xCELLigence RTCA DP InstrumentFlexible Real-Time Cell MonitoringFor life science research only.Not for use in diagnostic p...
RTCA Control Unit RTCA DP AnalyzerThe RTCA DP Instrument expands the throughput and application options of the xCELLigence...
E-Plate 16 and E-Plate VIEW 16:Cellular Assays in a 16-Well Format„ Quantitatively monitor changes in cell number, cell ad...
CIM-Plate 16LidMicroelectrodesMicroporous MembraneAdherent CellUpper ChamberCellsGel Layer(user provided)ChemoattractantLo...
E-Plate Insert 16:Co-Culture in Real-Time„ Continuously monitor indirect cell-cell interactions.„ Assess short- and long-t...
1. Cell Invasion and MigrationMicroRNA-200c Represses Migration and Invasion of Breast Cancer Cells by Targeting Actin-Reg...
5. Receptor-mediated SignalingImpedance responses reveal b2-adrenergic receptor signaling pluridimensionality and allow cl...
Ordering Information for xCELLigence RTCA DP SystemProduct Cat. No. Pack SizexCELLigence RTCA DP InstrumentRTCA DP Analyze...
Ƶ
ISSUE04Focus ApplicationCell MigrationFor life science research only.Not for use in diagnostic procedures.xCELLigence Syst...
different growth factors encountered during angiogenesis,Vasculife VEGF medium containing VEGF, EGF, IGF, orbFGF with 2% f...
Figure 2: HUVEC cell migration in response to the growth factors, VEGF (A, C) and HGF (B, D) using the CIM-Plate 16 and xC...
Published byACEA Biosciences, Inc.6779 Mesa Ridge Road Ste 100San Diego, CA 92121U.S.A.www.aceabio.com© 2013 ACEA Bioscien...
Comparative Analysis of Dynamic Cell Viability,Migration and Invasion Assessments by Novel Real-TimeTechnology and Classic...
study cell migration and chemotaxis in vitro (reviewed in [6]). TheTransmembrane/Boyden chamber assay is based on a chemot...
Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 3 October 2012 | Volume 7 | Issue 10 | e46536
CytotoxicityCompound cytotoxicity was assessed after exposure of MDA-MB-231 and A549 cells to a concentration range of pac...
RTCA device has been reported to generate compound-specifickinetic profiles on cultured cells, hereby demonstrating associ...
Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 6 October 2012 | Volume 7 | Issue 10 | e46536
of a Matrigel layer implies the introduction of an important addedvariable. Consequent Matrigel thawing and handling on ic...
www.lgcstandards-atcc.org), were cultured in DMEM andRPMI1640 respectively, each supplemented with 10% fetal bovineserum (...
instructions. The obtained STR profiles were matched withreference ATCC DNA fingerprints (www.lgcstandards-atcc.org)and wi...
plate was fixed by the first step of the SRB assay: culture mediumwas aspirated prior to fixation of the cells by addition...
Each concentration was tested six times within the sameexperiment. After 72 hours incubation with paclitaxel, survivalwas ...
on background (SF)-reduced signals representing pure chemoat-traction. Comparison of background migratory (negative contro...
Mechanistic modeling of the effectsof myoferlin on tumor cell invasionMarisa C. Eisenberga,1, Yangjin Kimb, Ruth Lic, Will...
Mathematical ModelSpatial Setup. The conventional modified Boyden chamber setupincludes two chambers with a semipermeable ...
∂P∂t¼ Dp∇2P þ λ31nρ|fflffl{zfflffl}production by cells− λ32P|ffl{zffl}degradation[5]∂G∂t¼ DG∇2G − k21CmwR1nG|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}bind...
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
Izasa.App-notes-Acea.Migracion-invasion
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Izasa.App-notes-Acea.Migracion-invasion

  1. 1. xCELLigence RTCA DP InstrumentFlexible Real-Time Cell MonitoringFor life science research only.Not for use in diagnostic procedures.Migración e Invasión
  2. 2. RTCA Control Unit RTCA DP AnalyzerThe RTCA DP Instrument expands the throughput and application options of the xCELLigenceReal-Time Cell Analyzer (RTCA) portfolio. Featuring a dual-plate (DP) format, the instrumentmeasures impedance-based signals in both cellular and cell invasion/migration (CIM)assays – without the use of exogenous labels. With outstanding application flexibility, the RTCADP Instrument supports multiple users performing short-term and long-term experiments.The xCELLigence RTCA DP InstrumentFlexible Real-Time Cell MonitoringExplore 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 cellsFigure 1: Reveal cytotoxic effects through continuousmonitoring. HT1080 cells were seeded in an E-Plate at twodifferent densities (5,000 and 10,000 cells) and treated 24 hourslater with 12.5 nM Paclitaxel, or DMSO as a control. As shownby the Cell Index profile, which reflects cell adherence, theantimitotic effect of Paclitaxel was observed in HT1080 cells thatwere proliferating, whereas confluent cells showed no response.Cytotoxicity Analysis in E-PlatesThe xCELLigence System continuously and non-invasivelydetects cell responses throughout an experiment, without the useof exogenous labels that can disrupt the natural cell environment.„ Obtain complete, continuous data profiles from cellresponses generated during in vitro experiments (Figure 1).„ Take advantage of real-time data to identify optimal timepoints for downstream assays.„ Combine real-time monitoring of cellular responses withcomplementary functional endpoint assays, and maximizedata quality before, during, and after your experiment.Compact. Convenient. Versatile.The RTCA DP Instrument consists of two components: the RTCAControl Unit and the RTCA DP Analyzer with three integratedstations 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 theRTCA DP Analyzer and plates into standard CO2 incubators.2
  3. 3. 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 usingmicroscopes.E-Plate 16E-Plate 16 E-Plate VIEW 16500 μmObtain detailed information about your cells with the versatile RTCA DP Instrument, which supportsup 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 assaysmay be run simultaneously.E-Plates for the RTCA DP InstrumentMore Flexibility. More Data. More Insight.Figure 3: Continuously monitor cells and determine optimal timepoints for assessing cytotoxicity. Cell proliferation and cell deathwere continuously monitored using the xCELLigence RTCA DP Instrument.The optimal time points for visual inspection of HeLa cells weredetermined and images taken 4 and 22 hours after compound treatmentusing a Z16 Apo Microscope with light base (Leica Microsystems).Control300 μMAntimycin ANormalizedCellIndex1.90.9-0.19 18 27 36 45 54Time (Hours)Antimycin AadministrationControl5 μM100 μM300 μM4 h 22 hFigure 2: Easily visualize cells while measuring cell response withxCELLigence System E-Plate VIEW technology. A modified versionof the standard E-Plate 16, the E-Plate VIEW 16 enables image acqui-sition using microscopes or automated cell-imaging systems. For themodification, four rows of microelectrode sensors were removed in eachwell to create a window for visualizing cells. Approximately 70% of eachwell bottom is covered by the microelectrodes, providing cell impedancemeasurements nearly identical to those obtained with the standardE-Plate 16. Both plate types can be used in parallel.3
  4. 4. CIM-Plate 16LidMicroelectrodesMicroporous MembraneAdherent CellUpper ChamberCellsGel Layer(user provided)ChemoattractantLower ChamberCIM-Plate 16:Quantitative Cell Invasion/Migration Analysis„ Monitor cell invasion and migration continuously in real timeover 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 invasionwhile concurrently assessing migration. HT1080 cells (2 x 104) wereseeded in the upper chamber of CIM-Plate wells coated with varying dilutionsof Matrigel, or in wells with no coating. Serum was added to the lowerchamber of selected wells as a chemoattractant. Invasion was observed andmigration monitored continuously over a 70-hour period. All serum-starvedsamples resulted in base-line Cell Index levels, indicating the absence ofinvasion/migration, while those wells with chemoattractant induced migration.Invasion/Migration Analysis in CIM-PlatesFigure 5: Analyze invasion/migration in real time with theCIM-Plate 16. The plate features two separable sections for easeof experimental setup. Cells seeded in the upper chamber movethrough the microporous membrane into the lower chamber thatcontains a chemoattractant. Cells adhering to the microelectrodesensors lead to an increase in impedance, which is measured inreal time by the RTCA DP Instrument.4
  5. 5. E-Plate Insert 16:Co-Culture in Real-Time„ Continuously monitor indirect cell-cell interactions.„ Assess short- and long-term cell response withoutlabor-intensive labeling and microscopy.„ Co-culture different cell typtes under physiological conditionsfor a broad range of applications, including:Cancer Research: Assess paracrine stimulation ofcancer cell proliferation by fibroblasts.Immunology: Investigate immune cell interactions.Stem Cell Research: Monitor proliferation anddifferentiation in the presence of stimulation cells.Toxicology: Determine cytotoxicity of agents andassess effects of cytokine release.Figure 6. Real-time monitoring of co-culture-induced proliferationstimulation and its inhibition using the E-Plate Insert. Intercellularinteractions play an important role in normal cell development andtumorigenesis. Results show that the proliferation of hormone-responsivetumor cells is likely mediated by hormones and growth factors exchangedbetween the two cell populations separated by the E-Plate Insert.Elevated T47D cell proliferation on the E-Plate (green trace „ ) wasinduced by hormone secretion of H295R cells in the insert, and inhibitedby the hormone synthesis inhibitor Prochloraz (blue trace „ ). Incubationof T47D cells with only the E-Plate Insert did not affect proliferation (redtrace „ ).LidE-Plate 16E-Plate Insert5
  6. 6. 1. Cell Invasion and MigrationMicroRNA-200c Represses Migration and Invasion of Breast Cancer Cells by Targeting Actin-RegulatoryProteins 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/ApoptosisScreening and identification of small molecule compounds perturbing mitosis using time-dependentcellular 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, XuX, Abassi YA.Anal Chem. 2010; 82(15):6495-503.Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-targeteffects.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 CytotoxicityReal-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 SpreadingA 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, RuddCE, 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 Instrument6
  7. 7. 5. Receptor-mediated SignalingImpedance 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 CytopathogenicityNovel, 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 CellsRapid and quantitative assessment of cell quality, identity, and functionality for cell-based assays usingreal-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 FunctionAn 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 continued7
  8. 8. Ordering Information for xCELLigence RTCA DP SystemProduct Cat. No. Pack SizexCELLigence RTCA DP InstrumentRTCA DP AnalyzerRTCA Control Unit0038060105005469759001054544170011 Bundled Package1 Instrument1 Notebook PCE-Plate 16E-Plate VIEW 16E-Plate Insert 1605469830001054698130010632473800106324746001064653820016 Plates6 x 6 Plates6 Plates6 x 6 Plates1 x 6 Devices (6 16-Well Inserts)CIM-Plate 16 05665817001056658250016 Plates6 x 6 PlatesCIM-Plate 16, Assembly Tool 05665841001 1 Assembly ToolLearn more about the enabling technology of the xCELLigence System and its broad range ofapplications at www.aceabio.comXCELLIGENCE, E-PLATE, CIM-PLATE, and ACEA BIOSCIENCES are registered trademarks of ACEA Biosciences, Inc. in the USand other countries.All other product names and trademarks are the property of their respective owners.For life science research only.Not for use in diagnostic procedures.Published byACEA Biosciences, Inc.6779 Mesa Ridge Road Ste. 100San Diego, CA 92121U.S.A.www.aceabio.com© 2013 ACEA Biosciences, Inc.All rights reserved.
