Susan Christine Massey

Direct inhibition of myosin II effectively blocks glioma invasion in the presence of multiple motogens

ETOC: Brain invasion by gliomas makes these tumors particularly malignant. In this paper, we demonstrate that these tumors need myosin II to drive this process and that the need for myosin II cannot be replaced by stimulating the upstream signal transduction cascades that are pathogenic in this disease.

Increased Re‐Entry into Cell Cycle Mitigates Age‐Related Neurogenic Decline in the Murine Subventricular Zone

Although new neurons are produced in the subventricular zone (SVZ) of the adult mammalian brain, fewer functional neurons are produced with increasing age. The age‐related decline in neurogenesis has been attributed to a decreased pool of neural progenitor cells (NPCs), an increased rate of cell death, and an inability to undergo neuronal differentiation and develop functional synapses. The time between mitotic events has also been hypothesized to increase with age, but this has not been directly investigated. Studying primary‐cultured NPCs from the young adult and aged mouse forebrain, we observe that fewer aged cells are dividing at a given time; however, the mitotic cells in aged cultures divide more frequently than mitotic cells in young cultures during a 48‐hour period of live‐cell time‐lapse imaging. Double‐thymidine‐analog labeling also demonstrates that fewer aged cells are dividing at a given time, but those that do divide are significantly more likely to re‐enter the cell cycle within a day, both in vitro and in vivo. Meanwhile, we observed that cellular survival is impaired in aged cultures. Using our live‐cell imaging data, we developed a mathematical model describing cell cycle kinetics to predict the growth curves of cells over time in vitro and the labeling index over time in vivo. Together, these data surprisingly suggest that progenitor cells remaining in the aged SVZ are highly proliferative. STEM CELLS 2011;29:2005–2017.

Glial progenitor cell recruitment drives aggressive glioma growth: Mathematical and experimental modelling

Currently available glioma treatments remain unsuccessful at prolonging disease-free remission. Recent evidence suggests that tumour recruitment of glial progenitor cells by platelet-derived growth factor (PDGF) may play a role in the development and progression of these tumours. Building upon our recent experimental results and previous proliferation–invasion (PI) reaction–diffusion model, in this study, we created a proliferation–invasion–recruitment (PIR) model that includes a mechanism for progenitor cell recruitment, wherein paracrine PDGF signalling stimulates migration and proliferation of progenitors derived from the local brain environment. Parametrizing this mathematical model with data obtained from the PDGF-driven rat glioma model, we explored the consequences of recruitment, using the PIR model to compare the effects of high versus low PDGF secretion rates on tumour growth and invasion dynamics. The mathematical model predicts correlation between high levels of recruitment and both increased radial velocity of expansion on magnetic resonance imaging and less diffusely invasive edges. Thus, the PIR model predicts that PDGF levels correlate with tumour aggressiveness, and results are consistent with both human and experimental data, demonstrating that the effects of progenitor cell recruitment provide a novel mechanism to explain the variability in the rates of proliferation and dispersion observed in human gliomas.

Comparative dynamics of microglial and glioma cell motility at the infiltrative margin of brain tumours

Microglia are a major cellular component of gliomas, and abundant in the centre of the tumour and at the infiltrative margins. While glioma is a notoriously infiltrative disease, the dynamics of microglia and glioma migratory patterns have not been well characterized. To investigate the migratory behaviour of microglia and glioma cells at the infiltrative edge, we performed two-colour time-lapse fluorescence microscopy of brain slices generated from a platelet-derived growth factor-B (PDGFB)-driven rat model of glioma, in which glioma cells and microglia were each labelled with one of two different fluorescent markers. We used mathematical techniques to analyse glioma cells and microglia motility with both single cell tracking and particle image velocimetry (PIV). Our results show microglia motility is strongly correlated with the presence of glioma, while the correlation of the speeds of glioma cells and microglia was variable and weak. Additionally, we showed that microglia and glioma cells exhibit different types of diffusive migratory behaviour. Microglia movement fit a simple random walk, while glioma cell movement fits a super diffusion pattern. These results show that glioma cells stimulate microglia motility at the infiltrative margins, creating a correlation between the spatial distribution of glioma cells and the pattern of microglia motility.

Hybrid approach for parameter estimation in agent-based models

Agent-based models are valuable in cancer research to show how different behaviors emerge from individual interactions between cells and their environment. However, calibrating such models can be difficult, especially if the parameters that govern the underlying interactions are hard to measure experimentally. Herein, we detail a new method to converge on parameter sets that fit an agent-based model to multiscale data using a model of glioblastoma as an example.

Reaction-Diffusion Model of PDGF-driven Recruitment in Experimental Glioblastoma

Platelet-derived growth factor (PDGF) drives the formation of gliomas in an experimental animal model, which notably involves the recruitment of large numbers of glial progenitor cells. (Assanah 2006). In order to understand the underlying mechanism, particularly what factors influence the degree of recruitment, and how varied amounts of PDGF would affect the gross characteristics and overall appearance of tumors in the brain, we adapted a reaction diffusion model of glioma, which has been used for analyzing clinical data, to model the interactions at play in these experimental models.