Molecular signature of stem-like glioma cells (SLGCs) from human glioblastoma and gliosarcoma.

Glioblastoma multiforme (GBM) and the GBM variant gliosarcoma (GS) are among the tumors with the highest morbidity and mortality, providing only palliation. Stem-like glioma cells (SLGCs) are involved in tumor initiation, progression, therapy resistance, and relapse. The identification of general features of SLGCs could contribute to the development of more efficient therapies. Commercially available protein arrays were used to determine the cell surface signature of eight SLGC lines from GBMs, one SLGC line obtained from a xenotransplanted GBM-derived SLGC line, and three SLGC lines from GSs. By means of non-negative matrix factorization expression metaprofiles were calculated. Using the cophenetic correlation coefficient (CCC) five metaprofiles (MPs) were identified, which are characterized by specific combinations of 7-12 factors. Furthermore, the expression of several factors, that are associated with GBM prognosis, GBM subtypes, SLGC differentiation stages, or neural identity was evaluated. The investigation encompassed 24 distinct SLGC lines, four of which were derived from xenotransplanted SLGCs, and included the SLGC lines characterized by the metaprofiles. It turned out that all SLGC lines expressed the epidermal growth factor EGFR and EGFR ligands, often in the presence of additional receptor tyrosine kinases. Moreover, all SLGC lines displayed a neural signature and the IDH1 wildtype, but differed in their p53 and PTEN status. Pearson Correlation analysis identified a positive association between the pluripotency factor Sox2 and the expression of FABP7, Musashi, CD133, GFAP, but not with MGMT or Hif1α. Spherical growth, however, was positively correlated with high levels of Hif1α, CDK4, PTEN, and PDGFRß, whereas correlations with stemness factors or MGMT (MGMT expression and promoter methylation) were low or missing. Factors highly expressed by all SLGC lines, irrespective of their degree of stemness and growth behavior, are Cathepsin-D, CD99, EMMPRIN/CD147, Intß1, the Galectins 3 and 3b, and N-Cadherin.

Dimensional Complexity of the Resting Brain in Healthy Aging, Using a Normalized MPSE.

Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE). It allows functional brain complexity analyses at varying levels of EV energy, independent from global shifts in data variance. Especially the restriction of the EV spectrum to the first dimensions, carrying the most prominent data variance, can act as a filter to reveal the most discriminant factors of dependent variables. Here we look at the effects of healthy aging on the dimensional complexity of brain activity. We employ a large open access dataset, providing a great number of high quality fast multiband recordings. Using nMPSE on whole brain, regional, network and searchlight approaches, we were able to find many age related changes, i.e., in sensorimotoric and right inferior frontal brain regions. Our results implicate that research on dimensional complexity of functional MRI recordings promises to be a unique resource for understanding brain function and for the extraction of biomarkers.