ADVERTISEMENT
2 /3 FREE ARTICLES LEFT THIS MONTH Remaining
Chemistry matters. Join us to get the news you need.

If you have an ACS member number, please enter it here so we can link this account to your membership. (optional)

ACS values your privacy. By submitting your information, you are gaining access to C&EN and subscribing to our weekly newsletter. We use the information you provide to make your reading experience better, and we will never sell your data to third party members.

ENJOY UNLIMITED ACCES TO C&EN

Analytical Chemistry

Raman Helps Delineate Brain Tumors

Label-free method stacks up against conventional pathology

by Celia Henry Arnaud
September 9, 2013 | APPEARED IN VOLUME 91, ISSUE 36

[+]Enlarge
Credit: Sci. Transl. Med.
A Raman image (left) shows glioma cells (blue) infiltrating brain tissue. A conventional stained histology image is shown for comparison.
09136-scicon-glioblastomacxd.jpg
Credit: Sci. Transl. Med.
A Raman image (left) shows glioma cells (blue) infiltrating brain tissue. A conventional stained histology image is shown for comparison.

The goal of brain cancer surgery is to strike a balance between removing diseased tissue and leaving healthy tissue. But to the surgeon, cancer tissue and normal tissue can be difficult to tell apart. Imaging methods based on chemical signatures might be able to help distinguish between the two. To that end, a research team led by Harvard University’s X. Sunney Xie has used label-free, two-color stimulated Raman scattering microscopy to detect human glioblastoma in mice and in tumors removed from human patients (Sci. Transl. Med. 2013, DOI: 10.1126/scitranslmed.3005954). The researchers acquired images of excised brain tissue using Raman signals at 2,845 cm−1 and 2,930 cm−1, which are representative of proteins and lipids, respectively. They colored the lipid signal green and the protein signal blue. Tumors show up as mostly blue. Neuropathologists classified brain tissue in the samples using both Raman and conventional histological staining, making only two errors out of 225 Raman samples.

X

Article:

This article has been sent to the following recipient:

Leave A Comment

*Required to comment