1. INVESTIGATING MECHANISMS OF
GLIOBLASTOMACELL MIGRATION WITHINA 3D
BIOMIMETIC MICROENVIRONMENT
Amanda Powell, Chandra Kothapalli
Department of Chemical & Biomedical Engineering
Cleveland State University
Presented at the Society for Biomaterials Annual Meeting,
April 17, 2014, Denver, CO

2. Background
• Cancer cell phenotype
• Uncontrollable cellular proliferation
• Formation of localized or remote tumor sites
• Progressive acquisition of organs and various vital systems
• What influences proliferation, migration, tumor formations etc. of
these cells?
• What is glioblastoma?
• Most prevalent and aggressive malignant primary brain tumor
• Accounts for about 52% of all primary brain tumor cases
• High mortality rates
• Highly migratory and invasive, often resulting in new tumor sites
throughout the body
• Current treatment options
• Surgical intervention
• Radiotherapy and chemotherapy
• Corticosteroids
• Antiangiogenic therapy
• Various clinical trials

3. Objectives of this project
• Develop a microfluidic device to mimic 3D physiological
microenvironment of cancer cells, and that allows for in
situ monitoring
• Investigate the role of chemogradients and matrix
stiffness on glioblastoma cell chemotaxis
• Investigate cancer cell- endothelial cell interactions within
this device

4. Microfluidics
[Y.Toh et al., 2007, Lab Chip] [H.Wu et al., 2006, JACS]
[T.Frisk et al., 2007, Electrophoresis] [A.Taylor et al., 2005, Nat. Method]
[M.Kim et al., 2007, Biomed Microdev] [F.Q.Nie et al., 2007, Biomaterials]
[A.Wong et al., 2008, Biomaterials] [G.Walker et al., 2005, Lab Chip]
[W.Saadi et al., 2007, Biomed Microdev] [N.Jeon et al., 2002, Nat Biotech]
[A.Paguirigan., 2006, Lab Chip] [B.Chung, 2005, Lab Chip]
[S.Cheng et al., 2007, Lab Chip] [S.Wang et al., 2004, Exp. Cell Res.]
[Y.Ling et al., 2007, Lab Chip]
• Physiologically-relevant length and time
scales
• precise control
• micro-environment
• in situ monitoring
• live cell imaging
• minimal resources
• simple and inexpensive (< $1/device)
• high-magnification investigation
• quantification

5. Outlay of device
Growth Factor
Cancer
cells
Gel-loading
port
Cell chamber
Growth
factor
chamber
3D gel
region

6. Experimental conditions: Glioma cells & Growth factors
Controls
• 1 mg/ml
collagen-1
• 2 mg/ml
collagen-1
• 3 mg/ml
collagen-1
1 mg/ml
Collagen-1
• VEGF
• 0.1 mM
• 1.0 mM
• 10 mM
• EGF
• 0.1 mM
• 1.0 mM
• 10 mM
2 mg/ml
Collagen-1
• VEGF
• 0.1 mM
• 1.0 mM
• 10 mM
• EGF
• 0.1 mM
• 1.0 mM
• 10 mM
3 mg/ml
Collagen-1
• VEGF
• 0.1 mM
• 1.0 mM
• 10 mM
• EGF
• 0.1 mM
• 1.0 mM
• 10 mM

