Izasa.App-notes-Acea.Migracion-invasion

xCELLigence RTCA DP Instrument
Flexible Real-Time Cell Monitoring
For life science research only.
Not for use in diagnostic procedures.
Migración e Invasión

RTCA Control Unit RTCA DP Analyzer
The RTCA DP Instrument expands the throughput and application options of the xCELLigence
Real-Time Cell Analyzer (RTCA) portfolio. Featuring a dual-plate (DP) format, the instrument
measures impedance-based signals in both cellular and cell invasion/migration (CIM)
assays – without the use of exogenous labels. With outstanding application flexibility, the RTCA
DP Instrument supports multiple users performing short-term and long-term experiments.
The xCELLigence RTCA DP Instrument
Flexible Real-Time Cell Monitoring
Explore the wide range of applications
„ Cell invasion and migration assays
„ Compound- and cell-mediated cytotoxicity
„ Cell adhesion and cell spreading
„ Cell proliferation and cell differentiation
„ Receptor-mediated signaling
„ Virus-mediated cytopathogenicity
„ Continuous quality control of cells
Figure 1: Reveal cytotoxic effects through continuous
monitoring. HT1080 cells were seeded in an E-Plate at two
different densities (5,000 and 10,000 cells) and treated 24 hours
later with 12.5 nM Paclitaxel, or DMSO as a control. As shown
by the Cell Index profile, which reflects cell adherence, the
antimitotic effect of Paclitaxel was observed in HT1080 cells that
were proliferating, whereas confluent cells showed no response.
Cytotoxicity Analysis in E-Plates
The xCELLigence System continuously and non-invasively
detects cell responses throughout an experiment, without the use
of exogenous labels that can disrupt the natural cell environment.
„ Obtain complete, continuous data profiles from cell
responses generated during in vitro experiments (Figure 1).
„ Take advantage of real-time data to identify optimal time
points for downstream assays.
„ Combine real-time monitoring of cellular responses with
complementary functional endpoint assays, and maximize
data quality before, during, and after your experiment.
Compact. Convenient. Versatile.
The RTCA DP Instrument consists of two components: the RTCA
Control Unit and the RTCA DP Analyzer with three integrated
stations for measuring cell responses in parallel or independently.
„ Choose from three types of impedance-based 16-well plates:
— E-Plate 16 and E-Plate VIEW 16 for cellular assays
— CIM-PLATE 16 for cell invasion/migration assays
„ Use all three different plate types in any combination.
„ Easily achieve optimal cell culture conditions by placing the
RTCA DP Analyzer and plates into standard CO2 incubators.
2

E-Plate 16 and E-Plate VIEW 16:
Cellular Assays in a 16-Well Format
„ Quantitatively monitor changes in cell number, cell adhesion,
cell viability, and cell morphology.
„ Easily add compounds during an experiment.
„ Assess short- and long-term cellular effects.
„ With the E-Plate VIEW 16, observe measured changes using
microscopes.
E-Plate 16
E-Plate 16 E-Plate VIEW 16
500 μm
Obtain detailed information about your cells with the versatile RTCA DP Instrument, which supports
up to three plates of any type – E-Plate 16, E-Plate VIEW 16, or CIM-Plate 16 – in any combination.
For example, cell invasion/migration assays and cytotoxicity assays or short- and long-term assays
may be run simultaneously.
E-Plates for the RTCA DP Instrument
More Flexibility. More Data. More Insight.
Figure 3: Continuously monitor cells and determine optimal time
points for assessing cytotoxicity. Cell proliferation and cell death
were continuously monitored using the xCELLigence RTCA DP Instrument.
The optimal time points for visual inspection of HeLa cells were
determined and images taken 4 and 22 hours after compound treatment
using a Z16 Apo Microscope with light base (Leica Microsystems).
Control
300 μM
Antimycin A
NormalizedCellIndex
1.9
0.9
-0.1
9 18 27 36 45 54
Time (Hours)
Antimycin A
administration
Control
5 μM
100 μM
300 μM
4 h 22 h
Figure 2: Easily visualize cells while measuring cell response with
xCELLigence System E-Plate VIEW technology. A modified version
of the standard E-Plate 16, the E-Plate VIEW 16 enables image acqui-
sition using microscopes or automated cell-imaging systems. For the
modification, four rows of microelectrode sensors were removed in each
well to create a window for visualizing cells. Approximately 70% of each
well bottom is covered by the microelectrodes, providing cell impedance
measurements nearly identical to those obtained with the standard
E-Plate 16. Both plate types can be used in parallel.
3

CIM-Plate 16
Lid
Microelectrodes
Microporous Membrane
Adherent Cell
Upper Chamber
Cells
Gel Layer
(user provided)
Chemoattractant
Lower Chamber
CIM-Plate 16:
Quantitative Cell Invasion/Migration Analysis
„ Monitor cell invasion and migration continuously in real time
over the entire time course of an experiment.
„ Eliminate time-consuming manual detection (Figure 4).
„ Perform CIM analysis in a convenient one-well system (Figure 5).
Figure 4: Quantitatively measure the rate and onset of invasion
while concurrently assessing migration. HT1080 cells (2 x 104) were
seeded in the upper chamber of CIM-Plate wells coated with varying dilutions
of Matrigel, or in wells with no coating. Serum was added to the lower
chamber of selected wells as a chemoattractant. Invasion was observed and
migration monitored continuously over a 70-hour period. All serum-starved
samples resulted in base-line Cell Index levels, indicating the absence of
invasion/migration, while those wells with chemoattractant induced migration.
Invasion/Migration Analysis in CIM-Plates
Figure 5: Analyze invasion/migration in real time with the
CIM-Plate 16. The plate features two separable sections for ease
of experimental setup. Cells seeded in the upper chamber move
through the microporous membrane into the lower chamber that
contains a chemoattractant. Cells adhering to the microelectrode
sensors lead to an increase in impedance, which is measured in
real time by the RTCA DP Instrument.
4

E-Plate Insert 16:
Co-Culture in Real-Time
„ Continuously monitor indirect cell-cell interactions.
„ Assess short- and long-term cell response without
labor-intensive labeling and microscopy.
„ Co-culture different cell typtes under physiological conditions
for a broad range of applications, including:
Cancer Research: Assess paracrine stimulation of
cancer cell proliferation by fibroblasts.
Immunology: Investigate immune cell interactions.
Stem Cell Research: Monitor proliferation and
differentiation in the presence of stimulation cells.
Toxicology: Determine cytotoxicity of agents and
assess effects of cytokine release.
Figure 6. Real-time monitoring of co-culture-induced proliferation
stimulation and its inhibition using the E-Plate Insert. Intercellular
interactions play an important role in normal cell development and
tumorigenesis. Results show that the proliferation of hormone-responsive
tumor cells is likely mediated by hormones and growth factors exchanged
between the two cell populations separated by the E-Plate Insert.
Elevated T47D cell proliferation on the E-Plate (green trace „ ) was
induced by hormone secretion of H295R cells in the insert, and inhibited
by the hormone synthesis inhibitor Prochloraz (blue trace „ ). Incubation
of T47D cells with only the E-Plate Insert did not affect proliferation (red
trace „ ).
Lid
E-Plate 16
E-Plate Insert
5

1. Cell Invasion and Migration
MicroRNA-200c Represses Migration and Invasion of Breast Cancer Cells by Targeting Actin-Regulatory
Proteins FHOD1 and PPM1Ferences.
Jurmeister S, Baumann M, Balwierz A, Keklikoglou I, Ward A, Uhlmann S, Zhang JD, Wiemann S, Sahin O.
Mol Cell Biol. 2012; 32(3):633–651.
c-Myb regulates matrix metalloproteinases 1/9, and cathepsin D: implications for matrix-dependent bre-
ast cancer cell invasion and metastasis.
Knopfová L, Beneš P, Pekarēíková L, Hermanová M, MasaƎík M, Pernicová Z, Souēek K, Smarda J.
Mol Cancer. 2012; 11:15.
Comparative Analysis of Dynamic Cell Viability, Migration and Invasion Assessments by Novel Real-
Time Technology and Classic Endpoint Assays.
Limame R, Wouters A, Pauwels B, Fransen E, Peeters M, Lardon F, De Wever O, Pauwels P.
PLoS One. 2012; 7(10): e46536.
2. Compound-mediated Cytotoxicity/Apoptosis
Screening and identification of small molecule compounds perturbing mitosis using time-dependent
cellular response profiles.
Ke N, Xi B, Ye P, Xu W, Zheng M, Mao L, Wu MJ, Zhu J, Wu J, Zhang W, Zhang J, Irelan J, Wang X, Xu
X, Abassi YA.
Anal Chem. 2010; 82(15):6495-503.
Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-target
effects.
Abassi YA, Xi B, Zhang W, Ye P, Kirstein SL, Gaylord MR, Feinstein SC, Wang X, Xu X.
Chem Biol. 2009; 16(7):712-23.
3. Cell-mediated Cytotoxicity
Real-time profiling of NK cell killing of human astrocytes using xCELLigence technology.
Moodley K, Angel CE, Glass M, Graham ES.
J Neurosci Methods. 2011; 200(2): 173-180.
Unique functional status of natural killer cells in metastatic stage IV melanoma patients and its modula-
tion by chemotherapy.
Fregni G, Perier A, Pittari G, Jacobelli S, Sastre X, Gervois N, Allard M, Bercovici N, Avril MF, Caignard A.
Clin Cancer Res. 2011; 17(9): 2628–37.
4. Cell Adhesion and Cell Spreading
A role for adhesion and degranulation-promoting adapter protein in collagen-induced platelet activati-
on mediated via integrin a2b1.
Jarvis GE, Bihan D, Hamaia S, Pugh N, Ghevaert CJ, Pearce AC, Hughes CE, Watson SP, Ware J, Rudd
CE, Farndale RW.
Journal of Thromb Haemost. 2012; 10(2): 268–277.
Dynamic monitoring of cell adhesion and spreading on microelectronic sensor arrays.
Atienza JM, Zhu J, Wang X, Xu X, Abassi Y.
J Biomol Screen. 2005; 10(8): 795-805.
Selected Publications for the RTCA DP Instrument
6