  9. 9. Ƶ
  10. 10. ISSUE04Focus ApplicationCell MigrationFor life science research only.Not for use in diagnostic procedures.xCELLigence System Real-Time Cell AnalyzerIntroductionCell migration and invasion are mechanically integratedmolecular processes and fundamental components ofembryogenesis, vasculogenesis, immune responses, as wellas pathophysiological events such as cancer cell metastasis(1, 2). Cell migration and invasion involve morphologicalchanges due to actin cytoskeleton rearrangement and theemergence of protrusive membrane structures followedby contraction of the cell body, uropod detachment, andsecretion of matrix degrading enzymes (1, 2). Thesemulti-step processes are influenced by extracellular andintracellular factors and signaling events through specializedmembrane receptors.The integrated nature of cell migration is exemplified byangiogenesis. Angiogenesis or neo-angiogenesis refers tothe formation of new blood vessels from pre-existing vesselsand is critical for development, wound healing and tumorgrowth. Endothelial cell migration is an important compo-nent of angiogenesis, involving chemotactic, haptotacticand mechanotactic (shear stress) induced cell migration (3).Chemotactic cell migration is typically induced by solublegrowth factors such as vascular endothelial growth factor(VEGF) and its isoforms, fibroblast growth factor (bFGF)and hepatocyte growth factor (HGF) amongst others. Thesegrowth factors interact with their cognate receptor tyrosinekinases on he surface of endothelial cells activating signalingpathways culminating in directed cell migration.In the present study, we used the new CIM-Plate 16 withthe xCELLigence RTCA DP Instrument to monitor growthfactor-mediated migration of endothelial cells in realtimeusing label-free conditions. The CIM-Plate 16 is a 16-wellmodified Boyden chamber composed of an upper chamber(UC) and a lower chamber (LC). The UC and LC easily snaptogether to form a tight seal. The UC is sealed at its bottomby a microporous Polyethylene terephthalate (PET) mem-brane. These micropores permit the physical translocation ofcells from the upper part of the UC to the bottom side of themembrane. The bottom side of the membrane (the sidefacing the LC) contains interdigitated gold microelectrodesensors which will come in contact with migrated cells andgenerate an impedance signal. The LC contains 16 wells,each of which serves as a reservoir for a chemoattractantsolution on the bottom side of the wells, separated fromeach other by pressure-sensitive O-ring seals.ResultsTo analyze endothelial cell migration using the CIM-Plate16, human umbilical vein endothelial cells (HUVEC) fromLifeline Cell Technologies were cultured in Vasculife VEGFcell culture medium, according to the manufacturer’srecommendations. Cells were serum starved in VasculifeBasal 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 toFeatured Study:Automated Continuous Monitoring ofGrowth Factor-Mediated Endothelial CellMigration using the CIM-Plate 16 andxCELLigence RTCA DP InstrumentJieying Wu and Jenny ZhuACEA Biosciences, Inc., San Diego, USA.
  11. 11. different growth factors encountered during angiogenesis,Vasculife VEGF medium containing VEGF, EGF, IGF, orbFGF with 2% fetal bovine serum, was serially dilutedwith Vasculife Basal medium and transferred to the lowerchamber of the CIM-Plate 16 (see Figure 1). For optimalHUVEC migration, it was determined from previousexperiments that extracellular matrix (ECM) proteins,such as fibronectin (FN) are necessary. The PET membranewas 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. TheCIM-Plate 16 was placed in the RTCA DP Instrumentequilibrated in a CO2 incubator. HUVEC migration wascontinuously monitored using the RTCA DP Instrument.Figure 1 shows the time- and dose-dependent directionalmigration of HUVEC cells from the upper chamber to thelower chamber. The combination of growth factors andserum provides a strong chemoattractant signal whichtogether induce the directional migration of HUVEC cellsthrough the micropores of the CIM-Plate 16. Migratingcells are detected by the electronic sensing microelectrodes,producing changes in the measured Cell Index values(see Figure 1). HUVEC migration has been shown to beinfluenced by a number of growth factors including VEGF,HGF, and bFGF. These growth factors are known to besecreted by tumors and cells within the tumor stroma, aswell as endothelial cells inducing migration and angiogenesis.To measure HUVEC migration in response to individualgrowth factors using the CIM-Plate 16, HUVEC cellsfrom Lonza were starved for 6 hours and detached. At thesame time, titration of HGF, and separately of VEGF, wasperformed in basal media (complete HUVEC media dilutedat a ratio of 1:125 with EGM media from Lonza). Each ofthese growth factors was then transferred to the wells ofthe lower chamber (see Figure 2). The PET membrane wascoated with FN as described above.After CIM-Plate 16 assembly, HUVEC cells were added at30,000 cells/well for VEGF-induced migration and 15,000cells/well for HGF-induced migration. Time-dependentHUVEC migration was monitored using the RTCA DPInstrument. Both VEGF and HGF induced the migrationof HUVEC cells in a time- and dose-dependent manner(see Figure 2A and 2B). The RTCA Software 1.2 was used tocalculate time-dependent EC50 values for both VEGF- andHGF-mediated HUVEC migration (see Figure 2C and 2D).Angiogenesis is a compelling target for cancer therapy.Monoclonal antibodies targeting angiogenesis play animportant role in colon and lung cancer therapy (4). Forthis reason, the migration of endothelial cells in responseto angiogenic factors such as VEGF is a good in vitro modelsystem for studying and screening potential inhibitors ofthis process (see Figure 3). For the quantitative and time-dependent assessment of inhibition of VEGF-induced endo-thelial cell migration, HUVEC cells were added tothe CIM-Plate 16, as described above, in the presence ofincreasing amounts of a VEGF receptor inhibitor. Asshown in Figure 3A, this inhibitor was found to potentlyblock VEGF-induced cell migration in a time- and dose-dependent manner. The inhibition of VEGF-inducedHUVEC cell migration by this compound was quantifiedusing the RTCA Software 1.2. Time-dependent IC50values, shown in Figure 3B, demonstrate that thisparticular VEGF receptor inhibitor blocks the kinaseactivity of all three VEGF receptor isoforms with IC50values 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 assessHUVEC cell migration, Vasculife VEGF medium, containing VEGF, EGF, IGF, bFGF and 2% fetal bovine serum, was serially diluted with VasculifeBasal 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 16.0Time (in Hour)6.55.54.53.52.51.50.5-0.5CellIndexCompleteMedia1:31:91:271:811:2431:729BasalMedia
  12. 12. 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 CellIndex 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 21.0Time (in Hour)VEGF-induced cell migration HGF-induced cell migration3.52.51.50.5-0.5CellIndex60 ng/mL20 ng/mL6.7 ng/mL2.2 ng/mL0.7 ng/mL0.2 ng/mL0 ng/mLEGM Media2.01.00.00.0 3.0 6.0 9.0 12.0 15.0 18.0Time (in Hour)CellIndex100 ng/mL33.3 ng/mL200 ng/mL3.7 ng/mL1.2 ng/mLEGM Media11.1 ng/mL3.60003.00002.40001.80001.20000.60000.0000CellIndex-10.50 -10.00 -9.50 -9.00 -8.50 -8.00 -7.50 -7.00 -6.50Log of concentration (g/ml)2.10001.80001.50001.20000.90000.60000.30000.0000CellIndex-9.50 -9.00 -8.50 -8.00 -7.50 -7.00 -6.50 -6.0Log of concentration (g/ml)T1 EC50= 5.5 ng/mLT2 EC50= 5.3 ng/mLT1 EC50= 3.2 ng/mLT2 EC50= 2.7 ng/mLCA BDA0.04 nM0.12 nM0.4 nM1.1 nM3.3 nM10 nMInhibitor0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0Time (in Hour)2.52.01.51.00.50.0CellIndexB2.40002.00001.60001.20000.80000.40000.0000CellIndex-11.00 -10.50 -10.00 -9.50 -9.00 -8.50 -8.00 -7.50Log of concentration (M)T1 EC50= 78 pMT2 EC50= 179 pMFigure 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 VEGFreceptor inhibitor; see text in Results for details.ConclusionData presented in this application note demonstrate thatgrowth-factor-mediated endothelial cell migration can bemonitored quantitatively and in realtime using the CIM-Plate 16 with the RTCA DP Instrument. The xCELLigenceSystem proved to be ideal for assessing and screening aninhibitor of endothelial cell migration and angiogenesis.The CIM-Plate 16 combines the benefits of continuouslabel-free impedance-based technology with the classicBoyden chamber permitting automated, real-time, andquantitative measurements of cell migration and invasion.Classic cell migration techniques utilizing standard andtranswell Boyden chambers are labor intensive, producingresults that can be difficult to reproduce. The non-invasiveCIM-Plate 16 does not require manual cell counting or celllabeling. Moreover, the continuous real-time data obtainedusing the CIM-Plate 16 identifies optimal time points forperforming parallel gene expression and functional analysesof HUVEC migration. The above described features andbenefits of the CIM-Plate 16 and the RTCA DP Instrumentdescribe an ideal system for the in vitro analysis of thecellular and molecular events associated with cell migrationand invasion.T1 T2 T1 T2T1 T2
  13. 13. Published byACEA Biosciences, Inc.6779 Mesa Ridge Road Ste 100San Diego, CA 92121U.S.A.www.aceabio.com© 2013 ACEA Biosciences, Inc.All rights reserved.Ordering InformationReferences1. 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.For life science research only.Not for use in diagnostic procedures.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 detectionProduct Cat. No. Pack SizexCELLigence RTCA DP InstrumentRTCA DP AnalyzerRTCA Control Unit0038060105005469759001054544170011 Bundled Package1 Instrument1 Notebook PCE-Plate 16E-Plate VIEW 16E-Plate Insert 1605469830001054698130010632473800106324746001064653820016 Plates6 x 6 Plates6 Plates6 x 6 Plates1 x 6 Devices (6 16-Well Inserts)CIM-Plate 16 05665817001056658250016 Plates6 x 6 Plates
