ß 2014. Published by The Company of Biologists Ltd | Journal of Cell Science (2014) 127, 1805–1815 doi:10.1242/jcs.139733

RESEARCH ARTICLE

Fluctuation of Rac1 activity is associated with the phenotypic andtranscriptional heterogeneity of glioma cells
Hiroko Yukinaga1, Clara Shionyu2, Eishu Hirata2, Kumiko Ui-Tei3, Takeshi Nagashima4,5, Shinji Kondo4,
Mariko Okada-Hatakeyama6, Honda Naoki7 and Michiyuki Matsuda1,2,8,*

Phenotypic heterogeneity of cancer cells is caused not only by
genetic and epigenetic alterations but also by stochastic variation of
intracellular signaling molecules. Using cells that stably expressFörster resonance energy transfer (FRET) biosensors, we show
here a correlation between a temporal fluctuation in the activity ofRac1 and the invasive properties of C6 glioma cells. By using longterm time-lapse imaging, we found that Rac1 activity in C6 glioma
cells fluctuated over a timescale that was substantially longer thanthat of the replication cycle. Because the relative level of Rac1
activity in each cell was unaffected by a suspension–adhesion
procedure, we were able to sort C6 glioma cells according to thelevels of Rac1 activity, yielding Rac1high and Rac1low cells. The
Rac1high cells invaded more efficiently than did Rac1low cells in a
Matrigel invasion assay. We assessed the transcriptional profiles ofRac1high and Rac1low cells and performed gene ontology analysis.
Among the 14 genes that were most associated with the term‘membrane’ (membrane-related genes) in Rac1high cells, we
identified four genes that were associated with glioma invasion
and Rac1 activity by using siRNA knockdown experiments. Amongthe transcription factors upregulated in Rac1high cells, Egr2 wasfound to positively regulate expression of the four membranerelated invasion-associated genes. The identified signaling network
might cause the fluctuations in Rac1 activity and the heterogeneity
in the invasive capacity of glioma cells.
KEY WORDS: Rac1, Invasion, Heterogeneity, MMP15, MT2-MMP,
Tetraspanin 17, TSN17, Pstpip2, Freq, NCS1, Elmo1

1
Department of Pathology and Biology of Diseases, Graduate School of Medicine,
Kyoto University, Kyoto 606-8501, Japan. 2Laboratory of Bioimaging and Cell
Signaling, Graduate School of Biostudies, Kyoto University, Kyoto 606-8501,
Japan. 3Department of Biophysics and Biochemistry, Graduate School of
Science, University of Tokyo, Tokyo 606-8501, Japan. 4Research Unit for
Immunodynamics, RIKEN Research Center for Allergy and Immunology,
Kanagawa 230-0045, Japan. 5Division of Cell Proliferation, United Centers for
Advanced Research and Translational Medicine, Tohoku University Graduate
School of Medicine, Sendai, Miyagi, 980-8575, Japan. 6Laboratory for Cellular
Systems Modeling, RIKEN Research Center for Allergy and Immunology,
Kanagawa 230-0045, Japan. 7Imaging Platform for Spatio-Temporal Information,
Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan. 8Institutefor Integrated Cell-Material Sciences, Kyoto University, Kyoto 606-8501, Japan.

*Author for correspondence (matsuda.michiyuki.2c@kyoto-u.ac.jp)
This is an Open Access article distributed under the terms of the Creative Commons Attribution
License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution
and reproduction in any medium provided that the original work is properly attributed.

Received 1 August 2013; Accepted 23 January 2014

INTRODUCTION

Cancer cells that originate from a single cell acquire phenotypic
heterogeneity because of genomic instability or heritableepigenetic changes (Lengauer et al., 1998; Shackleton et al.,2009). This heterogeneity is advantageous for the progression ofcancer and promotes its highly invasive nature in tissues (Heppner,
1984; Rubin, 1990; Shackleton et al., 2009). Recently, however, it
has been reported that the fate and behavior of mammalian cells,
including cancer cells, can be determined by stochastic variation ingene expression (Brock et al., 2009). For example, heterogenous
signaling patterns in monoclonal cancer cells can generate cellswith diverse phenotypes, which have different drug sensitivities(Singh et al., 2010).
A typical example of a cancer that exhibits extensive
heterogeneity is glioblastoma, which was previously termed
glioblastoma ‘multiforme’, reflecting its histopathologicaldivergence in size, shape, karyotype, etc. (Louis, 2006). Amongthe many experimental models of glioblastoma, the C6 glioma
cell model has been frequently used to study the invasiveness ofglioblastoma cells (Grobben et al., 2002). Glioblastomas formed
from C6 glioma cells implanted into syngeneic Wistar rats sharemany histological hallmarks with human glioblastoma and C6
glioblastoma cells preferentially migrate along neuronal fibers
and through the perivascular space, a pattern which resembles the
spread of human glioblastoma.
Rho-family GTPases regulate cytoskeletal dynamics and,
thereby, affect many cellular processes, including cell polarity,
migration, vesicle trafficking and cytokinesis (EtienneManneville and Hall, 2002). In cancer cells, Rho-family
GTPases play crucial roles in the acquisition of cancer-cellspecific behavior (Sahai and Marshall, 2002). Rac1, for example,
accelerates tumorigenesis by regulating apoptosis, cell cycle
progression, assembly and disassembly of tight junctions andadherens junctions, cell migration and cell invasion (Mack et al.,2011). Importantly, these pleiotropic functions of Rho-family
GTPases have been characterized by comparing cancer cells with
non-cancer cells. However, little is known about theheterogeneity and changes in the activity of Rho-family
GTPases within cancer cell populations.
The ability to sort cells with respect to a property of interest is
essential in order to study the heterogeneity of cancer cells by
genetic, epigenetic, biochemical or cell biological analyses. Cellsurface markers and cognitive antibodies have been routinely
used for this purpose, but the methods used to sort cells
depending on the intracellular activity of a signaling molecule
are limited. Biosensors based on the principle of Förster
resonance energy transfer (FRET) have been widely used tomonitor the activity of the signaling molecules (Kiyokawa et al.,2006; Miyawaki, 2011); however, due to a lack of methods
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ABSTRACTfor the stable expression of the FRET biosensors, cell sortingwith FRET-biosensor-expressing cells has been a difficult
task.
Recently, we have reported the development of methods for the
stable expression of FRET biosensors in cell lines and transgenic
mice (Kamioka et al., 2012; Komatsu et al., 2011). By using C6
rat glioma cells that stably expressed a FRET biosensor for
Rac1, we found that C6 glioma cells penetrating the brain
parenchyma show higher Rac1 and Cdc42 activities and lower
RhoA activity than those advancing in the perivascular regions(Hirata et al., 2012). This observation led us to investigate themechanism by which the heterogeneity of Rho-family GTPase
activity is generated, and the role of the heterogeneity in theinvasion of glioma cells. For this purpose, we established amethod to sort cells with respect to their levels of Rho-family
GTPase activity. Then, by using next generation sequencers, we
identified genes whose expression was correlated with Rac1
activity. Using this approach, we show here that fluctuations in
the activity of Rac1 are associated with the heterogeneity ofglioma invasion.
RESULTS
Distribution of Rac1 activity among C6 glioma cells