5. Receptor-mediated Signaling
Impedance responses reveal b2-adrenergic receptor signaling pluridimensionality and allow classifica-
tion of ligands with distinct signaling profiles.
Stallaert W, Dorn JF, van der Westhuizen E, Audet M, Bouvier M.
PLoS One. 2012; 7(1): e29420.
Label-free impedance responses of endogenous and synthetic chemokine receptor CXCR3 agonists cor-
relate with Gi-protein pathway activation.
Watts AO, Scholten DJ, Heitman LH, Vischer HF, Leurs R.
Biochem Biophys Res Commun. 2012; 419(2):412-8.
Impedance measurement: A new method to detect ligand-biased receptor signaling.
Kammermann M, Denelavas A, Imbach A, Grether U, Dehmlow H, Apfel CM, Hertel C.
Biochem Biophys Res Commun. 2011; 412(3): 419-424.
6. Virus-mediated Cytopathogenicity
Novel, real-time cell analysis for measuring viral cytopathogenesis and the efficacy of neutralizing anti-
bodies to the 2009 influenza A (H1N1) virus.
Tian D, Zhang W, He J, Liu Y, Song Z, Zhou Z, Zheng M, Hu Y.
PloS One. 2012; 7(2):e31965.
Real-time monitoring of flavivirus induced cytopathogenesis using cell electric impedance technology.
Fang Y, Ye P, Wang X, Xu X, Reisen W.
J Virol Methods. 2011; 173(2):251–8.
7. Quality of Control of Cells
Rapid and quantitative assessment of cell quality, identity, and functionality for cell-based assays using
real-time cellular analysis.
Irelan JT, Wu MJ, Morgan J, Ke N, Xi B, Wang X, Xu X, Abassi YA.
J Biomol Screen. 2011; 16(3):313-22.
Live cell quality control and utility of real-time cell electronic sensing for assay development.
Kirstein SL, Atienza JM, Xi B, Zhu J, Yu N, Wang X, Xu X, Abassi YA.
Assay Drug Dev Technol. 2006; 4(5):545-53.
8. Endothelial Barrier Function
An inverted blood-brain barrier model that permits interactions between glia and inflammatory stimuli.
Sansing HA, Renner NA, MacLean AG.
J Neurosci Methods. 2012; 207(1):91–6.
A dynamic real-time method for monitoring epithelial barrier function in vitro.
Sun M, Fu H, Cheng H, Cao Q, Zhao Y, Mou X, Zhang X, Liu X, Ke Y.
Anal Biochem. 2012; 425(2):96–103.
Selected Publications continued
7

Ordering Information for xCELLigence RTCA DP System
Product Cat. No. Pack Size
RTCA DP Analyzer
RTCA Control Unit
00380601050
05469759001
05454417001
1 Bundled Package
1 Instrument
1 Notebook PC
E-Plate 16
E-Plate VIEW 16
E-Plate Insert 16
05469830001
05469813001
06324738001
06324746001
06465382001
6 Plates
6 x 6 Plates
6 Plates
6 x 6 Plates
1 x 6 Devices (6 16-Well Inserts)
CIM-Plate 16 05665817001
05665825001
6 Plates
6 x 6 Plates
CIM-Plate 16, Assembly Tool 05665841001 1 Assembly Tool
Learn more about the enabling technology of the xCELLigence System and its broad range of
applications at www.aceabio.com
XCELLIGENCE, E-PLATE, CIM-PLATE, and ACEA BIOSCIENCES are registered trademarks of ACEA Biosciences, Inc. in the US
and other countries.
All other product names and trademarks are the property of their respective owners.
Published by
ACEA Biosciences, Inc.
6779 Mesa Ridge Road Ste. 100
San Diego, CA 92121
U.S.A.
www.aceabio.com
© 2013 ACEA Biosciences, Inc.
All rights reserved.

ISSUE04
Focus Application
Cell Migration
xCELLigence System Real-Time Cell Analyzer
Introduction
Cell migration and invasion are mechanically integrated
molecular processes and fundamental components of
embryogenesis, vasculogenesis, immune responses, as well
as pathophysiological events such as cancer cell metastasis
(1, 2). Cell migration and invasion involve morphological
changes due to actin cytoskeleton rearrangement and the
emergence of protrusive membrane structures followed
by contraction of the cell body, uropod detachment, and
secretion of matrix degrading enzymes (1, 2). These
multi-step processes are influenced by extracellular and
intracellular factors and signaling events through specialized
membrane receptors.
The integrated nature of cell migration is exemplified by
angiogenesis. Angiogenesis or neo-angiogenesis refers to
the formation of new blood vessels from pre-existing vessels
and is critical for development, wound healing and tumor
growth. Endothelial cell migration is an important compo-
nent of angiogenesis, involving chemotactic, haptotactic
and mechanotactic (shear stress) induced cell migration (3).
Chemotactic cell migration is typically induced by soluble
growth factors such as vascular endothelial growth factor
(VEGF) and its isoforms, fibroblast growth factor (bFGF)
and hepatocyte growth factor (HGF) amongst others. These
growth factors interact with their cognate receptor tyrosine
kinases on he surface of endothelial cells activating signaling
pathways culminating in directed cell migration.
In the present study, we used the new CIM-Plate 16 with
the xCELLigence RTCA DP Instrument to monitor growth
factor-mediated migration of endothelial cells in realtime
using label-free conditions. The CIM-Plate 16 is a 16-well
modified Boyden chamber composed of an upper chamber
(UC) and a lower chamber (LC). The UC and LC easily snap
together to form a tight seal. The UC is sealed at its bottom
by a microporous Polyethylene terephthalate (PET) mem-
brane. These micropores permit the physical translocation of
cells from the upper part of the UC to the bottom side of the
membrane. The bottom side of the membrane (the side
facing the LC) contains interdigitated gold microelectrode
sensors which will come in contact with migrated cells and
generate an impedance signal. The LC contains 16 wells,
each of which serves as a reservoir for a chemoattractant
solution on the bottom side of the wells, separated from
each other by pressure-sensitive O-ring seals.
Results
To analyze endothelial cell migration using the CIM-Plate
16, human umbilical vein endothelial cells (HUVEC) from
Lifeline Cell Technologies were cultured in Vasculife VEGF
cell culture medium, according to the manufacturer’s
recommendations. Cells were serum starved in Vasculife
Basal medium, detached using a trypsin-EDTA solution,
and the cell density was adjusted to 300,000 cells/mL.
To assess general HUVEC cell migration in response to
Featured Study:
Automated Continuous Monitoring of
Growth Factor-Mediated Endothelial Cell
Migration using the CIM-Plate 16 and
Jieying Wu and Jenny Zhu
ACEA Biosciences, Inc., San Diego, USA.

different growth factors encountered during angiogenesis,
Vasculife VEGF medium containing VEGF, EGF, IGF, or
bFGF with 2% fetal bovine serum, was serially diluted
with Vasculife Basal medium and transferred to the lower
chamber of the CIM-Plate 16 (see Figure 1). For optimal
HUVEC migration, it was determined from previous
experiments that extracellular matrix (ECM) proteins,
such as fibronectin (FN) are necessary. The PET membrane
was therefore coated on both sides with 20 μg/mL FN.
After CIM-Plate 16 assembly, 100 μL of cell suspension
(30,000 cells) were added to each well of the UC. The
CIM-Plate 16 was placed in the RTCA DP Instrument
equilibrated in a CO2 incubator. HUVEC migration was
continuously monitored using the RTCA DP Instrument.
Figure 1 shows the time- and dose-dependent directional
migration of HUVEC cells from the upper chamber to the
lower chamber. The combination of growth factors and
serum provides a strong chemoattractant signal which
together induce the directional migration of HUVEC cells
through the micropores of the CIM-Plate 16. Migrating
cells are detected by the electronic sensing microelectrodes,
producing changes in the measured Cell Index values
(see Figure 1). HUVEC migration has been shown to be
influenced by a number of growth factors including VEGF,
HGF, and bFGF. These growth factors are known to be
secreted by tumors and cells within the tumor stroma, as
well as endothelial cells inducing migration and angiogenesis.
To measure HUVEC migration in response to individual
growth factors using the CIM-Plate 16, HUVEC cells
from Lonza were starved for 6 hours and detached. At the
same time, titration of HGF, and separately of VEGF, was
performed in basal media (complete HUVEC media diluted
at a ratio of 1:125 with EGM media from Lonza). Each of
these growth factors was then transferred to the wells of
the lower chamber (see Figure 2). The PET membrane was
coated with FN as described above.
After CIM-Plate 16 assembly, HUVEC cells were added at
30,000 cells/well for VEGF-induced migration and 15,000
cells/well for HGF-induced migration. Time-dependent
HUVEC migration was monitored using the RTCA DP
Instrument. Both VEGF and HGF induced the migration
of HUVEC cells in a time- and dose-dependent manner
(see Figure 2A and 2B). The RTCA Software 1.2 was used to
calculate time-dependent EC50 values for both VEGF- and
HGF-mediated HUVEC migration (see Figure 2C and 2D).
Angiogenesis is a compelling target for cancer therapy.
Monoclonal antibodies targeting angiogenesis play an
important role in colon and lung cancer therapy (4). For
this reason, the migration of endothelial cells in response
to angiogenic factors such as VEGF is a good in vitro model
system for studying and screening potential inhibitors of
this process (see Figure 3). For the quantitative and time-
dependent assessment of inhibition of VEGF-induced endo-
thelial cell migration, HUVEC cells were added to
the CIM-Plate 16, as described above, in the presence of
increasing amounts of a VEGF receptor inhibitor. As
shown in Figure 3A, this inhibitor was found to potently
block VEGF-induced cell migration in a time- and dose-
dependent manner. The inhibition of VEGF-induced
HUVEC cell migration by this compound was quantified
using the RTCA Software 1.2. Time-dependent IC50
values, shown in Figure 3B, demonstrate that this
particular VEGF receptor inhibitor blocks the kinase
activity of all three VEGF receptor isoforms with IC50
values in the picomolar range.
Figure 1: Time- and dose-dependent directional migration of HUVEC cells from the upper to the lower CIM-Plate 16 chamber. To assess
HUVEC cell migration, Vasculife VEGF medium, containing VEGF, EGF, IGF, bFGF and 2% fetal bovine serum, was serially diluted with Vasculife
Basal medium and transferred to the lower chamber of the CIM-Plate 16.
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0
Time (in Hour)
6.5
5.5
4.5
3.5
2.5
1.5
0.5
-0.5
CellIndex
Complete
Media
1:3
1:9
1:27
1:81
1:243
1:729
Basal
Media

