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J. Cell Biol. Vol. 192 No. 1 55–67
www.jcb.org/cgi/doi/10.1083/jcb.201004026 JCB 55
Correspondence to Michael A. Mancini: firstname.lastname@example.org
Abbreviations used in this paper: C/EBP, CAAT/enhancer binding protein-;
CMBl, CellMask blue; CV, coefﬁcient of variation; HCA, high-content analysis;
HTM, high throughput microscopy; IBMX, 3-isobutyl-1-methylxanthine; PPAR,
peroxisome proliferator activated receptor ; qPCR, quantitative RT-PCR; seFRET,
sensitized emission ﬂuorescence resonance energy transfer; SRC, steroid recep-
The dominant cellular basis of obesity is increased fat cell size
during the adipocyte differentiation process. The process is
marked by accretion of triglycerides within intracellular lipid
droplets (Farmer, 2006). Adipogenesis is tightly regulated by
peroxisome proliferator activated receptor (PPAR), a mem-
ber of the ligand-activated nuclear receptor superfamily of
transcription factors. Mechanistically, exogenous (thiazolidine-
diones) or endogenous (eicosanoids) ligands activate PPAR by
stabilizing the active conformation of the ligand-binding domain
(Nolte et al., 1998) to induce or repress a wide array of differ-
entiation-dependent and adipose-specic genes. PPAR mRNA
and protein expression are robustly induced in a feed-forward
loop with CAAT/enhancer binding protein- (C/EBP) during
adipogenesis (Wu et al., 1999; Rosen et al., 2002). The process
is initially stimulated by several up-stream transcription factors:
C/EBP, C/EBP (Yeh et al., 1995; Wu et al., 1996; Zuo et al.,
2006), and coregulators, including the p160 class of steroid re-
ceptor coactivators (SRCs; Louet and O’Malley, 2007).
A critical step required for adipogenesis is the down-
regulation of kinase signaling pathways targeting PPAR to
permit its full transcriptional activity (Hu et al., 1996; Adams
et al., 1997; Camp and Tafuri, 1997). Specically, the pro-
adipogenic function of PPAR is decreased by mitogen-activated
protein kinase (MAPK) phosphorylation in the N-terminal A/B
region (mouse S112/human S114), which concomitantly re-
duces thiazolidinedione afnity for PPAR (Shao et al., 1998).
Overexpression of a nonphosphorylatable form of PPAR pro-
motes insulin sensitization and elevated adipogenesis in 3T3L1
(Hu et al., 1996; Shao et al., 1998). Additionally, mouse em-
bryonic broblasts expressing a serine-to-alanine substitution at
codon 112 (Rangwala et al., 2003) exhibit a similar effect. PPAR
phosphorylation at S112/S114 also decreases interactions with
The related coactivators SRC-2 and SRC-3 interact
with peroxisome proliferator activated receptor
(PPAR) to coordinate transcriptional circuits to pro-
mote adipogenesis. To identify potential coactivator re-
dundancy during human adipogenesis at single cell
resolution, we used high content analysis to quantify
links between PPAR, SRC-2, SRC-3, and lipogenesis.
Because we detected robust increases and signiﬁcant
cell–cell heterogeneity in PPAR and lipogenesis, with-
out changes in SRC-2 or SRC-3, we hypothesized that
permissive coregulator levels comprise a necessary adi-
pogenic equilibrium. We probed this equilibrium by
down-regulating SRC-2 and SRC-3 while simultaneously
quantifying PPAR. Individual or joint knockdown
equally inhibits lipid accumulation by preventing lipo-
genic gene engagement, without affecting PPAR protein
levels. Supporting dominant, pro-adipogenic roles for
SRC-2 and SRC-3, SRC-1 knockdown does not affect
adipogenesis. SRC-2 and SRC-3 knockdown increases
the proportion of cells in a PPARhi/lipidlo state
while increasing phospho-PPAR–S114, an inhibitor
of PPAR transcriptional activity and adipogenesis.
Together, we demonstrate that SRC-2 and SRC-3 con-
comitantly promote human adipocyte differentiation by
attenuating phospho-PPAR–S114 and modulating PPAR
Homeostatic levels of SRC-2 and SRC-3 promote
early human adipogenesis
Sean M. Hartig,1 Bin He,1 Weiwen Long,1 Benjamin M. Buehrer,2 and Michael A. Mancini1
1Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030
2Zen-Bio, Inc., Research Triangle Park, NC 27709
© 2011 Hartig et al. This article is distributed under the terms of an Attribution–
Noncommercial–Share Alike–No Mirror Sites license for the ﬁrst six months after the pub-
lication date (see http://www.rupress.org/terms). After six months it is available under a
Creative Commons License (Attribution–Noncommercial–Share Alike 3.0 Unpor ted license,
as described at http://creativecommons.org/licenses/by-nc-sa/3.0/).
THE JOURNAL OF CELL BIOLOGY
JCB • VOLUME 192 • NUMBER 1 • 2011 56
to SRC-3/ mice, which provides evidence of a dominant pro-
adipogenic role for SRC-3.
Although the similarities observed among SRC-2 and
SRC-3 knockout mice indicate a functional overlap and domi-
nant pro-adipogenic roles, no data exists on the contributions of
cell–cell variability between SRC-2, SRC-3, and PPAR that
collectively and/or redundantly promote human adipogenesis.
Accordingly, the purpose of this study was to focus upon SRC-2
and SRC-3 and dissect the early interplay between these co-
activators and PPAR that converts a human preadipocyte into a
mature fat cell. Here, we developed and used a high-throughput
microscopy-based, high-content analysis (HCA) approach to
quantify the effects of SRC loss of function on the cell-to-cell
population dynamics of PPAR. Our results emphasize the
novel regulatory role of steady-state levels of SRC-2 and SRC-3
in human adipogenesis, specically by promoting lipid accu-
mulation under both high and low PPAR phenotypes marked
by attenuation of PPAR phosphorylation at S114.
Human adipocyte differentiation occurs
independently of static SRC mRNA proﬁles
SRCs have been shown to be critical elements of the murine
adipogenic gene program (Picard et al., 2002; Louet et al., 2006;
SRCs (Shao et al., 1998), resulting in a potential negative
cooperative effect on PPAR-regulated, adipocentric genes.
