations and protein-defined chondrocyte states in the adult human osteoarthritic joint

Authors

Müller, L., Mimpen, J. Y., Todd, C. J. A., Inns, J., Frey, A. M., Orr, S. E., Brumwell, A., Deehan, D. J., Hilkens, C. M. U., Snelling, S. J. B., Trost, M., Rice, S. J.

Abstract

Objective Single-cell RNA sequencing has transformed our understanding of cellular heterogeneity within osteoarthritic (OA) joint tissues, yet transcript abundance provides only an indirect measure of cellular phenotype and function. Recent advances in mass spectrometry now enable proteins to be measured directly at single-cell resolution. Here, we applied single-cell proteomics (SCP) to primary human OA cartilage and synovium to determine whether cellular heterogeneity within the diseased joint can be resolved directly at the proteome level. Design Cartilage and synovium were obtained from patients undergoing total knee arthroplasty for OA. Low-input proteomic profiling was first performed to establish proteome depth from limited cell numbers and validate biologically relevant clustering. SCP was then applied to individual cells, followed by unsupervised clustering, differential protein abundance analysis and iterative biological annotation. Results Low-input proteomics identified 7,800 proteins from 1,000 cells while retaining tissue- and cell-associated biological differences. Across the SCP dataset, ~2,300 unique proteins were detected, resolving chondrocyte, synovial fibroblast and immune-cell populations that closely paralleled those previously defined by single-cell transcriptomics. Higher-resolution analysis distinguished lining and sublining fibroblasts, discrete immune populations and multiple protein-defined chondrocyte states. Conclusions SCP enables direct characterisation of cellular heterogeneity within the human OA joint at the level of the proteome. By independently recovering established cellular populations while revealing protein-defined chondrocyte states, our findings establish SCP as a powerful complement to single-cell transcriptomics and open a new route to investigating functional cellular states and disease mechanisms that cannot be inferred from RNA abundance alone.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 02 Oct 2026.

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