  14. 14. Comparative Analysis of Dynamic Cell Viability,Migration and Invasion Assessments by Novel Real-TimeTechnology and Classic Endpoint AssaysRidha Limame1*, An Wouters1, Bea Pauwels1, Erik Fransen2, Marc Peeters1,3, Filip Lardon1, Olivier DeWever4, Patrick Pauwels1,51 Center for Oncological Research (CORE), University of Antwerp, Antwerp, Belgium, 2 StatUA Center for Statistics, University of Antwerp, Antwerp, Belgium, 3 Departmentof Oncology, Antwerp University Hospital, Edegem (Antwerp), Belgium, 4 Laboratory of Experimental Cancer Research, Department of Radiotherapy and NuclearMedicine, Ghent University Hospital, Ghent, Belgium, 5 Laboratory of Pathology, Antwerp University Hospital, Edegem (Antwerp), BelgiumAbstractBackground: Cell viability and motility comprise ubiquitous mechanisms involved in a variety of (patho)biological processesincluding cancer. We report a technical comparative analysis of the novel impedance-based xCELLigence Real-Time CellAnalysis detection platform, with conventional label-based endpoint methods, hereby indicating performancecharacteristics and correlating dynamic observations of cell proliferation, cytotoxicity, migration and invasion on cancercells in highly standardized experimental conditions.Methodology/Principal Findings: Dynamic high-resolution assessments of proliferation, cytotoxicity and migration wereperformed using xCELLigence technology on the MDA-MB-231 (breast cancer) and A549 (lung cancer) cell lines. Proliferationkinetics were compared with the Sulforhodamine B (SRB) assay in a series of four cell concentrations, yielding fair to goodcorrelations (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 pixelarea 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 referencemethod and correlated by OD reading for two Matrigel densities (Rho.0.95). Variance component analysis revealedincreased variances associated with impedance-based detection of migration and invasion, potentially caused by thesensitive nature of this method.Conclusions/Significance: The xCELLigence RTCA technology provides an accurate platform for non-invasive detection ofcell viability and motility. The strong correlations with conventional methods imply a similar observation of cell behaviorand interchangeability with other systems, illustrated by the highly correlating kinetic invasion profiles on differentplatforms applying only adapted matrix surface densities. The increased sensitivity however implies standardizedexperimental 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 Assessmentsby Novel Real-Time Technology and Classic Endpoint Assays. PLoS ONE 7(10): e46536. doi:10.1371/journal.pone.0046536Editor: Aamir Ahmad, Wayne State University School of Medicine, United States of AmericaReceived April 24, 2012; Accepted August 31, 2012; Published October 19, 2012Copyright: ß 2012 Limame et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted 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’ adherenceto all the PLOS ONE policies on sharing data and materials.* E-mail: ridha.limame@ua.ac.beIntroductionAmong the most fundamental hallmarks of cancer are loss ofpre-existing tissue architecture by sustained proliferation andextracellular matrix infiltration of cancer cells. Cancer cells maysustain proliferative signaling in an autocrine or paracrine fashionby producing growth factors themselves, by overexpression ofgrowth factor receptors or by a constitutive activation ofdownstream signaling components [1]. Monitoring of cell prolif-eration and cell viability is critical in biomedical research, in orderto understand the pathways regulating proliferation and viability,and to develop agents that modulate these processes. Thesulforhodamine B (SRB) test is a high throughput and reproduc-ible colorimetric assay, based on the binding of SRB to proteinbasic amino acid residues, providing a sensitive index of cellularprotein content that is linear over a cell density range [2].Matrix penetration necessitates activation of the cellular motilityapparatus and can occur by either individual cells or cell strands,sheets or clusters [3]. A phenomenon predominantly involved inthis process is chemotaxis, whereby cell movement is directedalong an extracellular chemical gradient of secreted factors in themicroenvironment [4]. Already in the early stages of embryogen-esis, formation of complex tissues and organs is orchestrated byfine-tuned chemotactic migration of cell chains. In malignantprocesses however, cancer cells tend to adopt similar, if notidentical mechanisms to metastasize to distant organ sites [5].Several well-established experimental approaches are available toPLOS ONE | www.plosone.org 1 October 2012 | Volume 7 | Issue 10 | e46536
  15. 15. study cell migration and chemotaxis in vitro (reviewed in [6]). TheTransmembrane/Boyden chamber assay is based on a chemotactic-driven cell transit through a filter [7]. An important feature of theendpoint in this experimental set-up is that cells need to exhibitactive migratory behavior to end up at the other side of themembrane.The xCELLigence RTCA technology (Roche Applied Science) hasemerged as an alternative non-invasive and label-free approach toassess cellular proliferation, migration and invasion in real time ona cell culture level [8]. This system makes use of impedancedetection for continuous monitoring of cell viability, migration andinvasion (reviewed in [9]) (Fig. 1).Here we report data of in vitro assessment of four cellularprocesses (proliferation, cytotoxicity, migration and invasion) onthe MDA-MB-231 and A549 cancer cell lines using xCELLigenceRTCA DP (Roche Applied Science) in comparison with dataresulting from parallel experiments applying a previously existingand well-established measuring method (to be considered as a‘‘gold standard’’ method) for each process. Both these cell lines areextensively characterized and used as models representing twodifferent highly incidental tumor types (breast cancer, lung cancer).Furthermore, these cell lines show a strong degree of motility inthe wild-type state, thus providing useful examples for thedistinction between chemotactic and random motility.Importantly, all of the comparative techniques are traditionallabel-based endpoint assays that have been selected due to theirwidespread application within the scientific community andsimilarity in working principle with xCELLigence. Although theyhave been slightly modified to match with the xCELLigence setupand its ability to acquire time-dependent kinetics of cultured cellbehavior, the fundamental handling and detection principles ofeach classic assay have been maintained. Cell proliferation andcytotoxicity testing has been performed using the SRB assay [10],with the microtubule stabilizer paclitaxel as cytotoxic agent in thelatter experiments. Being widely used for the treatment of a varietyof tumor types, inhibitory effects of this anti-mitotic compound oncell proliferation have been described extensively. Furthermore,previous reports on the use of the xCELLigence device includedpaclitaxel as a reference compound in their studies [8,11], makingthis a suitable agent for this study. Cell migration and invasionexperiments were performed using conventional Transwell platesand quantified by both pixel area calculation of stainedmembranes and optical density reading of solubilized dye. Forthe first time, results from ‘‘tried-and-tested’’ assay setups areconfronted with parallel data recorded using a novel, commer-cially available technology, providing an objective technicalcomparison of dynamic observations on cultured cells in highlystandardized experimental conditions.ResultsProliferationThe dynamic assessment of proliferation kinetics was modeledby performing SRB testing on both MDA-MB-231 and A549 cells.Growth curve studies were performed over a ten-day period andproliferation curves were established for four different platingdensities. 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 wereperformed in duplicates and the resulting high-resolution datawere extrapolated to the matching data points of the counterpartmethod as described in the Materials and Methods section.For MDA-MB-231 cells, cell doubling time was27.7865.14 hours and 29.9262.85 hours measured with theSRB assay and the xCELLigence system respectively. Similarly,for A549 cells, doubling time was 27.9361.75 hours and29.1861.87 hours measured with the SRB assay and thexCELLigence system respectively.Spearman’s Rho (r) correlations were calculated on globalaverage results that had been normalized to the highest value inthe data set per method (SRB or xCELLigence) to eliminate units ofmeasurement (Fig. 2A, B, shown as ‘‘scaled’’). Both MDA-MB-231and A549 cells revealed fair to good correlation rates for allapplied seeding densities, noting however that proliferation did notset off at the lowest cell seeding density (100 cells/cm2) of MDA-MB-231 on RTCA (Fig. 2A). A549 cells showed only minimalproliferative activity at this density as well (Fig. 2B). Correlationsobserved between SRB-based and impedance-based quantitationreached higher values at the medium cell seeding densities of 500cells/cm2and 1000 cells/cm2for both cell lines tested (Spearman’sr = 0.835 resp. 0.790 for MDA-MB-231 and r = 0.964 resp. 0.883for A549). Altogether, correlation values ranged from 0.880 to0.964 for A549 and 0.688 to 0.835 for MDA-MB-231. Variancecomponent analysis on proliferation data of A549 cells resultingfrom both techniques indicated smaller intra- and inter-experi-mental variances on xCELLigence when compared to SRB.Conversely, detection of proliferation kinetics of MDA-MB-231cells resulted in a higher degree of (intra- and interexperimental)variance when performed with the xCELLigence system (quantifiedas sw and sb in Table S1).Figure 1. xCELLigence RTCA: impedance-based detection of cellviability and motility. Interdigitated gold microelectrodes on thewell bottom (viability – E-plate) or on the bottom side of a filtermembrane (motility – CIM-plate 16) detect impedance changes, causedby the presence of cells and expressed as a Cell Index. This detectionmethod is proportional to both cell number (left and above) andmorphology as increased cell spreading is reflected by a higher CellIndex value (right). When starting an experiment, a baseline Cell Indexvalue is recorded in medium only before cell addition.doi:10.1371/journal.pone.0046536.g001Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 2 October 2012 | Volume 7 | Issue 10 | e46536