7. Quantification of diffusion gradients
• Device parameters
• Cell loading area
• Growth factor loading area
• Collagen matrix injection port
• Einstein-stokes Equation
• Time For complete diffusion across
channel
𝑫 𝑪 =
𝒌 𝑩 𝑻
𝟔𝝅ɳ𝒓
𝝉 =
𝑳 𝟐
𝟒𝝅 𝟐 𝑫 𝑪
Zone5
Zone4
Zone3
Zone2
Zone1
0.00E+00
1.00E-04
0hr
2hr
4hr
6hr
18hr
30hr
42hr
ConcentrationofVEGF
Diffusion concentrations of VEGF through
1 mg/ml collagen per zone over 48 h
Zone5
Zone4
Zone3
Zone2
Zone1
0.00E+00
1.00E-04
0hr
2hr
4hr
6hr
18hr
30hr
42hr
ConcentrationofVEGF
Diffusion concentrations of VEGF through
2 mg/ml collagen- per zone over 48 h
Zone5
Zone4
Zone3
Zone2
Zone1
0.00E+00
1.00E-04
0hr
2hr
4hr
6hr
18hr
30hr
42hr
ConcentrationofVEGF
Diffusion concentration of VEGF through
3 mg/ml collagen-1 per zone over 48 h

8. Cancer cell migration over 48 h within 3D scaffolds
1 mg/mL collagen scaffold
1 mM VEGF gradient
VEGF EGF
Cellsmigratedover48h
0
100
200
300
400
500
600
0 mM
0.1 mM
1 mM
10 mM
1 mg/mL collagen
VEGF EGF
Cellsmigratedover48h
0
10
20
30
40
50
60
70 0 mM
0.1 mM
1 mM
10 mM
2 mg/mL collagen
VEGF EGF
Cellsmigratedover48h
0
5
10
15
20
25
30
35
0 mM
0.1 mM
1 mM
10 mM
3 mg/mL collagen

9. Average migration distance over 48 h
Time (min)
0 500 1000 1500 2000 2500 3000
Averagedistancemigrated(mm)
0
20
40
60
80
0.1 mM
1 mM
10 mM
VEGF in 1 mg/mL collagen
Time (min)
0 500 1000 1500 2000 2500 3000
Averagedistancemigrated(mm)
0
20
40
60
80
VEGF in 2 mg/mL collagen
Time (min)
0 500 1000 1500 2000 2500 3000
Averagedistancemigrated(mm)
0
20
40
60
80 VEGF in 3 mg/mL collagen
Time (min)
0 500 1000 1500 2000 2500 3000
Averagedistancemigrated(mm)
0
20
40
60
80
EGF in 1 mg/mL collagen
Time (min)
0 500 1000 1500 2000 2500 3000
Averagedistancemigrated(mm)
0
20
40
60
80 EGF in 2 mg/mL collagen
Time (min)
0 500 1000 1500 2000 2500 3000
Averagedistancemigrated(mm) 0
20
40
60
80 EGF in 3 mg/mL collagen
1.0 µM VEGF in 2 mg/ml Collagen-1

10. Glioblastoma cell shape index (CSI) at 48 h
CSI =
4𝜋∗𝐴
𝑃2
In controls, CSI ~ 1
0 h 12 h 24 h 48 h
*1.0 µM EGF in 2 mg/ml collagen
20 µm 20 µm 20 µm
0.0
0.2
0.4
0.6
0.8
1.0
1
2
3
4 0.1
1
10
Cellshapeindex
C
ollagen
(m
g/m
L)
VEGF dosage (mM)
0.0
0.2
0.4
0.6
0.8
1.0
1
2
3
0.1
1
10
Cellshapeindex
Collagen (mg/mL)
EG
F
dose
(mM
)
20 µm

11. Average cell speed over 48 h
Time (min)
0 500 1000 1500 2000 2500 3000
Averagecellspeed(mm/h)
0
2
4
6
8
10
12
14
VEGF-0.1 mM
VEGF-1 mM
VEGF-10 mM
EGF-0.1 mM
EGF-1 mM
EGF-10 mM
1 mg/mL collagen
Time (min)
0 500 1000 1500 2000 2500 3000
Averagecellspeed(mm/h)
0
2
4
6
8
10
12
14
VEGF-0.1 mM
VEGF-1 mM
VEGF-10 mM
EGF-0.1 mM
EGF-1 mM
EGF-10 mM
3 mg/mL collagen
Time (min)
0 500 1000 1500 2000 2500 3000
Averagecellspeed(mm/h)
0
2
4
6
8
10
12
14
VEGF-0.1 mM
VEGF-1 mM
VEGF-10 mM
EGF-0.1 mM
EGF-1 mM
EGF-10 mM
2 mg/mL collagen