We have shown previously that C6 glioma cells that invade at the
periphery of a tumor mass in the rat brain, or a three-dimensional(3D) spheroid, exhibit higher Rac1 activity than those trailingsuch leader cells (Hirata et al., 2012). We speculated that such a
distribution of Rac1 activity among C6 glioma cells might beautonomously generated during cell growth and spreading. To
test this hypothesis, we prepared C6 glioma cells that stably

Fig. 1. Activity variation of Rac1. (A) The Raichu-Rac1 FRET biosensor
comprises YFP, Rac1, the CRIB domain of PAK1, CFP and the C-terminal
domain of K-Ras. Upon binding of GTP to Rac1, PAK1 binds to Rac1, whichbrings YFP into close proximity to CFP, thereby, increasing FRET. (B) C6
glioma cells that stably expressed Raichu-Rac1 were grown on glassbottomed dishes and imaged to visualize the FRET to CFP ratio (FRET/CFP)in the intensity display mode with the FRET to CFP ratio ranges as indicatedin the figures. Scale bar: 200 mm. (C) The FRET to CFP ratio averaged foreach cell in B is shown, which could be a normal distribution (P50.92,
Kolmogorov–Smirnov test and P50.3, Shapiro-Wilk normality test). Analyses
were performed by R (R Version 2.12.1). (D) The FRET to CFP ratio and YFPintensity in each cell are plotted to show the independence of the Rac1
activity from the concentration of the biosensor.

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express the FRET biosensor Raichu-Rac1, and visualized Rac1
activity on glass-bottomed dishes (Fig. 1A). We detected
substantial variation in Rac1 activity, which exhibited a typicalnormal distribution (Fig. 1B,C). Correlation between the activityof Rac1 and the expression level of the biosensor was notobserved (Fig. 1D).
Temporal fluctuations and resilience of Rac1 activity

The normal distribution of Rac1 activity probably reflected thenoise of the system (Brock et al., 2009). To study the mechanism
underlying the generation of the noise, we used time-lapse
imaging to record Rac1 activity in C6 glioma cells for 5 days
(Fig. 2A; supplementary material Movie 1). C6 glioma cells that
expressed Raichu-Rac1 were seeded onto a glass-bottomed dishthat had a 282-mm-diameter spot, which prevented cells frommoving out of the visual field. To maintain the cell density
within the optimal range for cell growth, the Raichu-Rac1expressing cells were co-cultured with parental C6 glioma cells.
We chose spots that had a single biosensor-expressing cell, andbetween one and several parental C6 cells for the tracking. Rac1
activity was averaged over the entire cell area and thenplotted against time (Fig. 2B). Except for the rapid decline
and increase during cell division, Rac1 activity exhibitedchanges over timescales that were longer than the duration of
the cell cycle (.40 hours), which we refer to as fluctuations.
Consequently, after 5 days, when the single cells had
proliferated to give six to eight cells, Rac1 activity varied
substantially among the daughter cells (supplementary material
Fig. S1A). Analysis of plots that had been generated using power
spectrum did not reveal any periodicity in the fluctuations in
Rac1 activity (Fig. 2C). Of note, even after the substantialchange during cell division, Rac1 activity returned to a level as
before cell division, suggesting that the level of Rac1 activity is
maintained by a mechanism that is unaffected by cell division(Fig. 2D). The range of Rac1 activity after 5 days (Fig. 2B)
was similar to the range of Rac1 activity observed for thecell population (Fig. 1C); therefore, we concluded that the
distribution of Rac1 activity was generated, primarily, by
the fluctuations over timescales that occurred over periods
longer than the duration of the cell cycle. To understandthe biological relevance of the observed distribution of Rac1
activity, we examined the correlation between the cell area andRac1 activity (Fig. 2E), and found a weak positive correlation.
The positive, though weak, correlation between the cell area andRac1 activity probably reflects the high level of Rac1 activity inlamellipodia (Itoh et al., 2002; Kraynov et al., 2000). We also
examined the velocity of the migration (Fig. 2E) but could notobserve any clear correlation with Rac1 activity.
Next, to examine the resilience of the level of Rac1 activity
to suspension and re-adhesion procedures, we detached the cells
by using trypsin and then induced cell adhesion by using
trypsin inhibition in situ (Fig. 2F). Trypsinization induced
cell rounding and decreased Rac1 activity, whereas trypsin
inhibition induced cell adhesion and restored Rac1 activity.
Notably, the relative Rac1 activity of each cell to other cells
undergoing the suspension–adhesion protocol did not change
before, during or after trypsinization (Fig. 2F). This observationagrees with the previous report that the suspension of adherent
cells reduces Rac1 activity (del Pozo et al., 2000) and alsosuggests that the level of Rac1 activity in each cell ismaintained by a mechanism that is unaffected by thesuspension–adhesion procedure.