  16. 16. Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 3 October 2012 | Volume 7 | Issue 10 | e46536
  17. 17. CytotoxicityCompound 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 responsewas detected by both SRB and xCELLigence, with comparable IC50values of 4.7860.90 nM and 6.4461.90 nM respectively (t-test,p = 0.244) in MDA-MB-231 cells. The normalized toxic responsecorrelated highly between SRB-based and xCELLigence-baseddetection for both cell lines (Spearman’s r = 0.970 and 0.976 forMDA-MB-231 and A549 resp.) (Fig. 2C).Cell migrationConventional Transwell plates have been organized in asequential setup to perform dynamic observations of cancer cellmigration in a time-dependent manner, yielding a series of datasimilar to the xCELLigence system (Fig. 3A, B). Both chemotacticmigration to medium containing 10% FBS and random migrationwith SF medium on either side of the membrane have beenconsidered. High correlation values for both cell lines wereobtained when comparing area calculation and OD withxCELLigence Cell Index (CI) (Fig. 4A). Importantly, it must benoted that correlation coefficients were calculated for overall meanvalues resulting from three independent experiments, eachperformed in duplicates for all time points. As described in theMaterials and Methods section, all raw data obtained werenormalized to the single maximal value over three experiments perquantitation method (CI, pixel area or OD) to eliminate units ofmeasurement. Subsequently, values from random (SF) migrationwere subtracted from chemotactic (FBS) migration per time pointto generate signals of net chemoattraction (Fig. 4B). Pixel areacalculations, averaged over three fields per insert membrane(Fig. 4C), correlated with xCELLigence CI measurements for bothMDA-MB-231 and A549 cells (Spearman’s r = 0.90 for both celllines). However, OD measurements showed even strongercorrelations with xCELLigence data (Spearman’s r = 0.96 and1.00 for MDA-MB-231 and A549 resp.). Area calculationcorrelated with OD measurements in a similar fashion as withxCELLigence for both MDA-MB-231 and A549 (Spearman’sr = 0.89 and r = 0.90 resp.) (Fig. 4A).At later time points of the experiments, xCELLigence generatedlarger variances between intra-experimental replicates whencompared to area calculations or OD values derived from classicTranswells (Table S1). Briefly, variance between replicates withinone experiment increased over time, creating a funnel shapedtime-dependent pattern (Fig. 4D, E). Variance component analysisindeed revealed an increase of intra-experimental variance onxCELLigence 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 similarresults (results not shown).A significant difference was observed between background(serum-free, SF) signals generated by the three methodologies ofcell migration quantitation. These signals derived from randommovement of cells without exposure to any chemoattractant and,serving as a negative control, showed a different pattern whengenerated by xCELLigence or by both quantitation techniques usingTranswells (Fig. 5). Paired comparisons between time-dependenttracking of background migration were performed between alltechniques using a likelihood ratio test and revealed a significantdifference between xCELLigence and pixel area calculation(p,0.001) for both MDA-MB-231 and A549. OD measurementsdiffered significantly from xCELLigence (p,0.001) for MDA-MB-231 cells, but showed a similar pattern when compared withxCELLigence data generated from A549 cells (p = 0.22).Cell invasionImpedance-based detection of MDA-MB-231 cell invasion wascompared with results derived from a Transwell system as appliedfor migration experiments, with Matrigel as extracellular matrixcomponent added on top of the microporous membranes (Fig. 6A).It was found that Matrigel dilutions of 10% (v/v, SF medium) onxCELLigence yielded high correlations when compared to a dilutionof 20% on Transwells in dynamic invasion profile recordingduring a 48-hour incubation (Spearman’s r = 0.939). Similarly,invasion through a Matrigel dilution of 3.3% on xCELLigencecorrelated highly with a dilution of 7.7% on Transwell plates(Spearman’s r = 0.927) (Fig. 6B, C). Variance component analysisof Transwell and xCELLigence data revealed slightly increaseddegrees of intra-experimental variance in the latter. The variancebetween independent experiments was increased for xCELLigencein comparison with Transwell assays (Table S1). All Matrigeldilutions have been synchronized regarding seeding surface areafor both systems and theoretical calculations for correlatingMatrigel dilutions are shown in Table 1.DiscussionxCELLigence technology measures impedance changes in ameshwork of interdigitated gold microelectrodes located at thewell bottom (E-plate) or at the bottom side of a microporousmembrane (CIM16-plate). These changes are caused by thegradual increase of electrode surface occupation by (proliferated/migrated/invaded) cells during the course of time and thus canprovide an index of cell viability, migration and invasion. Thismethod of quantitation is directly proportional to cellularmorphology, spreading, ruffling and adhesion quality as well ascell number [8,9] (Fig. 1). Cell proliferation and paclitaxelcytotoxicity kinetics, as assessed by the xCELLigence platform, werecompared with an SRB-based approach, showing good correla-tions for both cell lines tested, implying that both methods detectsimilar process kinetics when performed in standardized condi-tions. However, correlations were generally stronger for the A549than for the MDA-MB-231 cell line, which may indicate a possiblecell type-dependent cause. MDA-MB-231 cells show a heteroge-neous morphotype with round and spread cells, which maydifferentially influence impedance based measurements or crystalviolet uptake. Paclitaxel was chosen as it is widely used astreatment for a variety of tumor types and previous reports on theuse of the xCELLigence system as a tool for cytotoxicity screeningincluded paclitaxel as a reference compound [8,11]. Nevertheless,it must be underlined that the highly correlative nature ofcytotoxic kinetics as detected by both techniques for paclitaxel maynot apply for certain other compounds. Indeed, the xCELLigenceFigure 2. Time-dependent proliferation and cytotoxicity profiles of MDA-MB-231 and A549. A. Proliferation curves of MDA-MB-231 cellsas generated by xCELLigence RTCA (red) and SRB (black) for different seeding densities of 100 (top left), 500 (bottom left), 1000 (top right) and 2000cells/cm2(bottom right) during a ten-day incubation. B. Same as (A) for A549 cells. All graphs represent results from three independent experiments 6SD. C. Cytotoxicity profiles relating to 72 hours of exposure to paclitaxel (0–100 nM). Cells were allowed to attach and propagate during 24 hoursprior to start of treatment. Toxicity data from xCELLigence RTCA were derived from normalized plots. All graphs represent results from threeindependent experiments 6 SD.doi:10.1371/journal.pone.0046536.g002Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 4 October 2012 | Volume 7 | Issue 10 | e46536
  18. 18. RTCA device has been reported to generate compound-specifickinetic 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 areaand OD quantitation, as these methods are widespread within thescientific community and also take morphologic features intoaccount. Both area calculation and OD measurement correlatedhighly with cell migration, as detected by xCELLigence, confirmingthat the observed kinetic cell behavior is strongly similar, providedthat equal cell seeding densities are applied. The closestassociations were found between OD measurements and RTCACI, generating nearly identical migration patterns. OD valueswere determined on cell lysates with extracted crystal violet stain,derived from entire Transwell membranes, and thus provide amore reliable quantitation when compared with pixel areacalculation, which was based on averaging three microscopicfields per Transwell membrane. This indicates that impedance-based measurements have smaller limits of detection, resulting inhighly reliable migration estimates. Analysis of background signals,resulting from random migration in a serum-free environment(negative control), revealed a significant difference in signaldetection between xCELLigence and both classic techniques(Fig. 5). Weak signals resulting from limited baseline cell migrationwere indeed more accurately detected by the RTCA platform,which implies a higher sensitivity, compared to classic detectionmethods. OD measurements correlated more closely withxCELLigence data in all experiments and did not show a significantdifference when background signals were compared with xCELLi-gence for the A549 cell line, suggesting a smaller detection limit andthus a more accurate method of quantifying cell migration whenusing Transwells. Additionally, a smaller limit of detection canexplain the observed time-dependent increase of variabilitybetween intra-experimental replicates during the course of anexperiment. Data derived from cell culture-based experiments aresubject to inter- as well as intra-individual variation regarding cellcounting, pipetting, preparation of chemotactic factors andgeneral cell culture handling. As a consequence, small handlingdifferences during the preparation of an experiment can result insignal differences between technical replicates and this will bereflected in variations increasing with time within one experimentwhen compared to area calculation or OD measurement, that donot detect this variability to this extent. This is also illustrated bycell culture-based invasion experiments, as the uniform applicationFigure 3. Conventional Transwell design for detection of time-dependent cell migration. A. A Transwell setup consists of an upperchamber (insert) that is placed onto a lower chamber (well). The insert contains a microporous membrane (8 mm pores) allowing passage of tumorcells. After a period of serum starvation a serum-free cell suspension is seeded in the insert and exposed to medium containing potentialchemoattractants (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 baselinemigration (toward SF medium, no chemoattraction) as a negative control were included. Two times two 24-well Transwell plates were used toexamine migration to FBS (positive control – top row) and baseline migration (negative control – bottom row). At ten time points during a 24-hourincubation period inserts were fixed and stained in duplicate. Two inserts containing cell-free media (grey fill) have been included throughout theexperiment and fixed and stained after 12 hours incubation to assess background absorption in optical density (OD) measurements. In addition, toexclude influence of inter-plate variability on observed migration rates, each plate contained duplicate two-hour control inserts.doi:10.1371/journal.pone.0046536.g003Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 5 October 2012 | Volume 7 | Issue 10 | e46536
