The proteomic landscape of glioma stem-like cells

Cheryl F. Lichti; Norelle C. Wildburger; Alexander S. Shavkunov; Ekaterina Mostovenko; Huiling Liu; Erik P. Sulman; Carol L. Nilsson

doi:10.1016/j.euprot.2015.06.008

The proteomic landscape of glioma stem-like cells

Cheryl F. Lichti, Norelle C. Wildburger, Alexander S. Shavkunov, Ekaterina Mostovenko, Huiling Liu, Erik P. Sulman, Carol L. Nilsson

Pharmacology & Toxicology

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Glioma stem-like cells (GSCs) are hypothesized to provide a repository of cells in tumors that can self-replicate and are radio- and chemo-resistant. GSC lines, representing several glioma subtypes, have been isolated and characterized at the transcript level. We sought to characterize 35 GSC lines at the protein level using label-free quantitative proteomics. Resulting relative fold changes were used to drive unsupervised hierarchical clustering for the purpose of classifying the cell lines based on proteomic profiles. Bioinformatics analysis identified synoviolin, serine/arginine-rich splicing factor 2, symplekin, and IL-5 as molecules of interest in progression and/or treatment of glioma.

Original language	English (US)
Pages (from-to)	85-93
Number of pages	9
Journal	EuPA Open Proteomics
Volume	8
DOIs	https://doi.org/10.1016/j.euprot.2015.06.008
State	Published - Sep 1 2015

Keywords

DAVID
Glioblastoma
Glioma stem cells
Ingenuity Pathway Analysis
Label-free quantitative proteomics
Unsupervised hierarchical clustering

ASJC Scopus subject areas

Biochemistry

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Cite this

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title = "The proteomic landscape of glioma stem-like cells",

abstract = "Glioma stem-like cells (GSCs) are hypothesized to provide a repository of cells in tumors that can self-replicate and are radio- and chemo-resistant. GSC lines, representing several glioma subtypes, have been isolated and characterized at the transcript level. We sought to characterize 35 GSC lines at the protein level using label-free quantitative proteomics. Resulting relative fold changes were used to drive unsupervised hierarchical clustering for the purpose of classifying the cell lines based on proteomic profiles. Bioinformatics analysis identified synoviolin, serine/arginine-rich splicing factor 2, symplekin, and IL-5 as molecules of interest in progression and/or treatment of glioma.",

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AU - Lichti, Cheryl F.

AU - Wildburger, Norelle C.

AU - Shavkunov, Alexander S.

AU - Mostovenko, Ekaterina

AU - Liu, Huiling

AU - Sulman, Erik P.

AU - Nilsson, Carol L.

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