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FRET-based cell sorting to select Rac1high and Rac1low
populations

To understand the mechanisms underlying the fluctuations in
Rac1 activity, we attempted to examine the transcriptomes of C6
glioma cells that had different levels of Rac1 activity. Given thatthe suspension–adhesion procedure did not alter the relative Rac1
activity of each cell, we sorted C6 glioma cells according totheir Rac1 activity by using fluorescence-activated cell sorting(FACS). The FRET to cyan fluorescent protein (CFP) ratio wasused as the index of FRET efficiency, as is used in microscopy.
The FRET:CFP ratio was independent of the expression level ofthe biosensor (Fig. 3A) as observed under 2D conditions(Fig. 1D). C6 glioma cells in the highest and lowest decile,with respect to the FRET:CFP ratio, were named the Rac1high andRac1low populations, respectively, and sorted (Fig. 3B).
Because Rac1 activity is closely associated with cellattachment (del Pozo et al., 2000), it was unknown whether the
Rac1 activity monitored by the FRET:CFP ratio in FACS
reflected the Rac1 activity of cells grown on the culture dishes.
In Fig. 2F, we showed that the suspension–adhesion procedure
did not alter the relative Rac1 activity of each cell in the cell
population; here, we quantified the GTP that was bound to the1807

Journal of Cell ScienceFig. 2. Fluctuations in and the resilience of Rac1 activity. (A) C6 glioma cells that expressed Raichu-Rac1 were plated on glass-bottomed dishes with parent
C6 glioma cells, which served as feeder cells at a low cell density. Cells were imaged in time-lapse for 5 days (supplementary material Movie 1). Representative
images of the FRET to CFP ratio (FRET/CFP) (top row), and differential interference contrast images (bottom row) are shown. (B) The timecourse of asingle cell and its derivatives after smoothing by the Savitzky–Golay filter, except for the mitosis phase, during which period a surge of Rac1 activity wasobserved (asterisks and thin lines). Each newborn cell is depicted with a different color line. The data are also shown in supplementary material Fig. S1.
(C) Power spectrum of Rac1 activity. Blue and red lines indicate normalized power spectra of analyzed cells (n558) and the average, respectively.
(D) Correlation of Rac1 activities before and after cell division. (E) The relationship between Rac1 activity and the cell area (left panel) and velocity of cells(right panel). (F) C6 glioma cells expressing Raichu-Rac1 in serum-free medium were imaged in time-lapse. During the imaging, 1.25% trypsin was added at
0.5 h. At 0.75 h, FBS was added to inactivate trypsin. Left panel: representative images of the FRET/CFP images (top row), and differential interference contrast
images (bottom row) are shown; right panel, Rac1 activities in four cells were quantified. Scale bars: 20 mm.

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Fig. 3. Isolation of Rac1high and Rac1low cell populations by FACS.
(A) C6 glioma cells expressing Raichu-Rac1 were analyzed by using FACS.
The Rac1 activity (measured by using the FRET to CFP ratio, FRET/CFP)
did not correlate with the FRET biosensor concentration (YFP) in each cell.
(B) The top and bottom 10% of cells were sorted to obtain Rac1high (red) andRac1low (blue) populations, respectively, (left panel). Small fractions ofRac1high and Rac1low cells were reanalyzed after sorting (right panel).
(C) Rac1high and Rac1low cells were collected and analyzed by pulldown
assay (left panel). Cells expressing CFP–Rac1V12 (constitutively active) and
CFP–Rac1N17 (dominant negative) were used as positive and negative
controls, respectively. White and black arrows indicate CFP–Rac1 andendogenous Rac1, respectively, quantification is shown in the right panel.
(D) Rac1high and Rac1low cells were plated on dishes and cultured for
3 hours. Cells were labeled with 32P for 2 hours, lysed andimmunoprecipitated by using an antibody against GFP, followed by TLC
analysis (left panel) and quantification of GTP and GDP bound to the FRET
biosensor (right panel). P-values were derived by using two-tailed Student’s
t-test. (E) Rac1high and Rac1low cells were used for the Matrigel invasion
assay. (F) Rac1high, Rac1low cells, Cdc42high and Cdc42low cells were platedon dishes, cultured for the indicated periods and then the activity of Rac1 andCdc42, respectively, was re-analyzed by FACS.

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Transcriptional signatures of Rac1high and Rac1low cells

To investigate the association of the variation in Rac1 activity
with transcriptional signatures, RNA sequencing (RNA-Seq)
analysis was performed on C6 glioma cells that had been sorted
by using FACS. mRNA was isolated from the Rac1high andRac1low populations of C6 glioma cells and sequenced. The
difference in the expression of genes between Rac1high andRac1low cells was plotted against the calculated average
expression (Fig. 4A). Similar analyses were performed for thecells expressing the biosensors for Cdc42 and RhoA to

Journal of Cell Scienceendogenous Rac1 by using a pulldown assay. The amount of
GTP–Rac1 in Rac1high cells was larger than that in Rac1low cells(Fig. 3C). Furthermore, we directly measured the GTP:GDP ratioby thin-layer chromatography (TLC) after cell sorting. Both
Rac1high and Rac1low cells were plated on the culture dishes andradiolabeled with 32P for 2 hours, followed by TLC analysis tomeasure the GTP:GDP ratio on the biosensor (Fig. 2D). Although
the difference in the GTP:GDP ratio between Rac1high andRac1low was smaller than the difference between wild-type and
GTPase-deficient mutant Rac1 proteins, the GTP:GDP ratio of
Rac1high cells was constantly higher than that of Rac1low cells,
providing biochemical evidence that the FRET-based cell sortingreflects the Rac1 activity level. In addition, we found that cells in
the G2/M phase were enriched in the Rac1low population(supplementary material Fig. S1B), which agrees with theobservation that Rac1 activity was transiently reduced during
cell division (Fig. 2B).
We then examined the invasion of Rac1high and Rac1low cells
into Matrigel by using a Boyden chamber assay. Cells that hadreached the lower side of the filter were counted after 22 hours.
Although the efficiency of invasion varied in each experiment, weconstantly observed that Rac1high cells invaded into Matrigel
significantly faster than did Rac1low cells (Fig. 3E). Thisobservation agrees with the findings of our previous 3D
spheroid assay, which showed that cells with higher Rac1
activity invaded into Matrigel first and guided cells with lower
Rac1 activity (Fig. 1B) (Hirata et al., 2012).
Finally, to investigate further our hypothesis that thefluctuations in the activity of Rac1 caused the observed
distribution of Rac1 activity, the Rac1high and Rac1low cells
were cultured for up to 9 days and re-analyzed by FACS. On
the first day, the distribution of Rac1 activity within thesorted populations remained discrete, but after one week the
distribution of Rac1 activity within each population was
identical, supporting our hypothesis (Fig. 3F). We performed
similar experiments with C6 glioma cells expressing a FRET
biosensor for Cdc42. In agreement with the previous findingthat glioma cells invading at the front exhibit high Rac1 activity
and high Cdc42 activity (Hirata et al., 2012), a similar result
was obtained using Cdc42high and Cdc42low cells (Fig. 3F).
Furthermore, we retrospectively analyzed long-term time-lapse
images (supplementary material Fig. S1C), and found that, inagreement with the FACS data, cells in the highest and lowest
decile, with respect to the FRET to CFP ratio, displayed similar
profiles at 50 hours.