Figure 2: HUVEC cell migration in response to the growth factors, VEGF (A, C) and HGF (B, D) using the CIM-Plate 16 and xCELLigence RTCA DP Instrument, showing Cell
Index profiles (A, B) and EC50 plots (C, D); see text in Results for details.
0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0
Time (in Hour)
VEGF-induced cell migration HGF-induced cell migration
3.5
2.5
1.5
0.5
-0.5
CellIndex
60 ng/mL
20 ng/mL
6.7 ng/mL
2.2 ng/mL
0.7 ng/mL
0.2 ng/mL
0 ng/mL
EGM Media
2.0
1.0
0.0
0.0 3.0 6.0 9.0 12.0 15.0 18.0
Time (in Hour)
CellIndex
100 ng/mL
33.3 ng/mL
200 ng/mL
3.7 ng/mL
1.2 ng/mL
EGM Media
11.1 ng/mL
3.6000
3.0000
2.4000
1.8000
1.2000
0.6000
0.0000
CellIndex
-10.50 -10.00 -9.50 -9.00 -8.50 -8.00 -7.50 -7.00 -6.50
Log of concentration (g/ml)
2.1000
1.8000
1.5000
1.2000
0.9000
0.6000
0.3000
0.0000
CellIndex
-9.50 -9.00 -8.50 -8.00 -7.50 -7.00 -6.50 -6.0
Log of concentration (g/ml)
T1 EC50= 5.5 ng/mL
T2 EC50= 5.3 ng/mL
T1 EC50= 3.2 ng/mL
T2 EC50= 2.7 ng/mL
C
A B
D
A
0.04 nM
0.12 nM
0.4 nM
1.1 nM
3.3 nM
10 nM
Inhibitor
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0
Time (in Hour)
2.5
2.0
1.5
1.0
0.5
0.0
CellIndex
B
2.4000
2.0000
1.6000
1.2000
0.8000
0.4000
0.0000
CellIndex
-11.00 -10.50 -10.00 -9.50 -9.00 -8.50 -8.00 -7.50
Log of concentration (M)
T1 EC50= 78 pM
T2 EC50= 179 pM
Figure 3: Real-time continuous HUVEC cell monitoring showing the Cell Index profiles (A) and IC50 plot (B) for the inhibition of VEGF-induced cell migration by a VEGF
receptor inhibitor; see text in Results for details.
Conclusion
Data presented in this application note demonstrate that
growth-factor-mediated endothelial cell migration can be
monitored quantitatively and in realtime using the CIM-
Plate 16 with the RTCA DP Instrument. The xCELLigence
System proved to be ideal for assessing and screening an
inhibitor of endothelial cell migration and angiogenesis.
The CIM-Plate 16 combines the benefits of continuous
label-free impedance-based technology with the classic
Boyden chamber permitting automated, real-time, and
quantitative measurements of cell migration and invasion.
Classic cell migration techniques utilizing standard and
transwell Boyden chambers are labor intensive, producing
results that can be difficult to reproduce. The non-invasive
CIM-Plate 16 does not require manual cell counting or cell
labeling. Moreover, the continuous real-time data obtained
using the CIM-Plate 16 identifies optimal time points for
performing parallel gene expression and functional analyses
of HUVEC migration. The above described features and
benefits of the CIM-Plate 16 and the RTCA DP Instrument
describe an ideal system for the in vitro analysis of the
cellular and molecular events associated with cell migration
and invasion.
T1 T2 T1 T2
T1 T2

Published by
ACEA Biosciences, Inc.
6779 Mesa Ridge Road Ste 100
San Diego, CA 92121
U.S.A.
www.aceabio.com
© 2013 ACEA Biosciences, Inc.
All rights reserved.
Ordering Information
References
1. Lauffenburger DA, Horwitz AF. (1996).
“Cell migration: a physically integrated molecular process.”
Cell 84(3):359-69.
2. Ridley AJ, Schwartz MA, Burridge K, et al. (2003).
“Cell migration: integrating signals from front to back.”
Science 302(5651):1704-9.
3. Lamalice L, Le Boeuf F, Huot J. (2007).
“Endothelial cell migration during angiogenesis.”
Circulation Research 24: 100: 782-794.
4. Folkman J. (2007).
“Angiogenesis: an organizing principle for drug discovery?”
Nature Reviews Drug Discovery 6(4): 273-286.
Trademarks:
XCELLIGENCE, CIM-PLATE, E-PLATE, and ACEA BIOSCIENCES are registered trademarks of ACEA Biosciences, Inc.
in the US and other countries.
Other brands or product names are trademarks of their respective holders.
Key Words:
Growth factor-mediated cell migration, xCELLigence System,
RTCA DP Instrument, real-time migration monitoring, CIM-Plate 16,
non-invasive and label-free migration detection
Product Cat. No. Pack Size
RTCA DP Analyzer
RTCA Control Unit
00380601050
05469759001
05454417001
1 Bundled Package
1 Instrument
1 Notebook PC
E-Plate 16
E-Plate VIEW 16
E-Plate Insert 16
05469830001
05469813001
06324738001
06324746001
06465382001
6 Plates
6 x 6 Plates
6 Plates
6 x 6 Plates
1 x 6 Devices (6 16-Well Inserts)
CIM-Plate 16 05665817001
05665825001
6 Plates
6 x 6 Plates

Comparative Analysis of Dynamic Cell Viability,
Migration and Invasion Assessments by Novel Real-Time
Technology and Classic Endpoint Assays
Ridha Limame1
*, An Wouters1
, Bea Pauwels1
, Erik Fransen2
, Marc Peeters1,3
, Filip Lardon1
, Olivier De
Wever4
, Patrick Pauwels1,5
1 Center for Oncological Research (CORE), University of Antwerp, Antwerp, Belgium, 2 StatUA Center for Statistics, University of Antwerp, Antwerp, Belgium, 3 Department
of Oncology, Antwerp University Hospital, Edegem (Antwerp), Belgium, 4 Laboratory of Experimental Cancer Research, Department of Radiotherapy and Nuclear
Medicine, Ghent University Hospital, Ghent, Belgium, 5 Laboratory of Pathology, Antwerp University Hospital, Edegem (Antwerp), Belgium
Abstract
Background: Cell viability and motility comprise ubiquitous mechanisms involved in a variety of (patho)biological processes
including cancer. We report a technical comparative analysis of the novel impedance-based xCELLigence Real-Time Cell
Analysis detection platform, with conventional label-based endpoint methods, hereby indicating performance
characteristics and correlating dynamic observations of cell proliferation, cytotoxicity, migration and invasion on cancer
cells in highly standardized experimental conditions.
Methodology/Principal Findings: Dynamic high-resolution assessments of proliferation, cytotoxicity and migration were
performed using xCELLigence technology on the MDA-MB-231 (breast cancer) and A549 (lung cancer) cell lines. Proliferation
kinetics were compared with the Sulforhodamine B (SRB) assay in a series of four cell concentrations, yielding fair to good
correlations (Spearman’s Rho 0.688 to 0.964). Cytotoxic action by paclitaxel (0–100 nM) correlated well with SRB (Rho.0.95)
with similar IC50 values. Reference cell migration experiments were performed using Transwell plates and correlated by pixel
area calculation of crystal violet-stained membranes (Rho 0.90) and optical density (OD) measurement of extracted dye
(Rho.0.95). Invasion was observed on MDA-MB-231 cells alone using Matrigel-coated Transwells as standard reference
method and correlated by OD reading for two Matrigel densities (Rho.0.95). Variance component analysis revealed
increased variances associated with impedance-based detection of migration and invasion, potentially caused by the
sensitive nature of this method.
Conclusions/Significance: The xCELLigence RTCA technology provides an accurate platform for non-invasive detection of
cell viability and motility. The strong correlations with conventional methods imply a similar observation of cell behavior
and interchangeability with other systems, illustrated by the highly correlating kinetic invasion profiles on different
platforms applying only adapted matrix surface densities. The increased sensitivity however implies standardized
experimental conditions to minimize technical-induced variance.
Citation: Limame R, Wouters A, Pauwels B, Fransen E, Peeters M, et al. (2012) Comparative Analysis of Dynamic Cell Viability, Migration and Invasion Assessments
by Novel Real-Time Technology and Classic Endpoint Assays. PLoS ONE 7(10): e46536. doi:10.1371/journal.pone.0046536
Editor: Aamir Ahmad, Wayne State University School of Medicine, United States of America
Received April 24, 2012; Accepted August 31, 2012; Published October 19, 2012
Copyright: ß 2012 Limame et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: These authors have no support or funding to report.
Competing Interests: Olivier De Wever, listed as co-author, is currently serving as an academic editor for PLOS ONE. This does not alter the authors’ adherence
to all the PLOS ONE policies on sharing data and materials.
* E-mail: ridha.limame@ua.ac.be
Introduction
Among the most fundamental hallmarks of cancer are loss of
pre-existing tissue architecture by sustained proliferation and
extracellular matrix infiltration of cancer cells. Cancer cells may
sustain proliferative signaling in an autocrine or paracrine fashion
by producing growth factors themselves, by overexpression of
growth factor receptors or by a constitutive activation of
downstream signaling components [1]. Monitoring of cell prolif-
eration and cell viability is critical in biomedical research, in order
to understand the pathways regulating proliferation and viability,
and to develop agents that modulate these processes. The
sulforhodamine B (SRB) test is a high throughput and reproduc-
ible colorimetric assay, based on the binding of SRB to protein
basic amino acid residues, providing a sensitive index of cellular
protein content that is linear over a cell density range [2].
Matrix penetration necessitates activation of the cellular motility
apparatus and can occur by either individual cells or cell strands,
sheets or clusters [3]. A phenomenon predominantly involved in
this process is chemotaxis, whereby cell movement is directed
along an extracellular chemical gradient of secreted factors in the
microenvironment [4]. Already in the early stages of embryogen-
esis, formation of complex tissues and organs is orchestrated by
fine-tuned chemotactic migration of cell chains. In malignant
processes however, cancer cells tend to adopt similar, if not
identical mechanisms to metastasize to distant organ sites [5].
Several well-established experimental approaches are available to
PLOS ONE | www.plosone.org 1 October 2012 | Volume 7 | Issue 10 | e46536

