Details

Autor(en) / Beteiligte

• Gularyan, Samvel K.
• Gulin, Alexander A.
• Anufrieva, Ksenia S.
• Shender, Victoria O.
• Shakhparonov, Michail I.
• Bastola, Soniya
• Antipova, Nadezhda V.
• Kovalenko, Tatiana F.
• Rubtsov, Yury P.
• Latyshev, Yaroslav A.
• Potapov, Alexander A.
• Pavlyukov, Marat S.

Titel

Investigation of Inter- and Intratumoral Heterogeneity of Glioblastoma Using TOF-SIMS

Ist Teil von

• Molecular & cellular proteomics, 2020-06, Vol.19 (6), p.960-970

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2020

Quelle

MEDLINE

Beschreibungen/Notizen

• Clinical samples obtained from 57 glioma patients were analyzed by TOF-SIMS. The analysis allowed to separately cluster normal brain and cancer samples and demonstrated differences between primary and recurrent tumors as well as differences between morphologically distinct regions of the same tumor. Simultaneous detection of proteins and metabolites by TOF-SIMS allowed to visualize Caveolin 1 positive cholesterol rich population of cancer cells that might represent glioma stem cells. [Display omitted] Highlights •TOF-SIMS allows to visualize normal brain and tumor border.•Glioma samples can be subdivided into clinically relevant groups by TOF-SIMS data.•TOF-SIMS allows to simultaneously detect proteins and metabolites in clinical samples. Glioblastoma (GBM) is one of the most aggressive human cancers with a median survival of less than two years. A distinguishing pathological feature of GBM is a high degree of inter- and intratumoral heterogeneity. Intertumoral heterogeneity of GBM has been extensively investigated on genomic, methylomic, transcriptomic, proteomic and metabolomics levels, however only a few studies describe intratumoral heterogeneity because of the lack of methods allowing to analyze GBM samples with high spatial resolution. Here, we applied TOF-SIMS (Time-of-flight secondary ion mass spectrometry) for the analysis of single cells and clinical samples such as paraffin and frozen tumor sections obtained from 57 patients. We developed a technique that allows us to simultaneously detect the distribution of proteins and metabolites in glioma tissue with 800 nm spatial resolution. Our results demonstrate that according to TOF-SIMS data glioma samples can be subdivided into clinically relevant groups and distinguished from the normal brain tissue. In addition, TOF-SIMS was able to elucidate differences between morphologically distinct regions of GBM within the same tumor. By staining GBM sections with gold-conjugated antibodies against Caveolin-1 we could visualize border between zones of necrotic and cellular tumor and subdivide glioma samples into groups characterized by different survival of the patients. Finally, we demonstrated that GBM contains cells that are characterized by high levels of Caveolin-1 protein and cholesterol. This population may partly represent a glioma stem cells. Collectively, our results show that the technique described here allows to analyze glioma tissues with a spatial resolution beyond reach of most of other omics approaches and the obtained data may be used to predict clinical behavior of the tumor.