Open AccessGlioblastoma cells labeled by robust Raman tags for enhancing imaging contrast
Author(s) -
Li Huang,
Yung Sheng Chang,
Yi Wu,
Wei Sun,
Chan Chuan Liu,
Chun I. Sze,
Shiuan Yeh Chen
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.002142
Subject(s) - autofluorescence , raman spectroscopy , fluorescence lifetime imaging microscopy , cancer cell , fluorescence , cell , epidermal growth factor receptor , glioblastoma , contrast (vision) , pathology , biomedical engineering , cancer research , medicine , cancer , computer science , chemistry , optics , artificial intelligence , physics , biochemistry
Complete removal of a glioblastoma multiforme (GBM), a highly malignant brain tumor, is challenging due to its infiltrative characteristics. Therefore, utilizing imaging agents such as fluorophores to increase the contrast between GBM and normal cells can help neurosurgeons to locate residual cancer cells during image guided surgery. In this work, Raman tag based labeling and imaging for GBM cells in vitro is described and evaluated. The cell membrane of a GBM adsorbs a substantial amount of functionalized Raman tags through overexpression of the epidermal growth factor receptor (EGFR) and "broadcasts" stronger pre-defined Raman signals than normal cells. The average ratio between Raman signals from a GBM cell and autofluorescence from a normal cell can be up to 15. In addition, the intensity of these images is stable under laser illuminations without suffering from the severe photo-bleaching that usually occurs in fluorescent imaging. Our results show that labeling and imaging GBM cells via robust Raman tags is a viable alternative method to distinguish them from normal cells. This Raman tag based method can be used solely or integrated into an existing fluorescence system to improve the identification of infiltrative glial tumor cells around the boundary, which will further reduce GBM recurrence. In addition, it can also be applied/extended to other types of cancer to improve the effectiveness of image guided surgery.