OBJECTIVE: To test the hypothesis that Fourier transform infrared (FTIR) spectral imaging, coupled with multivariate data processing techniques, can image the spatial distribution of matrix constituents in native and engineered cartilage samples. METHODS: Tissue sections from native and trypsin-digested bovine nasal cartilage (BNC) and from engineered cartilage, generated by chick sternal chondrocytes grown in a hollow fiber bioreactor, were placed either on calcium fluoride windows for FTIR analysis or gelatinized microscope slides for histologic analysis. Based on the assumption that cartilage is predominantly chondroitin sulfate (CS) and type II collagen, chemical images were extracted from FTIR spectral imaging data sets using 2 multivariate methods: the Euclidean distance algorithm and a least-squares approach. RESULTS: Least-squares analysis of the FTIR data of native BNC yielded a collagen content of 54 +/- 13% and a CS content of 37 +/- 16% (mean +/- SD). Euclidean distance analysis of measurements made on trypsin-digested BNC demonstrated only trace amounts of CS. For engineered cartilage, the CS content was significantly lower (15 +/- 5%), while the collagen content (73 +/- 6%) was significantly higher than biochemically determined values (CS 34%, collagen 5%, protein 61%). These differences are due to the fact that the dimethylmethylene blue assay overestimated the CS content of the tissue because it is not specific for CS, while the FTIR spectral imaging technique overestimated the collagen content because it lacks specificity for different proteins. CONCLUSION: FTIR spectral imaging combines histology-like spatial localization with the quantitative capability of bulk chemical analysis. For molecules with a unique spectral signature, such as CS, the FTIR technique coupled with multivariate analysis can define a unique spatial distribution. However, for some applications, the lack of specificity of this technique for different types of proteins may be a limitation.
OBJECTIVE: To test the hypothesis that Fourier transform infrared (FTIR) spectral imaging, coupled with multivariate data processing techniques, can image the spatial distribution of matrix constituents in native and engineered cartilage samples. METHODS: Tissue sections from native and trypsin-digested bovine nasal cartilage (BNC) and from engineered cartilage, generated by chick sternal chondrocytes grown in a hollow fiber bioreactor, were placed either on calcium fluoride windows for FTIR analysis or gelatinized microscope slides for histologic analysis. Based on the assumption that cartilage is predominantly chondroitin sulfate (CS) and type II collagen, chemical images were extracted from FTIR spectral imaging data sets using 2 multivariate methods: the Euclidean distance algorithm and a least-squares approach. RESULTS: Least-squares analysis of the FTIR data of native BNC yielded a collagen content of 54 +/- 13% and a CS content of 37 +/- 16% (mean +/- SD). Euclidean distance analysis of measurements made on trypsin-digested BNC demonstrated only trace amounts of CS. For engineered cartilage, the CS content was significantly lower (15 +/- 5%), while the collagen content (73 +/- 6%) was significantly higher than biochemically determined values (CS 34%, collagen 5%, protein 61%). These differences are due to the fact that the dimethylmethylene blue assay overestimated the CS content of the tissue because it is not specific for CS, while the FTIR spectral imaging technique overestimated the collagen content because it lacks specificity for different proteins. CONCLUSION: FTIR spectral imaging combines histology-like spatial localization with the quantitative capability of bulk chemical analysis. For molecules with a unique spectral signature, such as CS, the FTIR technique coupled with multivariate analysis can define a unique spatial distribution. However, for some applications, the lack of specificity of this technique for different types of proteins may be a limitation.
Authors: Yang Xia; Daniel Mittelstaedt; Nagarajan Ramakrishnan; Matthew Szarko; Aruna Bidthanapally Journal: Microsc Res Tech Date: 2011-02 Impact factor: 2.769
Authors: David A Reiter; Onyi Irrechukwu; Ping-Chang Lin; Somaieh Moghadam; Sarah Von Thaer; Nancy Pleshko; Richard G Spencer Journal: NMR Biomed Date: 2012-01-29 Impact factor: 4.044
Authors: Karen A Esmonde-White; Gurjit S Mandair; Farhang Raaii; Jon A Jacobson; Bruce S Miller; Andrew G Urquhart; Blake J Roessler; Michael D Morris Journal: J Biomed Opt Date: 2009 May-Jun Impact factor: 3.170