The p160 family of SRCs (SRC-1, SRC-2, and SRC-3) de-
ned the rst class of coregulators (CoR) that enhance nuclear
receptor transactivation in a ligand-dependent manner, bridging
NRs to other components of the basal transcriptional machinery
and integrating both genomic and nongenomic signals (Oñate
et al., 1995; Anzick et al., 1997; Hong et al., 1997). Depending
on the ligand context, biochemical assays have shown that each
of the SRCs potentiate the transcriptional activity of PPAR
through direct interactions (McInerney et al., 1998; Kodera
et al., 2000; Rocchi et al., 2001; Louet et al., 2006). However,
SRC-2 and SRC-3 share the highest degree of sequence ho-
mology and promote adipogenesis in knockout mouse models.
SRC-2/ mice are protected from obesity because of enhanced
energy expenditure, decreased white adipocyte differentiation,
and increased thermogenic activity of brown adipose (Picard
et al., 2002). SRC-3 ablation leads to lean mice with increased
energy expenditure and decreased adipogenesis (Louet et al.,
2006; Coste et al., 2008). However, upon high-fat diet feed-
ing, SRC-1/ knockout mice are slightly prone to obesity be-
cause of both a reduced capacity for fatty acid oxidation and
decreased energy expenditure (Picard et al., 2002). Moreover,
double knockout of SRC-1 and SRC-3 results in a lean pheno-
type and increased metabolic rate (Wang et al., 2006), similar
Figure 1. Expression of SRC-2, SRC-3, PPAR, and adipocentric genes during human adipocyte differentiation. Subcutaneous human preadipocytes were
differentiated for 4 d, and total mRNA was isolated at each indicated interval. The mRNA levels of SRC-1, SRC-2, and SRC-3 (A); and PPAR2, C/EBP,
SREBP1c, ADFP, and FASN (B) were determined by qPCR. -actin served as an internal control. RNA was collected from at least two independent isolations
and error bars represent SEM. Asterisks indicate levels of induction statistically different from 0 h (P < 0.05).
57p160 coactivators promote adipocyte heterogeneity • Hartig et al.
After 7 d, 88.1% of cells were positive for lipids, which sug-
gests that the preadipocyte cell population responds robustly
to chemical induction of adipogenesis in a unidirectional man-
ner (Fig. S1 A). Consistent with the qPCR results (Fig. 1 A),
SRC-1 was signicantly increased (50%), whereas modest,
statistically insignicant changes in SRC-2 (15%) and SRC-3
(22%) were detected. Using additional software with built-in
lipid droplet analysis tools (McDonough et al., 2009), we
detected increases in the number of lipid droplets/nuclei
(+250%; Fig. 2 D), total lipid droplet area (+120%; Fig. 2 E),
and lipid content/cell (12-fold induction; Fig. 2 F). Dose
response experiments using rosiglitazone (BRL49653) or a
natural PPAR ligand (15-deoxy-12,14-prostaglandin J2;
15dPGJ2) showed that both ligands increase lipogenesis
(Fig. S1 B), PPAR (Fig. S1 C), and SRC-1 levels (Fig. S1 D).
Increasing concentrations of 15dPGJ2 and BRL49653 did not
signicantly alter the protein expression of SRC-2 (Fig. S1 E)
and SRC-3 (Fig. S1 F).
Cell-to-cell measurements of lipids as a
function of PPAR represent population
heterogeneity during human adipogenesis
At the population level, the time-dependent induction of
PPAR was positively correlated with the accumulation of
lipids, which is consistent with previously established deter-
ministic paradigms of PPAR-mediated adipogenesis (Rosen
et al., 1999). However, when the differentiating fraction of
preadipocytes was quantitatively examined at the single cell
level, a large degree of population heterogeneity was observed,
which is in agreement with recent studies on the 3T3L1 dif-
ferentiation program (Loo et al., 2009). These subpopulation
changes were apparent as early as 24 h after MIX treatment,
and more visible at 96 h (Fig. 3 A). This heterogeneity is
illustrated in Fig. 3 B by plotting, on a cell-by-cell basis,
Coste et al., 2008). Given these results, we used quantitative
RT-PCR (qPCR) to measure mRNA expression during the
first 4 d of human adipocyte differentiation with rosigli-
tazone, dexamethasone, 3-isobutyl-1-methylxanthine (IBMX),
and insulin (MIX). Surprisingly, we found that although
SRC-1 mRNA levels were increased twofold, SRC-2 and
SRC-3 were not changed during the rst 4 d of differentiation
(Fig. 1 A). We also measured mRNA levels of several tran-
scription factors that stimulate differentiation and markers
of lipogenesis (Fig. 1 B). The mRNA levels of C/EBP (40-
fold), PPAR (10-fold), SREBP1c (10-fold), FASN (20-fold),
and ADFP (threefold) were up-regulated in response to 96 h
of treatment with MIX. These initial experiments suggested
that human adipocytes maintain constant SRC-2 and SRC-3
mRNA levels while up-regulating SRC-1 transcripts during
HCA of human adipocyte cell populations
validates mRNA results for SRCs
To further understand the dynamics of SRC-1, SRC-2, SRC-3,
and PPAR during differentiation, we validated the mRNA
proling (Fig. 1) with protein and lipid measurements at the
single cell level using HCA. Subcutaneous preadipocytes were
differentiated for 4 d, then xed and labeled for DNA (DAPI),
SRC-1, SRC-2, SRC-3, or PPAR. Lipid droplets were labeled
with a uorescent neutral lipid dye to mark differentiating
cells. We next quantied changes in these properties for each
cell (≥1,000 cells/condition/experiment), by automated cell
and nucleus identication (Fig. 2 A) with both in-house algo-
rithms and commercially available software. As indicated
in Fig. 2 B, mean PPAR protein levels increased monotoni-
cally (2.8-fold) over the rst 4 d, which correlates with an ap-
proximately eightfold induction of lipid accumulation (Fig. 2 C).
Figure 2. Development of an image-based
analysis platform to study PPAR and co-
activator expression in human adipocytes.
(A) Shown are representative grayscale images
of adipocytes differentiated for 96 h, immuno-
labeled with antibodies to SRC-2 or SRC-3,
and then stained with DAPI, CMBl, and Lipid-
TOX (Lipid). Binary nuclear and cellular masks
were generated by a combination of watershed
and threshold image transformations (Pipeline
Pilot; Accelrys). Nuclear masks are indicated
in green; whole cell masks are shown in red.
