In postmenopausal osteoporosis, an impairment in enzymatic cross-links (ECL) occurs, leading in part to a decline in bone biomechanical properties. Biochemical methods by high performance liquid chromatography (HPLC) are currently used to measure ECL. Another method has been proposed, by Fourier Transform InfraRed Imaging (FTIRI), to measure a mature PYD/immature DHLNL cross-links ratio, using the 1660/1690 cm−1 area ratio in the amide I band. However, in bone, the amide I band composition is complex (collagens, non-collagenous proteins, water vibrations) and the 1660/1690 cm−1 by FTIRI has never been directly correlated with the PYD/DHLNL by HPLC. A study design using lathyritic rats, characterized by a decrease in the formation of ECL due to the inhibition of lysyl oxidase, was used in order to determine the evolution of 1660/1690 cm−1 by FTIR Microspectroscopy in bone tissue and compare to the ECL quantified by HPLC. The actual amount of ECL was quantified by HPLC on cortical bone from control and lathyritic rats. The lathyritic group exhibited a decrease of 78% of pyridinoline content compared to the control group. The 1660/1690 cm−1 area ratio was increased within center bone compared to inner bone, and this was also correlated with an increase in both mineral maturity and mineralization index. However, no difference in the 1660/1690 cm−1 ratio was found between control and lathyritic rats. Those results were confirmed by principal component analysis performed on multispectral infrared images. In bovine bone, in which PYD was physically destructed by UV-photolysis, the PYD/DHLNL (measured by HPLC) was strongly decreased, whereas the 1660/1690 cm−1 was unmodified. In conclusion, the 1660/1690 cm−1 is not related to the PYD/DHLNL ratio, but increased with age of bone mineral, suggesting that a modification of this ratio could be mainly due to a modification of the collagen secondary structure related to the mineralization process.

We present, to the best of our knowledge, the first demonstration of mid-infrared supercontinuum (SC) tissue imaging at wavelengths beyond 5 μm using a fiber-coupled SC source spanning 2-7.5 μm. The SC was generated in a tapered large-mode-area chalcogenide photonic crystal fiber in order to obtain broad bandwidth, high average power, and single-mode output for diffraction-limited imaging performance. Tissue imaging was demonstrated in transmission at selected wavelengths between 5.7 (1754  cm) and 7.3 μm (1370  cm) by point scanning over a sub-millimeter region of colon tissue, and the results were compared to images obtained from a commercial instrument.

Histopathology remains the gold standard method for colon cancer diagnosis. Novel complementary approaches for molecular level diagnosis of the disease are need of the hour. Infrared (IR) imaging could be a promising candidate method as it probes the intrinsic chemical bonds present in a tissue, and provides a "spectral fingerprint" of the biochemical composition. To this end, IR spectral histopathology, which combines IR imaging and data processing techniques, was employed on seventy seven paraffinized colon tissue samples (48 tumoral and 29 non-tumoral) in the form of tissue arrays. To avoid chemical deparaffinization, a digital neutralization of the spectral interference of paraffin was implemented. Clustering analysis was used to partition the spectra and construct pseudo-colored images, for assigning spectral clusters to various tissue structures (normal epithelium, malignant epithelium, connective tissue etc.). Based on the clustering results, linear discriminant analysis was then used to construct a stringent prediction model which was applied on samples without a priori histopathological information. The predicted spectral images not only revealed common features representative of the colonic tissue biochemical make-up, but also highlighted additional features like tumor budding and tumor-stroma association in a label-free manner. This novel approach of IR spectral imaging on paraffinized tissues showed 100% sensitivity and allowed detection and differentiation of normal and malignant colonic features based purely on their intrinsic biochemical features. This non-destructive methodology combined with multivariate statistical image analysis appears as a promising tool for colon cancer diagnosis and opens up the way to the concept of numerical spectral histopathology.