  19. 19. Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 6 October 2012 | Volume 7 | Issue 10 | e46536
  20. 20. of a Matrigel layer implies the introduction of an important addedvariable. Consequent Matrigel thawing and handling on ice, usingonly cooled consumables, and hands-on coating experience cancontribute to homogenous gelification and thus to enhanced assayreproducibility. Comparative invasion results have shown corre-lation between xCELLigence and Transwells when similar cellseeding densities were applied and, more importantly, when theamount of Matrigel per square area unit is synchronized. Dilutingthe matrix barrier, and thus changing the degree of matrixfenestration, gives rise to similar invasion rates on both setups withdifferent seeding areas, although equal volumes of Matrigel havebeen applied.In conclusion, the real-time label-free xCELLigence systemprovides a suitable and accurate platform for high-throughputkinetic screenings and for determination of cell motility dynamics.In contrast with classic endpoint assays, the impedance-baseddetection method is generally less labor-intensive, provides kineticinformation on the studied processes and does not affect cellviability, potentially generating further experimentation possibil-ities. Moreover, the correlating observations as performed withconventional approaches make methods interchangeable toperform functional studies when larger cell populations of interestare needed. However although impedance measurement providesa sensitive cell-based detection method, it should be applied as acomplementary tool to further functional confirmation.Importantly, this is the first study illustrating the highlycorrelative nature of invasion kinetics detected by two differentsetups applying synchronized matrix densities. The increasedsensitivity, however, necessitates standardized experimental con-ditions and user experience, to minimize variance increments onthe xCELLigence system.Materials and MethodsCell cultureTwo malignant cell lines (A549, lung adenocarcinoma; MDA-MB-231, breast adenocarcinoma), obtained from the AmericanType 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 therandom migration signal (empty squares in panel A, B), with associated Spearman’s Rho values. All results originate from three independent duplicateexperiments 6 SD. B. Normalization procedure of migration patterns. Raw data (left panel) were normalized to a (0–1) scale (middle panel) throughdivision 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, exp3 - black). Triangle and circle markers represent negative (SF) and positive (FBS) control data respectively. C. ImageJ-based picture processing. Originalpictures were color thresholded to obtain a binary image displaying cellular content as saturated black areas on a white background. Thresholdedimages 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 theoriginal high-resolution plot by extrapolating data from the corresponding time points (right). All results represent original data from threeindependent duplicate experiments 6 SD. Picture string (obj. 2.56) shows migratory status of MDA-MB-231 cells, stained as described, at fivedifferent stages during 24 hours of incubation. E. Same as (D) for A549 cells.doi:10.1371/journal.pone.0046536.g004Figure 5. Time-dependent random migration profile of MDA-MB-231 and A549. Comparison of random migration signals (negativecontrol – SF) between three quantitation methods: pixel area calculation – black, OD - red, xCELLigence - green. A likelihood ratio test revealed asignificant difference in slope between area calculation and OD (p,0.001) and area calculation and xCELLigence (p,0.001) for both cell lines and ODand xCELLigence (p,0.001) for MDA-MB-231 only. OD and xCELLigence slopes did not differ significantly (p = 0.22) for A549 cells.doi:10.1371/journal.pone.0046536.g005Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 7 October 2012 | Volume 7 | Issue 10 | e46536
  21. 21. www.lgcstandards-atcc.org), were cultured in DMEM andRPMI1640 respectively, each supplemented with 10% fetal bovineserum (FBS), 1% penicillin/streptomycin, 1% L-glutamine andadditionally, 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. Celllines were maintained at 37uC and 5% CO2/95% air in ahumidified incubator and confirmed free of mycoplasma infectionthrough regular testing (MycoAlertH Mycoplasma Detection Kit, Lonza,Belgium). All cell lines have been validated in-house by shorttandem repeat (STR) profiling using the Cell IDTMSystem(Promega, Madison, WI, USA) according to the manufacturer’sFigure 6. Time-dependent invasion profile of MDA-MB-231. A. Experimental design to quantify MDA-MB-231 Matrigel invasion. Two timestwo 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) after24 hours of serum starvation. At ten time points during a 48-hour incubation period inserts were fixed and stained in duplicate. Two insertscontaining cell-free media (grey fill) have been included throughout the experiment and fixed and stained after 24 hours incubation to assessbackground 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% (filledcircles) and 3.3% (filled squares) to medium+10% FBS with associated Spearman’s rank correlation coefficients (Rho). All results are from threeindependent duplicate experiments with SD. C. Sequential pictures showing invasive MDA-MB-231 cells at the indicated time points during a 48-hourincubation 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.doi:10.1371/journal.pone.0046536.g006Table 1. Matrigel surface densities corresponding withdegree of dilution for a fixed volume of 20 mL.Matrigel densityxCELLigence RTCA Transwell% mg/cm2*%10.0 189.50 23.13.3 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.t001Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 8 October 2012 | Volume 7 | Issue 10 | e46536
  22. 22. instructions. The obtained STR profiles were matched withreference ATCC DNA fingerprints (www.lgcstandards-atcc.org)and with the Cell Line Integrated Molecular Authentication(CLIMA) database (http://bioinformatics.istge.it/clima) [12] toauthenticate cell line identity.xCELLigence Real-Time Cell Analysis (RTCA): proliferationand cytotoxicityExperiments were carried out using the xCELLigence RTCA DPinstrument (Roche Diagnostics GmbH, Mannheim, Germany)which was placed in a humidified incubator at 37uC and 5% CO2.Cell proliferation and cytotoxicity experiments were performedusing modified 16-well plates (E-plate, Roche Diagnostics GmbH,Mannheim, Germany). Microelectrodes were attached at thebottom 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. Afterleaving the devices at room temperature for 30 min, thebackground impedance for each well was measured. Cells wereharvested from exponential phase cultures by a standardizeddetachment procedure using 0.05% Trypsin-EDTA (InvitrogenNV/SA, Merelbeke, Belgium) and counted automatically with aScepter 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/wellfor cytotoxicity experiments). The cell concentrations of 20, 100,200 and 400 cells/well were considered for correlation with theSRB method described below. After leaving the plates at roomtemperature for 30 min to allow cell attachment, in accordancewith the manufacturer’s guidelines, they were locked in the RTCADP device in the incubator and the impedance value of each wellwas automatically monitored by the xCELLigence system andexpressed as a Cell Index value (CI). Water was added to the spacesurrounding the wells of the E-plate to avoid interference fromevaporation. For proliferation assays, the cells were incubatedduring ten days in growth medium (10% FBS) and CI wasmonitored every 15 min during the first six hours, and every hourfor the rest of the period. Two replicates of each cell concentrationwere used in each test. For cytotoxicity experiments, CI of eachwell was automatically monitored with the xCELLigence systemevery 15 min during the overnight recovery period. Twenty-fourhours after cell seeding, cells were treated during a period of72 hours with paclitaxel (0, 1, 2, 5, 10, 20, 50 and 100 nM)dissolved in phosphate buffered saline (PBS). PBS alone was addedto control wells. Each concentration was tested in duplicate withinthe same experiment. CI was monitored every 15 min during theexperiment. Three days after the start of treatment with paclitaxel,CI measurement was ended.xCELLigence Real-Time Cell Analysis (RTCA): migrationand invasionCell migration and invasion experiments were performed usingmodified 16-well plates (CIM-16, Roche Diagnostics GmbH,Mannheim, Germany) with each well consisting of an upper and alower chamber separated by a microporous membrane containingrandomly distributed 8 mm-pores. This setup corresponds toconventional Transwell plates with microelectrodes attached tothe underside of the membrane for impedance-based detection ofmigrated cells. Prior to each experiment, cells were deprived ofFBS during 24 hours. Initially, 160 mL and 30 mL of media wasadded to the lower and upper chambers respectively and the CIM-16 plate was locked in the RTCA DP device at 37uC and 5% CO2during 60 minutes to obtain equilibrium according to themanufacturer’s guidelines. After this incubation period, ameasurement step was performed as a background signal,generated by cell-free media. To initiate an experiment, cellswere detached using TrypLE ExpressTM(Invitrogen, Merelbeke,Belgium), resuspended in serum-free (SF) medium, counted