(B–F) Nuclear and cellular masks were used to
extract pixel-based measurements that describe
nuclear PPAR, SRC-1, SRC-2, SRC-3 levels
(B) and lipid accumulation (C) during a 96-h
differentiation period. Additional software
(CyteSeer; Vala Sciences) was used to calcu-
late the number of lipid droplets/nuclei (D),
lipid droplet area (E), and fold induction of
lipid (F). Experiments shown are the mean of
11 independent experiments. Values are the
mean fold induction. Error bars indicate SEM
(*, P < 0.05 compared with 0 h). Bar, 50 µm.
JCB • VOLUME 192 • NUMBER 1 • 2011 58
SRC-2 and SRC-3 levels are correlated
with PPAR-dependent lipogenesis
Biochemical studies and mouse models have indicated that pro-
adipogenic transcriptional activity of PPAR is maintained by
functional interactions with the coactivators SRC-2 and SRC-3
(McInerney et al., 1998; Rocchi et al., 2001; Louet et al., 2006).
Therefore, we sought to understand the single cell relationship
between these SRCs and PPAR. First, we analyzed the level
of heterogeneity that existed for both SRC-2 and SRC-3 in
individual cells during the rst 96 h of human adipocyte differen-
tiation. Analysis of the cell–cell heterogeneity in both SRC-2
(Fig. 4 A) and SRC-3 (Fig. 4 B) levels indicated a <10-fold
lower range of expression levels compared with PPAR (>100-
fold). This tighter range of SRC-2 or SRC-3 levels was consis-
tent with (96 h) and without (0 h) differentiation, whereas lipid
content increased independent of single cell SRC-2 and SRC-3
expression. We further represented this contrast in variability
by calculating the coefcient of variation (CV = /) after
96 h of differentiation, where a higher CV indicates wider sys-
tem heterogeneity. In agreement with scatter plot representations
of PPAR (Fig. 3 B), SRC-2 (Fig. 4 A), and SRC-3 (Fig. 4 B),
CVs for PPAR and lipid were signicantly greater than those
calculated for SRC-2 and SRC-3.
Next, we simultaneously detected SRC/PPAR (Fig. 4 F)
to determine if there were cells with SRC-2 or SRC-3 levels
that correlated with PPAR and/or lipids. Individual cell
measurements of PPAR and SRC were normalized to their
respective median intensities at 96 h. PPAR normalization
cellular lipid content as a function of nuclear PPAR protein
levels. At 96 h after induction by MIX, 100-fold variation
in PPAR and a range of three orders of magnitude in lipid
content were observed. This was an undetectable response at
the median protein (immunouorescence; Fig. 2) and mRNA
levels (qPCR; Fig. 1). To explore this heterogeneity across
experiments, we subdivided the cell populations. For each
experiment and time point, individual cell measurements of
PPAR and lipid were normalized to the median intensity at
96 h, which set the threshold for quadrant subdivision for
both properties arbitrarily equal to 1. This threshold was then
applied to each time point. Subsequently, this created four
subpopulation quadrants (Fig. 3 C): high PPAR/high lipid
(PPARhi /lipidhi), high PPAR/low lipid (PPARhi/lipidlo),
low PPAR/low lipid (PPARlo/lipidlo), and low PPAR/high
lipid (PPARlo/lipidhi). Temporal analysis of these quadrants
showed that the PPARhi/lipidhi population increased from 2%
after 24 h of differentiation to 33% at 96 h. More interestingly,
however, was the up-regulation of the PPARhi/lipidlo popula-
tion at 24 h (21% change) followed by a decrease (12%) at
96 h. Based on these results, the nonessential role of SRC-1
(Fig. S2), and the central functions of SRC-2 and SRC-3 in
adipogenesis (Picard et al., 2002; Louet et al., 2006; Wang
et al., 2006; Coste et al., 2008), we proposed that variation in
PPAR (Fig. 3 C) might be dictated by factors, specically
SRC-2 and SRC-3, whose overall expression level was not
regulated by differentiation but nonetheless were important
for the early human adipogenic phenotype.
Figure 3. Population dynamics of PPAR protein
expression as a function of lipid content during
the early phases of human adipocyte differen-
tiation. (A) Representative images of adipocytes
during differentiation were immunolabeled for
PPAR, and lipids were labeled and imaged by
high throughput microscopy (HTM). Bar, 50 µm.
(B) Cell-to-cell variation in PPAR and lipids dur-
ing the ﬁrst 96 h of differentiation was monitored.
One representative experiment is shown. Individ-
ual cell measurements of PPAR and lipid were
normalized to the median intensity at 96 h, which
set a threshold (dotted lines) that was applied
to each time point to create the PPARx/lipidy
populations. An example cell is shown from each
quadrant. Bar, 20 µm. (C) Pie charts are shown
that indicate the change in population distribution
over this time period after median threshold appli-
cation (n = 5 independent experiments).
59p160 coactivators promote adipocyte heterogeneity • Hartig et al.
and lipids exhibited a less pronounced relationship. Speci-
cally, SRC-2 and SRC-3 intensities within cell subpopulations
dened by PPAR and lipid levels in Fig. 3 showed that the
PPARhi/lipidhi cells had signicantly higher levels of SRC-2
or SRC-3 than the other quadrants (Fig. 4 H). Interestingly,
this population also had signicantly higher correlation co-
efcients between SRC-2/PPAR and SRC-3/PPAR. In con-
trast, PPARhi /lipidlo cells exhibited the lowest SRC/PPAR
correlation (Table I).