study cell migration and chemotaxis in vitro (reviewed in [6]). The
Transmembrane/Boyden chamber assay is based on a chemotactic-
driven cell transit through a filter [7]. An important feature of the
endpoint in this experimental set-up is that cells need to exhibit
active migratory behavior to end up at the other side of the
membrane.
The xCELLigence RTCA technology (Roche Applied Science) has
emerged as an alternative non-invasive and label-free approach to
assess cellular proliferation, migration and invasion in real time on
a cell culture level [8]. This system makes use of impedance
detection for continuous monitoring of cell viability, migration and
invasion (reviewed in [9]) (Fig. 1).
Here we report data of in vitro assessment of four cellular
processes (proliferation, cytotoxicity, migration and invasion) on
the MDA-MB-231 and A549 cancer cell lines using xCELLigence
RTCA DP (Roche Applied Science) in comparison with data
resulting from parallel experiments applying a previously existing
and well-established measuring method (to be considered as a
‘‘gold standard’’ method) for each process. Both these cell lines are
extensively characterized and used as models representing two
different highly incidental tumor types (breast cancer, lung cancer).
Furthermore, these cell lines show a strong degree of motility in
the wild-type state, thus providing useful examples for the
distinction between chemotactic and random motility.
Importantly, all of the comparative techniques are traditional
label-based endpoint assays that have been selected due to their
widespread application within the scientific community and
similarity in working principle with xCELLigence. Although they
have been slightly modified to match with the xCELLigence setup
and its ability to acquire time-dependent kinetics of cultured cell
behavior, the fundamental handling and detection principles of
each classic assay have been maintained. Cell proliferation and
cytotoxicity testing has been performed using the SRB assay [10],
with the microtubule stabilizer paclitaxel as cytotoxic agent in the
latter experiments. Being widely used for the treatment of a variety
of tumor types, inhibitory effects of this anti-mitotic compound on
cell proliferation have been described extensively. Furthermore,
previous reports on the use of the xCELLigence device included
paclitaxel as a reference compound in their studies [8,11], making
this a suitable agent for this study. Cell migration and invasion
experiments were performed using conventional Transwell plates
and quantified by both pixel area calculation of stained
membranes and optical density reading of solubilized dye. For
the first time, results from ‘‘tried-and-tested’’ assay setups are
confronted with parallel data recorded using a novel, commer-
cially available technology, providing an objective technical
comparison of dynamic observations on cultured cells in highly
standardized experimental conditions.
Results
Proliferation
The dynamic assessment of proliferation kinetics was modeled
by performing SRB testing on both MDA-MB-231 and A549 cells.
Growth curve studies were performed over a ten-day period and
proliferation curves were established for four different plating
densities. To correct for seeding area differences between SRB-
experiments and xCELLigence, cell seeding densities were synchro-
nized between both techniques (100, 500, 1000 and 2000 cells/
cm2
). Corresponding experiments on the xCELLigence system were
performed in duplicates and the resulting high-resolution data
were extrapolated to the matching data points of the counterpart
method as described in the Materials and Methods section.
For MDA-MB-231 cells, cell doubling time was
27.7865.14 hours and 29.9262.85 hours measured with the
SRB assay and the xCELLigence system respectively. Similarly,
for A549 cells, doubling time was 27.9361.75 hours and
29.1861.87 hours measured with the SRB assay and the
xCELLigence system respectively.
Spearman’s Rho (r) correlations were calculated on global
average results that had been normalized to the highest value in
the data set per method (SRB or xCELLigence) to eliminate units of
measurement (Fig. 2A, B, shown as ‘‘scaled’’). Both MDA-MB-231
and A549 cells revealed fair to good correlation rates for all
applied seeding densities, noting however that proliferation did not
set off at the lowest cell seeding density (100 cells/cm2
) of MDA-
MB-231 on RTCA (Fig. 2A). A549 cells showed only minimal
proliferative activity at this density as well (Fig. 2B). Correlations
observed between SRB-based and impedance-based quantitation
reached higher values at the medium cell seeding densities of 500
cells/cm2
and 1000 cells/cm2
for both cell lines tested (Spearman’s
r = 0.835 resp. 0.790 for MDA-MB-231 and r = 0.964 resp. 0.883
for A549). Altogether, correlation values ranged from 0.880 to
0.964 for A549 and 0.688 to 0.835 for MDA-MB-231. Variance
component analysis on proliferation data of A549 cells resulting
from both techniques indicated smaller intra- and inter-experi-
mental variances on xCELLigence when compared to SRB.
Conversely, detection of proliferation kinetics of MDA-MB-231
cells resulted in a higher degree of (intra- and interexperimental)
variance when performed with the xCELLigence system (quantified
as sw and sb in Table S1).
Figure 1. xCELLigence RTCA: impedance-based detection of cell
viability and motility. Interdigitated gold microelectrodes on the
well bottom (viability – E-plate) or on the bottom side of a filter
membrane (motility – CIM-plate 16) detect impedance changes, caused
by the presence of cells and expressed as a Cell Index. This detection
method is proportional to both cell number (left and above) and
morphology as increased cell spreading is reflected by a higher Cell
Index value (right). When starting an experiment, a baseline Cell Index
value is recorded in medium only before cell addition.
doi:10.1371/journal.pone.0046536.g001
Real-Time Technology Compared with Endpoint Assays

Cytotoxicity
Compound cytotoxicity was assessed after exposure of MDA-
MB-231 and A549 cells to a concentration range of paclitaxel (0–
100 nM for 72 hours). In both cell lines, a similar toxic response
was detected by both SRB and xCELLigence, with comparable IC50
values of 4.7860.90 nM and 6.4461.90 nM respectively (t-test,
p = 0.244) in MDA-MB-231 cells. The normalized toxic response
correlated highly between SRB-based and xCELLigence-based
detection for both cell lines (Spearman’s r = 0.970 and 0.976 for
MDA-MB-231 and A549 resp.) (Fig. 2C).
Cell migration
Conventional Transwell plates have been organized in a
sequential setup to perform dynamic observations of cancer cell
migration in a time-dependent manner, yielding a series of data
similar to the xCELLigence system (Fig. 3A, B). Both chemotactic
migration to medium containing 10% FBS and random migration
with SF medium on either side of the membrane have been
considered. High correlation values for both cell lines were
obtained when comparing area calculation and OD with
xCELLigence Cell Index (CI) (Fig. 4A). Importantly, it must be
noted that correlation coefficients were calculated for overall mean
values resulting from three independent experiments, each
performed in duplicates for all time points. As described in the
Materials and Methods section, all raw data obtained were
normalized to the single maximal value over three experiments per
quantitation method (CI, pixel area or OD) to eliminate units of
measurement. Subsequently, values from random (SF) migration
were subtracted from chemotactic (FBS) migration per time point
to generate signals of net chemoattraction (Fig. 4B). Pixel area
calculations, averaged over three fields per insert membrane
(Fig. 4C), correlated with xCELLigence CI measurements for both
MDA-MB-231 and A549 cells (Spearman’s r = 0.90 for both cell
lines). However, OD measurements showed even stronger
correlations with xCELLigence data (Spearman’s r = 0.96 and
1.00 for MDA-MB-231 and A549 resp.). Area calculation
correlated with OD measurements in a similar fashion as with
xCELLigence for both MDA-MB-231 and A549 (Spearman’s
r = 0.89 and r = 0.90 resp.) (Fig. 4A).
At later time points of the experiments, xCELLigence generated
larger variances between intra-experimental replicates when
compared to area calculations or OD values derived from classic
Transwells (Table S1). Briefly, variance between replicates within
one experiment increased over time, creating a funnel shaped
time-dependent pattern (Fig. 4D, E). Variance component analysis
indeed revealed an increase of intra-experimental variance on
xCELLigence data in comparison with Transwell data. However,
early (,10 h) pixel area values showed similar degrees of variance.
Additional experiments using an identical setup yielded similar
results (results not shown).
A significant difference was observed between background
(serum-free, SF) signals generated by the three methodologies of
cell migration quantitation. These signals derived from random
movement of cells without exposure to any chemoattractant and,
serving as a negative control, showed a different pattern when
generated by xCELLigence or by both quantitation techniques using
Transwells (Fig. 5). Paired comparisons between time-dependent
tracking of background migration were performed between all
techniques using a likelihood ratio test and revealed a significant
difference between xCELLigence and pixel area calculation
(p,0.001) for both MDA-MB-231 and A549. OD measurements
differed significantly from xCELLigence (p,0.001) for MDA-MB-
231 cells, but showed a similar pattern when compared with
xCELLigence data generated from A549 cells (p = 0.22).
Cell invasion
Impedance-based detection of MDA-MB-231 cell invasion was
compared with results derived from a Transwell system as applied
for migration experiments, with Matrigel as extracellular matrix
component added on top of the microporous membranes (Fig. 6A).
It was found that Matrigel dilutions of 10% (v/v, SF medium) on
xCELLigence yielded high correlations when compared to a dilution
of 20% on Transwells in dynamic invasion profile recording
during a 48-hour incubation (Spearman’s r = 0.939). Similarly,
invasion through a Matrigel dilution of 3.3% on xCELLigence
correlated highly with a dilution of 7.7% on Transwell plates
(Spearman’s r = 0.927) (Fig. 6B, C). Variance component analysis
of Transwell and xCELLigence data revealed slightly increased
degrees of intra-experimental variance in the latter. The variance
between independent experiments was increased for xCELLigence
in comparison with Transwell assays (Table S1). All Matrigel
dilutions have been synchronized regarding seeding surface area
for both systems and theoretical calculations for correlating
Matrigel dilutions are shown in Table 1.
Discussion
xCELLigence technology measures impedance changes in a
meshwork of interdigitated gold microelectrodes located at the
well bottom (E-plate) or at the bottom side of a microporous
membrane (CIM16-plate). These changes are caused by the
gradual increase of electrode surface occupation by (proliferated/
migrated/invaded) cells during the course of time and thus can
provide an index of cell viability, migration and invasion. This
method of quantitation is directly proportional to cellular
morphology, spreading, ruffling and adhesion quality as well as
cell number [8,9] (Fig. 1). Cell proliferation and paclitaxel
cytotoxicity kinetics, as assessed by the xCELLigence platform, were
compared with an SRB-based approach, showing good correla-
tions for both cell lines tested, implying that both methods detect
similar process kinetics when performed in standardized condi-
tions. However, correlations were generally stronger for the A549
than for the MDA-MB-231 cell line, which may indicate a possible
cell type-dependent cause. MDA-MB-231 cells show a heteroge-
neous morphotype with round and spread cells, which may
differentially influence impedance based measurements or crystal
violet uptake. Paclitaxel was chosen as it is widely used as
treatment for a variety of tumor types and previous reports on the
use of the xCELLigence system as a tool for cytotoxicity screening
included paclitaxel as a reference compound [8,11]. Nevertheless,
it must be underlined that the highly correlative nature of
cytotoxic kinetics as detected by both techniques for paclitaxel may
not apply for certain other compounds. Indeed, the xCELLigence
Figure 2. Time-dependent proliferation and cytotoxicity profiles of MDA-MB-231 and A549. A. Proliferation curves of MDA-MB-231 cells
as generated by xCELLigence RTCA (red) and SRB (black) for different seeding densities of 100 (top left), 500 (bottom left), 1000 (top right) and 2000
cells/cm2
(bottom right) during a ten-day incubation. B. Same as (A) for A549 cells. All graphs represent results from three independent experiments 6
SD. C. Cytotoxicity profiles relating to 72 hours of exposure to paclitaxel (0–100 nM). Cells were allowed to attach and propagate during 24 hours
prior to start of treatment. Toxicity data from xCELLigence RTCA were derived from normalized plots. All graphs represent results from three
independent experiments 6 SD.

