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A mul&-‐scale approach to inves&gate microenvironmental influences on invasion characteris&cs of heterogeneous glial cells
Joseph D. Juliano1, Andrea Hawkins-‐Daarud2, Russ Rockne2, Peter Canoll3, Kris&n R. Swanson2 1. College of Liberal Arts and Sciences, Arizona State University 2. Department of Neurological Surgery, Northwestern University 3. Department of Pathology and Cell Biology, Columbia University Medical Center
In previous work[5], the PIR model been used to explore how PDGF recruitment could impact the overall dynamics of GBM. For the the PIR model to be predic&ve, es&ma&on of specific model parameters for both popula&ons is required. Complica&ons arise as cell tracking is prone to bias, and certain cells do not fit a persistent random walk model (see supplementary material). Also PDGF concentra&on is difficult to measure but current work is underway to alleviate both of these concerns. We have shown microenvironmental influences on invasion characteris3cs of heterogeneous glial cells at 2, 5 and 10 days a=er infec3on and a mul3-‐scale approach to take informa3on from individual and popula3on cell tracking data to es3mate PIR diffusion values.
I would like to thank Barre[ the Honors college at ASU for the financial support and the Center for Biology and Society. I want to thank Dr. Swanson, Dr. Hawkins-‐Daarud, and the rest of the BONK team for their support and mentorship.
Background: Glioblastoma mul&forme (GBM) is the most aggressive primary brain tumor. To study the in vivo behavior of GBM, a rat model was developed by injec&ng a PDGF expressing retrovirus into rat glial progenitor cells[1,2]. Once deposited in the host, the retrovirally infected cells secreted high levels of PDGF which transformed surrounding normal glial progenitors into a malignant, invasive phenotype with similar characteris&cs to human GBM. These studies have lead to the development of the Prolifera&on-‐Invasion-‐Recruitment (PIR) mathema&cal model, which focuses on the consequences of PDGF-‐driven paracrine recruitment to GBM growth dynamics Current Study: • Es&mate cellular diffusion rates from cell tracking data by fiang mean-‐squared-‐
distances (MSD) consistent with a persistent random walk to inform PIR model parameters.
• Determine how cell speed and diffusion varies across days aber infec&on of PDGF expressing retrovirus
1Assanah et al. Glial Progenitors in Adult White Ma4er Are Driven to Form Malignant Gliomas by Platelet-‐Derived Growth Factor-‐ Expressing Retroviruses. Journal of Neuroscience, 2006. 2Assanah et al. PDGF SGmulates the Massive Expansion of Glial Progenitors in the Neonatal Forebrain. Glia, 2009. 3Cynthia L. Stokes, Douglas A. Lauffenburger, and Stuart K. Williams. MigraGon of individual microvessel endothelial cells: stochasGc model and parameter measurement. Cell Science, 1991. 4Meijering et al. Methods for Cell and ParGcle Tracking. Methods in Enzymology, 2012 5S. C. Massey, M. C. Assanah, K. A. Lopez, P. Canoll, K. R. Swanson. Progenitor cell recruitment drives aggressive glioma growth: mathemaGcal and experimental modeling. J. Roy. Soc. Interface, 2012.
Introduc3on
Cell Tracking Method
Future Work
Discussion
Acknowledgements and References
Results
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Mean Cell Speed (µ m/ hr)
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Applying Cellular Diffusion Coefficients To 2D PIR Model Simula3ons
Compare Virtual Simula3ons with Observed Tumors
Figure 1. Recruited glial progenitors labeled by GFP virus. Infected glial progenitors labeled by PDGF-‐IRES-‐DSRED virus (leb panel). Green lines indicate cell movement recorded every three minutes (right panel).
Recruited Cells Infected Cells
2 DAYS AFTER INFECTION OF PDGF VIRUS
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MSD Model Predic3ons
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MSD = 2S2P[t −P(1− e−tP )]
Black lines represent infected cell tracks, blue lines represent recruited cell tracks. Each box represents 350x350 μm colored by average veloci&es of tracks in each box on a log10 scale.
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Confinement ra&o is defined by the distance traveled by cells from the start to end point divided total distance traveled. This is used as a simple measure to characterize direc&onal persistence of cell popula&ons[4].
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Es&ma&on for PIR Model Parameters
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