The correlations between SRCs and PPAR established
a quantitative relationship between lipid, PPAR, and SRC
levels in human adipocytes, but did not indicate the nature
of the interaction between these proteins. To visualize the
interactions between PPAR/SRC-2 and PPAR/SRC-3 occur-
ring in response to differentiation cues, we used sensitized
emission uorescence resonance energy transfer (seFRET).
set the threshold for binary subdivision into PPARlo and
PPARhi populations arbitrarily equal to 1. This threshold was
then applied to each time point, and normalized SRC levels
were calculated. As shown in Fig. 4 D, SRC-2 levels were
higher in the PPARhi population across all time points. SRC-3
(Fig. 4 E) showed a similar pattern at 24 h and 96 h only. The
SRC-2 and SRC-3 levels in the PPARlo and PPARhi were
not affected by differentiation, which suggested that these
populations might represent, in terms of SRC and PPAR,
similar biological states. Single cell analysis of the correla-
tion (Pearson’s r [Pr]) between SRC and PPAR levels at 96 h
showed signicant, positive correlation between both SRC-2
and PPAR (Pr = 0.39, n = 6) and SRC-3 and PPAR (Pr =
0.33, n = 6). Contour mapping (Fig. 4 G) of lipid intensity,
as a function of SRC and PPAR, showed that high SRC-2
and PPAR correlated with increased lipids. SRC-3, PPAR,
Figure 4. Cell-to-cell relationships between SRC-2, SRC-3, and PPAR. (A and B) SRC-2 (A) and SRC-3 (B) were monitored along with lipids at the single
cell level at the indicated time points. (C) Cell–cell variability, measured as the CV (/µ), of the indicated properties at 96 h. (D and E) SRC-2 (D) and SRC-3
(E) normalized median intensities were monitored as a function of time in PPARlo and PPARhi cell populations by immunoﬂuorescence. For each experiment
and time point, individual cell measurements of PPAR and SRC were normalized to the median intensity at 96 h. PPAR normalization set the threshold
for binary subdivision into PPARlo and PPARhi populations arbitrarily equal to 1. This threshold was then applied to each time point, and normalized SRC
levels were calculated (*, P < 0.05 for PPARlo vs. PPARhi). (F) Shown are immunoﬂuorescence images of SRC/PPAR/lipid (L) after 96 h of adipocyte
differentiation from one representative experiment with (G) contour mapping of SRC/PPAR/lipid relationships. Density plots show normalized lipid expres-
sion as a function of normalized SRC and PPAR levels (n ≥ 1,900 cells). (H) Cells were divided into four quadrants based on their median PPAR and lipid
levels, followed by calculation of the normalized median SRC-2 or SRC-3 intensity inside each population. In all experiments, SRC levels were normalized
to the median intensity at 96 h (n = 6 independent experiments; *, P < 0.05). Error bars indicate SEM. Bars, 50 µm.
JCB • VOLUME 192 • NUMBER 1 • 2011 60
SRC-2 and SRC-3 are essential for the
The combination of qPCR, HCA, and FRET results led us to
hypothesize that static, permissive SRC-2 and SRC-3 levels,
occurring in the entire population contribute to an equilib-
rium condition that controls human adipogenesis. To perturb
the postulated SRC-2 and SRC-3 equilibrium, we performed
siRNA-based knockdowns (individually or in tandem) while
simultaneously using antibodies to detect cell-to-cell changes in
individual SRC protein levels. This approach uniquely allows
cell-by-cell monitoring of target knockdown and any effect on
differentiation. After a 48-h siRNA knockdown, preadipocytes
were induced to differentiate for up to 96 h, and the extent of
lipid accumulation and SRC levels was quantied by HCA.
Shown in Fig. 6 A, single knockdown of SRC-2 or SRC-3 re-
sulted in decreased lipid accumulation. In line with previous
observations (Louet et al., 2006), SRC-1 siRNA had no effect
on lipogenesis or adipocentric gene expression (Fig. S2). More-
over, there was no apparent synergistic or additive inhibition
of adipogenesis with dual SRC-2/SRC-3 siRNA knockdown.
Additionally, siRNA targeting SRC-2 or SRC-3 did not alter
the expression of the other CoR detected at the immunouores-
cence level (Fig. S3 A) and quantied by HCA (Fig. 6 B). qPCR
analyses of SRC levels in the median population levels for
single or dual siRNA (Figs. 6 C and S3, B–F) showed consistent
knockdown for both messages, validating our protein (HCA)
measurements 6 d after transfection.
CFP PPAR2/YFP SRCs were cotransfected for 48 h and
subsequently treated with MIX or vehicle (DMSO) for 2 h.
A strong FRET signal, chiey localized in a heterotypic pat-
tern within the nucleus, was observed when CFP-PPAR/
YFP–SRC-2 or CFP-PPAR2/YFP–SRC-3 were coexpressed
in HeLa cells (Fig. 5 A). FRET was measured both treatment
conditions implying differentiation-independent interactions
between SRCs and PPAR. Although vehicle treatment ex-
hibited a high basal level of FRET, statistically signicant
increases (>1.5-fold change) in FRET were detected in the
presence of MIX (Fig. 5 B). Cells coexpressing YFP-SRC/
ECFP-NLS or CFP-PPAR2/EYFP-NLS were used as nega-
tive controls and exhibited signicantly less FRET (Fig. 5 C)
than PPAR–SRC fusion pairs.
Figure 5. SRC-2 and SRC-3 interact with
PPAR in a differentiation-independent manner.
(A) seFRET was used to evaluate the inter-
actions between CFP-PPAR2/YFP SRC-2 or
CFP-PPAR2/YFP SRC-3 after exposure to
either vehicle (DMSO) or MIX for 2 h in wild-
type HeLa cells. Representative images are
shown from one experiment for a single channel
(CFP or YFP) with the calculated FRET image.
Bars, 10 µm. (B) For each cell, the net FRET
between CFP-PPAR2 and YFP-SRC was de-
termined using the softWoRx user interface.
FRET was measured within nucleoplasmic re-
gions of interest only. (C) Control plasmids,
ECFP or EYFP fused to a NLS sequence, were
coexpressed, and FRET was determined. On
average, CFP-PPAR2/YFP-SRC FRET signals
were 8–20× greater than those measured in
vector control experiments (n ≥ 22 cells mea-
sured over three independent experiments;
*, P < 0.05 compared with vehicle treatment).
FRET signals were scaled between minimum
and maximum signals (0–1,200 pixels), and
intensity was colored as shown. Error bars
Table I. Pearson’s product moment correlation coefﬁcients
were calculated for PPAR/SRC-2 and PPAR/SRC-3 inside of
Phenotype Lipidlo Lipidhi
PPARlo 0.21 ± 0.05 0.25 ± 0.10
PPARhi 0.16 ± 0.06 0.43 ± 0.10
PPARlo 0.17 ± 0.10 0.13 ± 0.06
PPARhi 0.09 ± 0.08 0.33 ± 0.13
Data are presented as the mean ± SEM. n = 6.