12. Cell migration trajectories 1 mg/ml
collagen
2 mg/ml
collagen
3 mg/ml
collagen

13. Cell-cell coculture configurations
Kothapalli et al., Biomicrofluidics, 2011, 5, 013406

14. Effect of cell-cell paracrine signaling on cancer cell migration
Human
endothelial cells
Human
Glioblatoma
cells
*U-87 cells in 1 mg/ml Collagen-1
Collagen concentration
1 mg/mL 2 mg/mL 3 mg/mL
Numberofcellsmigrated
over144h
1
10
100
1000
U87
HMVEC

15. Migration parameters for U87MG and EC cells
Time (h)
0 20 40 60 80 100 120 140 160
Averagedistancemigrated(mm)
0
10
20
30
40
50
60
U87
HMVEC
1 mg/mL collagen
Time (h)
0 20 40 60 80 100 120 140 160
Averagedistancemigrated(mm) 0
10
20
30
40
50
60
U87
HMVEC
2 mg/mL collagen
Time (h)
0 20 40 60 80 100 120 140 160
Averagedistancemigrated(mm)
0
5
10
15
20
25
U87
HMVEC
3 mg/mL collagen
Time (h)
0 20 40 60 80 100 120 140 160
Averagecellspeed(mm/h)
0.0
0.1
0.2
0.3
0.4
0.5
U87
HMVEC
1 mg/mL collagen
Time (h)
0 20 40 60 80 100 120 140 160
Averagecellspeed(mm/h)
0.00
0.01
0.02
0.03
0.04
0.40
0.45
0.50
U87
HMVEC
2 mg/mL collagen
Time (h)
0 20 40 60 80 100 120 140 160Averagecellspeed(mm/h)
0.00
0.05
0.10
0.15
0.20
0.40
0.45
0.50
U87
HMVEC
3 mg/mL collagen

16. Comparison of our results with literature
Group Culture
Period
Platform Cell Count Distance Speed Conditions
**Our
Research**
48 h
3D Microfluidic
device 12 < n < 455 25 - 50 µm 2 – 14 µm/h
Gliomas
w/EGF &
VEGF
168 h
3D Microfluidic
device ~ 600 5 - 55 µm
0.4 – 0.45
µm/h
Gliomas
w/HMVECs
Wick et al.
(2001) 96 h
48 well chemotaxis
chamber ~ 200 900 µm
Standard
gliomas
Agudelo-
Garcia et al.
(2011)
48 h
Nanofiber-coated
agar plates ~ 250
Standard
gliomas
Burgoyne et
al. (2009) 48 h
Brain slice assay
280 µm
Standard
gliomas
Kim et al.
(2008) 10 h
3D collagen matrix
on cell culture
dishes
15 -19 µm/h
Gliomas
w/EGF
Schichor et al.
(2006) 24 h
Boyden chamber,
spheroid migration
in Laminin
~ 2 per
visual field
~350 µm
Gliomas
w/VEGF
Schichor et al.
(2006) 24 h
Boyden chamber,
spheroid migration
in Laminin
~6 µm
Gliomas
w/HMSCs

17. Conclusions from this study
• Microfluidic devices offer a unique platform to study cancer cell
biology under diffusive chemogradients within 3D matrices
• Strong role of matrix concentration (and thereby stiffness, porosity,
pore-size, etc.) on glioblastoma cell migration evident
• Within each collagen scaffold, cell migration is influenced by the type
(VEGF vs. EGF) and dosage (0-10 mM) of growth factor
• Higher migration of HMVECs towards cancer cells noted, suggesting
onset of angiogenesis
• Future work to screen and identify the benefits of delivering
therapeutic drugs to inhibit cancer cell migration and/or angiogenesis