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characterize the Rac1high and Rac1low populations (Fig. 4B). The
difference in the expression of genes between Rac1high andRac1low cells was positively correlated with that of Cdc42high andCdc42low cells. However, there was no correlation between theexpression of genes in Rac1high and Rac1low cells with RhoAhighand RhoAlow cells, or those of Cdc42high and Cdc42low and
RhoAhigh and RhoAlow cells. Again, this is in agreement with ourprevious observation that both the activity of Rac1 and Cdc42
were high in glioma cells migrating at the leading edge in rat
brains and in 3D Matrigel experiments (Hirata et al., 2012). To
identify genes that were expressed differently, we used theweighted average difference method (WAD). The WAD method

identified 713 differentially expressed genes using a cutoff of the
top 5% of ranked genes. Gene ontology analysis, based on terms
associated with biological processes, showed that the Rac1highphenotype is associated most with the G-protein-coupled receptor
signaling pathway, followed by cell–matrix adhesion and then theelectron transport chain (Fig. 4C). The Rac1low phenotype isassociated with terms pertaining to cell division, the cell cycle
and mitosis. In addition, analysis of terms relating to cellularcomponents showed that genes that are associated with therespiratory chain, focal adhesion and mitochondrial respiratory
chain complex I were upregulated in the Rac1high population, andthat the expression of genes relating to the cytoplasm and1809

Journal of Cell ScienceFig. 4. RNA-Seq analysis of Rac1high and Rac1low cell populations. (A) Poly(A)-selected RNA was isolated from Rac1high and Rac1low cell populations andused for RNA-Seq analysis. The relationship between the average expression [log2(Rac1high6Rac1low)/2] and expression difference between Rac1high andRac1low cells [log2(Rac1high/Rac1low)] is shown in a log ratio against abundance (M–A) plot. The WAD method identified 713 differentially expressed genes using
cutoffs of the top 5% ranked genes. Genes enriched in Rac1high and Rac1low cells are depicted with pink and blue dots, respectively. The top 14 genes
for the cellular component term ‘membrane’ are marked in orange, except for Pstpip2, Freq/NCS1, MMP15 and Tsn17, which are shown with red dots. Elmo1and Egr2 are shown as green dots. (B) RNA-Seq analysis was similarly performed for the Cdc42high, Cdc42low, RhoAhigh and RhoAlow cell populations. Scatterplots of the expression differences are shown. Correlation coefficient, r, between measures was calculated by using R software. (C,D) Gene ontology
analysis with biological process terms (C) or cellular component terms (D) is shown for Rac1high and Rac1low cells (left and right panels, respectively). P-values
were calculated by Pearson product–moment correlation coefficients.

nucleus, and that are integral to membrane, was increased in theRac1low population (Fig. 4D).
Identification of genes that regulate C6 glioma cell invasion

Based on the RNA-Seq data, we attempted to identify genes that
regulate glioma invasion. For this purpose, we focused on the 23
genes that were most upregulated in the Rac1high population andrelated to the cell component term ‘membrane’, which we term‘membrane-related genes’ (supplementary material Table S1).
Notably, 18 genes out of these 23 genes were upregulated in
Cdc42high cells in comparison with Cdc42low cells, strongly
suggesting that there is large overlap between gene expression in
Rac1high cells and Cdc42high cells. The difference in gene

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expression between Rac1high cells and Rac1low cells was first
confirmed by using quantitative (q)PCR, except for Ecop, forwhich we failed to prepare specific primers. Next, to characterize
the membrane-related genes that were upregulated in Rac1highcells, we knocked down the ten genes that were most upregulated
in the Rac1high population by using three different siRNAs,
except for cathepsin L1 (Ctsl), against which we failed to prepare
three effective siRNAs. From the remaining 12 genes, we
arbitrarily chose and knocked down Ntrk2, Freq (also known asNcs1), Il1rap and Pstpip2. C6 glioma cells were then examined
for their invasive potential by using the Matrigel invasion assay.
Of the 14 membrane-related genes, knockdown of matrix
metalloproteinase 15 (MMP15, also known as MT2-MMP),

Fig. 5. The effect of knockdown of genes upregulated in the Rac1high cell population on invasion and Rac1 activity. (A) For the top 14 genes thatwere most associated with the cellular component term ‘membrane’, three siRNAs for each gene were used to knockdown the target genes in C6 glioma
cells. Cells were used for invasion analysis as described in the text. The results of two independent experiments are shown. *P,0.05, **P,0.01 and***P,0.001 (two-tailed paired Student’s t-test). (B) Four genes associated with the invasive phenotype in A, and Lgals3 and Rgs2 (as controls), were knocked
down and the FRET to CFP ratio values (FRET/CFP) were quantified for each C6 glioma cell. The numbers of cells analyzed are shown at the bottom. In thebox-whisker plot, the median and the 15 and 75 percentiles are represented by bold lines and boxes, respectively. (C) Representative images of FRET:CFP ratioin C6 glioma cells transfected with the indicated siRNAs. (D–F) The effect of the knockdown of Egr2, Elmo1 and PRex1 was also examined by invasion
assay (D) (results are plotted as means6s.d., P-values were calculated using paired two-tailed Student’s t-test), quantification of the FRET to CFP (FRET/CFP)
ratio (E) and imaging (F) upon treatment with siRNA as previously described in A, B and C, respectively. Scale bars: 100 mm (C,F).

