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

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(SiO_(2)@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.

Urinary analysis based on surface-enhanced Raman scattering for the noninvasive screening of lung cancer

We developed a reliable simple analysis of adenosine,creatinine and other nucleosides(cytidine and guanosine)with single measurement based on surface-enhanced Raman spectroscopy(SERS)using DSP-AuNPs with Cu^(2+)ions probe and applied it to achieve noninvasive screening of lung cancer.The gold nanoparticles(AuNPs)were modified with 3,3′-dithiodipropionic acid di(N-hydroxysuccinimide ester)(DSP),which could react with above mentioned molecules followed with the aggregation of AuNPs at the presence of Cu^(2+),thus form the plasmonic hot spots to dramatically increase their fingerprint Raman signal.The probe of DSP-AuNPs was applied for the detail analysis of urinary adenosine of healthy people and lung cancer patients.The SERS measurement indicated a higher concentration of urinary adenosine for lung cancer patients than that of healthy people,which was consistent with the results measured by high-performance liquid chromatography(HPLC).In combination of Raman spectroscopy with the orthogonal partial least squares discriminant analysis(OPLS-DA)model,we are able to discriminate the lung cancer patients from healthy people with the urinary test.The results indicated that besides of adenosine,other metabolites including uric acid,guanine and creatinine may also be the potential tumor markers in urine for the noninvasive lung cancer diagnosis.Such a method paves a way for the noninvasive cancer screening and it can be further modified for the detection of other molecules on medical diagnosis.