61p160 coactivators promote adipocyte heterogeneity • Hartig et al.
down-regulation of SRC-2, SRC-3, or SRC-2/SRC-3 resulted in
decreases in central adipogenic transcription factors (Fig. 6 H):
PPAR (>29%) and C/EBP (>40%). Further, reductions in lipo-
genic gene expression were also measured (Fig. 6 H): ADFP
(>57%), SREBP1c (>59%), and FASN (>64%). To probe the
action of SRCs in gain-of-function experiments, SRC-1, SRC-2,
and SRC-3 were overexpressed by lentiviral infection. Impor-
tantly, we ignored expression level artifacts that present them-
selves as protein aggregates (Stenoien et al., 2000, 2002; Feige
et al., 2005). Although single or double SRC-2/SRC-3 siRNA
decreased lipogenesis, moderate (1.5–2.5 times endogenous
protein) overexpression of SRC-1, SRC-2, and SRC-3 showed
modest increases in lipogenesis (<1.6-fold compared with
FLAG control) after 96 h of differentiation (Fig. S5). Each of
these results suggested that equilibrium levels of only SRC-2
and SRC-3 are needed for human adipocyte differentiation and
lipogenic gene regulation.
Quantication over a large span of experiments (n = 7
independent replications, >1,000 cells/condition) showed that
a >60% reduction in either or both SRC-2 and SRC-3 led to
a 40% decrease (Fig. 6 D) in the number of lipid droplets/
nuclei without altering lipid droplet size (Fig. 6 E). We next
determined the effect of SRC-2/SRC-3 on the rate of lipo-
genesis in the presence of a synthetic (BRL49653/rosiglitazone)
or the natural PPAR agonist, 15dPGJ2 (Forman et al., 1995;
Kliewer et al., 1995). In these loss-of-function experiments,
SRC-2/SRC-3 single or joint knockdown slowed lipogenesis
at 96 h by at least 50% (compared with scrambled siRNA con-
trol) without signicantly affecting the induction of PPAR.
Additionally, the effect was observed under differentiation with
rosiglitazone (Fig. 6 F) or 15dPGJ2 (Fig. 6 G). This result sug-
gests that SRC-2 and SRC-3 are critical components of the basal
adipogenic machinery, in agreement with in vivo data (Picard
et al., 2002; Louet et al., 2006; Coste et al., 2008). By qPCR,
Figure 6. Single or double siRNA knockdown of SRC-2 and SRC-3 disrupts adipocyte differentiation without affecting PPAR protein induction. (A) Pre-
adipocytes were reverse-transfected with scrambled (scR) or siRNA to SRC-2, SRC-3, or both SRC-2/SRC-3 for 48 h followed by induction of differentia-
tion, and imaging. Bar, 50 µm. (B) HCA detection of p160 levels after siRNA transfection. (C) qPCR was used to validate measurements of SRC-2 or SRC-3
knockdown by HCA. (D and E) Lipid droplet count (D) and lipid droplet area (E) were determined after siRNA knockdown and 4 d of differentiation (n = 7
independent experiments). (F and G) The effects on the rate of lipogenesis and PPAR induction were determined by differentiation of preadipocytes for the
indicated time points in the presence of either 3 µM BRL49653 (rosiglitazone; F) or 30 µM 15dPGJ2 (G) after SRC siRNA transfection (n = 3 independent
experiments). (H) Heat map summary of the downstream effects of siRNA to SRC-2/SRC-3 on lipid accumulation markers as measured by qPCR: PPAR2,
C/EBP, ADFP, FASN, and SREBP1c. RNA was isolated from two independent experiments. Asterisks indicate measured variables statistically different from
the nontargeting siRNA control at the 95% conﬁdence level (*, P < 0.05). Error bars indicate SEM.
JCB • VOLUME 192 • NUMBER 1 • 2011 62
suggested: (a) up-regulation of phospho-PPAR levels coincided
with (b) reduction of lipid accumulation and (c) enrichment of
cells in a PPARhi/lipidlo state. Simultaneous immunouores-
cence detection of phospho-PPAR and total PPAR in our
subpopulation analyses also showed that, with respect to the
nontargeting control, the mean single cell intensity ratio of
phospho-PPAR to PPAR (ph-PPAR/PPAR) was highest in
the lipidlo populations (Fig. 7 E). This nding was consistent
with our hypothesis that the observed defect in lipogenesis was
caused by a specic subpopulation up-regulation of phospho-
PPAR. Collectively, data from Figs. 6 and 7 suggest that per-
missive levels of the coactivators SRC-2 and SRC-3 attenuate
phospho-PPAR to promote a full adipogenic response.
The contribution of transcription factors and the associated regula-
tory machinery to the development of functional heterogeneity
among white fat depots remains largely undiscovered, especially
in the context of human adipogenesis. Recent cell culture studies
(Le and Cheng, 2009; Loo et al., 2009) have indicated that concur-
rent physiological and molecular states may exist in differentiating
3T3L1 preadipocytes largely as a response to systemic or growth
factor stimulation. Additionally, PAR titrated knock-in transgenic
mice (Tsai et al., 2009) demonstrated that selective reduction of
PPAR only affected the accumulation of perigonadal fat, without
decreasing retroperitoneal, inguinal, mesenteric, or subcapsular
adipose mass, an indication that PPAR functional heterogeneity
at the whole animal level can exist without compromising general-
ized adipogenesis. Our single cell–oriented data are consistent and
expand upon these ndings, showing a wide cell-to-cell variability
(Fig. 3) in the early human adipocyte differentiation cascade.
During this period, cells exhibit and support continuous PPAR
states with and without lipids, even while robust activation of pro-
adipogenic genes occurs at the population level (Fig. 1).
Synthetic and natural ligands bind PPAR in a relatively
large, promiscuous ligand-binding pocket that alters receptor
conformation to assemble active transcriptional machinery
(Nolte et al., 1998). Distinct from the hormonal up-regulation of
PPAR and its effects upon its downstream targets, bulk mRNA
(Fig. 1) and protein (Fig. 2) levels of pro-adipogenic coregulators
SRC-2 and SRC-3 remain quite constant during the rst 96 h of
differentiation independent of natural (15dPGJ2) and synthetic
(rosiglitazone) ligands (Fig. S2). Further examination of the
cell–cell correlations between SRC-2, SRC-3, and PPAR re-
vealed that (a) PPARhi/lipidhi cells also exhibited the highest
SRC levels and a correlation between PPAR/SRC, whereas
(b) all other PPAR/lipid populations showed little or no correla-
tion between PPAR and SRC (Table I). In addition to correlations
between SRCs, PPAR, and lipids, we have also shown a ligand-
independent direct (FRET) interaction between SRC-2/PPAR
and SRC-3/PPAR. Recent data suggests that the N-terminal
A/B domain of PPAR can act as a docking site for coregula-
tors, in the absence of a ligand, to maintain a basal level of
constitutive transcriptional activity, but it can also direct and
enhance target gene specicity of the receptor (Gelman et al.,
1999; Feige et al., 2005; Molnár et al., 2005; Tudor et al., 2007).
