Article

Label-free differentiation of human pituitary adenomas by FT-IR spectroscopic imaging.

Faculty of Medicine Carl Gustav Carus, Dresden University of Technology, Clinical Sensoring and Monitoring, 01307, Dresden, Germany.
Analytical and Bioanalytical Chemistry (Impact Factor: 3.66). 04/2012; 403(3):727-35. DOI: 10.1007/s00216-012-5824-y
Source: PubMed

ABSTRACT Fourier transform infrared (FT-IR) spectroscopic imaging has been used to characterize different types of pituitary gland tumors and normal pituitary tissue. Freshly resected tumor tissue from surgery was prepared as thin cryosections and examined by FT-IR spectroscopic imaging. Tissue types were discriminated via k-means cluster analysis and a supervised classification algorithm based on linear discriminant analysis. Spectral classification allowed us to discriminate between tumor and non-tumor cells, as well as between tumor cells that produce human growth hormone (hGH+) and tumor cells that do not produce that hormone (hGH-). The spectral classification was compared and contrasted with a histological PAS and orange G stained image. It was further shown that hGH+ pituitary tumor cells show stronger amide bands than tumor cells that do not produce hGH. This study demonstrates that FT-IR spectroscopic imaging can not only potentially serve as a fast and objective approach for discriminating pituitary gland tumors from normal tissue, but that it can also detect hGH-producing tumor cells.

0 Bookmarks
 · 
100 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Abstract Purpose: Vibrational spectroscopy enables the label-free characterization of cells and tissue by probing the biochemical composition. Here, we evaluated these techniques to identify glioblastoma stem cells. Materials and methods: The biochemical fingerprints of glioblastoma cells were established in human cell lines with high and low content of CD133 (cluster of differentiation 133) -positive cells using attenuated total reflection Fourier-transform infrared (ATR FT-IR) on vital cells and FT-IR mapping, which delivers spatially resolved spectroscopic datasets. After data preprocessing, unsupervised cluster analysis was applied. CD133 was addressed with flow cytometry and immunohistochemistry and used as a stemness marker. Results: In all preparations, the algorithm was able to correctly classify the spectra, differentiating CD133-rich and -poor populations. The main spectral differences were found in the region of 1000 cm(-1) to 1150 cm(-1) that can be assigned to vibrations of chemical bonds of DNA, RNA, carbohydrates and phospholipids. Interestingly, this spectral region is a key feature to discern glioblastoma from normal brain parenchyma, as FT-IR spectroscopic mapping of experimental brain tumors demonstrated. Conclusions: We were able to show biochemical differences between glioblastoma cell populations with high and low content of cancer stem cells that are presumably related to changes in the RNA/DNA content.
    International Journal of Radiation Biology 03/2014; · 1.84 Impact Factor