Cluster analysis of infrared spectra can differentiate intact and repaired articular cartilage

Development of a Hybrid Biomimetic Enamel-Biocomposite Interface and a Study of Its Molecular Features Using Synchrotron Submicron ATR-FTIR Microspectroscopy and Multivariate Analysis Techniques

Using a biomimetic strategy and bioinspired materials, our work proposed a new technological approach to create a hybrid transitional layer between enamel and dental biocomposite. For this purpose, an amino acid booster conditioner based on a set of polar amino acids (lysine, arginine, hyaluronic acid), calcium alkali, and a modified adhesive based on BisGMA and nanocrystalline carbonate-substituted hydroxyapatite are used during dental enamel restoration. The molecular properties of the hybrid interface formed using the proposed strategy were understood using methods of multivariate statistical analysis of spectral information collected using the technique of synchrotron infrared microspectroscopy. The results obtained indicate the possibility of forming a bonding that mimics the properties of natural tissue with controlled molecular properties in the hybrid layer. The diffusion of the amino acid booster conditioner component, the calcium alkali, and the modified adhesive with nanocrystalline carbonate-substituted hydroxyapatite in the hybrid interface region creates a structure that should stabilize the reconstituted crystalline enamel layer. The developed technology can form the basis for an individualized, personalized approach to dental enamel restorations.

High-resolution infrared microspectroscopic characterization of cartilage cell microenvironment

The lateral resolution of infrared spectroscopy has been inadequate for accurate biochemical characterization of the cell microenvironment, a region regulating biochemical and biomechanical signals to cells. In this study, we demonstrate the capacity of a high-resolution Fourier transform infrared microspectroscopy (HR-FTIR-MS) to characterize the collagen content of this region. Specifically, we focus on the collagen content in the cartilage cell (chondrocyte) microenvironment of healthy and osteoarthritic (OA) cartilage. Human tibial cartilage samples (N = 28) were harvested from 7 cadaveric donors and graded for OA severity (healthy, early OA, advanced OA). HR-FTIR-MS was used to analyze the collagen content of the chondrocyte microenvironment of five distinct zones across the tissue depth. HR-FTIR-MS successfully showed collagen content distribution across chondrocytes and their environment. In zones 2 and 3 (10 - 50% of the tissue thickness), we observed that collagen content was smaller (P < 0.05) in early OA compared to the healthy tissue in the vicinity of cells (pericellular region). The collagen content loss was extended to the extracellular matrix in advanced OA tissue. No significant differences in the collagen content of the chondrocyte microenvironment were observed between the groups in the most superficial (0-10%) and deep zones (50-100%). HR-FTIR-MS revealed collagen loss in the early OA cartilage pericellular region before detectable changes in the extracellular matrix in advanced OA. HR-FTIR-MS-based compositional assessment enables a better understanding of OA-related changes in tissues. This technique can be used to identify new disease mechanisms enabling better intervention strategies. STATEMENT OF SIGNIFICANCE: Osteoarthritis (OA) is the most common degenerative joint disease causing pain and disability. While significant progress has been made in OA research, OA pathogenesis is still poorly understood and current OA treatments are mainly palliative. This study demonstrates that high-resolution FTIR microspectroscopy (HR-FTIR-MS) can characterize OA-induced compositional changes in the cell microenvironment (pericellular matrix) during the early disease stages before tissue changes in the extracellular matrix become apparent. This technique may further enable the identification of new OA mechanisms and improve our current understanding of OA pathogenesis, thus, enabling the development of better treatment methods.

Fourier Transform Infrared Microspectroscopy Combined with Principal Component Analysis and Artificial Neural Networks for the Study of the Effect of β-Hydroxy-β-Methylbutyrate (HMB) Supplementation on Articular Cartilage

The potential of Fourier Transform infrared microspectroscopy (FTIR microspectroscopy) and multivariate analyses were applied for the classification of the frequency ranges responsible for the distribution changes of the main components of articular cartilage (AC) that occur during dietary β-hydroxy-β-methyl butyrate (HMB) supplementation. The FTIR imaging analysis of histological AC sections originating from 35-day old male piglets showed the change in the collagen and proteoglycan contents of the HMB-supplemented group compared to the control. The relative amount of collagen content in the superficial zone increased by more than 23% and in the middle zone by about 17%, while no changes in the deep zone were observed compared to the control group. Considering proteoglycans content, a significant increase was registered in the middle and deep zones, respectively; 62% and 52% compared to the control. AFM nanoindentation measurements collected from animals administered with HMB displayed an increase in AC tissue stiffness by detecting a higher value of Young’s modulus in all investigated AC zones. We demonstrated that principal component analysis and artificial neural networks could be trained with spectral information to distinguish AC histological sections and the group under study accurately. This work may support the use and effectiveness of FTIR imaging combined with multivariate analyses as a quantitative alternative to traditional collagenous tissue-related histology.