RTCA device has been reported to generate compound-specific
kinetic profiles on cultured cells, hereby demonstrating associa-
tions with the respective mechanisms of action [11].
To quantify cell migration through conventional setups,
detection by crystal violet was selected, followed by pixel area
and OD quantitation, as these methods are widespread within the
scientific community and also take morphologic features into
account. Both area calculation and OD measurement correlated
highly with cell migration, as detected by xCELLigence, confirming
that the observed kinetic cell behavior is strongly similar, provided
that equal cell seeding densities are applied. The closest
associations were found between OD measurements and RTCA
CI, generating nearly identical migration patterns. OD values
were determined on cell lysates with extracted crystal violet stain,
derived from entire Transwell membranes, and thus provide a
more reliable quantitation when compared with pixel area
calculation, which was based on averaging three microscopic
fields per Transwell membrane. This indicates that impedance-
based measurements have smaller limits of detection, resulting in
highly reliable migration estimates. Analysis of background signals,
resulting from random migration in a serum-free environment
(negative control), revealed a significant difference in signal
detection between xCELLigence and both classic techniques
(Fig. 5). Weak signals resulting from limited baseline cell migration
were indeed more accurately detected by the RTCA platform,
which implies a higher sensitivity, compared to classic detection
methods. OD measurements correlated more closely with
xCELLigence data in all experiments and did not show a significant
difference when background signals were compared with xCELLi-
gence for the A549 cell line, suggesting a smaller detection limit and
thus a more accurate method of quantifying cell migration when
using Transwells. Additionally, a smaller limit of detection can
explain the observed time-dependent increase of variability
between intra-experimental replicates during the course of an
experiment. Data derived from cell culture-based experiments are
subject to inter- as well as intra-individual variation regarding cell
counting, pipetting, preparation of chemotactic factors and
general cell culture handling. As a consequence, small handling
differences during the preparation of an experiment can result in
signal differences between technical replicates and this will be
reflected in variations increasing with time within one experiment
when compared to area calculation or OD measurement, that do
not detect this variability to this extent. This is also illustrated by
cell culture-based invasion experiments, as the uniform application
Figure 3. Conventional Transwell design for detection of time-dependent cell migration. A. A Transwell setup consists of an upper
chamber (insert) that is placed onto a lower chamber (well). The insert contains a microporous membrane (8 mm pores) allowing passage of tumor
cells. After a period of serum starvation a serum-free cell suspension is seeded in the insert and exposed to medium containing potential
chemoattractants (by default: medium+FBS). During incubation at 37uC and 5% CO2, cells migrate toward the bottom side of the membrane. B.
Experimental design to assess time-dependent migratory behavior of cultured cells. Both migration toward FBS-containing medium and baseline
migration (toward SF medium, no chemoattraction) as a negative control were included. Two times two 24-well Transwell plates were used to
examine migration to FBS (positive control – top row) and baseline migration (negative control – bottom row). At ten time points during a 24-hour
incubation period inserts were fixed and stained in duplicate. Two inserts containing cell-free media (grey fill) have been included throughout the
experiment and fixed and stained after 12 hours incubation to assess background absorption in optical density (OD) measurements. In addition, to
exclude influence of inter-plate variability on observed migration rates, each plate contained duplicate two-hour control inserts.

of a Matrigel layer implies the introduction of an important added
variable. Consequent Matrigel thawing and handling on ice, using
only cooled consumables, and hands-on coating experience can
contribute to homogenous gelification and thus to enhanced assay
reproducibility. Comparative invasion results have shown corre-
lation between xCELLigence and Transwells when similar cell
seeding densities were applied and, more importantly, when the
amount of Matrigel per square area unit is synchronized. Diluting
the matrix barrier, and thus changing the degree of matrix
fenestration, gives rise to similar invasion rates on both setups with
different seeding areas, although equal volumes of Matrigel have
been applied.
In conclusion, the real-time label-free xCELLigence system
provides a suitable and accurate platform for high-throughput
kinetic screenings and for determination of cell motility dynamics.
In contrast with classic endpoint assays, the impedance-based
detection method is generally less labor-intensive, provides kinetic
information on the studied processes and does not affect cell
viability, potentially generating further experimentation possibil-
ities. Moreover, the correlating observations as performed with
conventional approaches make methods interchangeable to
perform functional studies when larger cell populations of interest
are needed. However although impedance measurement provides
a sensitive cell-based detection method, it should be applied as a
complementary tool to further functional confirmation.
Importantly, this is the first study illustrating the highly
correlative nature of invasion kinetics detected by two different
setups applying synchronized matrix densities. The increased
sensitivity, however, necessitates standardized experimental con-
ditions and user experience, to minimize variance increments on
the xCELLigence system.
Materials and Methods
Cell culture
Two malignant cell lines (A549, lung adenocarcinoma; MDA-
MB-231, breast adenocarcinoma), obtained from the American
Type Culture Collection (ATCC, Manassas, VA, USA) (http://
Figure 4. Time-dependent migratory pattern of MDA-MB-231 and A549. A. MDA-MB-231 (left) and A549 (right) cell migration profiles,
detected by Transwell experiments (black) and xCELLigence (red). Graphs represent scaled signals (0–1) of net chemoattraction after subtraction of the
random migration signal (empty squares in panel A, B), with associated Spearman’s Rho values. All results originate from three independent duplicate
experiments 6 SD. B. Normalization procedure of migration patterns. Raw data (left panel) were normalized to a (0–1) scale (middle panel) through
division of all data by the maximum value obtained in three independent experiments. Subsequently, random migration (SF) signals (triangle markers)
were subtracted from the positive (FBS) control counterparts (circle markers) per experiment to obtain a pure chemotactic signal (right panel).
Example shown is the migratory pattern of MDA-MB-231 cells estimated by pixel area calculation in three experiments (exp 1 - red, exp 2 - green, exp
3 - black). Triangle and circle markers represent negative (SF) and positive (FBS) control data respectively. C. ImageJ-based picture processing. Original
pictures were color thresholded to obtain a binary image displaying cellular content as saturated black areas on a white background. Thresholded
images were masked to exclude non-cellular particles from the final area calculation. Pictures shown are migrated MDA-MB-231 cells after four hours
(top row) and 16 hours (bottom row) of incubation. D. Migratory behavior of MDA-MB-231 cells toward medium+FBS (positive control – filled squares)
and background migration (empty squares) as detected by conventional Transwell experiments at ten time points spread over 24 hours of incubation.
Area calculation (left) of stained cells and optical density (OD – middle) were compared to the xCELLigence migration pattern, reconstructed from the
original high-resolution plot by extrapolating data from the corresponding time points (right). All results represent original data from three
independent duplicate experiments 6 SD. Picture string (obj. 2.56) shows migratory status of MDA-MB-231 cells, stained as described, at five
different stages during 24 hours of incubation. E. Same as (D) for A549 cells.
Figure 5. Time-dependent random migration profile of MDA-MB-231 and A549. Comparison of random migration signals (negative
control – SF) between three quantitation methods: pixel area calculation – black, OD - red, xCELLigence - green. A likelihood ratio test revealed a
significant difference in slope between area calculation and OD (p,0.001) and area calculation and xCELLigence (p,0.001) for both cell lines and OD
and xCELLigence (p,0.001) for MDA-MB-231 only. OD and xCELLigence slopes did not differ significantly (p = 0.22) for A549 cells.

www.lgcstandards-atcc.org), were cultured in DMEM and
RPMI1640 respectively, each supplemented with 10% fetal bovine
serum (FBS), 1% penicillin/streptomycin, 1% L-glutamine and
additionally, 1% sodium pyruvate was added to RPMI1640 only.
All cell culture reagents were purchased from Invitrogen NV/SA
(Merelbeke, Belgium). For proliferation and cytotoxicity experi-
ments, normal growth medium containing FBS was used. Cell
lines were maintained at 37uC and 5% CO2/95% air in a
humidified incubator and confirmed free of mycoplasma infection
through regular testing (MycoAlertH Mycoplasma Detection Kit, Lonza,
Belgium). All cell lines have been validated in-house by short
tandem repeat (STR) profiling using the Cell IDTM
System
(Promega, Madison, WI, USA) according to the manufacturer’s
Figure 6. Time-dependent invasion profile of MDA-MB-231. A. Experimental design to quantify MDA-MB-231 Matrigel invasion. Two times
two 24-well Transwell plates were used to examine invasion to FBS through a 20% (v/v) (top row) and 7.7% (v/v) Matrigel layer (bottom row) after
24 hours of serum starvation. At ten time points during a 48-hour incubation period inserts were fixed and stained in duplicate. Two inserts
containing cell-free media (grey fill) have been included throughout the experiment and fixed and stained after 24 hours incubation to assess
background absorption in optical density (OD) measurements. In addition, to exclude influence of inter-plate variability on observed migration rates,
each plate contained duplicate 24-hour control inserts. B. MDA-MB-231 dynamic cell invasion profiles, generated by Transwell experiments (black)
and xCELLigence (red). Graphs represent normalized signals (scaled values 0–1) of invasion through 20% (open circles), 10% (open squares), 7.7% (filled
circles) and 3.3% (filled squares) to medium+10% FBS with associated Spearman’s rank correlation coefficients (Rho). All results are from three
independent duplicate experiments with SD. C. Sequential pictures showing invasive MDA-MB-231 cells at the indicated time points during a 48-hour
incubation on Transwells coated with 20% (top row) and 7.7% Matrigel (bottom row). Pictures (obj. 2.56) show cells fixed and stained in 20%
methanol/0.1% crystal violet.
Table 1. Matrigel surface densities corresponding with
degree of dilution for a fixed volume of 20 mL.
Matrigel density
xCELLigence RTCA Transwell
% mg/cm2*
%
10.0 189.50 23.1
3.3 63.17 7.7
*Matrigel (Basement Membrane Matrix, growth factor reduced, BD Biosciences)
delivered as a 613.55 mg/mL stock.
doi:10.1371/journal.pone.0046536.t001

