Nondestructive Assessment of Engineered Cartilage Composition by Near Infrared Spectroscopy

Abstract

Tissue engineering presents a strategy to overcome the limitations of current tissue healing methods. Scaffolds, cells, external growth factors and mechanical input are combined in an effort to obtain constructs with properties that mimic native tissues. However, engineered constructs developed using similar culture environments can have very different matrix composition and biomechanical properties. Accordingly, a nondestructive technique to assess constructs during development such that appropriate compositional endpoints can be defined is desirable. Near infrared spectroscopy (NIRS) analysis is a modality being investigated to address the challenges associated with current evaluation techniques, which includes nondestructive compositional assessment. In the present study, cartilage tissue constructs were grown using chondrocytes seeded onto polyglycolic acid (PGA) scaffolds in similar environments in three separate tissue culture experiments and monitored using NIRS. Multivariate partial least squares (PLS) analysis models of NIR spectra were calculated and used to predict tissue composition, with biochemical assay information used as the reference data. Results showed that for combined data from all tissue culture experiments, PLS models were able to assess composition with significant correlations to reference values, including engineered cartilage water (at 5200 cm(-1), R = 0.68, p = 0.03), proteoglycan (at 4310 cm(-1), R = 0.82, p = 0.007), and collagen (at 4610 cm(-1), R = 0.84, p = 0.005). In addition, degradation of PGA was monitored using specific NIRS frequencies. These results demonstrate that NIR spectroscopy combined with multivariate analysis provides a nondestructive modality to assess engineered cartilage, which could provide information to determine the optimal time for tissue harvest for clinical applications.

Keywords: Cartilage; Collagen; Multivariate data analysis; Near infrared spectroscopy; Proteoglycan; Tissue engineering.

FIGURE 1

NIR probe data collection from engineered constructs.

FIGURE 2

(a) Cartilage constructs were significantly thicker compared to initial PGA scaffolds for all timepoints (*significantly different compared to pre-culture). (b) Cartilage matrix formation within the scaffold mesh changes the size, morphology and color of constructs over time.

FIGURE 3

Water (a), sGAG (b), and collagen (c) content assessed using gravimetric measurements and biochemical assays for constructs in culture Experiments 1, 2, and 3. Construct composition varied with culture duration and with the specific experiment, although all constructs were grown using the same conditions. *Significant difference among values at different timepoints in a specific experiment, or between different experiments at the same timepoint (p < 0.05).

FIGURE 4

NIR raw spectra (a) and second derivative spectra (b) of engineered cartilage constructs from Experiment 2 as a function of time showing the water absorbance dominance and peak broadening. A spectrum from native cartilage of similar thickness is included for comparison. Spectral details are resolved using second derivative spectra, and peaks reflecting specific matrix and water components are noted with dashed lines. Peaks at 4200, 4310, and 4610 cm⁻¹ reflect PGA, PG and collagen respectively, while the peaks at 5200 and 7150 cm⁻¹ arise from water.

FIGURE 5

The second derivative peak heights at 4200, 4310, 4610, 5200 cm⁻¹ were used to reflect the NIR-derived concentration of water (a), PG (b), collagen (c), and PGA (d). A similar pattern of increase in water, PG, and collagen, and decrease in PGA, is observed among different experiments. However, the actual experimental values of specific components differ. *Significant difference among values at different timepoints in a specific experiment, or between different experiments at the same timepoint (p < 0.05).

FIGURE 6

Engineered construct composition predicted using PLS models vs. experimental values obtained from gravimetric and biochemical analysis, with regression lines. Significant correlations (R values shown, all p < 0.05) were found between the predicted and experimental values for all parameters.