Engineering of a Biomimetic Interface between a Native Dental Tissue and Restorative Composite and Its Study Using Synchrotron FTIR Microscopic Mapping

The aim of this work is to develop a biomimetic interface between the natural tooth tissue and the restorative composite and to study it on the basis of synchrotron micro-FTIR mapping and multidimensional processing of the spectral data array. Using hierarchical cluster analysis of 3D FTIR data revealed marked improvements in the formation of the dentine/adhesive/dental hybrid interface using a biomimetic approach. The use of a biomimetic strategy (application of an amino acid-modified primer, alkaline calcium and a nano-c-HAp-modified adhesive) allowed the formation of a matrix that can be structurally integrated with natural dentine and dental composite. The biomimetic hybrid layer was characterised by homogeneous chemical composition and a higher degree of conversion of the adhesive during polymerisation, which should provide optimal integration of the dental composite with the dentine.

Synchrotron IR-microspectroscopy-based visualization of molecular and chemical interactions between dental cement, biomimetic composite and native dental tissue

The low affinity of composite materials for the hard tissue of human teeth poses a challenge to restorative dentists. This study was undertaken to explore molecular and chemical characteristics of the interface between the dental cement, the buffer layer formed from a next generation biomimetic material that mimics the organic mineral composition of human enamel and dentin, and the intact native hard dental tissue. Seven plane-parallel dental slices were analyzed using synchrotron IR microspectroscopy. The obtained absorption spectra of functional molecular groups were organized into cluster maps. This allowed us to identify the intact tissue, the adhesive agent and the biomimetic layer at their interface and to localize and measure concentrations of functional groups involved in the integration of the biomimetic composite into the hard tissue of the human tooth. The proposed biomimetic material is based on nanocrystal carbonate-substituted calcium hydroxyapatite synthesized from a biogenic calcium source and a complex of basic polar amino acids copying the composition of the human tooth and can form a functional bond with hard dental tissue.

Lumican delays melanoma growth in mice and drives tumor molecular assembly as well as response to matrix-targeted TAX2 therapeutic peptide

Lumican is a small leucine-rich proteoglycan (SLRP) being known as a key regulator of collagen fibrillogenesis. However, little attention has been given so far in studying its influence on tumor-associated matrix architecture. Here, we investigate the role of host lumican on tumor matrix organization as well as on disease progression considering an immunocompetent model of melanoma implanted in Lum ^-/- vs. wild type syngeneic mice. Conjointly, lumican impact on tumor response to matrix-targeted therapy was evaluated considering a previously validated peptide, namely TAX2, that targets matricellular thrombospondin-1. Analysis of available genomics and proteomics databases for melanoma first established a correlation between lumican expression and patient outcome. In the B16 melanoma allograft model, endogenous lumican inhibits tumor growth and modulates response to TAX2 peptide. Indeed, IHC analyses revealed that lumican deficiency impacts intratumoral distribution of matricellular proteins, growth factor and stromal cells. Besides, innovative imaging approaches helped demonstrating that lumican host expression drives biochemical heterogeneity of s.c. tumors, while modulating intratumoral collagen deposition as well as organization. Altogether, the results obtained present lumican as a strong endogenous inhibitor of tumor growth, while identifying for the first time this proteoglycan as a major driver of tumor matrix coherent assembly.

Imaging of Osteoarthritic Human Articular Cartilage using Fourier Transform Infrared Microspectroscopy Combined with Multivariate and Univariate Analysis

The changes in chemical composition of human articular cartilage (AC) caused by osteoarthritis (OA) were investigated using Fourier transform infrared microspectroscopy (FTIR-MS). We demonstrate the sensitivity of FTIR-MS for monitoring compositional changes that occur with OA progression. Twenty-eight AC samples from tibial plateaus were imaged with FTIR-MS. Hyperspectral images of all samples were combined for K-means clustering. Partial least squares regression (PLSR) analysis was used to compare the spectra with the OARSI grade (histopathological grading of OA). Furthermore, the amide I and the carbohydrate regions were used to estimate collagen and proteoglycan contents, respectively. Spectral peak at 1338 cm(-1) was used to estimate the integrity of the collagen network. The layered structure of AC was revealed using the carbohydrate region for clustering. Statistically significant correlation was observed between the OARSI grade and the collagen integrity in the superficial (r = -0.55) and the deep (r = -0.41) zones. Furthermore, PLSR models predicted the OARSI grade from the superficial (r = 0.94) and the deep (r = 0.77) regions of the AC with high accuracy. Obtained results suggest that quantitative and qualitative changes occur in the AC composition during OA progression, and these can be monitored by the use of FTIR-MS.

