Osteoarthritis and Cartilage Open (Dec 2020)

The zonal evolution of collagen-network morphology quantified in early osteoarthritic grades of human cartilage

  • Phoebe Szarek,
  • Magnus B. Lilledahl,
  • Nancy C. Emery,
  • Courtland G. Lewis,
  • David M. Pierce

Journal volume & issue
Vol. 2, no. 4
p. 100086

Abstract

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Summary: Objective: We aimed to directly quantify the zone-specific evolution in morphology of collagen fibers and networks in human cartilage during the progression of early osteoarthritis. Collagen fibers exhibit depth-dependent orientations and diameters crucial to their mechanical roles. Cartilage degenerates in osteoarthritis, affecting the morphology of the collagen network and ultimately the intra-tissue mechanics. Design: We obtained specimens of human cartilage from healthy human knees (n=3) and from total knee arthroplasties (n=5). We utilized TEM and custom image analyses to visualize and quantify distributions in principal orientation, dispersion (about the principal orientation), and diameter of collagen fibers in the early grades of OA within each through-thickness zone. We then used histological and statistical analyses to probe for significant changes in the zone-specific evolution in collagen-network morphology as a function of Osteoarthritis Research Society International (OARSI) grade. Results: Dispersion in the alignment of collagen fibers increased with progression of early OA in both the superficial and deep zones, and decreased in the middle zone, while principal orientation did not change significantly. The non-normal and right-skewed distributions in fiber diameters did not evolve with the progression of OA. Conclusions: We provide the research community with quantitative data (1) on the through-thickness morphology of collagen in healthy cartilage and (2) on the evolution of through-thickness morphology of collagen with progressing early OA. Such quantitative data facilitate an improved mechanistic understanding of the progression of OA, and may facilitate identifying image-based biomarkers and treatment targets, and ultimately finding clinical interventions for OA.

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