18. REFERENCES
IMAGES
1. Image 1: McLaren, Iren. “Brain food or how to eat to your mental advantage.” Master Cook- Blog About Cooking. 10
Nov 2008, Retrieved 20 June 2012 from http://mastercookblog.blogspot.com/2008/11/brain-food-or-how-to-eat-to-
your-mental.html.
2. Image 2: “Cancer requires multiple mutations from NIHen.png” Wikipedia.com. 31 Aug 2004, Retrieved 17 June
2012 from http://en.wikipedia.org/wiki/File:Cancer_requires_multiple_mutations_from_NIHen.png.
3. Image 3: “Glioblastoma Multiforme.” ThirdAge.com 2009, Retrieved 17 June 2012 from
http://www.thirdage.com/hc/c/what-is-glioblastoma-multiforme.
4. Image 4: Bruce, J. “Glioblastoma Multiforme.” Medscape.com. 6 Dec. 2006, Retrieved 17 June 2012 from
http://emedicine.medscape.com/article/283252-overview.
5. Image 5&6: “Molecular and Cellular Biology.” Wikipedia.com 14 Dec. 2013. Retrieved 12 June 2012 from
http://commons.wikimedia.org/wiki/Molecular_and_Cellular_Biology.
6. Image 7: “Neurotransmitter Acetylcholine.” Dutchpipesmoker.wordpress.com 4 Aug. 2013. Retrieved 19 Jan. 2014
from http://dutchpipesmoker.wordpress.com/tag/neurotransmitter-acetylcholine/.
LITERATURE
1. Kim HD et al. (2008) “Epidermal growth factor-induced enhancement of glioblastoma cell migration in 3D arises from
an intrinsic increase in speed but an extrinsic matrix and proteolysis-dependent increase in persistence.” Molecular
Biology of the Cell. 19:4249-89.
2. “Glioblastoma.” abta.org. 2012, Retrieved 17 June 2012 from http://www.abta.org/understanding-brain-tumors/types-
of-tumors/glioblastoma.html.
3. “Glioblastoma Multiforme (GBM).” NeuroOncologia. Retrieved 18 June 2012 from
http://www.neurooncologia.com/en/tumortypes/GBM/diagnosis.html.
4. Chung, S. et al. “Cell migration into scaffolds under co-culture conditions in a microfluidic platform.” The Royal
Society of Chemistry. 2008: 1-8.
5. Vickerman, V. et al. “Design, fabrication, and implementation of a novel multi parameter control microfluidic platform
for three-dimensional cell culture and real-time imaging.” Lab Chip. 2008 Sept., 8(9): 1468-1477.
6. Kothapalli, C. “Mechanisms of glioma cell migration.”
7. Ngalim. S. H. et. al. “How do cells make decisions: engineering micro- and nanoenvironments for cell migration.”
Journal of Oncology. 1 April 2010, 2010: 1-7.
8. Huang, Y. et. al. “Microfluidics-based devices: new tools for studying cancer and cancer stem cell migration.”
Biomicrofluidics, 5, 013412 (2011).

19. Acknowledgements
PERSONNEL
• Chandra Kothapalli, Ph.D., Advisor
• Joanne Belovich, Ph.D., provided U87-MG cells
• Kurt Farrell, lab mate
FUNDING
• Choose Ohio First Scholarship Program
• Travel Scholarship from Choose Ohio First Scholarship to
attend this conference
• CSU Start-Up Funds
• FRD Grant from CSU

20. THANK YOU

21. Multi-step model of invasion metastasis
Primary tumor
Secondary tumor
guidance intravasation
The Biology of Cancer (© Garland Science 2007)
extravasation
metastasis
Breast tumor progression
Vargo-Gogola
et al, 2008