andseeded in the upper chamber applying 36104cells in 100 mL.After cell addition, CIM-16 plates were incubated during30 minutes at room temperature in the laminar flow hood toallow the cells to settle onto the membrane according to themanufacturer’s guidelines. To prevent interference from evapora-tion during the experiments, SF medium was added to the entireempty space surrounding the wells on the CIM-16 plates. Lowerchambers contained media with or without FBS in order to assesschemotactic migration when exposed to FBS and backgroundmigration to SF medium as a negative control accordingly. Signalsrepresenting net chemoattraction were obtained by subtractingbackground (SF) values from the positive control (mediumcontaining FBS) signals. Each condition was performed inquadruplicate with a programmed signal detection schedule ofeach three minutes during the first 11 hours of incubation followedby each five minutes for three hours and finally each 15 minutes to24 hours of incubation.A protocol identical to the above migration experiments wasfollowed for invasion experiments added with the application of alayer of Matrigel on the upper side of the membranes and dynamicprocess follow-up during 50 hours. Aliquoted Matrigel (BasementMembrane Matrix, growth factor reduced, BD Biosciences,Erembodegem, Belgium) was thawed overnight on ice and mixedwith ice cold SF medium to obtain two dilutions correspondingwith 6190 mg/mL (10%, v/v) and 663 mg/mL (3.3%, v/v)(Table 1). All Matrigel handling materials as well as the sealedpacks containing CIM-16 upper chambers were stored ice coldovernight. Establishment of a Matrigel layer on the CIM-16 upperchamber membranes was achieved by adding 50 mL of thedilution sequentially on top of four membranes followed byremoval of 30 mL, leaving a total of 20 mL Matrigel dilution.Subsequently, the coated upper chambers were incubated at 37uCto homogenously gelify during a minimum of four hours, followedby addition of 160 mL media to the lower and 30 mL SF media tothe upper chambers. Equilibration and cell addition was carriedout as described above.All data have been recorded by the supplied RTCA software (vs.1.2.1). As described below in the ‘‘Prestatistical data processingand statistical analysis’’ section, original high-resolution data setsgenerated by xCELLigence were exported to MS Excel andreconstructed at a lower resolution by selecting only the datapoints corresponding with the respective time points of signaldetection by the endpoint methods (Fig. 7). CI-data fromcytotoxicity experiments have been normalized using the RTCAsoftware to the last data point prior to treatment start. All otherresults (proliferation, migration and invasion) are based on rawdata without CI-normalization and were processed as describedabove for comparison with conventional methodology.SRB assay: proliferationCells were harvested from exponential phase cultures bytrypsinization, counted and plated in 48-well plates. To determinea proliferation curve and calculation of the doubling time, seedingdensities ranged from 100 to 2000 A549 or MDA-MB-231 cells/well. Each concentration was tested six times within the sameexperiment. General cell culture conditions and culture mediumused for this method were similar to those applied for thexCELLigence counterpart experiments, as well as applied celldensities (100, 500, 1000 and 2000 cells/cm2). Every day, oneReal-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 9 October 2012 | Volume 7 | Issue 10 | e46536
  23. 23. plate was fixed by the first step of the SRB assay: culture mediumwas aspirated prior to fixation of the cells by addition of 200 mlcold 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 ofthe SRB test were performed as described previously [13,14].Shortly, the cells were stained with 200 ml 0.1% SRB dissolved in1% acetic acid for at least 15 minutes and subsequently washedfour times with 1% acetic acid to remove unbound stain. Theplates were left to dry at room temperature and bound proteinstain was solubilized with 200 ml 10 mM unbuffered TRIS base(tris(hydroxymethyl)aminomethane) and transferred to 96 wellsplates for optical density reading at 540 nm (Biorad 550microplate reader, Nazareth, Belgium). Cell doubling time wascalculated from the exponential phase of the growth curve.SRB assay: cytotoxicityCells were harvested as described above. In order to assureexponential growth during the experiments, seeding density was103A549 cells per well and 103MDA-MB-231 cells/well. After anovernight 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. Rawdata with high time resolution (filled and empty circles), resulting from independent xCELLigence experiments (1, 2, 3 and grey arrows) are reduced to alower 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 scalethat 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 exampleshows migration (left) and invasion (right) of MDA-MB-231 cells through two densities of Matrigel. The ten time points in the Transwell method (blackarrows) were selected from the xCELLigence plots (grey and blue) to reconstruct a low-resolution graph (black), directly comparable to the Transwelldata. An identical approach was applied for all other processes studied.doi:10.1371/journal.pone.0046536.g007Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 10 October 2012 | Volume 7 | Issue 10 | e46536
  24. 24. Each concentration was tested six times within the sameexperiment. After 72 hours incubation with paclitaxel, survivalwas 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 assayComparative migration experiments were conducted using aconventional 24-well Transwell system (6.5 mm TranswellH(#3422), CorningH, NY, USA) with each well separated by amicroporous polycarbonate membrane (10 mm thickness, 8 mmpores) into an upper (‘‘insert’’) and a lower chamber (‘‘well’’). After24 hours of serum deprivation, cells were detached usingTrypLETMExpress (Invitrogen, Merelbeke, Belgium), countedand resuspended in media without FBS to obtain equal celldensities (2.16105cells/cm2) as applied in the xCELLigence RTCADP system with respect to the membrane seeding surface of bothtechniques (classic TranswellH membrane surface 0.33 cm2,RTCA DP 0.143 cm2). A volume of 250 mL containing 76104cells was plated to each insert and 600 mL medium was added tothe wells. For each experiment, both chemotactic migration tomedium containing 10% FBS and random migration with SFmedium on both sides of the membrane have been assessed inparallel Transwell plates. At ten predetermined time points afterincubation start (Fig. 3B), inserts were fixed and stained induplicates and migration was quantitated using two commonlyused methods. At each time point, cells were fixed and stained in a20% methanol/0.1% crystal violet solution during three minutesat room temperature, followed by washing in deionized water toremove redundant staining [15]. Non-migrated cells remaining atthe upper side of the membranes were carefully removed withcotton swabs and inserts were dried in darkness overnight. Asfixing and staining was performed per set of two inserts with theremaining inserts to be further incubated until the following timepoint, inserts to be fixed and stained at a time point weretransferred to a companion 24-well plate and the remaining insertsimmediately replaced in the incubator. Using this approachunfavorable influences caused by continuous switching incubatingcells between 37uC and ambient temperatures could be avoided.The following day stained membranes were pictured in threerandom non-overlapping fields at 106objective and 106eyepieceon a transmitted-light microscope (Leica DMBR, Leica Micro-systems GmbH, Wetzlar, Germany) equipped with an AxioCamHRc camera (Carl Zeiss MicroImaging GmbH, Jena, Germany).A first method of quantitation was performed by processing allobtained images using ImageJ software (http://rsbweb.nih.gov/ij/). Each image (Fig. 4C, left) was color thresholded to obtain abinary (black white – 8 bit) image with cellular materialportrayed as saturated black areas (Fig. 4C, middle). As a next stepall non-cellular artifacts, predominantly visible shadows of emptypores and debris, were removed from each image by performingthe particle analysis function with a mask excluding all particlessmaller than 100 to 250 pixels dependent on the experiment(Fig. 4C, right). Masking thresholds were set by comparing binaryimages with their original phase contrast counterparts [16].Degree of migration for each time point per experiment wasdetermined by calculating the average pixel area of the three fieldsin duplicate. Inserts were subsequently submerged in 300 mL 1%SDS/16 PBS in order to lyse migrated cells and extract crystalviolet stain [17]. Submerged inserts were incubated in darknessovernight on a plate shaker at medium speed to ensure completelysis. The following day 200 mL of each lysate was transferred to a96-well plate for optical density (OD) measurement at 590 nmusing a Powerwave X microplate scanning spectrophotometer(Bio-Tek, Bad Friedrichshall, Germany), representing a second cellmigration quantitation method. Cell-free inserts containing onlymedium had been included in duplicate throughout eachexperiment as OD background controls. Reported OD datarepresent average background-corrected values 6 SD obtainedfrom three independent experiments in duplicate.Transwell Matrigel Invasion assayReference cell invasion experiments were carried out using aTranswell plate system as described for migration experiments,added with the application of Matrigel as extracellular matrixcomponent. Matrigel dilutions were prepared as described abovefor the xCELLigence RTCA invasion assay. In order to obtainMatrigel surface area densities synchronized with the CIM-16plates used for xCELLigence, two dilutions of 20% and 7.7% (v/v)have been prepared in ice cold SF medium corresponding with6190 mg/mL and 663 mg/mL respectively, as applied in a volumeof 20 mL per insert membrane, identical to the CIM-16 upperchamber coating volume (Table 1). All other conditions regardingculturing, cell seeding density and serum deprivation wereidentical to the Transwell migration assays described above. At apanel of ten predetermined time points, inserts