Articular cartilage is a hyaline cartilage that lines the subchondral bone in a diarthrodial joint. Near infrared (NIR) spectroscopy has been emerging as a nondestructive modality for evaluation of cartilage pathology. However, studies of the depth of penetration of NIR radiation into cartilage are lacking. The average thickness of human cartilage is about 1-3 mm, and it becomes even thinner as OA progresses. To ensure that the spectral data collected is restricted to the tissue of interest i.e. cartilage in this case, and not from the underlying subchondral bone, it is necessary to determine the depth of penetration of NIR radiation in different wavelength (frequency) regions. In the current study we establish how the depth of penetration varies throughout the NIR frequency range (4000-10000 cm-1). NIR spectra were collected from cartilage samples of different thicknesses (0.5 mm to 5 mm) with and without polystyrene placed underneath. A separate NIR spectrum of polystyrene was collected as a reference. It was found that the depth of penetration varied from ∼1 mm to 2 mm in the 4000-5100 cm-1 range, to ∼3 mm in the 5100-7000 cm-1 range, and to ∼5 mm in the 7000-9000 cm-1 frequency range. These findings suggest that the best NIR region to evaluate cartilage only with no subchondral bone contribution is between 4000-7000 cm-1.

In diseased conditions of cartilage such as osteoarthritis, there is typically an increase in water content from the average normal of 60–85% to greater than 90%. As cartilage has very little capability for self-repair, methods of early detection of degeneration are required, and assessment of water could prove to be a useful diagnostic method. Current assessment methods are either destructive, time consuming or have limited sensitivity. Here, we investigated the hypotheses that non-destructive near infrared spectroscopy (NIRS) of articular cartilage can be used to differentiate between free and bound water, and to quantitatively assess water content. The absorbances centered at 5200 cm−1 and 6890 cm−1 were attributed to a combination of free and bound water, and to free water only, respectively. The integrated areas of both absorbance bands were found to correlate linearly with the absolute water content (R=0.87 and R= 0.86) and with percent water content (R=0.97 and R=0.96) of the tissue. Partial least square models were also successfully developed and were used to predict water content, and percent free water. These data demonstrate that NIRS can be utilized to quantitatively determine water content in articular cartilage, and may aid in early detection of degenerative tissue changes in a laboratory setting, and with additional validations, possibly in a clinical setting.

Bone fracture risk increases with age, disease states, and with use of certain therapeutics, such as acid-suppressive drugs, steroids and high-dose bisphosphonates. Historically, investigations into factors that underlie bone fracture risk have focused on evaluation of bone mineral density (BMD). However, numerous studies have pointed to factors other than BMD that contribute to fragility, including changes in bone collagen and water. The goal of this study is to investigate the feasibility of using near infrared spectral imaging (NIRSI) to determine the spatial distribution and relative amount of water and organic components in whole cross-sections of bone, and to compare those results to those obtained using magnetic resonance imaging (MRI) methods. Cadaver human whole-section tibiae samples harvested from 18 donors of ages 27–97 years underwent NIRSI and ultrashort echo time (UTE) MRI. As NIRSI data is comprised of broad absorbances, second derivative processing was evaluated as a means to narrow peaks and obtain compositional information. The (inverted) second derivative peak heights of the NIRSI absorbances correlated significantly with the mean peak integration of the water, collagen and fat NIR absorbances, respectively, indicating that either processing method could be used for compositional assessment. The 5797 cm−1 absorbance was validated as arising from the fat present in bone marrow, as it completely disappeared after ultrasonication. The MRI UTE-determined bound water content in tibial cortical bone samples ranged from 62 to 91%. The NIRSI water peaks at 5152 cm−1 and at 7008 cm−1 correlated significantly with the UTE data, with r = 0.735, p = 0.016, and r = 0.71, p =.0096, respectively. There was also a strong correlation between the intensity of the NIRSI water peak at 7008 cm−1 and the intensity of the collagen peak at 4608 cm−1 (r = 0.69, p = 0.004). Since NIRSI requires minimal to no sample preparation, this approach has great potential to become a gold standard modality for the investigation of changes in water content, distribution, and environment in pre-clinical studies of bone pathology and therapeutics.