instructions. The obtained STR profiles were matched with
reference ATCC DNA fingerprints (www.lgcstandards-atcc.org)
and with the Cell Line Integrated Molecular Authentication
(CLIMA) database (http://bioinformatics.istge.it/clima) [12] to
authenticate cell line identity.
xCELLigence Real-Time Cell Analysis (RTCA): proliferation
and cytotoxicity
Experiments were carried out using the xCELLigence RTCA DP
instrument (Roche Diagnostics GmbH, Mannheim, Germany)
which was placed in a humidified incubator at 37uC and 5% CO2.
Cell proliferation and cytotoxicity experiments were performed
using modified 16-well plates (E-plate, Roche Diagnostics GmbH,
Mannheim, Germany). Microelectrodes were attached at the
bottom of the wells for impedance-based detection of attachment,
spreading and proliferation of the cells. Initially, 100 mL of cell-
free growth medium (10% FBS) was added to the wells. After
leaving the devices at room temperature for 30 min, the
background impedance for each well was measured. Cells were
harvested from exponential phase cultures by a standardized
detachment procedure using 0.05% Trypsin-EDTA (Invitrogen
NV/SA, Merelbeke, Belgium) and counted automatically with a
Scepter 2.0 device (Merck Millipore SA/NV, Overijse, Belgium),
Fifty mL of the cell suspension was seeded into the wells (20, 40, 80,
100, 200, 400 and 800 cells/well for proliferation, 1000 cells/well
for cytotoxicity experiments). The cell concentrations of 20, 100,
200 and 400 cells/well were considered for correlation with the
SRB method described below. After leaving the plates at room
temperature for 30 min to allow cell attachment, in accordance
with the manufacturer’s guidelines, they were locked in the RTCA
DP device in the incubator and the impedance value of each well
was automatically monitored by the xCELLigence system and
expressed as a Cell Index value (CI). Water was added to the space
surrounding the wells of the E-plate to avoid interference from
evaporation. For proliferation assays, the cells were incubated
during ten days in growth medium (10% FBS) and CI was
monitored every 15 min during the first six hours, and every hour
for the rest of the period. Two replicates of each cell concentration
were used in each test. For cytotoxicity experiments, CI of each
well was automatically monitored with the xCELLigence system
every 15 min during the overnight recovery period. Twenty-four
hours after cell seeding, cells were treated during a period of
72 hours with paclitaxel (0, 1, 2, 5, 10, 20, 50 and 100 nM)
dissolved in phosphate buffered saline (PBS). PBS alone was added
to control wells. Each concentration was tested in duplicate within
the same experiment. CI was monitored every 15 min during the
experiment. Three days after the start of treatment with paclitaxel,
CI measurement was ended.
xCELLigence Real-Time Cell Analysis (RTCA): migration
and invasion
Cell migration and invasion experiments were performed using
modified 16-well plates (CIM-16, Roche Diagnostics GmbH,
Mannheim, Germany) with each well consisting of an upper and a
lower chamber separated by a microporous membrane containing
randomly distributed 8 mm-pores. This setup corresponds to
conventional Transwell plates with microelectrodes attached to
the underside of the membrane for impedance-based detection of
migrated cells. Prior to each experiment, cells were deprived of
FBS during 24 hours. Initially, 160 mL and 30 mL of media was
added to the lower and upper chambers respectively and the CIM-
16 plate was locked in the RTCA DP device at 37uC and 5% CO2
during 60 minutes to obtain equilibrium according to the
manufacturer’s guidelines. After this incubation period, a
measurement step was performed as a background signal,
generated by cell-free media. To initiate an experiment, cells
were detached using TrypLE ExpressTM
(Invitrogen, Merelbeke,
Belgium), resuspended in serum-free (SF) medium, counted and
seeded in the upper chamber applying 36104
cells in 100 mL.
After cell addition, CIM-16 plates were incubated during
30 minutes at room temperature in the laminar flow hood to
allow the cells to settle onto the membrane according to the
manufacturer’s guidelines. To prevent interference from evapora-
tion during the experiments, SF medium was added to the entire
empty space surrounding the wells on the CIM-16 plates. Lower
chambers contained media with or without FBS in order to assess
chemotactic migration when exposed to FBS and background
migration to SF medium as a negative control accordingly. Signals
representing net chemoattraction were obtained by subtracting
background (SF) values from the positive control (medium
containing FBS) signals. Each condition was performed in
quadruplicate with a programmed signal detection schedule of
each three minutes during the first 11 hours of incubation followed
by each five minutes for three hours and finally each 15 minutes to
24 hours of incubation.
A protocol identical to the above migration experiments was
followed for invasion experiments added with the application of a
layer of Matrigel on the upper side of the membranes and dynamic
process follow-up during 50 hours. Aliquoted Matrigel (Basement
Membrane Matrix, growth factor reduced, BD Biosciences,
Erembodegem, Belgium) was thawed overnight on ice and mixed
with ice cold SF medium to obtain two dilutions corresponding
with 6190 mg/mL (10%, v/v) and 663 mg/mL (3.3%, v/v)
(Table 1). All Matrigel handling materials as well as the sealed
packs containing CIM-16 upper chambers were stored ice cold
overnight. Establishment of a Matrigel layer on the CIM-16 upper
chamber membranes was achieved by adding 50 mL of the
dilution sequentially on top of four membranes followed by
removal of 30 mL, leaving a total of 20 mL Matrigel dilution.
Subsequently, the coated upper chambers were incubated at 37uC
to homogenously gelify during a minimum of four hours, followed
by addition of 160 mL media to the lower and 30 mL SF media to
the upper chambers. Equilibration and cell addition was carried
out as described above.
All data have been recorded by the supplied RTCA software (vs.
1.2.1). As described below in the ‘‘Prestatistical data processing
and statistical analysis’’ section, original high-resolution data sets
generated by xCELLigence were exported to MS Excel and
reconstructed at a lower resolution by selecting only the data
points corresponding with the respective time points of signal
detection by the endpoint methods (Fig. 7). CI-data from
cytotoxicity experiments have been normalized using the RTCA
software to the last data point prior to treatment start. All other
results (proliferation, migration and invasion) are based on raw
data without CI-normalization and were processed as described
above for comparison with conventional methodology.
SRB assay: proliferation
Cells were harvested from exponential phase cultures by
trypsinization, counted and plated in 48-well plates. To determine
a proliferation curve and calculation of the doubling time, seeding
densities ranged from 100 to 2000 A549 or MDA-MB-231 cells/
well. Each concentration was tested six times within the same
experiment. General cell culture conditions and culture medium
used for this method were similar to those applied for the
xCELLigence counterpart experiments, as well as applied cell
densities (100, 500, 1000 and 2000 cells/cm2
). Every day, one

plate was fixed by the first step of the SRB assay: culture medium
was aspirated prior to fixation of the cells by addition of 200 ml
cold 10% trichloroacetic acid. After one hour incubation at 4uC,
cells were washed five times with deionized water and left to dry.
After collection of all plates during ten days, the following steps of
the SRB test were performed as described previously [13,14].
Shortly, the cells were stained with 200 ml 0.1% SRB dissolved in
1% acetic acid for at least 15 minutes and subsequently washed
four times with 1% acetic acid to remove unbound stain. The
plates were left to dry at room temperature and bound protein
stain was solubilized with 200 ml 10 mM unbuffered TRIS base
(tris(hydroxymethyl)aminomethane) and transferred to 96 wells
plates for optical density reading at 540 nm (Biorad 550
microplate reader, Nazareth, Belgium). Cell doubling time was
calculated from the exponential phase of the growth curve.
SRB assay: cytotoxicity
Cells were harvested as described above. In order to assure
exponential growth during the experiments, seeding density was
103
A549 cells per well and 103
MDA-MB-231 cells/well. After an
overnight recovery period, treatment with paclitaxel (0–100 nM)
dissolved in PBS was started. Control wells were added with PBS.
Figure 7. Prestatistical data processing. A. Schematic depiction of processing kinetic data generated by SRB, xCELLigence and Transwells. Raw
data with high time resolution (filled and empty circles), resulting from independent xCELLigence experiments (1, 2, 3 and grey arrows) are reduced to a
lower time resolution by selecting only the data points corresponding with the time points of endpoint detection (filled circles only). Subsequently,
data have been normalized by dividing all values by the highest value recorded over all experiments per method, resulting in a modified Y-axis scale
that ranges from 0 to 1. Finally, the normalized data have been averaged with calculation of SD for the three independent experiments per method.
B. Reduction of high-resolution data, generated by xCELLigence, to a low resolution comparable with data from conventional assays. The example
shows migration (left) and invasion (right) of MDA-MB-231 cells through two densities of Matrigel. The ten time points in the Transwell method (black
arrows) were selected from the xCELLigence plots (grey and blue) to reconstruct a low-resolution graph (black), directly comparable to the Transwell
data. An identical approach was applied for all other processes studied.