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tetraspanin 17 (TSN17), Pstpip2 and Freq, which we callmembrane-related invasion-associated genes, significantly
inhibited C6 glioma cell invasion (Fig. 5A). Except for
MMP15, knockdown of the membrane-related invasionassociated genes suppressed Rac1 activity, suggesting that
TSN17, Pstpip2 and Freq promote invasion through activationof Rac1 (Fig. 5B). Knockdown of the membrane-related but
invasion-irrelevant genes Lgals3 and Rgs2 did not affect Rac1
activity. Notably, knockdown of TSN17 and Freq, but not
Pstpip2, caused rounding of the cell shape (Fig. 5C). These twogenes might be associated with Rac1-mediated membrane
protrusion.
We next examined transcription factors that were upregulatedin Rac1high cells (supplementary material Table S2) and foundthat expression of Egr2 was reproducibly increased in Rac1highcells and Cdc42high cells. Similar to this, Elmo1 and PRex1,
which are positive regulators of Rac1 activity, were found to be
upregulated in Rac1high cells (supplementary material Table S3).
Knockdown of Egr2 and Elmo1, but not PRex1, suppressed C6
cell invasion and decreased Rac1 activity (Fig. 5D,E). Unlike theknockdown of the membrane-related genes TSN17 and Freq,
knockdown of Egr2 or Elmo1 decreased Rac1 activity without
affecting the cell shape (Fig. 5F).
Thus, we identified the genes of four membrane-related
proteins, a transcription factor and a Rac1 activator as invasionassociated genes that are upregulated in Rac1high cells. Notably,knockdown of Egr2, Elmo1, Pstpip2, Freq and TSN17 not only
decreased the average Rac1 activity but also suppressed thefluctuations in the activity of Rac1 (supplementary material Fig.
S2), suggesting that the fluctuations in the activity of Rac1 might
be associated with the basal level of Rac1 activity.
Hierarchy of the invasion-associated genes upregulated inRac1high cells

To delineate the signaling network of the invasion-associatedgenes that were upregulated in Rac1high cells, we first transiently
activated Rac1 by using the rapamycin-induced Rac1 activation
system (Yagi et al., 2012). Upon rapamycin-induced membranetranslocation of Tiam1, a guanine-nucleotide-exchange factor for
Rac1, Rac1 activation was clearly detected by both FACS
(Fig. 6A) and pulldown assay (Fig. 6B). Of the genes tested, only
expression of Egr2 was rapidly and transiently induced(supplementary material Fig. S3). We next knocked down eachgene and quantified the expression of the other invasionassociated genes by using qPCR (supplementary material Fig.
S4). The comprehensive knockdown experiments revealed
intriguing features of the signaling network that regulates Rac1
activity (Fig. 6C). First, the membrane-related invasionassociated genes, Pstpip2, TSN17, Freq and MMP15 wereclustered. Second, and more importantly, we could determine
the hierarchy of the genes by assuming that the knockdown of agene decreases the expression of the downstream genes andincreases the expression of the upstream gene(s) by a negativefeedback loop. For example, Egr2 knockdown decreased
expression of all four membrane-related invasion-associatedgenes but not the invasion-irrelevant genes, such as Rgs2 or
Elmo1. By contrast, knockdown of the membrane-related
invasion-associated genes increased Egr2 expression, suggestingthat there is a negative-feedback loop to Egr2. Because theeffect of Egr2 knockdown on the expression of Pstpip2 was
not significant, this gene could be part of a different signaling
pathway. From these data, we propose a model of the gene

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network that regulates Rac1 activity and the invasive capacity of
C6 glioma cells (Fig. 6D).
DISCUSSION

The phenotypic heterogeneity of cancer cell populations is caused
by genetic, epigenetic and non-genetic mechanisms. The nongenetic mechanisms that cause the variations in gene expression
include transcriptional and translational noises (Brock et al.,2009). Although the precise nature of such noise remains largely
elusive, we can speculate that the variation in gene expression
reflects the intracellular signaling activities. Here, we haveestablished a technique to sort cells according to the activities of
intracellular signaling molecules and to examine the effect of thevariation in signaling molecule activity on the biological ortranscriptional heterogeneity of cancer cells.
FRET-based cell sorting

This technique is based on two assumptions: first, that the activityof the molecule of interest is maintained during FACS; second,that the transcriptome is not substantially perturbed during FACS.
Given that it has been reported previously that the suspension ofadherent cells reduces their Rac1 activity (del Pozo et al., 2000),
it was of concern that the cell preparation process – i.e.
trypsinization and suspension of adherent C6 glioma cells –might mask the intercellular variation in the activities of the Rhofamily GTPases that are observed under both 2D and 3D
conditions. Contrary to our expectation, the intercellular variationin Rac1 activity was reproduced after the cell preparation process.
Re-analysis of Rac1 activity by FACS, TLC or pulldown assay
demonstrated that Rac1 activity was conserved in both theRac1high and Rac1low cell populations (Fig. 3A,B). Time-lapse
imaging confirmed that the relative Rac1 activity of each cell wasmaintained before and after cell division or the suspension–
adhesion procedure (Fig. 2D). These observations suggest thatthe activity of Rac1 in a single cell is dependent on both basal andexternal cues. The activation of external cues, which include
growth factors and signaling mediated by integrin, rapidlychanges Rac1 activity (Heasman and Ridley, 2008). Meanwhile,the basal Rac1 activity is determined by the status of intrinsic
signals, which are unaffected by external cues and fluctuate overlonger timescales.
The second assumption that requires further consideration isthe effect of cell sorting on the transcriptome. The ontology
analysis of genes that were upregulated in Rac1high cells showed aclose correlation with terms that are associated with biologicalprocesses that are linked to the function of Rac1 (Fig. 4C). The
first and second most highly scored terms were the GPCR
pathway and cell–matrix adhesion, respectively. Both of these are
related to cell migration, with which the functions of Rac1 are
most often associated (Sahai and Marshall, 2002; Sander andCollard, 1999). Another major function of Rac1 is the regulationof the NADH-mediated production of reactive oxygen species(Heasman and Ridley, 2008); therefore, it is not surprising thatthe electron transport chain was scored third. Furthermore, inagreement with the finding that Rac1 activity rapidly reduces
during cell division (Yoshizaki et al., 2003), the first to thirdscores of genes upregulated in Rac1low cells were assigned to thepathways of cell division, cell cycle and mitosis, respectively(Fig. 4C). Furthermore, among the 23 genes that wereupregulated in the Rac1high population and classified under thecell component term ‘membrane’, 13 genes are known to be
involved in cancer cell invasion (supplementary material Table
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S1). These observations support our assumption that thetranscriptional profiles are reasonably conserved during FACS.
Genes associated with the Rac1high phenotype

