Growth characteristics of glioblastoma spheroids.
Cell spheroids have been proposed as models of early tumor growth from which a better understanding of tumor cell heterogeneity and its effects on treatment response might be gained. Results of experiments performed to understand the underlying dynamics of cell growth within a spheroid formed by SNB19, a high-grade glioblastoma cell line, are presented. We discuss the spatiotemporal distribution of the cells and their cell cycle status based on physical measurements, immunohistochemistry, and flow cytometry analysis. The size of the spheroids and their growth rates were dependent on the initial cell number, the proliferation was mostly limited to the outermost region as the spheroids grew in size, and the number of dead cells increased with age and size as well. Interestingly, though the population of the proliferating cells became localized to the outer rim as spheroids grew, the fraction of proliferating cells did not change drastically. Also, our data reveal that the calculated density varied with respect to age of the spheroid as well as position within the spheroid. We show that a simple exponential model is not adequate for modelling the growth characteristics that have been seen by these experiments. In contradiction to available studies, we report that an acellular (necrotic) center appeared and then disappeared during the period of investigation. Furthermore, after the acellular region disappeared, a few proliferative cells appeared in the center area, raising many questions about the growth-related dynamics of the spheroids formed by this particular cell type.
Nirmala
C
C
Department of Biomedical and Therapeutic Sciences, College of Medicine at Peoria, University of Illinois, Peoria, IL 61656 USA.
Rao
J S
JS
Ruifrok
A C
AC
Langford
L A
LA
Obeyesekere
M
M
D000951:Antigens, Neoplasm / Q000378:metabolism; D001932:Brain Neoplasms / Q000378:metabolism / Q000473:pathology*; D002453:Cell Cycle; D002455:Cell Division; D005434:Flow Cytometry; D005909:Glioblastoma / Q000378:metabolism / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008432:Mathematical Computing; D008433:Mathematics; D008954:Models, Biological; D018874:Spheroids, Cellular / Q000378:metabolism / Q000473:pathology*; D014407:Tumor Cells, Cultured
D000951:Antigens, Neoplasm