In diseased conditions of cartilage such as osteoarthritis, there is typically an increase in water content from the average normal of 60-85% to greater than 90%. As cartilage has very little capability for self-repair, methods of early detection of degeneration are required, and assessment of water could prove to be a useful diagnostic method. Current assessment methods are either destructive, time consuming or have limited sensitivity. Here, we investigated the hypotheses that non-destructive near infrared spectroscopy (NIRS) of articular cartilage can be used to differentiate between free and bound water, and to quantitatively assess water content. The absorbances centered at 5200 cm −1 and 6890 cm −1 were attributed to a combination of free and bound water, and to free water only, respectively. The integrated areas of both absorbance bands were found to correlate linearly with the absolute water content (R=0.87 and R= 0.86) and with percent water content (R=0.97 and R=0.96) of the tissue. Partial least square models were also successfully developed and were used to predict water content, and percent free water. These data demonstrate that NIRS can be utilized to quantitatively determine water content in articular cartilage, and may aid in early detection of degenerative tissue changes in a laboratory setting, and with additional validations, possibly in a clinical setting.

Bone mineral crystallinity is an important factor determining bone quality and strength. The gold standard method to quantify crystallinity is X-ray diffraction (XRD), but vibrational spectroscopic methods present powerful alternatives to evaluate a greater variety of sample types. We describe original approaches by which transmission Fourier transform infrared (FT-IR), attenuated total reflection (ATR) FT-IR, and Raman spectroscopy can be confidently used to quantify bone mineral crystallinity. We analyzed a range of biological and synthetic apatite nanocrystals (10-25 nm) and found strong correlations between different spectral factors and the XRD determination of crystallinity. We highlight striking differences between FT-IR spectra obtained by transmission and ATR. In particular, we show for the first time the absence of the 1030 cm crystalline apatite peak in ATR FT-IR spectra, which excludes its use for analyzing crystallinity using the traditional 1030/1020 cm ratio. The νPO splitting ratio was also not adequate to evaluate crystallinity using ATR FT-IR. However, we established original approaches by which ATR FT-IR can be used to determine apatite crystallinity, such as the 1095/1115 and 960/1115 cm peak ratios in the second derivative spectra. Moreover, we found a simple unified approach that can be applied for all three vibrational spectroscopy modalities: evaluation of the νPO peak position. Our results allow the recommendation of the most reliable analytical methods to estimate bone mineral crystallinity by vibrational spectroscopy, which can be readily implemented in many biomineralization, archeological and orthopedic studies. In particular, we present a step forward in advancing the use of the increasingly utilized ATR FT-IR modality for mineral research.

The MRI-derived porosity index (PI) is a non-invasively obtained biomarker based on an ultrashort echo time sequence that images both bound and pore water protons in bone, corresponding to water bound to organic collagenous matrix and freely moving water, respectively. This measure is known to strongly correlate with the actual volumetric cortical bone porosity. However, it is unknown whether PI may also be able to directly quantify bone organic composition and/or mechanical properties. We investigated this in human cadaveric tibiae by comparing PI values to near infrared spectral imaging (NIRSI) compositional data and mechanical compression data. Data were obtained from a cohort of eighteen tibiae from male and female donors with a mean ± SD age of 70 ± 21 years. Biomechanical stiffness in compression and NIRSI-derived collagen and bound water content all had significant inverse correlations with PI ( r = −0.79, −0.73, and −0.95 and p = 0.002, 0.007, and <0.001, respectively). The MRI-derived bone PI alone was a moderate predictor of bone stiffness ( R 2 = 0.63, p = 0.002), and multivariate analyses showed that neither cortical bone cross-sectional area nor NIRSI values improved bone stiffness prediction compared to PI alone. However, NIRSI-obtained collagen and water data together were a moderate predictor of bone stiffness (R 2 = 0.52, p = 0.04). Our data validates the MRI-derived porosity index as a strong predictor of organic composition of bone and a moderate predictor of bone stiffness, and also provides preliminary evidence that NIRSI measures may be useful in future pre-clinical studies on bone pathology.

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