Among the 14 genes that were classified under the cellcomponent term ‘membrane’ and were upregulated in Rac1highcells, knockdown of four genes inhibited C6 glioma cell invasionin the Matrigel invasion assay. Previous reports have indicated orsuggested that proteins encoded by the four genes are more or lessassociated with invasion of cancer cells. MMP15 is expressed
predominantly in glioblastoma (Lampert et al., 1998; Nakada
et al., 1999), suggesting that MMP15 plays a major role in the1812

degradation of the extracellular matrix during glioma invasion.
TSN17 has been recently shown to regulate ADAM10, which has
been shown to be involved in cancer progression (Dornier et al.,2012; Haining et al., 2012; Mochizuki and Okada, 2007). Pstpip2
regulates F-actin bundling and enhances the formation offilopodia (Chitu et al., 2005), which strongly points to a role ofthis protein in glioma invasion. Freq/NCS1 is a Ca2+-binding
protein that is expressed predominantly in neuronal cells (Dasonet al., 2012; Nakamura et al., 2006). In primary cultured adultcortical neurons, overexpression of NCS1 induces neurite
sprouting; however, the role of NCS1 in glioma invasion has
not been determined.

Journal of Cell ScienceFig. 6. Identification of a signaling network comprising the genes that are upregulated in the Rac1high cell population and associated with invasion.
(A,B) C6 glioma cells expressing Raichu-Rac1 alone or Raichu-Rac1 plus plasma-membrane-targeted Lyn-FKBP12-rapamycin-binding domain (LDR) and
FK506-binding protein (FKBP) fused with Tiam1 were stimulated with 10 mM rapamycin (solid line) or the solvent DMSO (dashed line) for 30 minutes. Rac1
activity was examined by using FACS (A) or pulldown assay (B). C6 glioma cells expressing CFP–Rac1N17 or CFP–Rac1V12 were used as negative andpositive controls, respectively. White and black arrows indicate CFP–Rac1 and endogenous Rac1, respectively. Densitometry for GTP-bound Rac1 wasnormalized to the amount of the total Rac1 (right panel). (C) The genes listed in the left column were knocked down as described in Fig. 5, or Rac1 was activatedas in A. qPCR was used to analyse the levels of the genes in the top row. Fold changes relative to the control siRNA-transfected cells are shown in thelog2 scale. The genes were clustered by using the nearest neighbor method. The data on the invasion and Rac1 activity that is shown in Fig. 5 are also included.
Black cells indicate knockdown efficiency, yellow cells indicate upregulation (.50%), blue cells indicate downregulation (,250%), white cells indicate others.
(D) A proposed model of Rac1 activity regulation in C6 glioma cells.

Genes responsive to the activation of Rac1 or Cdc42 have been
identified by overexpressing constitutively active Q61L mutantsof Rac1 or Cdc42 in NIH 3T3 cells (Teramoto et al., 2003). There
are some similarities between this previous work and our present
study. First, the expression profile of Rac1QL-expressing cells
resembles that of Cdc42QL-expressing cells in the previous
studies. We also found that the expression profile of Rac1highcells resembles that of Cdc42high cells (Fig. 4B; supplementarymaterial Table S1). Second, in cells expressing Rac1QL or
Cdc42QL, genes related to the extracellular matrix and celladhesion are upregulated and genes related to the cell cycle aresuppressed, as in Rac1high cells (Fig. 4C). However, there are also
some discrepancies. For example, the collagen a1 chain precursor
was upregulated 3.1-fold in Rac1QL-expressing cells but was
suppressed to 56% in Rac1high cells. This difference might havebeen caused by the lack of GTPase activity in cells expressing the
Rac1QL mutant because the cycling between the GDP-bound and
GTP-bound forms has been shown to play an important role incell migration (Parrini et al., 2011). In another study, different
expression levels of Rac1 have been used in colorectal cancer
cells to identify the target genes of Rac1 by using microarray
analysis (Gómez del Pulgar et al., 2007); however, we could not
find any similarity to our data. In another study, C6 rat glioma
cells were selected both in vitro and in vivo for phenotypes ofhigh and low migratory capacity (Tatenhorst et al., 2005). By
using microarray analysis, that study found 31 genes weredifferentially expressed in association with the migratory
phenotypes. We could not detect a substantial resemblance
between the gene expression profiles of that study and our present
findings. Thus, the constitutive activation (or suppression) andintrinsic fluctuations in Rac1 activity might cause different
transcriptional phenotypes. Alternatively, the effect of Rac1 ontranscriptional profiles might be specific to the cell type. In any
event, different approaches led to the identification of various
genes related to glioma invasion. Further analyses will be
required to find the cause of such divergence.
Hierarchy of invasion-associated genes that are upregulated
in the Rac1high population

The comprehensive knockdown experiments strongly pointed to
the role of Egr2 as a master regulator of C6 glioma invasion.
Knockdown of Egr2 suppressed the expression of four
membrane-related invasion-associated genes. By contrast,
knockdown of the four membrane-related invasion-associatedgenes or Elmo1 increased the expression of Egr2, which suggeststhat there are negative-feedback loops between the invasive
phenotype and Egr2 expression. Microarray analyses haverevealed upregulation of Egr2 in metastatic squamous cell
carcinomas (Kim et al., 2006; Liu et al., 2008). Furthermore, infibroblasts infected with Kaposi sarcoma-associated herpesvirus,
Egr2 induces MMPs and extracellular matrix metalloproteinaseinducer (Emmprin) (Dai et al., 2012). These observations strongly
support the proposal that Egr2 is a key regulator of glioma
invasion.
Origin of the heterogeneity of Rac1 activity

