Near infrared spectroscopic assessment of developing engineered tissues: correlations with compositional and mechanical properties.

Articular cartilage degeneration causes pain and reduces the mobility of millions of people annually. Regeneration of cartilage is challenging, due in part to its avascular nature, and thus tissue engineering approaches for cartilage repair have been studied extensively. Current techniques to assess the composition and integrity of engineered tissues, including histology, biochemical evaluation, and mechanical testing, are destructive, which limits real-time monitoring of engineered cartilage tissue development in vitro and in vivo. Near infrared spectroscopy (NIRS) has been proposed as a non-destructive technique to characterize cartilage. In the current study, we describe a non-destructive NIRS approach for assessment of engineered cartilage during development, and demonstrate correlation of these data to gold standard mid infrared spectroscopic measurements, and to mechanical properties of constructs. Cartilage constructs were generated using bovine chondrocyte culture on polyglycolic acid (PGA) scaffolds for six weeks. BMP-4 growth factor and ultrasound mechanical stimulation were used to provide a greater dynamic range of tissue properties and outcome variables. NIR spectra were collected daily using an infrared fiber optic probe in diffuse reflectance mode. Constructs were harvested after three and six weeks of culture and evaluated by the correlative modalities of mid infrared (MIR) spectroscopy, histology, and mechanical testing (equilibrium and dynamic stiffness). We found that specific NIR spectral absorbances correlated with MIR measurements of chemical composition, including relative amount of PGA (R = 0.86, p = 0.02), collagen (R = 0.88, p = 0.03), and proteoglycan (R = 0.83, p = 0.01). In addition, NIR-derived water content correlated with MIR-derived proteoglycan content (R = 0.76, p = 0.04). Both equilibrium and dynamic mechanical properties generally improved with cartilage growth from three to six weeks. In addition, significant correlations between NIRS-derived parameters and mechanical properties were found for constructs that were not treated with ultrasound (PGA (R = 0.71, p = 0.01), water (R = 0.74, p = 0.02), collagen (R = 0.69, p = 0.04), and proteoglycan (R = 0.62, p = 0.05)). These results lay the groundwork for extension to arthroscopic engineered cartilage assessment in clinical studies.

Near infrared spectroscopic imaging assessment of cartilage composition: Validation with mid infrared imaging spectroscopy.

Disease or injury to articular cartilage results in loss of extracellular matrix components which can lead to the development of osteoarthritis (OA). To better understand the process of disease development, there is a need for evaluation of changes in cartilage composition without the requirement of extensive sample preparation. Near infrared (NIR) spectroscopy is a chemical investigative technique based on molecular vibrations that is increasingly used as an assessment tool for studying cartilage composition. However, the assignment of specific molecular vibrations to absorbance bands in the NIR spectrum of cartilage, which arise from overtones and combinations of primary absorbances in the mid infrared (MIR) spectral region, has been challenging. In contrast, MIR spectroscopic assessment of cartilage is well-established, with many studies validating the assignment of specific bands present in MIR spectra to specific molecular vibrations. In the current study, NIR imaging spectroscopic data were obtained for compositional analysis of tissues that served as an in vitro model of OA. MIR spectroscopic data obtained from the identical tissue regions were used as the gold-standard for collagen and proteoglycan (PG) content. MIR spectroscopy in transmittance mode typically requires a much shorter pathlength through the sample (≤10 microns thick) compared to NIR spectroscopy (millimeters). Thus, this study first addressed the linearity of small absorbance bands in the MIR region with increasing tissue thickness, suitable for obtaining a signal in both the MIR and NIR regions. It was found that the linearity of specific, small MIR absorbance bands attributable to the collagen and PG components of cartilage (at 1336 and 856 cm(-1), respectively) are maintained through a thickness of 60 µm, which was also suitable for NIR data collection. MIR and NIR spectral data were then collected from 60 µm thick samples of cartilage degraded with chondroitinase ABC as a model of OA. Partial least squares (PLS) regression using NIR spectra as input predicted the MIR-determined compositional parameters of PG/collagen within 6% of actual values. These results indicate that NIR spectral data can be used to assess molecular changes that occur with cartilage degradation, and further, the data provide a foundation for future clinical studies where NIR fiber optic probes can be used to assess the progression of cartilage degradation.

Nondestructive Assessment of Engineered Cartilage Composition by Near Infrared Spectroscopy.

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.

Monitoring the Progression of Spontaneous Articular Cartilage Healing with Infrared Spectroscopy.

OBJECTIVE: Evaluation of early compositional changes in healing articular cartilage is critical for understanding tissue repair and for therapeutic decision-making. Fourier transform infrared imaging spectroscopy (FT-IRIS) can be used to assess the molecular composition of harvested repair tissue. Furthermore, use of an infrared fiber-optic probe (IFOP) has the potential for translation to a clinical setting to provide molecular information in situ. In the current study, we determined the feasibility of IFOP assessment of cartilage repair tissue in a rabbit model, and assessed correlations with gold-standard histology. DESIGN: Bilateral osteochondral defects were generated in mature white New Zealand rabbits, and IFOP data obtained from defect and adjacent regions at 2, 4, 6, 8, 12, and 16 weeks postsurgery. Tissues were assessed histologically using the modified O'Driscoll score, by FT-IRIS, and by partial least squares (PLS) modeling of IFOP spectra. RESULTS: The FT-IRIS parameters of collagen content, proteoglycan content, and collagen index correlated significantly with modified O'Driscoll score (P = 0.05, 0.002, and 0.02, respectively), indicative of their sensitivity to tissue healing. Repair tissue IFOP spectra were distinguished from normal tissue IFOP spectra in all samples by PLS analysis. However, the PLS model for prediction of histological score had a high prediction error, which was attributed to the spectral information being acquired from the tissue surface only. CONCLUSION: The strong correlations between FT-IRIS data and histological score support further development of the IFOP technique for clinical applications, although further studies to optimize data collection from the full sample depths are required.

Assessment of hyaline cartilage matrix composition using near infrared spectroscopy.

Changes in the composition of the extracellular matrix (ECM) are characteristic of injury or disease in cartilage tissue. Various imaging modalities and biochemical techniques have been used to assess the changes in cartilage tissue but lack adequate sensitivity, or in the case of biochemical techniques, result in destruction of the sample. Fourier transform near infrared (FT-NIR) spectroscopy has shown promise for the study of cartilage composition. In the current study NIR spectroscopy was used to identify the contributions of individual components of cartilage in the NIR spectra by assessment of the major cartilage components, collagen and chondroitin sulfate, in pure component mixtures. The NIR spectra were obtained using homogenous pellets made by dilution with potassium bromide. A partial least squares (PLS) model was calculated to predict composition in bovine cartilage samples. Characteristic absorbance peaks between 4000 and 5000 cm(-1) could be attributed to components of cartilage, i.e. collagen and chondroitin sulfate. Prediction of the amount of collagen and chondroitin sulfate in tissues was possible within 8% (w/dw) of values obtained by gold standard biochemical assessment. These results support the use of NIR spectroscopy for in vitro and in vivo applications to assess matrix composition of cartilage tissues, especially when tissue destruction should be avoided.