SRC-2 and SRC-3 promote adipocyte
heterogeneity and attenuate
Although our experiments revealed a central function of SRC-2
and SRC-3 in promoting human adipogenesis both phenotypi-
cally (HCA) and transcriptionally (qPCR), follow-up experi-
ments showed that when PPAR mRNA was reduced by 29%
(Fig. 6 H), PPAR protein levels and induction were not signi-
cantly altered (Fig. 6, F and G). We then analyzed the cell–cell
variability of PPAR and lipids after SRC siRNA knockdown
(Fig. 7 A). For each experiment and transfection, individual cell
measurements of PPAR and lipid were normalized to the me-
dian intensity of the scrambled control, which set the threshold
for quadrant subdivision for both properties arbitrarily equal to 1.
This threshold was then applied to each siRNA transfection to
create the PPARx/lipidy populations. As validation of the
PPAR/lipid gating, PPAR siRNA inhibits lipid accumula-
tion (Fig. S4 C) and shifts the cells to a predominantly (73%)
PPARlo/lipidlo state by decreasing PPARhi fractions (Fig. S4 D).
Although the total PPAR was largely unchanged at the whole
population (Fig. 6 F) and subpopulation level (Fig. 7 B), siRNA
to SRC-2 and/or SRC-3 caused shifts in each PPAR/lipid sub-
population. SRC siRNA increased the proportion of cells in
a PPARlo/lipidlo state by >4% while decreasing the PPARhi/
lipidhi percentage >6%. Concurrent with these changes in sub-
population distributions, more signicant effects were detected
on the PPARlo/lipidhi (13% to 15%) and the PPARhi/lipidlo
lipid populations (+15% to +19%). The changes in population
variation indicate a role for SRC-2 and SRC-3 in controlling
cell heterogeneity that promotes lipogenesis over a wide con-
tinuum of PPAR expression. Additionally, up-regulation of the
PPARhi/lipidlo population, along with decreases in downstream
PPAR-dependent (ADFP) and adipocentric/lipogenic genes
(Fig. 6 H), led us to hypothesize that the loss of the coactivators
resulted in higher levels of PPAR phosphorylation at S114,
leading to delayed/reduced adipogenesis.
MAPK–ERK phosphorylation of PPAR at S112/S114
diminishes its ligand afnity, transcriptional activity, adipogenic
capacity, and interactions with SRCs (Hu et al., 1996; Adams
et al., 1997; Shao et al., 1998; Rangwala et al., 2003). We pos-
tulated that our PPARhi/lipidlo populations might represent
higher levels of phospho-PPAR S114 and that the presence
of SRC-2 and/or SRC-3 minimizes this proportion of cells to
promote adipocyte differentiation. To test this hypothesis, we
knocked down SRC-2 and/or SRC-3 with siRNA and evaluated
the levels of phospho-PPAR S114 at 0, 24, and 96 h after dif-
ferentiation. Upon immunolabeling with a specic antibody to
phospho-PPAR S114 and total PPAR (Fig. 7 C), higher levels
of phospho-PPAR were present when SRC-2 and/or SRC-3
levels were reduced by siRNA. Further quantitative analysis in-
dicated increases in phospho-PPAR (Fig. 7 D) at each time
point for individual knockdowns of SRC-2 (2.89-fold, 96 h) or
SRC-3 (2.6-fold, 96 h), or when SRC-2 and SRC-3 (2.75-fold,
96 h) are both knocked down, respectively. Contrasting the
effect of SRC siRNA, scrambled siRNA (scR) reduces phospho-
PPAR S114 by 32% over the 96-h differentiation period.
As shown in Fig. 7 A and Fig. 7 D, several correlated ndings are
p160 coactivators promote adipocyte heterogeneity • Hartig et al.
Figure 7. SRC-2/SRC-3 single or double knockdown alters PPAR heterogeneity and phosphorylation status. (A) PPAR was immunolabeled and imaged
by HTM with DAPI/CMBl and lipid counterstains under conditions of SRC-2, SRC-3, or SRC-2/SRC-3 siRNA. The effects of SRC-2/SRC-3 siRNA on sub-
population distributions (*, P < 0.05, n = 3) were tabulated. For each experiment and transfection, individual cell measurements of PPAR and lipid were
normalized to the median intensity of the scrambled control, setting the threshold for quadrant subdivision for both properties arbitrarily equal to 1. This
threshold was then applied to each siRNA transfection to create the PPARx/lipidy populations. (B) The normalized median PPAR level was determined
in each subpopulation for scrambled (scR) and SRC siRNA conditions. (C) Human preadipocytes were reverse-transfected with siRNA to SRC-2, SRC-3, or
both coactivators and treated with MIX for up to 96 h. Subsequent to the perturbations, cells were immunolabeled with antibodies to phosphoPPAR-S114
and total PPAR, followed by HTM imaging. Bar, 50 µm. (D) After imaging, phosphoPPAR-S114 was quantiﬁed for 0, 24, and 96 h of differentiation
(*, P < 0.05; n = 3). (E) The single cell intensity ratios of phosphoPPAR-S114 to PPAR were determined for the lipidlo and lipidhi populations at 96 h
(*, P < 0.05; n = 3). Error bars indicate SEM.
JCB • VOLUME 192 • NUMBER 1 • 2011 64
implications for targeting the PPAR–SRC interaction surface
as strategies for new therapeutics to prevent the onset of obesity
associated with the treatment of type 2 diabetes.