Each concentration was tested six times within the same
experiment. After 72 hours incubation with paclitaxel, survival
was determined by the SRB assay as described above. IC50 values,
representing the drug concentration causing 50% growth inhibi-
tion, were calculated using WinNonlin software (Pharsight,
Mountain View, USA).
Transwell migration assay
Comparative migration experiments were conducted using a
conventional 24-well Transwell system (6.5 mm TranswellH
(#3422), CorningH, NY, USA) with each well separated by a
microporous polycarbonate membrane (10 mm thickness, 8 mm
pores) into an upper (‘‘insert’’) and a lower chamber (‘‘well’’). After
24 hours of serum deprivation, cells were detached using
TrypLETM
Express (Invitrogen, Merelbeke, Belgium), counted
and resuspended in media without FBS to obtain equal cell
densities (2.16105
cells/cm2
) as applied in the xCELLigence RTCA
DP system with respect to the membrane seeding surface of both
techniques (classic TranswellH membrane surface 0.33 cm2
,
RTCA DP 0.143 cm2
). A volume of 250 mL containing 76104
cells was plated to each insert and 600 mL medium was added to
the wells. For each experiment, both chemotactic migration to
medium containing 10% FBS and random migration with SF
medium on both sides of the membrane have been assessed in
parallel Transwell plates. At ten predetermined time points after
incubation start (Fig. 3B), inserts were fixed and stained in
duplicates and migration was quantitated using two commonly
used methods. At each time point, cells were fixed and stained in a
20% methanol/0.1% crystal violet solution during three minutes
at room temperature, followed by washing in deionized water to
remove redundant staining [15]. Non-migrated cells remaining at
the upper side of the membranes were carefully removed with
cotton swabs and inserts were dried in darkness overnight. As
fixing and staining was performed per set of two inserts with the
remaining inserts to be further incubated until the following time
point, inserts to be fixed and stained at a time point were
transferred to a companion 24-well plate and the remaining inserts
immediately replaced in the incubator. Using this approach
unfavorable influences caused by continuous switching incubating
cells between 37uC and ambient temperatures could be avoided.
The following day stained membranes were pictured in three
random non-overlapping fields at 106objective and 106eyepiece
on a transmitted-light microscope (Leica DMBR, Leica Micro-
systems GmbH, Wetzlar, Germany) equipped with an AxioCam
HRc camera (Carl Zeiss MicroImaging GmbH, Jena, Germany).
A first method of quantitation was performed by processing all
obtained images using ImageJ software (http://rsbweb.nih.gov/ij/
). Each image (Fig. 4C, left) was color thresholded to obtain a
binary (black white – 8 bit) image with cellular material
portrayed as saturated black areas (Fig. 4C, middle). As a next step
all non-cellular artifacts, predominantly visible shadows of empty
pores and debris, were removed from each image by performing
the particle analysis function with a mask excluding all particles
smaller than 100 to 250 pixels dependent on the experiment
(Fig. 4C, right). Masking thresholds were set by comparing binary
images with their original phase contrast counterparts [16].
Degree of migration for each time point per experiment was
determined by calculating the average pixel area of the three fields
in duplicate. Inserts were subsequently submerged in 300 mL 1%
SDS/16 PBS in order to lyse migrated cells and extract crystal
violet stain [17]. Submerged inserts were incubated in darkness
overnight on a plate shaker at medium speed to ensure complete
lysis. The following day 200 mL of each lysate was transferred to a
96-well plate for optical density (OD) measurement at 590 nm
using a Powerwave X microplate scanning spectrophotometer
(Bio-Tek, Bad Friedrichshall, Germany), representing a second cell
migration quantitation method. Cell-free inserts containing only
medium had been included in duplicate throughout each
experiment as OD background controls. Reported OD data
represent average background-corrected values 6 SD obtained
from three independent experiments in duplicate.
Transwell Matrigel Invasion assay
Reference cell invasion experiments were carried out using a
Transwell plate system as described for migration experiments,
added with the application of Matrigel as extracellular matrix
component. Matrigel dilutions were prepared as described above
for the xCELLigence RTCA invasion assay. In order to obtain
Matrigel surface area densities synchronized with the CIM-16
plates used for xCELLigence, two dilutions of 20% and 7.7% (v/v)
have been prepared in ice cold SF medium corresponding with
6190 mg/mL and 663 mg/mL respectively, as applied in a volume
of 20 mL per insert membrane, identical to the CIM-16 upper
chamber coating volume (Table 1). All other conditions regarding
culturing, cell seeding density and serum deprivation were
identical to the Transwell migration assays described above. At a
panel of ten predetermined time points, inserts were fixed and
stained in duplicates and invasion was quantified by OD reading
at 590 nm after overnight extraction of the crystal violet stain.
Reported OD data represent average background-corrected
values 6 SD obtained from three independent experiments in
duplicate.
Prestatistical data processing and statistical analysis
All data recorded using the xCELLigence RTCA system have
been processed using MS Excel in order to obtain data series with
the same resolution as the data recorded by the conventional
reference methods (SRB, Transwell). This has been performed by
selecting only the xCELLigence-generated values corresponding to
the time points that have been used in the reference methods, thus
leading to a reconstruction of the studied process dynamics at a
lower time resolution (Fig. 7A, B).
Furthermore, to eliminate differences in units of measurement
between the compared methods (xCELLigence CI, OD, pixel area),
all data have been reduced to a (0–1) scale. This was done by
considering all data gathered over three performed experiments
per method and subsequently dividing all data by the single
maximal value obtained, thus reducing this value to one (Fig. 4B
and 7A). As these interventions do not influence proportionality
nor variance levels of the data, comparable series of dynamically
generated results were obtained for further statistical processing.
For cell migration experiments, random migration signals (SF)
were subtracted from the chemotactic migration signals (FBS) per
time point to generate dynamic profiles of net chemoattraction
(Fig. 4B).
All statistical analyses were performed using the statistical
package R, version 2.13.1 (www.r-project.org). Correlations were
calculated according to the Spearman’s rank correlation method.
Intra- and inter-experimental variances were assessed for each
quantitation method separately using a mixed model approach
with time as fixed and biological replicate as random effect. The
standard deviation of the random intercept (inter-experimental
variance) as well as the residual standard deviation (intra-
experimental variance) was obtained through a variance compo-
nent analysis. Due to differences in these values regarding the cell
migration data, calculations were split up into ‘‘early’’ (before ten
hours incubation) and ‘‘late’’ (after ten hours incubation)
measurements. All cell migration data analyses were performed

on background (SF)-reduced signals representing pure chemoat-
traction. Comparison of background migratory (negative control –
SF) signal detection between the three quantitation techniques
(pixel area calculation, OD and RTCA CI) was performed by
fitting a mixed linear model where the signal was regressed on
time, technique and their interaction. Biological replicates were
entered as a random effect. A likelihood ratio test was performed
to test the significance, expressed as a p-value, of the interaction
term time-technique.
Supporting Information
Table S1 Variance component analysis of proliferation,
cytotoxicity, migration and invasion. All values expressed as
the square root of the variance (s2
). sb Variance between
independent experiments (‘‘between’’). sw Variance within one
experiment (‘‘within’’). Low 6102
and 656102
cells/cm2
. High
6103
and 626103
cells/cm2
. Early Before 10 hours incubation.
Late After 10 hours incubation. ND Not done. * Matrigel dilution
in SF medium (v/v).
(DOCX)
Acknowledgments
We thank Ken Op de Beeck (CORE and Center for Medical Genetics,
University of Antwerp) for assistance with cell line authentication, the
Laboratory for Pathophysiology (University of Antwerp) for granting access
to the microscope for picturing Transwell membranes and the Laboratory
for Experimental Medicine and Pediatrics (University of Antwerp) for
granting access to the microplate scanning spectrophotometer for OD
measurements. We gratefully acknowledge Marc Baay and Johan Ides
(CORE, Antwerp) for constructive discussions.
Author Contributions
Conceived and designed the experiments: RL AW BP EF MP FL ODW
PP. Performed the experiments: RL AW BP. Analyzed the data: RL AW
BP EF. Contributed reagents/materials/analysis tools: MP FL PP. Wrote
the paper: RL. Evaluated and interpreted results: RL AW BP EF MP FL
ODW PP. Evaluated manuscript text: AW EF MP FL ODW PP.
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Mechanistic modeling of the effects
of myoferlin on tumor cell invasion
Marisa C. Eisenberga,1
, Yangjin Kimb
, Ruth Lic
, William E. Ackermanc
, Douglas A. Knissc,d
, and Avner Friedmana,e,1
a
Mathematical Biosciences Institute, Ohio State University, Columbus, OH 43210; b
Department of Mathematics, University of Michigan, Dearborn, MI
48128; c
Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH 43210; d
Department of Biomedical Engineering, Ohio State
University, Columbus, OH 43210; and e
Department of Mathematics, Ohio State University, Columbus, OH 43210
Contributed by Avner Friedman, October 5, 2011 (sent for review August 18, 2011)
Myoferlin (MYOF) is a member of the evolutionarily conserved
ferlin family of proteins, noted for their role in a variety of mem-
brane processes, including endocytosis, repair, and vesicular trans-
port. Notably, ferlins are implicated in Caenorhabditis elegans
sperm motility (Fer-1), mammalian skeletal muscle development
and repair (MYOF and dysferlin), and presynaptic transmission in
the auditory system (otoferlin). In this paper, we demonstrate that
MYOF plays a previously unrecognized role in cancer cell invasion,
using a combination of mathematical modeling and in vitro experi-
ments. Using a real-time impedance-based invasion assay (xCELLi-
gence), we have shown that lentiviral-based knockdown of MYOF
significantly reduced invasion of MDA-MB-231 breast cancer cells
in Matrigel bioassays. Based on these experimental data, we de-
veloped a partial differential equation model of MYOF effects
on cancer cell invasion, which we used to generate mechanistic
hypotheses. The mathematical model predictions revealed that ma-
trix metalloproteinases (MMPs) may play a key role in modulating
this invasive property, which was supported by experimental data
using quantitative RT-PCR screens. These results suggest that MYOF
may be a promising target for biomarkers or drug target for meta-
static cancer diagnosis and therapy, perhaps mediated through
MMPs.
cancer invasion ∣ RNAi ∣ partial differential equation models ∣ metastasis
Amajority of cancer deaths are related not to the primary
tumor itself, but rather the formation of disseminated me-
tastases (1). Cancer spread requires that cells achieve atypical
motility, which enables them to invade surrounding tissues and
vessels of the blood and lymphatic systems (2–4). Thus, under-
standing the mechanisms and signaling processes that lead to
invasive cell behavior may lead to new therapeutic approaches for
controlling and treating cancer.
The fundamental mechanisms of invasive cancer cell move-
ment are largely conserved across a wide range of cell types, with
some of the protease dependent and protease independent move-
ment types demonstrated by cancer cells also seen in organisms as
diverse as unicellular organisms, slime molds, and white blood
cells. The ferlin family is an evolutionarily ancient family of pro-
teins (5), which are known to affect processes crucial to migration
and invasion, including membrane fusion and repair, vesicle
transport, endocytosis, protein recycling and stability, and cell
motility (6–13). Thus, one might expect the ferlin family to be
good candidates for cancer proteins, although they have not
previously been investigated in this capacity. In Caenorhabditis
elegans, spermatozoa exhibit amoeboid movement, and muta-
tions in the fer-1 gene [an orthologue of myoferlin (MYOF)]
result in immobility and infertility (13). In humans, MYOF has
been implicated in a variety of cellular processes, including myo-
blast fusion, growth factor receptor stability, endocytosis, and
endothelial cell membrane repair (6, 8, 10–12); however until
now its role in cancer cell movement has not been explored.
Although information on MYOF is currently limited, it has been
shown to be upregulated in breast cancer biopsies (14) and
expressed in breast cancer cell lines (15). Immunohistochemical
evidence available from the Human Protein Atlas (16) suggests
that MYOF is strongly expressed in several cancer types includ-
ing colorectal, breast, ovarian, cervical, endometrial, thyroid,
stomach, pancreatic, and liver cancer (14, 15, 17–26).
To explore the function of MYOF in cancer, a stable line
of MYOF-deficient malignant breast carcinoma cells (MDA-
MB-231) was generated using lentivirus-based delivery of shRNA
constructs targeting human MYOF mRNA (Sigma). A stable,
lentiviral control cell line was generated in tandem using lentivir-
al particles carrying a nonhuman gene targeting shRNA (Sigma).
MYOF depletion was validated by immunoblotting (SI Appendix,
Fig. 2). We used an electrode-impedance-based invasion assay
[xCELLigence (27)] to probe the effect of MYOF deficiency on
cell invasion. Compared to the control MBA-MB-231 cells,
MYOF-knockdown (MYOF-KD) cells exhibited reduced inva-
sive capacity (28).
Motivated by these experimental results, we developed a math-
ematical model that examines the role of MYOF in cancer cell
invasion. Because relatively little is known about the function of
MYOF in cancer, there is a useful opportunity for mathematical
modeling to suggest hypotheses, which can then be tested experi-
mentally. The model is described by a system of partial differen-
tial equations (PDEs). It builds on previous work on cancer cell
migration/invasion (29), now incorporating a submodel for
MYOF-mediated growth factor receptor recycling.
Using multiple MYOF-related datasets (8–12), we determined
several parameters which differed between wild-type/control
and MYOF-deficient cells. Our simulations suggest that one key
parameter—the matrix metalloproteinase (MMP) production
rate—is enough to reproduce the experimental data showing
reduced MYOF-KD cell invasion. Based on the mathematical
model, we hypothesized that MYOF affects MMP production
and/or secretion in MDA-MB-231 cells. Preliminary experimen-
tal results thus far confirm our hypothesis. Indeed, pilot PCR
results presented in this work show that MMPs may be signifi-
cantly downregulated by MYOF depletion.
We propose that MYOF may serve as a fundamental player
in cancer cell movement, by regulating the local behavior of
the plasma membrane and affecting trafficking of receptors and
proteins to and from the membrane. In particular, MYOF effects
on MMP production and release may be key to its role in regulat-
ing tumor cell invasivity. Metastasis requires cancer cells to de-
velop increased invasive capability, suggesting that MYOF may
play an important role in the ability of tumor cells to metastasize.
Author contributions: M.C.E., Y.K., R.L., W.E.A., D.A.K., and A.F. designed research,
performed research, and wrote the paper.
The authors declare no conflict of interest.
1
To whom correspondence may be addressed. E-mail: meisenberg@mbi.osu.edu or
afriedman@mbi.osu.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/
doi:10.1073/pnas.1116327108/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1116327108 PNAS Early Edition ∣ 1 of 6
APPLIED
MATHEMATICS
MEDICALSCIENCES

