Citation: Chengde Wang, Liping Huang, Shanshan Wang, Lihao Wu, Yi Wang, Jun Dong. A distinction of gliomas at cellular and tissue level by surface-enhanced Raman scattering spectroscopy[J]. Chinese Chemical Letters, ;2024, 35(5): 109383. doi: 10.1016/j.cclet.2023.109383 shu

A distinction of gliomas at cellular and tissue level by surface-enhanced Raman scattering spectroscopy

Chengde Wang ^{a, b, 1} ,
Liping Huang ^{c, d, 1} ,
Shanshan Wang ^c ,
Lihao Wu ^b ,
Yi Wang ^{c, *,} ,
Jun Dong ^{a, *,}

a.
Department of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
b.
Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
c.
School of Ophthalmology and Optometry, Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325001, China
d.
Department of Chemistry, School of Science and Research Center for Industries of the Future, Westlake University, Hangzhou 310030, China

yiwang@wmu.edu.cn

dongjun@suda.edu.cn

Received Date: 3 January 2023
Revised Date: 16 November 2023
Accepted Date: 4 December 2023
Available Online: 20 December 2023

Figures(5)

Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor in adults. The precise identification and distinction of GBM heterogeneity from surrounding brain parenchyma at the cellular level and even at the tissue level are important for GBM therapy. In this study, GBM cells are distinguished from normal astrocytes and non-central nervous system (CNS) tumor cells by surface-enhanced Raman scattering (SERS) based on gold nanoshell (SiO₂@Au) particles and support vector machine (SVM) algorithm. In addition, the gold nanoisland (AuNI) SERS substrates are further developed and explored for accurate detection of GBM at the tissue level. The distinction between glioma and trauma tissues, identification of different tumor grades, and IDH mutation are realized with the assistance of orthogonal partial least squares discriminant analysis (OPLS-DA) in a rapid, non-invasive, and convenient method. The results show that the developed SERS-based analytical method has the potential for practical application for the detection of GBM at the single-cell and tissue levels and even for real-time intraoperative diagnosis.
- Glioblastoma multiforme (GBM),
- Surface-enhanced Raman scattering (SERS),
- Nanoparticle,
- Nanoisland,
- Machine learning
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