Journal of Neuro-Oncology

, Volume 116, Issue 3, pp 477–485 | Cite as

Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections

  • Steven N. Kalkanis
  • Rachel E. Kast
  • Mark L. Rosenblum
  • Tom Mikkelsen
  • Sally M. Yurgelevic
  • Katrina M. Nelson
  • Aditya Raghunathan
  • Laila M. Poisson
  • Gregory W. Auner
Laboratory Investigation


The need exists for a highly accurate, efficient and inexpensive tool to distinguish normal brain tissue from glioblastoma multiforme (GBM) and necrosis boundaries rapidly, in real-time, in the operating room. Raman spectroscopy provides a unique biochemical signature of a tissue type, with the potential to provide intraoperative identification of tumor and necrosis boundaries. We aimed to develop a database of Raman spectra from normal brain, GBM, and necrosis, and a methodology for distinguishing these pathologies. Raman spectroscopy was used to measure 95 regions from 40 frozen tissue sections using 785 nm excitation wavelength. Review of adjacent hematoxylin and eosin sections confirmed histology of each region. Three regions each of normal grey matter, necrosis, and GBM were selected as a training set. Ten regions were selected as a validation set, with a secondary validation set of tissue regions containing freeze artifact. Grey matter contained higher lipid (1061, 1081 cm−1) content, whereas necrosis revealed increased protein and nucleic acid content (1003, 1206, 1239, 1255–1266, 1552 cm−1). GBM fell between these two extremes. Discriminant function analysis showed 99.6, 97.8, and 77.5 % accuracy in distinguishing tissue types in the training, validation, and validation with freeze artifact datasets, respectively. Decreased classification in the freeze artifact group was due to tissue preparation damage. This study shows the potential of Raman spectroscopy to accurately identify normal brain, necrosis, and GBM as a tool to augment pathologic diagnosis. Future work will develop mapped images of diffuse glioma and neoplastic margins toward development of an intraoperative surgical tool.