What causes the heterogeneity of Rac1 activity among C6 glioma
cells? The 5-day time-lapse imaging revealed that non-genetic
fluctuations in the activity of Rac1, which occurred overtimescales of more than 40 hours, caused the observed
distribution of Rac1 activity (Fig. 2A). This conclusion was
also supported by the observation that culture of the isolated

Journal of Cell Science (2014) 127, 1805–1815 doi:10.1242/jcs.139733

Rac1high or Rac1low cell population restored the original
distribution of Rac1 within 1 week (Fig. 3F; supplementarymaterial Fig. S1C). Notably, our conclusion agrees with thevariation of protein levels that has been seen previously in humanH1299 lung carcinoma cells (Sigal et al., 2006), in which
the expression levels of proteins have been shown to fluctuate
over a timescale of more than 40 hours. By using knockdown
experiments against genes upregulated in the Rac1high population,
we identified a gene network that regulates Rac1 activity(Fig. 6D). This network comprises both positive- and negativefeedback loops, which are sufficient to cause oscillation of thesignaling network. Although we have not been able to confirm thatthe variation in Rac1 activity in vivo is also driven by the samemechanism, fluctuations in gene expression over prolonged periodsof time, and the resulting fluctuations in Rac1 activity, could
generate glioma cells that have different invasive capacities.
MATERIALS AND METHODS
Biosensors and cell lines

C6 rat glioma cells were obtained from the American Type Culture
Collection and cultured in Dulbecco’s modified Eagle’s medium
containing 10% fetal bovine serum (FBS). The FRET biosensors for
Rac1, Cdc42 and RhoA were Raichu-Rac1, Raichu-Cdc42 and RaichuRhoA, respectively, and have been described previously (Itoh et al.,
2002; Yoshizaki et al., 2003). To establish stable cell lines that expressed
Raichu biosensors, we used two approaches: (1) we replaced CFP withteal fluorescent protein (TFP) and delivered the expression cassettes into
C6 glioma cells by using a retroviral vector as described previously(Hirata et al., 2012); (2) we used piggyBac transposon-mediated genetransfer, which, more recently, has been used to stably express Raichu
biosensors with higher sensitivity (Komatsu et al., 2011; Yusa et al.,2009). The cells were then single-cell cloned before further experiments
unless described otherwise.
Time-lapse FRET imaging

FRET images were obtained and processed using the same conditions andprocedures as previously reported (Aoki and Matsuda, 2009). Cells wereplated on 35-mm-diameter glass-bottomed dishes (AGC Techno Glass,
Shizuoka, Japan) or micro-patterned glass-bottomed dishes (CytoGraph;
Dai Nippon Printing, Tokyo, Japan). Cells were imaged at 37 ˚C under 5%
CO2 by using an inverted microscope (IX81; Olympus, Tokyo, Japan)
equipped with a 640 objective lens (UAPO/NA 1.35, Olympus), a 640
objective lens (UPLSAPO/NA 0.95, Olympus), a 660 objective lens(PlanApoPH/NA 1.40, Olympus), a cooled CCD camera (Cool SNAPHQ or Cool SNAP-K4; Roper Scientific, Tucson, AZ), an LED
illumination system (CoolLED precisExcite; Molecular Devices,
Sunnyvale, CA), an IX2-ZDC laser-based autofocusing system(Olympus) and an MD-XY30100T-Meta automatically programmable
xy stage (SIGMA KOKI, Tokyo, Japan). The following filters were used
for the dual-emission imaging studies and were obtained from Omega
Optical (Brattleboro, VT): an XF1071 (440AF21) excitation filter, an
XF2034 (455DRLP) dichroic mirror, and two emission filters, XF3075(480AF30) for CFP and XF3079 (535AF26) for yellow fluorescentprotein (YFP). After background subtraction, FRET:CFP ratio images
were created by using the MetaMorph software (Universal Imaging, West
Chester, PA) and were represented by an intensity-modulated display
mode. In the intensity-modulated display mode, eight colors from redthrough to blue are used to represent the FRET:CFP ratio, with theintensity of each color indicating the mean intensity of FRET and CFP.
For the quantification, the FRET and CFP intensities were averaged over
the whole cell area, and the results were exported to Excel software(Microsoft Corporation, Redmond, WA).
FRET-based cell sortingC6 glioma cells expressing Raichu-Rac1 were trypsinized, resuspended
in PBS containing 3% FBS and analyzed and/or sorted with a FACSAria

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machine (Becton Dickinson, Franklin Lakes, NJ). We used the following
combinations of lasers and emission filters for the detection offluorescence from the biosensor: for the donor fluorescence of TFP and
CFP, a 407-nm laser and a 480AF30 filter (Omega Optical); for thesensitized FRET from YFP, a 407-nm laser and a 535AF26 filter (Omega
Optical); and for the acceptor fluorescence of YFP, a 475-nm laser and a
535AF26 filter (Omega Optical). Cells were first gated for size andgranularity to exclude cell debris and aggregates. For cell sorting, C6
glioma cells in the highest and lowest decile with respect to the FRET to
CFP (or TFP) ratios were sorted as Rac1high and Rac1low populations,
respectively, into DMEM containing 10% FBS. Small fractions ofRac1high and Rac1low cells were re-analyzed for validation, and theremaining cells were snap-frozen and stored at 280 ˚C until RNA
extraction. Detailed data analysis was performed using FlowJo. 7.6
software (Tree Star, Ashland, OR).
TLC of guanine nucleotides bound to GTPases

Guanine nucleotides bound to Raichu biosensors or GFP-tagged Rac1
proteins were quantified as described previously (Gotoh et al., 1995).
Briefly, cells were sorted by FACS and plated on six-well dishes. After
3 hours, cells were metabolically labeled with 32P for 2 hours and lysedwith lysis buffer. The cell lysates were clarified by centrifugation andused to immunoprecipitate Raichu biosensors or GFP-tagged Rac1 byusing an antiserum against GFP and Protein-A–Sepharose. Guanine
nucleotides bound to the immuoprecipitates were separated by TLC andquantified with a BAS-1000 image analyzer.