were fixed andstained in duplicates and invasion was quantified by OD readingat 590 nm after overnight extraction of the crystal violet stain.Reported OD data represent average background-correctedvalues 6 SD obtained from three independent experiments induplicate.Prestatistical data processing and statistical analysisAll data recorded using the xCELLigence RTCA system havebeen processed using MS Excel in order to obtain data series withthe same resolution as the data recorded by the conventionalreference methods (SRB, Transwell). This has been performed byselecting only the xCELLigence-generated values corresponding tothe time points that have been used in the reference methods, thusleading to a reconstruction of the studied process dynamics at alower time resolution (Fig. 7A, B).Furthermore, to eliminate differences in units of measurementbetween the compared methods (xCELLigence CI, OD, pixel area),all data have been reduced to a (0–1) scale. This was done byconsidering all data gathered over three performed experimentsper method and subsequently dividing all data by the singlemaximal value obtained, thus reducing this value to one (Fig. 4Band 7A). As these interventions do not influence proportionalitynor variance levels of the data, comparable series of dynamicallygenerated results were obtained for further statistical processing.For cell migration experiments, random migration signals (SF)were subtracted from the chemotactic migration signals (FBS) pertime point to generate dynamic profiles of net chemoattraction(Fig. 4B).All statistical analyses were performed using the statisticalpackage R, version 2.13.1 (www.r-project.org). Correlations werecalculated according to the Spearman’s rank correlation method.Intra- and inter-experimental variances were assessed for eachquantitation method separately using a mixed model approachwith time as fixed and biological replicate as random effect. Thestandard deviation of the random intercept (inter-experimentalvariance) 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 cellmigration data, calculations were split up into ‘‘early’’ (before tenhours incubation) and ‘‘late’’ (after ten hours incubation)measurements. All cell migration data analyses were performedReal-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 11 October 2012 | Volume 7 | Issue 10 | e46536
  25. 25. 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 byfitting a mixed linear model where the signal was regressed ontime, technique and their interaction. Biological replicates wereentered as a random effect. A likelihood ratio test was performedto test the significance, expressed as a p-value, of the interactionterm time-technique.Supporting InformationTable S1 Variance component analysis of proliferation,cytotoxicity, migration and invasion. All values expressed asthe square root of the variance (s2). sb Variance betweenindependent experiments (‘‘between’’). sw Variance within oneexperiment (‘‘within’’). Low 6102and 656102cells/cm2. High6103and 626103cells/cm2. Early Before 10 hours incubation.Late After 10 hours incubation. ND Not done. * Matrigel dilutionin SF medium (v/v).(DOCX)AcknowledgmentsWe thank Ken Op de Beeck (CORE and Center for Medical Genetics,University of Antwerp) for assistance with cell line authentication, theLaboratory for Pathophysiology (University of Antwerp) for granting accessto the microscope for picturing Transwell membranes and the Laboratoryfor Experimental Medicine and Pediatrics (University of Antwerp) forgranting access to the microplate scanning spectrophotometer for ODmeasurements. We gratefully acknowledge Marc Baay and Johan Ides(CORE, Antwerp) for constructive discussions.Author ContributionsConceived and designed the experiments: RL AW BP EF MP FL ODWPP. Performed the experiments: RL AW BP. Analyzed the data: RL AWBP EF. Contributed reagents/materials/analysis tools: MP FL PP. Wrotethe paper: RL. Evaluated and interpreted results: RL AW BP EF MP FLODW PP. Evaluated manuscript text: AW EF MP FL ODW PP.References1. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell144: 646–674. doi:10.1016/j.cell.2011.02.013.2. Skehan P, Storeng R, Scudiero D, Monks A, McMahon J, et al. (1990) Newcolorimetric cytotoxicity assay for anticancer-drug screening. J Natl Cancer Inst82: 1107–1112.3. Friedl P, Gilmour D (2009) Collective cell migration in morphogenesis,regeneration and cancer. Nat Rev Mol Cell Biol 10: 445–457. doi:10.1038/nrm2720.4. Roussos ET, Condeelis JS, Patsialou A (2011) Chemotaxis in cancer. Nat RevCancer 11: 573–587. doi:10.1038/nrc3078.5. Friedl P, Wolf K (2003) Tumour-cell invasion and migration: diversity andescape mechanisms. Nat Rev Cancer 3: 362–374. doi:10.1038/nrc1075.6. Hulkower KI, Herber RL (2011) Cell Migration and Invasion Assays as Toolsfor Drug Discovery. Pharmaceutics 3: 107–124. doi:10.3390/pharmaceu-tics3010107.7. BOYDEN S (1962) The chemotactic effect of mixtures of antibody and antigenon polymorphonuclear leucocytes. J Exp Med 115: 453–466.8. Ke N, Wang X, Xu X, Abassi YA (2011) The xCELLigence system for real-timeand label-free monitoring of cell viability. Methods Mol Biol 740: 33–43.doi:10.1007/978-1-61779-108-6_6.9. Atienza JM, Yu N, Kirstein SL, Xi B, Wang X, et al. (2006) Dynamic and label-free cell-based assays using the real-time cell electronic sensing system. AssayDrug Dev Technol 4: 597–607. doi:10.1089/adt.2006.4.597.10. Vichai V, Kirtikara K (2006) Sulforhodamine B colorimetric assay forcytotoxicity screening. Nat Protoc 1: 1112–1116. doi:10.1038/nprot.2006.179.11. Abassi YA, Xi B, Zhang W, Ye P, Kirstein SL, et al. (2009) Kinetic Cell-BasedMorphological Screening: Prediction of Mechanism of Compound Action andOff-Target Effects. Chem Biol 16: 712–723. doi:10.1016/j.chem-biol.2009.05.011.12. Romano P, Manniello A, Aresu O, Armento M, Cesaro M, et al. (2009) CellLine Data Base: structure and recent improvements towards molecularauthentication of human cell lines. Nucleic Acids Res 37: D925–D932.doi:10.1093/nar/gkn730.13. Pauwels B, Korst AEC, de Pooter CMJ, Lambrechts HAJ, Pattyn GGO, et al.(2003) The radiosensitising effect of gemcitabine and the influence of the rescueagent amifostine in vitro. Eur J Cancer 39: 838–846.14. Pauwels B, Korst AEC, De Pooter CMJ, Pattyn GGO, Lambrechts HAJ, et al.(2003) Comparison of the sulforhodamine B assay and the clonogenic assay forin vitro chemoradiation studies. Cancer Chemother Pharmacol 51: 221–226.doi:10.1007/s00280-002-0557-9.15. Zhang D, LaFortune TA, Krishnamurthy S, Esteva FJ, Cristofanilli M, et al.(2009) Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor ReversesMesenchymal to Epithelial Phenotype and Inhibits Metastasis in InflammatoryBreast Cancer. Clinical Cancer Research 15: 6639–6648. doi:10.1158/1078-0432.CCR-09-0951.16. de Wever O, Hendrix A, de Boeck A, Westbroek W, Braems G, et al. (2010)Modeling and quantification of cancer cell invasion through collagen type Imatrices. Int J Dev Biol 54: 887–896. doi:10.1387/ijdb.092948ow.17. Huang J, Bridges LC, White JM (2005) Selective modulation of integrin-mediated cell migration by distinct ADAM family members. Mol Biol Cell 16:4982–4991. doi:10.1091/mbc.E05-03-0258.Real-Time Technology Compared with Endpoint AssaysPLOS ONE | www.plosone.org 12 October 2012 | Volume 7 | Issue 10 | e46536
  26. 26. Mechanistic modeling of the effectsof myoferlin on tumor cell invasionMarisa C. Eisenberga,1, Yangjin Kimb, Ruth Lic, William E. Ackermanc, Douglas A. Knissc,d, and Avner Friedmana,e,1aMathematical Biosciences Institute, Ohio State University, Columbus, OH 43210; bDepartment of Mathematics, University of Michigan, Dearborn, MI48128; cDepartment of Obstetrics and Gynecology, Ohio State University, Columbus, OH 43210; dDepartment of Biomedical Engineering, Ohio StateUniversity, Columbus, OH 43210; and eDepartment of Mathematics, Ohio State University, Columbus, OH 43210Contributed by Avner Friedman, October 5, 2011 (sent for review August 18, 2011)Myoferlin (MYOF) is a member of the evolutionarily conservedferlin 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 eleganssperm motility (Fer-1), mammalian skeletal muscle developmentand repair (MYOF and dysferlin), and presynaptic transmission inthe auditory system (otoferlin). In this paper, we demonstrate thatMYOF 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 MYOFsignificantly reduced invasion of MDA-MB-231 breast cancer cellsin Matrigel bioassays. Based on these experimental data, we de-veloped a partial differential equation model of MYOF effectson cancer cell invasion, which we used to generate mechanistichypotheses. The mathematical model predictions revealed that ma-trix metalloproteinases (MMPs) may play a key role in modulatingthis invasive property, which was supported by experimental datausing quantitative RT-PCR screens. These results suggest that MYOFmay be a promising target for biomarkers or drug target for meta-static cancer diagnosis and therapy, perhaps mediated throughMMPs.cancer invasion ∣ RNAi ∣ partial differential equation models ∣ metastasisAmajority of cancer deaths are related not to the primarytumor itself, but rather the formation of disseminated me-tastases (1). Cancer spread requires that cells achieve atypicalmotility, which enables them to invade surrounding tissues andvessels of the blood and lymphatic systems (2–4). Thus, under-standing the mechanisms and signaling processes that lead toinvasive cell behavior may lead to new therapeutic approaches forcontrolling and treating cancer.The fundamental mechanisms of invasive cancer cell move-ment are largely conserved across a wide range of cell types, withsome of the protease dependent and protease independent move-ment types demonstrated by cancer cells also seen in organisms asdiverse as unicellular organisms, slime molds, and white bloodcells. The ferlin family is an evolutionarily ancient family of pro-teins (5), which are known to affect processes crucial to migrationand invasion, including membrane fusion and repair, vesicletransport, endocytosis, protein recycling and stability, and cellmotility (6–13). Thus, one might expect the ferlin family to begood candidates for cancer proteins, although they have notpreviously been investigated