Glioblastoma In vivo Necrosis Raman spectroscopy 


  1. 1.
    Sanai N, Polley M-Y, McDermott MW, Parsa AT, Berger MS (2011) An extent of resection threshold for newly diagnosed glioblastomas. J Neurosurg 115:3–8PubMedCrossRefGoogle Scholar
  2. 2.
    Krafft C, Sobottka SB, Schackert G, Salzer R (2006) Raman and infrared spectroscopic mapping of human primary intracranial tumors: a comparative study. J Raman Spectrosc 37:367–375CrossRefGoogle Scholar
  3. 3.
    Pohling C, Buckup T, Pagenstecher A, Motzkus M (2011) Chemoselective imaging of mouse brain tissue via multiplex CARS microscopy. Biomed Opt Express 2:2110–2116PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Zhou Y, Liu CH, Sun Y, Pu Y, Boydston-White S, Liu Y, Alfano RR (2012) Human brain cancer studied by resonance Raman spectroscopy. J Biomed Opt 17:116021PubMedCrossRefGoogle Scholar
  5. 5.
    Mizuno A, Kitajima H, Kawauchi K, Muraishi S, Ozaki Y (1994) Near-infrared Fourier transform Raman spectroscopic study of human brain tissues and tumours. J Raman Spectrosc 25:25–29CrossRefGoogle Scholar
  6. 6.
    Mizuno A, Hayashi T, Tashibu K, Maraishi S, Kawauchi K, Ozaki Y (1992) Near-infrared FT-Raman spectra of the rat brain tissues. Neurosci Lett 141:47–52PubMedCrossRefGoogle Scholar
  7. 7.
    Amharref N, Beljebbar A, Dukic S, Venteo L, Schneider L, Pluot M, Manfait M (2007) Discriminating healthy from tumor and necrosis tissue in rat brain tissue samples by Raman spectral imaging. Biochim Biophys Acta 1768:2605–2615PubMedCrossRefGoogle Scholar
  8. 8.
    Beljebbar A, Dukic S, Amharref N, Manfait M (2010) Ex vivo and in vivo diagnosis of C6 glioblastoma development by Raman spectroscopy coupled to a microprobe. Anal Bioanal Chem 398:477–487PubMedCrossRefGoogle Scholar
  9. 9.
    Koljenovic S, L-Pi Choo-Smith, Bakker Schut TC, Kros JM, van den Berge HJ, Puppels GJ (2002) Discriminating vital tumor from necrotic tissue in human glioblastoma tissue samples by Raman spectroscopy. Lab Invest 82:1265–1277PubMedCrossRefGoogle Scholar
  10. 10.
    Krafft C, Sobottka SB, Schackert G, Salzer R (2005) Near infrared Raman spectroscopic mapping of native brain tissue and intracranial tumors. Analyst 130:1070–1077PubMedCrossRefGoogle Scholar
  11. 11.
    Koljenovic S, Schut TB, Vincent A, Kros JM, Puppels GJ (2005) Detection of meningioma in dura mater by Raman spectroscopy. Anal Chem 77:7958–7965PubMedCrossRefGoogle Scholar
  12. 12.
    Krafft C, Miljanic S, Sobottka SB, Schackert G, Salzer R (2003) Near infrared Raman spectroscopy to study the composition of human brain tissue and tumors. In: Wagnières G (ed) Diagnostic optical spectroscopy in biomedicine II: Proceedings of SPIE, vol 5141. SPIE, Munich, pp 5140–5230CrossRefGoogle Scholar
  13. 13.
    Krafft C, Kirsch M, Beleites C, Schackert G, Salzer R (2007) Methodology for fiber-optic Raman mapping and FTIR imaging of metastases in mouse brains. Anal Bioanal Chem 389:1133–1142PubMedCrossRefGoogle Scholar
  14. 14.
    Kirsch M, Schackert G, Salzer R, Krafft C (2010) Raman spectroscopic imaging for in vivo detection of cerebral brain metastases. Anal Bioanal Chem 398:1707–1713PubMedCrossRefGoogle Scholar
  15. 15.
    Beleites C, Geiger K, Kirsch M, Sobottka SB, Schackert G, Salzer R (2011) Raman spectroscopic grading of astrocytoma tissues: using soft reference information. Anal Bioanal Chem 400:2801–2816PubMedCrossRefGoogle Scholar
  16. 16.
    Bergner N, Bocklitz T, Romeike BFM, Reichart R, Kalff R, Krafft C, Popp J (2012) Identification of primary tumors of brain metastases by Raman imaging and support vector machines. Chemom Intell Lab Syst 117:224–232CrossRefGoogle Scholar
  17. 17.
    Meyer T, Bergner N, Bielecki C, Krafft C, Akimov D, Romeike BF, Reichart R, Kalff R, Dietzek B, Popp J (2011) Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis. J Biomed Opt 16:021113PubMedCrossRefGoogle Scholar
  18. 18.
    Leslie DG, Kast RE, Poulik JM, Rabah R, Sood S, Auner GW, Klein MD (2012) Identification of pediatric brain neoplasms using Raman spectroscopy. Pediatr Neurosurg 48:109–117PubMedCrossRefGoogle Scholar
  19. 19.
    Auner AW, Kast RE, Rabah R, Poulik JM, Klein MD (2013) Conclusions and data analysis: a 6-year study of Raman spectroscopy of solid tumors at a major pediatric institute. Pediatr Surg Int 29:129–140PubMedCrossRefGoogle Scholar