Journal of Cell Science (2014) 127, 1805–1815 doi:10.1242/jcs.139733

manufacturer’s protocol. Briefly, 26104 cells were seeded on themembrane with or without Matrigel pre-coating. After 22 hours, cells
were fixed, stained for nuclei with propidium iodide and imaged by using
an epifluorescence microscope. The number of nuclei was counted with
MetaMorph software (Universal Imaging). Data are expressed as thepercentage invasion through the Matrigel matrix and membrane relative
to the migration through the control membrane.
3D spheroid imaging

Organotypic culture was prepared as described previously (Gaggioli
et al., 2007). In a 12-well plate coated with poly-(2-hydroxyethyl
methacrylate) (Sigma-Aldrich, St Louis, MO) and containing 1 ml
serum-free CO2-independent medium (Invitrogen), 106 cells were
cultured overnight with slow agitation to form small aggregates. The
aggregates were embedded in 6 mg/ml Matrigel, maintained in complete
medium and observed under a two-photon microscope or a confocal
microscope for up to 18 hours in an incubation chamber.
Quantitative PCR

RNA was reverse-transcribed by using a High Capacity cDNA Reverse
Transcription kit (Applied Biosystems, Foster City, CA) according to the
manufacturer’s protocol. Then, the expression levels of each gene and
GAPDH, used as a standard, were analyzed by using the Power SYBRGreen PCR Master Mix (Applied Biosystems) and the ABI PRISM7300
Sequence Detection System (Applied Biosystems). The sequences ofprimers used for qPCR are shown in supplementary material Table S4.

Rac1 pulldown analysis

siRNA knockdown experiments

The Rac1 pulldown assay was performed according to the manufacturer’s
protocol (Cytoskelton, Denver, CO).

Stealth RNAi Negative Control Duplex and Stealth RNAi against
MMP15 were obtained from Invitrogen. Mission siRNAs against theother genes were purchased from Sigma-Aldrich (St Louis, MO). C6 cells
that stably expressed Raichu-Rac1 were transfected with 20 mM siRNAby using Lipofectamine 2000 (Invitrogen). After 2 days of transfection,
cells were used for invasion assay, qPCR or FRET imaging. The siRNAsequences are shown in supplementary material Tables S5.

RNA extraction

Total RNA was isolated by using a Qiagen RNeasy Micro Kit (Qiagen,Hilden, Germany) or a Qiagen RNeasy Mini Kit according to the
manufacturer’s protocol. RNA preparations were confirmed to be free ofproteins using a NanoDrop ND-1000 instrument (Thermo Fisher
Scientific, Waltham, MA), and the integrity of these measurements was
confirmed using a 2100 BioAnalyzer (Agilent Technologies, South
Queensferry, UK). RNA that had an RNA integrity number of §8.6 wasused for RNA-Seq.
Library preparation and sequencing

Total RNA was poly(A)-selected using poly(T) Dynabeads (Invitrogen,
San Diego, CA). Sequencing libraries were prepared according toIllumina’s mRNA-Seq protocol and sequenced at the Omics Science
Center (OSC) RIKEN Yokohama Institute. Two independent libraries
were analyzed for each data set. Sequence-read data have been submitted
to the Sequence Read Archive at the DNA Data Bank of Japan(submission no. DRA000605).
Mapping and processing of RNA-seq reads

The reads of each dataset were aligned to the rat reference genome (rn4,Nov. 2004, version 3.4) using TopHat v1.3.0 (Trapnell et al., 2009). The
resulting sequence alignment and map files in the BAM format wereanalyzed by using Cufflinks version 0.8.0 (Trapnell et al., 2010) tocompute fragments per kilobase of transcript per million mapped reads(FPKM). Genomic annotations were obtained from Ensembl in genetransfer format (GTF). We used only reads mapped to 20 or fewer sites
on the genome. The WAD method (Kadota et al., 2008) was thenperformed on the data of pairs of cells to generate expression differences.
Differentially expressed genes were filtered for a WAD ranking cutoff ofthe top 5.0%. Gene Ontology (GO) annotations were used to assign
biological functions to genes included in this study (Ashburner et al.,2000).

Rapamycin-induced Rac1 activation

Rapamycin-induced Rac1 activation with FK506-binding protein(FKBP)-Tiam1 has been reported previously (Yagi et al., 2012).
Acknowledgements
We thank Atsushi Miyawaki (RIKEN BSI, Wako, Japan), Tsuyoshi Akagi (KAN
Institute, Kobe, Japan), Jun-ichi Miyazaki (Osaka University, Japan), Kosuke
Yusa (Sanger Institute, Hinxton, UK) and Hitoshi Niwa (RIKEN CDB, Kobe,
Japan) for the plasmids; Yoshie Inaoka, Kyoko Hirano, Yurika Naruo, Kanako
Takakura and Akiko Kawagishi (Kyoto University, Kyoto, Japan) for their technicalassistance. We thank Fumi Hori (Omics Science Center, RIKEN Yokohama
Institute) and Nobuyuki Fujii, Kazuho Ikeo and Norikazu Monma (Cell Innovation
Project Data Analysis Center, National Institute of Genetics, Mishima, Japan) for
the sequencing and data analyses. We are grateful to the members of the
Matsuda Laboratory for their helpful discussions.

Competing interests
The authors declare no competing interests.

Author contributions
H.Y., C.S., E.H. and K.U.-T. performed experiments. T.N., S.K., M.O.-H and H.N.
analyzed and interpreted the data. H.Y. and M.M. conceived and designed theexperiments. H.Y. and M.M. wrote the manuscript.

Funding
M.M. was supported by the Research Program of Innovative Cell Biology by
Innovative Technology (Cell Innovation) from the Ministry of Education, Culture,
Sports and Science (MEXT), Japan. H.Y. was supported by the Global Center of
Excellence Program ‘‘Center for Frontier Medicine’’ and by a Grant-in-Aid forJapan Society for the Promotion of Science Fellows by MEXT, Japan. Depositedin PMC for immediate release.

3D Matrigel invasion assay

Supplementary material

3D Matrigel invasion assays were performed with a BD BioCoat Matrigel
Invasion Chamber (Becton Drive, Franklin Lakes, NJ) according to the

Supplementary material available online at
http://jcs.biologists.org/lookup/suppl/doi:10.1242/jcs.139733/-/DC1

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