in this capacity. In Caenorhabditiselegans, 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 hasbeen implicated in a variety of cellular processes, including myo-blast fusion, growth factor receptor stability, endocytosis, andendothelial cell membrane repair (6, 8, 10–12); however untilnow its role in cancer cell movement has not been explored.Although information on MYOF is currently limited, it has beenshown to be upregulated in breast cancer biopsies (14) andexpressed in breast cancer cell lines (15). Immunohistochemicalevidence available from the Human Protein Atlas (16) suggeststhat 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 lineof MYOF-deficient malignant breast carcinoma cells (MDA-MB-231) was generated using lentivirus-based delivery of shRNAconstructs 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 oncell 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 cellinvasion. Because relatively little is known about the function ofMYOF in cancer, there is a useful opportunity for mathematicalmodeling 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 cellmigration/invasion (29), now incorporating a submodel forMYOF-mediated growth factor receptor recycling.Using multiple MYOF-related datasets (8–12), we determinedseveral parameters which differed between wild-type/controland MYOF-deficient cells. Our simulations suggest that one keyparameter—the matrix metalloproteinase (MMP) productionrate—is enough to reproduce the experimental data showingreduced MYOF-KD cell invasion. Based on the mathematicalmodel, we hypothesized that MYOF affects MMP productionand/or secretion in MDA-MB-231 cells. Preliminary experimen-tal results thus far confirm our hypothesis. Indeed, pilot PCRresults presented in this work show that MMPs may be signifi-cantly downregulated by MYOF depletion.We propose that MYOF may serve as a fundamental playerin cancer cell movement, by regulating the local behavior ofthe plasma membrane and affecting trafficking of receptors andproteins to and from the membrane. In particular, MYOF effectson 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 mayplay 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.1To whom correspondence may be addressed. E-mail: meisenberg@mbi.osu.edu orafriedman@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 6APPLIEDMATHEMATICSMEDICALSCIENCES
  27. 27. Mathematical ModelSpatial Setup. The conventional modified Boyden chamber setupincludes two chambers with a semipermeable membrane betweenthem. There is typically laminin-rich matrix (e.g., Matrigel) ontop of the semipermeable membrane, which replicates the ECM,and invasion is measured by the number of cells which invadethrough the matrix and cross the membrane from the upper tothe lower chamber. For the xCELLigence data, the membraneis coupled with a microelectrode array at the bottom of the upperchamber. Cells begin in the upper chamber (Ω in Fig. 1), fromwhere they can migrate and invade through the ECM (region Sin Fig. 1) to reach the microelectrode array. They then cross themembrane/microelectrode array and attach to the bottom side ofthe array upon crossing. Growth factors (GF) may be introducedin the bottom well below the microelectrode array, to act as achemoattractant for the cells.We measure the approximate number of cells which haveadhered to the microelectrode array by measuring the change inimpedance, the cell index [although we note that cell index is alsodependent on other factors, such as cell adhesion and spreading(27)]. To simulate these conditions, we can use a similar setupas for the conventional modified Boyden chamber simulationsgiven in refs. 29 and 30, with several modifications to incorporatethe microelectrode array and measurement of cell index. Fullmathematical description and details on the spatial setup andboundary/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 combinedeffects with the variable P, which represents generic/nonspecificMMP concentration. Similarly, because our experimental datause fetal calf serum as a chemoattractant, the variable G repre-sents a combination of multiple growth factors (further detailsgiven in SI Appendix).Model Equations. The model equations are based on the modelvariable interactions shown in Fig. 2. MYOF has been shownin other contexts to affect membrane processes (6, 8, 11) andreceptor/protein recycling/transport (10, 12). Parameters relatingto 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-relatedparameters—one for wild-type/control cells and another forMYOF-KD cells, as described below.∂R1∂t¼ ðλ1 − k21R1G þ k13R3 − k01R1Þnn0[1]∂R2∂t¼ ðk21R1G − k32R2 þ k23R3Þnn0[2]∂R3∂t¼ ½k32R2 − ðk13 þ k23 þ k03ÞR3Šnn0[3]∂ρ∂t¼ −λ21Pρ|fflfflffl{zfflfflffl}degradationþ λ22ρ1 −ρρ0|fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl}reconstructionðsmallÞin 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 invadefollowing 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.
  28. 28. ∂P∂t¼ Dp∇2P þ λ31nρ|fflffl{zfflffl}production by cells− λ32P|ffl{zffl}degradation[5]∂G∂t¼ DG∇2G − k21CmwR1nG|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}binding− λ10G|ffl{zffl}degradation[6]∂n∂t¼ Dn∇2n þ λ11nð1 −nn0− μρÞR2R20|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}proliferation− ∇ ·χnn R2R20∇Gffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 þ λGj∇Gj2p|fflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflffl}chemotaxisþ χ0nISn∇ρffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 þ λρj∇ρj2q|fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}haptotaxis[7]Eqs. 1–3 constitute the receptor recycling ordinary differentialequation (ODE) submodel, collectively representing growthfactor receptor binding, internalization, degradation, and returnto the cell surface. We assume that all the internalized ligand isdegraded so that ligand return to the surface may be neglected.The receptor recycling submodel equations are multiplied byn∕n0 to scale the concentrations to the local cell density.The remaining PDEs are largely based on a previous modelof tumor growth and movement in a Boyden chamber (29), up-dated and with modifications to incorporate MYOF effects andour particular experimental setup. The ECM, in Eq. 4, undergoesdegradation by MMP (31) and includes a small remodeling term(30, 32–34). In Eq. 5, MMP is produced by the cells to degradethe matrix, and then degrades and diffuses. Because MMP isproduced in response to the presence of extracellular matrix,we have modified this term from ref. 29 to be dependent on bothn and ρ. Growth factor in Eq. 6 diffuses from below the xCELLi-gence well bottom, where it binds to free receptors on the cellsurface, and is degraded at a constant rate.Lastly, tumor cells Eq. 7 begin above the ECM in the upperchamber, from which they then undergo dispersion, chemotaxisfollowing the GF concentration gradient, haptotaxis through theECM, following the ECM concentration gradient, and growthfactor dependent proliferation based on the level of surface-bound growth factor (Fig. 2). Cell proliferation is modeled aslogistic growth, to which we add an additional ECM-dependentterm to account for additional crowding effects in the presenceof ECM (35). The diffusion constants and other parameters arepositive constants.Parameter Estimation. As discussed above, MYOF is known toaffect a variety of membrane processes (6, 8, 11) and receptor/protein recycling and transport (10, 12). Thus, we developedtwo versions of the membrane-related model parameters, under-lined in Eqs. 1–7, each characterizing the wild-type/lentiviralcontrol 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 experimentalreceptor internalization data from wild-type and MYOF-nullmyoblasts [MYOF knockout (MYOF-KO)] cells (12), with thefull details of the model parameterization given in SI Appendix.The resulting parameter estimates suggest that MYOF-deficientcells yield decreased GFR production, and increased receptorrecycling pathways leading to receptor degradation.There are three MYOF-dependent parameters in the full PDEmodel which remain unaccounted for—the MMP secretion rateand the chemotactic and haptotactic sensitivity parameters. AsMYOF-KD cells show decreased invasivity compared to wild-type/control cancer cells, we expect these parameters to decreasein the MYOF-KD case. The ODE submodel parameters showbetween a 13 and 40% change between wild-type and MYOF-KD parameter values, so we suppose χnm ¼ 0.75χn andχ0nm ¼ 0.75χ0n. As MMP has been shown to associate with MYOF(36) and MMP secretion is directly membrane-related, we wouldexpect that λ31 will be more strongly affected by the MYOF-KD.Indeed, we found the best fits to the xCELLigence invasion datafor a significantly lower value of λ31m, so that we take λ31m ¼λ31∕100. The remaining non-MYOF-dependent PDE modelparameters for both the wild-type/control and MYOF-KD cellswere determined based on literature values (29, 30, 36, 37) (seeSI 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. Tumorcell invasion is more significant in wild-type/control than MYOF-KD cells, matching experimental data. Bound and internalizedreceptors tend to follow the invading front of tumor cells, withcells toward the top of the upper well tending to have moreunbound receptors (as the GF has not diffused completely up thechamber). MMP also tends to roughly follow the invading front oftumor cells, as does degradation of ECM, with MMP productiondropping off outside the ECM region.Applications to Cancer Cell InvasionDecreased Invasion in MYOF-KD Cells. Figs. 3 and 4 show modelsimulations 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 recoverthe qualitative behavior of the experimental data, with a moresignificant decrease in invasivity for MYOF-KD cells in 100%Matrigel compared to 20% Matrigel.0 5 10 15 20 2500.050.10.150.20.250.30.35Time (h)CellindexControlMYOF KDMMP KDABFig. 3. Comparison between experimental data and simulation results with20% Matrigel. (A) Experimental results for lentiviral control (LTV-ctrl) andMYOF-KD cells. (B) Simulation results for wild-type/control, MYOF-KD, andhypothetical MMP KD cells.Eisenberg et al. PNAS Early Edition ∣ 3 of 6APPLIEDMATHEMATICSMEDICALSCIENCES

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