  20. 20.
    Bergner N, Krafft C, Geiger KD, Kirsch M, Schackert G, Popp J (2012) Unsupervised unmixing of Raman microspectroscopic images for morphochemical analysis of non-dried brain tumor specimens. Anal Bioanal Chem 403:719–725PubMedCrossRefGoogle Scholar
  21. 21.
    Tay L-L, Tremblay RG, Hulse J, Zurakowski B, Thompson M, Bani-Yaghoub M (2011) Detection of acute brain injury by Raman spectral signature. Analyst 136:1620–1626PubMedCrossRefGoogle Scholar
  22. 22.
    Ong CW, Shen ZX, He Y, Lee T, Tang SH (1999) Raman microspectroscopy of the brain tissues in the substantia nigra and MPTP-induced Parkinson’s disease. J Raman Spectrosc 30:91–96CrossRefGoogle Scholar
  23. 23.
    Chen P, Shen A, Zhao W, Baek S-J, Yuan H, Hu J (2009) Raman signature from brain hippocampus could aid Alzheimer’s disease diagnosis. Appl Opt 48:4743–4748PubMedCrossRefGoogle Scholar
  24. 24.
    Jyothi Lakshmi R, Kartha VB, Murali Krishna C, Solomon RJG, Ullas G, Uma Devi P (2002) Tissue Raman spectroscopy for the study of radiation damage: brain irradiation of mice. Radiat Res 157:175–182PubMedCrossRefGoogle Scholar
  25. 25.
    Wills H, Kast R, Stewart C, Rabah R, Pandya A, Poulik J, Auner G, Klein MD (2009) Raman spectroscopy detects and distinguishes neuroblastoma and related tissues in fresh and (banked) frozen specimens. J Pediatr Surg 44:386–391PubMedCrossRefGoogle Scholar
  26. 26.
    Bergner N, Romeike BFM, Reichart R, Kalff R, Krafft C, Popp J (2011) Raman and FTIR microspectroscopy for detection of brain metastasis. In: Ramanujam N, Popp J (eds) Clinical and biomedical spectroscopy and imaging II: Proceedings of SPIE, vol 8087. SPIE, Bellingham, p 8087Google Scholar
  27. 27.
    Krafft C, Bergner N, Romeike B, Reichart R, Kalff R, Geiger K, Kirsch M, Schackert G, Popp J (2012) Raman spectroscopic imaging as complementary tool for histopathologic assessment of brain tumors. In: Kollias N (ed) Photonic therapeutics and diagnostics VIII: Proceedings of SPIE, vol 8207. SPIE, Bellingham, p 8207FCrossRefGoogle Scholar
  28. 28.
    Klecka WR (1980) Discriminant analysis. Sage Publications, Newberry ParkGoogle Scholar
  29. 29.
    Phillips GR, Harris JM (1990) Polynomial filters for data sets with outlying or missing observations: application to charge-coupled-device-detected Raman spectra contaminated by cosmic rays. Anal Chem 62:2351–2357CrossRefGoogle Scholar
  30. 30.
    Mazet V, Carteret C, Brie D, Idier J, Humbert B (2005) Background removal from spectra by designing and minimising a non-quadratic cost function. Chemom Intell Lab Syst 76:121–133CrossRefGoogle Scholar
  31. 31.
    Eilers PHC (2003) A perfect smoother. Anal Chem 75:3631–3636PubMedCrossRefGoogle Scholar
  32. 32.
    Koljenovic S, Schut TCB, Wolthuis R, Vincent AJPE, Hendriks-Hagevi G, Santos L, Kros JM, Puppels GJ (2007) Raman spectroscopic characterization of porcine brain tissue using a single fiber-optic probe. Anal Chem 79:557–564PubMedCrossRefGoogle Scholar
  33. 33.
    Socrates G (2001) Infrared and Raman characteristic group frequencies. Wiley, ChichesterGoogle Scholar
  34. 34.
    Kohler M, Machill S, Salzer R, Krafft C (2009) Characterization of lipid extracts from brain tissue and tumors using Raman spectroscopy and mass spectrometry. Anal Bioanal Chem 393:1513–1520PubMedCrossRefGoogle Scholar
  35. 35.
    Movasaghi Z, Rehman S, Rehman IU (2007) Raman spectroscopy of biological tissues. Appl Spectrosc Rev 42:493–541CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Steven N. Kalkanis
    • 1
  • Rachel E. Kast
    • 5
  • Mark L. Rosenblum
    • 1
  • Tom Mikkelsen
    • 1
  • Sally M. Yurgelevic
    • 5
  • Katrina M. Nelson
    • 5
  • Aditya Raghunathan
    • 1
    • 3
  • Laila M. Poisson
    • 1
    • 2
  • Gregory W. Auner
    • 4
    • 5
  1. 1.Department of Neurosurgery, Hermelin Brain Tumor CenterHenry Ford Health SystemDetroitUSA
  2. 2.Department of Public Health ServicesHenry Ford Health SystemDetroitUSA
  3. 3.Department of Pathology and Laboratory MedicineHenry Ford Health SystemDetroitUSA
  4. 4.Department of SurgeryWayne State UniversityDetroitUSA
  5. 5.Department of Electrical and Computer EngineeringWayne State UniversityDetroitUSA

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