Investigation of intervertebral disc degeneration using multivariate FTIR spectroscopic imaging

Traditionally tissue samples are analysed using protein or enzyme specific stains on serial sections to build up a picture of the distribution of components contained within them. In this study we investigated the potential of multivariate curve resolution-alternating least squares (MCR-ALS) to deconvolute 2nd derivative spectra of Fourier transform infrared (FTIR) microscopic images measured in transflectance mode of goat and human paraffin embedded intervertebral disc (IVD) tissue sections, to see if this methodology can provide analogous information to that provided by immunohistochemical stains and bioassays but from a single section. MCR-ALS analysis of non-degenerate and enzymatically in vivo degenerated goat IVDs reveals five matrix components displaying distribution maps matching histological stains for collagen, elastin and proteoglycan (PG), as well as immunohistochemical stains for collagen type I and II. Interestingly, two components exhibiting characteristic spectral and distribution profiles of proteoglycans were found, and relative component/tissue maps of these components (labelled PG1 and PG2) showed distinct distributions in non-degenerate versus mildly degenerate goat samples. MCR-ALS analysis of human IVD sections resulted in comparable spectral profiles to those observed in the goat samples, highlighting the inter species transferability of the presented methodology. Multivariate FTIR image analysis of a set of 43 goat IVD sections allowed the extraction of semi-quantitative information from component/tissue gradients taken across the IVD width of collagen type I, collagen type II, PG1 and PG2. Regional component/tissue parameters were calculated and significant correlations were found between histological grades of degeneration and PG parameters (PG1: p = 0.0003, PG2: p < 0.0001); glycosaminoglycan (GAG) content and PGs (PG1: p = 0.0055, PG2: p = 0.0001); and MRI T2* measurements and PGs (PG1: p = 0.0021, PG2: p < 0.0001). Additionally, component/tissue parameters for collagen type I and II showed significant correlations with total collagen content (p = 0.0204, p = 0.0127). In conclusion, the presented findings illustrate, that the described multivariate FTIR imaging approach affords the necessary chemical specificity to be considered an important tool in the study of IVD degeneration in goat and human IVDs.

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.

Ultrasound can detect macroscopically undetectable changes in osteoarthritis reflecting the superficial histological and biochemical degeneration: ex vivo study of rabbit and human cartilage

Recognizing subtle cartilage changes in the preclinical stage of osteoarthritis (OA) is essential for early diagnosis. To this end, the ability of the ultrasound signal intensity to detect macroscopically undetectable cartilage change was investigated. In this study, cartilage of rabbit OA model and human OA samples was examined by macroscopic evaluation, ultrasound signal intensity, histology with Mankin scores, and Fourier transform infrared imaging (FTIRI) analysis. Rabbit OA was induced by anterior cruciate ligament transection and evaluated at 1, 2, 4 and 12 weeks. Twenty human samples were harvested during total knee arthroplasty from OA patients who had macroscopically normal human cartilage (ICRS grade 0) on the lateral femoral condyle. In the animal study, there was no macroscopic OA change at 2 weeks, but histology detected degenerative changes at this time point. Ultrasound signal intensity also detected degeneration at 2 weeks. In human samples, all samples were obtained from macroscopically intact site, however nearly normal (0≤ Mankin score <2), early OA (2≤ Mankin score <6), and moderate OA (6≤ Mankin score <10) samples were actually intermixed. Ultrasound signal intensity was significantly different among these 3 stages and was well correlated with Mankin scores (R = −0.80) and FTIR parameters related to collagen and proteoglycan content in superficial zone. In conclusion, ultrasound can detect microscopic cartilage deterioration when such changes do not exist macroscopically, reflecting superficial histological and biochemical changes.

Optimal variable selection for Fourier transform infrared spectroscopic analysis of articular cartilage composition

Articular cartilage (AC) is mainly composed of collagen, proteoglycans, chondrocytes, and water. These constituents are inhomogeneously distributed to provide unique biomechanical properties to the tissue. Characterization of the spatial distribution of these components in AC is important for understanding the function of the tissue and progress of osteoarthritis. Fourier transform infrared (FT-IR) absorption spectra exhibit detailed information about the biochemical composition of AC. However, highly specific FT-IR analysis for collagen and proteoglycans is challenging. In this study, a chemometric approach to predict the biochemical composition of AC from the FT-IR spectra was investigated. Partial least squares (PLS) regression was used to predict the proteoglycan content (n=32) and collagen content (n=28) of bovine cartilage samples from their average FT-IR spectra. The optimal variables for the PLS regression models were selected by using backward interval partial least squares and genetic algorithm. The linear correlation coefficients between the biochemical reference and predicted values of proteoglycan and collagen contents were r=0.923 (p<0.001) and r=0.896 (p<0.001), respectively. The results of the study show that variable selection algorithms can significantly improve the PLS regression models when the biochemical composition of AC is predicted.