Angela Carden

Application of vibrational spectroscopy to the study of mineralized tissues (review)

The infrared and Raman spectroscopy of bone and teeth tissues are reviewed. Characteristic spectra are obtained for both the mineral and protein components of these tissues. Vibrational spectroscopy is used to study the mineralization process, to define the chemical structure changes accompanying bone diseases, and to characterize interactions between prosthetic implants and tissues. Microspectroscopy allows acquisition of spatially resolved spectra, with micron scale resolution. Recently developed imaging modalities allow tissue imaging with chemical composition contrast.

Ultrastructural changes accompanying the mechanical deformation of bone tissue: a Raman imaging study.

Raman spectroscopy and imaging are known to be valuable tools for the analysis of bone, the determination of protein secondary structure, and the study of the composition of crystalline materials. We have utilized all of these attributes to examine how mechanical loading and the resulting deformation affects bone ultrastructure, addressing the hypothesis that bone spectra are altered, in both the organic and inorganic regions, in response to mechanical loading/deformation. Using a cylindrical indenter, we have permanently deformed bovine cortical bone specimens and investigated the ultrastructure in and around the deformed areas using hyperspectral Raman imaging coupled with multivariate analysis techniques. Indent morphology was further examined using scanning electron microscopy. Raman images taken at the edge of the indents show increases in the low-frequency component of the amide III band and high-frequency component of the amide I band. These changes are indicative of the rupture of collagen crosslinks due to shear forces exerted by the indenter passing through the bone. However, within the indent itself no evidence was seen of crosslink rupture, indicating that only compression of the organic matrix takes place in this region. We also present evidence of what is possibly a pressure-induced structural transformation occurring in the bone mineral within the indents, as indicated by the appearance of additional mineral factors in Raman image data from indented areas. These results give new insight into the mechanisms and causes of bone failure at the ultrastructural level.

Brittle IV mouse model for osteogenesis imperfecta IV demonstrates postpubertal adaptations to improve whole bone strength.

The Brtl mouse model for type IV osteogenesis imperfecta improves its whole bone strength and stiffness between 2 and 6 months of age. This adaptation is accomplished without a corresponding improvement in geometric resistance to bending, suggesting an improvement in matrix material properties.

Chemical Microstructure of Cortical Bone Probed by Raman Transects

Raman transects, microspectra taken at equal intervals along a line, are used to explore the microstructure of human cortical bone. Transects of 50 spectra taken at 2.5 μm intervals across an osteon show spatial differences in local mineral and protein composition as different physiological structures are traversed. Differences in mineral composition are seen near the rim of an osteon and further out in the lamellae. The blood vessel wall, primarily composed of collagen and elastin, is detected inside the Haversian canal. Factor analysis is used to explore the data set and reveals differences in mineral composition. Factor analysis also yields one bone matrix component, an osteoidal tissue component, and one blood vessel protein component. The 4 cm−1 spectral resolution and 2.5 μm spatial sampling facilitate the development of univariate metrics for bone development and health. Band integration is performed for important marker bands including phosphate v1 at 960 cm−1, monohydrogen phosphate v1 at 1003 cm−1, B-type carbonate v1 at ∼ 1070 cm−1, collagen CH2 wag at ∼ 1450 cm−1, and collagen amide I at ∼ 1650 cm−1. Mineral-to-matrix ratio, phosphate-to-monohydrogen phosphate ratio, and carbonate-to-phosphate ratio are calculated from these measured areas.

Spatial distribution of phosphate species in mature and newly generated Mammalian bone by hyperspectral Raman imaging.

Hyperspectral Raman images of mineral components of trabecular and cortical bone at 3 μm spatial resolution are presented. Contrast is generated from Raman spectra acquired over the 600-1400 cm-1 Raman shift range. Factor analysis on the ensemble of Raman spectra is used to generate descriptors of mineral components. In trabecular bone independent phosphate (PO4-3) and monohydrogen phosphate (HPO4-2) factors are observed. Phosphate and monohydrogen phosphate gradients extend from trabecular packets into the interior of a rod. The gradients are sharply defined in newly regenerated bone. There, HPO4-2 content maximizes near a trabecular packet and decreases to a minimum value over as little as a 20 μm distance. Incomplete mineralization is clearly visible. In cortical bone, factor analysis yields only a single mineral factor containing both PO4-3 and HPO4-2 signatures and this implies uniform distribution of these ions in the region imaged. Uniform PO4-3 and HPO4-2 distribution is verified by spectral band integration.

Raman imaging of bone mineral and matrix: composition and function

We discuss the use of Raman microprobe spectroscopy and Raman imaging to study the chemical composition of fresh, unmounted bone at a microscopic level. A specimen of human cortical bone was analyzed and evidence for the presence of amorphous-type calcium phosphate, a theoretical precursor in the bone formation process, was found. In general the amorphous4ype calcium phosphate appears away from osteons, in the interstitial tissue. This finding calls into question the role of amorphous-type calcium phosphate as a precursor to apatitic phosphate, since it was not found in the recently remodeled bone near the osteon center, but rather in older bone tissue. Some reasons for the presence of amorphous calcium phosphate are proposed. Possible relations ofthe amorphous mineral to bone damage and bone remodeling are discussed.

Bone microstructure deformation observed by Raman microscopy

While many macroscopic approaches have been used to study bones mechanical properties, little is known about the effect of mechanical stresses on bone tissue at the microstructural and submicrostructural levels. Raman microspectroscopy has been found to be an extremely sensitive technique for examining the effects of mechanical loading on polymer structures and thin films, as well as for the study of protein conformations. We present the first application of this technique to bone tissue. The organic component of bone is a highly ordered matrix composed mainly of collagen fibrils. Stress upon the bone tissue creates disorder in this structure; this disorder can easily be detected by Raman spectroscopy. Small changes in the matrix structure manifest themselves as band shifts in the Raman spectra.

Multivariate data reduction techniques for hyperspectral Raman imaging

Underlying the contrast in a hyperspectral Raman image are complete Raman spectra at each of tens or hundreds of thousands of pixels. Multivariate statistics allows reduction of these large data sets to manageable numbers of chemically significant descriptors that become the image contrast. In most cases an object can be viewed as containing a small number (usually fewer than ten) chemically discrete components, each with its own vibrational spectrum. Principal component analysis (PCA) and exploratory factor analysis (FA) can be used to generate descriptors from the experimentally observed Raman spectra in image data sets. Additionally, PCA and FA can be viewed as optimized weighted signal averaging techniques. FA contrast is generated from all regions of a spectrum that are attributable to one component. The result is better signal/noise ratio than is obtained using the height or area of a single band as image contrast. We will discuss a variety of preprocessing steps such as removing outliers and selecting spectral subregions for data analysis optimization. We will illustrate these concepts using an image of bone tissue.