Materials and methods
Primary cell culture and differentiation
Cryopreserved, subcutaneous primary human preadipocytes from normal
female donors with a mean body mass index of 27.51 were provided by
Zen-Bio Inc. Cells were maintained at 5% CO2/37°C in DME/F12 (Media-
tech, Inc.) with 10% FBS (Gemini Bio-Products), 100 U/ml penicillin, and
100 µg/ml streptomycin (growth media). Medium was replaced during
routine maintenance every 2 d. Cells were received at passage 2, and ex-
periments were performed before cells reached passage 10. Experiments
were performed using pooled human preadipocytes from ﬁve individual
female donors (Lot SL0033).
Unless otherwise indicated, all components were purchased from
Sigma-Aldrich. After seeding to the appropriate experimental format
(coverslips, 96- or 384-well plate format), cells were differentiated using
growth media supplemented with 100 nM human insulin, 0.250 mM IBMX,
500 nM dexamethasone, and either rosiglitazone (BRL49653; Cayman
Chemical Company) or 15dPGJ2 (Cayman Chemical Company). Unless
otherwise indicated, differentiation was performed with IBMX, dexametha-
sone, human insulin, and 3 µM rosiglitazone.
The following antibodies were purchased from commercial sources and
used for immunoﬂuorescence: mouse monoclonal AIB1/SRC-3 (BD), mouse
monoclonal TIF2/SRC-2 (BD), rabbit polyclonal phospho-PPAR S112/
S114 (Abcam), rabbit monoclonal PPAR (Cell Signaling Technology),
mouse monoclonal PPAR (clone E-8; Santa Cruz Biotechnology, Inc.),
SRC-1 (BD), and mouse monoclonal FLAG-M2 (Sigma-Aldrich). Rabbit poly-
clonal antibody to SRC-3 was provided by B. O’Malley (Baylor College
of Medicine, Houston, TX).
For ﬂuorescence detection of antibodies and neutral lipid content in multi-
well plates, the following protocol was performed on the BioMek NX (Beck-
man Coulter). The well plate systems used were: 96-well and 384-well
(Sensoplate Plus; Greiner). Aspirations and plate washes were performed
with an ELx405 (BioTek). After differentiation, media was aspirated, and
4% paraformaldehyde (ultrapure; Electron Microscopy Sciences) in PBS
was immediately added for 30 min at room temperature. Plates were then
quenched with 100 mM ammonium chloride. After quenching, plates were
washed three times with TBS. Fixed adipocytes were permeabilized with
0.1% Triton X-100 in TBS for 10 min and washed three times with TBS.
A 2% BSA in TBS/0.01% saponin (antibody diluent) blocking solution
was added for 30 min at room temperature followed by three TBS washes.
Antibodies were then diluted at a 1:200 concentration in antibody dilu-
ent and incubated overnight at 4°C. Subsequently, plates were washed
with TBS and incubated with secondary antibodies for 1 h at room tem-
perature. Alexa Fluor 647–-conjugated anti–mouse and Alexa Fluor 568–
conjugated anti–rabbit secondary antibodies (Invitrogen) were used.
Cells were again washed three times and incubated with 1 µg/ml Cell-
Mask blue (CMBl; Invitrogen), 1:1,000 LipidTOX green (Invitrogen), and
10 µg/ml DAPI in PBS for 45 min at room temperature. Dyes were then
aspirated and PBS/0.01% azide was added. Plates were then sealed and
CFP/YFP FRET experiments were performed using PPAR2-ECFP (provided
by F. Schaufele, University of California, San Francisco, CA), EYFP-SRC-2
(R. Michalides, Netherlands Cancer Institute, Amsterdam, Netherlands)
and EYFP-SRC-3 (Amazit et al., 2007) expressed in HeLa cells grown on
standard 12-mm glass coverslips. Constructs were cotransfected using Lipo-
fectamine 2000 (Invitrogen). Media was removed and replaced with fresh
DME/F12 with 5% FBS 24 h after transfection. After a further 24 h, cells
were treated for 2 h with either DMSO or differentiation cocktail (IBMX,
human insulin, dexamethasone, and rosiglitazone), both prepared in
growth media (DME/F12, 5% FBS). Treatment was followed with these
steps: ﬁxation 4% PFA (30 min), quench 100 mM NH4Cl (10 min), and
mount with SlowFade Gold (Invitrogen). After ﬁxation, cells were washed
with PBS++ three times, while all other wash steps were performed with
Pipes/Hepes/EGTA/MgCl2 (PEM) buffer, prepared at a ﬁnal pH of 6.8.
In contrast to the SRC-1/SRC-3 double knockout mouse
(Wang et al., 2006), the SRC-2/SRC-3 and SRC-1/SRC-2
mutant mice are lethal (Mark et al., 2004; Xu et al., 2009), mak-
ing our experiments the rst to analyze compensation between
SRC-2 and SRC-3 during human adipogenesis. Although over-
expression suggested that SRC-1, SRC-2, and SRC-3 modestly
increase adipogenesis, siRNA established that the endogenous
levels of SRC-2 and SRC-3 are essential for differentiation.
To determine if a functional overlap exists specically between
SRC-2 and SRC-3 during differentiation of cultured human
adipocytes, we used a dual siRNA approach. Strikingly, we found
that single or double knockdown of SRC-2 and SRC-3 inhibited
adipogenesis to the same extent for both natural (15dPGJ2) and
synthetic PPAR ligands with comparable PPAR induction.
Consistent with pro-adipogenic SRCs being dominant in adipo-
cyte development (Wang et al., 2006), SRC-1 siRNA had no
effect on differentiation. This result suggests that SRC-2 and
SRC-3 are fundamental components of the basal, preligand
adipogenic machinery driven by PPAR. The data at both the
phenotypic (Fig. 6, A–G) and gene regulatory level (Fig. 6 H) did
not exhibit any apparent compensation by the nontargeted SRCs
in single siRNA treatments. In support of this nding, it has
been proposed that SRC-2 and SRC-3 preferentially pair and
interact to promote gene transcription (Zhang et al., 2004).
Upon single or double SRC-2/SRC-3 siRNA treatment,
PPAR protein levels were unchanged; interestingly, there was
an enrichment of cells in a PPARhi/lipidlo state, with decreases
in the PPARhi/lipidhi and PPARlo/lipidhi proportions (Fig. 7 A).