Mathematical Model
Spatial Setup. The conventional modified Boyden chamber setup
includes two chambers with a semipermeable membrane between
them. There is typically laminin-rich matrix (e.g., Matrigel) on
top of the semipermeable membrane, which replicates the ECM,
and invasion is measured by the number of cells which invade
through the matrix and cross the membrane from the upper to
the lower chamber. For the xCELLigence data, the membrane
is coupled with a microelectrode array at the bottom of the upper
chamber. Cells begin in the upper chamber (Ω in Fig. 1), from
where they can migrate and invade through the ECM (region S
in Fig. 1) to reach the microelectrode array. They then cross the
membrane/microelectrode array and attach to the bottom side of
the array upon crossing. Growth factors (GF) may be introduced
in the bottom well below the microelectrode array, to act as a
chemoattractant for the cells.
We measure the approximate number of cells which have
adhered to the microelectrode array by measuring the change in
impedance, the cell index [although we note that cell index is also
dependent on other factors, such as cell adhesion and spreading
(27)]. To simulate these conditions, we can use a similar setup
as for the conventional modified Boyden chamber simulations
given in refs. 29 and 30, with several modifications to incorporate
the microelectrode array and measurement of cell index. Full
mathematical description and details on the spatial setup and
boundary/initial conditions are given in SI Appendix.
State Variable Definitions. We introduce the following variables:
R1, free growth factor receptor (GFR) (number∕cell)
R2, surface-bound GF-GFR complex (number∕cell)
R3, internalized GF-GFR complex (number∕cell)
ρ, concentration of ECM (g∕cm3
)
P, MMP concentration (g∕cm3)
G, GF concentration (g∕cm3)
n, density of breast cancer cells (cells∕cm3
)
Although MMPs are a diverse family of proteins with overlap-
ping yet distinct functions, for simplicity we model their combined
effects with the variable P, which represents generic/nonspecific
MMP concentration. Similarly, because our experimental data
use fetal calf serum as a chemoattractant, the variable G repre-
sents a combination of multiple growth factors (further details
given in SI Appendix).
Model Equations. The model equations are based on the model
variable interactions shown in Fig. 2. MYOF has been shown
in other contexts to affect membrane processes (6, 8, 11) and
receptor/protein recycling/transport (10, 12). Parameters relating
to these functions, e.g., receptor recycling parameters, are un-
derlined in Eqs. 1–7, indicating they are considered MYOF-
dependent. We developed two versions of the cell-related
parameters—one for wild-type/control cells and another for
MYOF-KD cells, as described below.
∂R1
∂t
¼ ðλ1 − k21R1G þ k13R3 − k01R1Þ
n
n0
[1]
∂R2
∂t
¼ ðk21R1G − k32R2 þ k23R3Þ
n
n0
[2]
∂R3
∂t
¼ ½k32R2 − ðk13 þ k23 þ k03ÞR3Š
n
n0
[3]
∂ρ
∂t
¼ −λ21Pρ
|fflfflffl{zfflfflffl}
degradation
þ λ22ρ

1 −
ρ
ρ0

|fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl}
reconstructionðsmallÞ
in S [4]
Fig. 1. xCELLigence well setup. The upper (Ω) and lower chambers are se-
parated by a semipermeable membrane and microelectrode array at x1 ¼ 0.
Matrigel/ECM sits on top of the microelectrode array (indicated by region S).
Fig. 2. Model for receptor recycling and cell migration and invasion, with processes marked in red affected by MYOF. Cells (n) proliferate, migrate, and invade
following these processes, with cell movement dependent on the growth factor gradient (chemotaxis) and the extracellular matrix gradient (haptotaxis).
2 of 6 ∣ www.pnas.org/cgi/doi/10.1073/pnas.1116327108 Eisenberg et al.

∂P
∂t
¼ Dp∇2P þ λ31nρ
|fflffl{zfflffl}
production by cells
− λ32P
|ffl{zffl}
degradation
[5]
∂G
∂t
¼ DG∇2
G − k21CmwR1nG
|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}
binding
− λ10G
|ffl{zffl}
degradation
[6]
∂n
∂t
¼ Dn∇2n þ λ11nð1 −
n
n0
− μρÞ
R2
R20
|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
proliferation
− ∇ ·

χn
n R2
R20
∇G
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1 þ λGj∇Gj2
p
|fflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
chemotaxis
þ χ0
n
IS
n∇ρ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1 þ λρj∇ρj2
q
|fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
haptotaxis

[7]
Eqs. 1–3 constitute the receptor recycling ordinary differential
equation (ODE) submodel, collectively representing growth
factor receptor binding, internalization, degradation, and return
to the cell surface. We assume that all the internalized ligand is
degraded so that ligand return to the surface may be neglected.
The receptor recycling submodel equations are multiplied by
n∕n0 to scale the concentrations to the local cell density.
The remaining PDEs are largely based on a previous model
of tumor growth and movement in a Boyden chamber (29), up-
dated and with modifications to incorporate MYOF effects and
our particular experimental setup. The ECM, in Eq. 4, undergoes
degradation by MMP (31) and includes a small remodeling term
(30, 32–34). In Eq. 5, MMP is produced by the cells to degrade
the matrix, and then degrades and diffuses. Because MMP is
produced in response to the presence of extracellular matrix,
we have modified this term from ref. 29 to be dependent on both
n and ρ. Growth factor in Eq. 6 diffuses from below the xCELLi-
gence well bottom, where it binds to free receptors on the cell
surface, and is degraded at a constant rate.
Lastly, tumor cells Eq. 7 begin above the ECM in the upper
chamber, from which they then undergo dispersion, chemotaxis
following the GF concentration gradient, haptotaxis through the
ECM, following the ECM concentration gradient, and growth
factor dependent proliferation based on the level of surface-
bound growth factor (Fig. 2). Cell proliferation is modeled as
logistic growth, to which we add an additional ECM-dependent
term to account for additional crowding effects in the presence
of ECM (35). The diffusion constants and other parameters are
positive constants.
Parameter Estimation. As discussed above, MYOF is known to
affect a variety of membrane processes (6, 8, 11) and receptor/
protein recycling and transport (10, 12). Thus, we developed
two versions of the membrane-related model parameters, under-
lined in Eqs. 1–7, each characterizing the wild-type/lentiviral
control and MYOF-KD cell types represented in our study.
The two versions of the receptor recycling submodel para-
meters in Eqs. 1–3 were determined by fitting to experimental
receptor internalization data from wild-type and MYOF-null
myoblasts [MYOF knockout (MYOF-KO)] cells (12), with the
full details of the model parameterization given in SI Appendix.
The resulting parameter estimates suggest that MYOF-deficient
cells yield decreased GFR production, and increased receptor
recycling pathways leading to receptor degradation.
There are three MYOF-dependent parameters in the full PDE
model which remain unaccounted for—the MMP secretion rate
and the chemotactic and haptotactic sensitivity parameters. As
MYOF-KD cells show decreased invasivity compared to wild-
type/control cancer cells, we expect these parameters to decrease
in the MYOF-KD case. The ODE submodel parameters show
between a 13 and 40% change between wild-type and MYOF-
KD parameter values, so we suppose χnm ¼ 0.75χn and
χ0
nm ¼ 0.75χ0
n. As MMP has been shown to associate with MYOF
(36) and MMP secretion is directly membrane-related, we would
expect that λ31 will be more strongly affected by the MYOF-KD.
Indeed, we found the best fits to the xCELLigence invasion data
for a significantly lower value of λ31m, so that we take λ31m ¼
λ31∕100. The remaining non-MYOF-dependent PDE model
parameters for both the wild-type/control and MYOF-KD cells
were determined based on literature values (29, 30, 36, 37) (see
SI Appendix, Tables 1 and 2 for individual references).
Simulation Results. The overall model behavior for the wild-type/
control and MYOF-KD cells in the xCELLigence wells with 20%
Matrigel coating are shown in SI Appendix, Figs. 3 and 4. Tumor
cell invasion is more significant in wild-type/control than MYOF-
KD cells, matching experimental data. Bound and internalized
receptors tend to follow the invading front of tumor cells, with
cells toward the top of the upper well tending to have more
unbound receptors (as the GF has not diffused completely up the
chamber). MMP also tends to roughly follow the invading front of
tumor cells, as does degradation of ECM, with MMP production
dropping off outside the ECM region.
Applications to Cancer Cell Invasion
Decreased Invasion in MYOF-KD Cells. Figs. 3 and 4 show model
simulations compared to cell index experimental data in xCEL-
Ligence wells for wild-type/control and MYOF-KD cells at 20%
and 100% Matrigel concentrations. Model simulations recover
the qualitative behavior of the experimental data, with a more
significant decrease in invasivity for MYOF-KD cells in 100%
Matrigel compared to 20% Matrigel.
0 5 10 15 20 25
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Time (h)
Cellindex
Control
MYOF KD
MMP KD
A
B
Fig. 3. Comparison between experimental data and simulation results with
20% Matrigel. (A) Experimental results for lentiviral control (LTV-ctrl) and
MYOF-KD cells. (B) Simulation results for wild-type/control, MYOF-KD, and
hypothetical MMP KD cells.
Eisenberg et al. PNAS Early Edition ∣ 3 of 6
APPLIED
MATHEMATICS
MEDICALSCIENCES

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