Raman spectroscopic imaging as complementary tool for histopathologic assessment of brain tumors

Raman spectroscopy enables label-free assessment of brain tissues and tumors based on their biochemical composition. Combination of the Raman spectra with the lateral information allows grading of tumors, determining the primary tumor of brain metastases and delineating tumor margins - even during surgery after coupling with fiber optic probes. This contribution presents exemplary Raman spectra and images collected from low grade and high grade regions of astrocytic gliomas and brain metastases. A region of interest in dried tissue sections encompassed slightly increased cell density. Spectral unmixing by vertex component analysis (VCA) and N-FINDR resolved cell nuclei in score plots and revealed the spectral contributions of nucleic acids, cholesterol, cholesterol ester and proteins in endmember signatures. The results correlated with the histopathological analysis after staining the specimens by hematoxylin and eosin. For a region of interest in non-dried, buffer immersed tissue sections image processing was not affected by drying artifacts such as denaturation of biomolecules and crystallization of cholesterol. Consequently, the results correspond better to in vivo situations. Raman spectroscopic imaging of a brain metastases from renal cell carcinoma showed an endmember with spectral contributions of glycogen which can be considered as a marker for this primary tumor.

[1]  L. Choo-Smith,et al.  Discriminating Vital Tumor from Necrotic Tissue in Human Glioblastoma Tissue Samples by Raman Spectroscopy , 2002, Laboratory Investigation.

[2]  S. Laffray,et al.  In vivo optical monitoring of tissue pathologies and diseases with vibrational contrast , 2009, Journal of biophotonics.

[3]  H von Holst,et al.  Increased levels of cholesterol esters in glioma tissue and surrounding areas of human brain. , 1997, British journal of neurosurgery.

[4]  Christoph Krafft,et al.  Near infrared Raman spectra of human brain lipids. , 2005, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[5]  Christoph Krafft,et al.  Disease recognition by infrared and Raman spectroscopy , 2009, Journal of biophotonics.

[6]  Jürgen Popp,et al.  A comparative Raman and CARS imaging study of colon tissue , 2009, Journal of biophotonics.

[7]  K. Bambery,et al.  A Fourier transform infrared microspectroscopic imaging investigation into an animal model exhibiting glioblastoma multiforme. , 2006, Biochimica et biophysica acta.

[8]  Christoph Krafft,et al.  Methodology for fiber-optic Raman mapping and FTIR imaging of metastases in mouse brains , 2007, Analytical and bioanalytical chemistry.

[9]  C. Kendall,et al.  Vibrational spectroscopy: a clinical tool for cancer diagnostics. , 2009, The Analyst.

[10]  Christian T. A. Brown,et al.  Discrimination of normal from pre‐malignant cervical tissue by Raman mapping of de‐paraffinized histological tissue sections , 2011, Journal of biophotonics.

[11]  Christoph Krafft,et al.  Analysis of human brain tissue, brain tumors and tumor cells by infrared spectroscopic mapping. , 2004, The Analyst.

[12]  Christoph Krafft,et al.  Identification of Primary Tumors of Brain Metastases by Infrared Spectroscopic Imaging and Linear Discriminant Analysis , 2006, Technology in cancer research & treatment.

[13]  N. Pavlidis,et al.  Brain metastasis of unknown primary: a diagnostic and therapeutic dilemma. , 2005, Cancer treatment reviews.

[14]  Christoph Krafft,et al.  Raman and infrared spectroscopic mapping of human primary intracranial tumors: a comparative study† , 2006 .

[15]  Jürgen Popp,et al.  Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis. , 2011, Journal of biomedical optics.

[16]  Christoph Krafft,et al.  Near infrared Raman spectroscopic mapping of native brain tissue and intracranial tumors. , 2005, The Analyst.

[17]  Christoph Krafft,et al.  Suitability of infrared spectroscopic imaging as an intraoperative tool in cerebral glioma surgery , 2009, Analytical and bioanalytical chemistry.

[18]  M. Manfait,et al.  Ex vivo and in vivo diagnosis of C6 glioblastoma development by Raman spectroscopy coupled to a microprobe , 2010, Analytical and bioanalytical chemistry.

[19]  Lisa Miller,et al.  FTIR-microspectroscopy of prion-infected nervous tissue. , 2006, Biochimica et biophysica acta.

[20]  Chit Yaw Fu,et al.  Clinical SERS: are we there yet? , 2011, Journal of biophotonics.

[21]  Abdelilah Beljebbar,et al.  Screening of biochemical/histological changes associated to C6 glioma tumor development by FTIR/PCA imaging. , 2010, The Analyst.

[22]  Stephen T. C. Wong,et al.  Chemically-selective imaging of brain structures with CARS microscopy. , 2007, Optics express.

[23]  Manuel Molina,et al.  Unknown primary , 2004, Journal of surgical oncology.

[24]  Frank Winkler,et al.  Therapy and prophylaxis of brain metastases , 2010, Expert review of anticancer therapy.

[25]  Max Diem,et al.  Spectral unmixing and clustering algorithms for assessment of single cells by Raman microscopic imaging , 2011 .

[26]  Christoph Krafft,et al.  Identification of primary tumors of brain metastases by SIMCA classification of IR spectroscopic images. , 2006, Biochimica et biophysica acta.

[27]  Hugh Barr,et al.  Raman spectroscopy: a potential tool for early objective diagnosis of neoplasia in the oesophagus , 2011, Journal of biophotonics.

[28]  B. Dietzek,et al.  Raman and CARS microspectroscopy of cells and tissues. , 2009, The Analyst.

[29]  R. Salzer,et al.  Raman spectroscopic imaging for in vivo detection of cerebral brain metastases , 2010, Analytical and bioanalytical chemistry.

[30]  R Campanella,et al.  Membrane lipids modifications in human gliomas of different degree of malignancy. , 1992, Journal of neurosurgical sciences.

[31]  G. Puppels,et al.  Detection of meningioma in dura mater by Raman spectroscopy. , 2005, Analytical chemistry.

[32]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[33]  Jürgen Popp,et al.  A comprehensive study of classification methods for medical diagnosis , 2009 .