Coincident with decreased expression of PPAR downstream
genes, the increased PPARhi/lipidlo population reected an in-
crease in the amount of phospho-PPAR S114 (Fig. 7 D). When
the levels of coactivator are reduced, the signal transduction
environment elevates phospho-PPAR S112/S114 status, low-
ers ligand (thiazolidinedione/eicosanoid) afnity, and increases
interactions with the corepressors SMRT (Shao et al., 1998) or
PER2 (Grimaldi et al., 2010) to reduce transcriptional activity
(Lavinsky et al., 1998). Collectively, these results imply that
SRC-2 and SRC-3, together, collaborate to promote adipocyte
differentiation through potential multimerization (McKenna
et al., 1998) and/or dimerization via protein dimerization/
interaction domains (Lodrini et al., 2008). To add to this model,
we combined quantitative analysis of SRC/PPAR/lipid kinet-
ics, correlations, FRET data, and loss-of-function experiments
at single cell resolution. These important single cell char-
acteristics suggest a novel mechanism of action. Specically,
permissive, homeostatic levels of SRC-2 and SRC-3 can inter-
act with the PPAR in the absence of MIX to regulate PPAR
heterogeneity, reduce inhibitory PPAR phosphorylation, and
Our quantitative, cell-by-cell approach has identied a
unique interplay between SRC-2, SRC-3, and PPAR that pro-
motes adipogenesis. Small molecule inhibitors that block SRC
recruitment or disrupt the predifferentiation complex between
SRCs and PPAR might maintain the positive (insulin sensiti-
zation) while reducing negative (weight gain) effects of thia-
zolidinediones (Reginato et al., 1998; Rocchi et al., 2001;
Michalik et al., 2006). Collectively, the data presented here have
65p160 coactivators promote adipocyte heterogeneity • Hartig et al.
TE2000-U) and a triple band ﬁlter set (Chroma 82000; Chroma Technol-
ogy Corp.). A progressive scan camera (COHU) functioned as the focusing
camera. The imaging camera (Hamamatsu Photonics) was set to capture
8-bit images at 2 × 2 binning (672 × 512 pixels, 0.684 × 0.684 µm2/pixel)
with ﬁve images captured per ﬁeld (DAPI, CMBl, LipidTOX, Alexa Fluor
568, and Alexa Fluor 647 secondary antibodies). All high-throughput
microscopy experiments were performed with an S Fluor 20×/0.75 NA
objective lens (Nikon). In general, 12–16 images were captured per well
for image analysis. All imaging was performed at room temperature.
Images were analyzed using custom algorithms developed with the Pipeline
Pilot (v7.5) software platform (Accelrys) in a similar workﬂow as described
previously (Szafran et al., 2008, 2009) and summarized in the following
steps. After background subtraction, nuclear and cell masks were gener-
ated using a combination of nonlinear least squares and watershed-from-
markers image manipulations of the DAPI images. Speciﬁcally, a nonlinear
least squares threshold was applied to a DAPI image to create a binary
image. This image was subsequently eroded and distance transformed
to generate a marker image identifying the approximate center for each
nuclei. This marker image in combination with the original DAPI image was
used in a watershed-from-markers operation to deﬁne the full nuclear mask
for each nucleus. A ﬁnal morphological open operation was used to gener-
ate the ﬁnal nuclear masks. Then, cellular masks were created by applying
watershed segmentation on the CellMask images using nuclei regions as
seeds. To prevent cell body oversegmentation, cell regions were trimmed
so their boundaries did not exceed an empirically determined maximal
distance from the nucleus. All events with whole cell masks bordering the
edge of the image were additionally eliminated from analysis. Both whole
cell and nucleus segmentation generate regions under which single cell
intensity features were extracted. Cell populations were ﬁltered to discard
events with cell aggregates, mitotic cells, apoptotic cells, cellular debris, or
poor segmentation. Applied gates were based upon nuclear area, nuclear
circularity, and cell size/nucleus ratio. In general, these ﬁlters removed
10% of the population of segmented cells. An additional image analysis
platform, CyteSeer (Vala Sciences), was also used to support the Pipe-
line Pilot-driven algorithms. Measurements extracted using lipid droplet
analysis intrinsic to CyteSeer software (McDonough et al., 2009) included
lipid droplet count, total integrated intensity of the lipid mask on the lipid
image, and lipid droplet area. Post-analysis measurements were exported
to spreadsheet software (Excel; Microsoft) for further analysis.
Data presented were acquired from a minimum of two (qPCR) or three
(HCA) independent experiments performed on multiple days, unless other-
wise indicated. Analysis of variance (ANOVA) was ﬁrst used to compare
the effects of time or siRNA treatment. If signiﬁcant differences were identi-
ﬁed, then data were compared with Tukey’s HSD post-hoc tests. All tests
were performed at the 95% conﬁdence interval using JMP-IN 7 (SAS).
Online supplemental material
Fig. S1 shows the extension of the human adipogenesis assay to later
time points and dose response experiments for BRL49653 and 15dPGJ2
with detection of SRC-1, SRC-2, SRC-3, PPAR, and lipid content. Fig. S2
shows the effect of SRC-1 siRNA on human adipogenesis. Fig. S3 dis-
plays immunoﬂuorescent detection of SRC-2 and SRC-3 under single
or double knockdown conditions. Fig. S3 also redisplays mRNA data
represented in Fig. 6 H. Fig. S4 shows the validation of subpopulation
analyses using PPAR siRNA. Fig. S5 describes SRC gain-of-function ex-
periments. Online supplemental material is available at http://www.jcb
The authors thank Drs. N. McKenna and Z.D. Sharp for critically reviewing the
manuscript; I.P. Uray for qPCR assay design; J.Y. Newberg, A.T. Szafran,
M.G. Mancini, L. Vergara, and J. Broughman for technical resource support;
and P. McDonough and J.H. Price (Vala Sciences) for longstanding support in
automated cytometry. Benjamin Buehrer is an employee of Zen-Bio, Inc.
This work was funded by National Institutes of Health (NIH) grant
5R01DK055622, the Hankamer Foundation, and pilot grant and equipment sup-
port from the John S. Dunn Gulf Coast Consortium for Chemical Genomics (to
M.A. Mancini). Additional funding was provided by NIH 1F32DK85979 (to
S.M. Hartig), 5T32HD007165 (to B.W. O’Malley), 5K01DK081446 (to B. He),
and 5R01CA090464 (to E. Chang). Imaging resources were supported by
Specialized Cooperative Centers Program in Reproduction U54 HD-007495
(to B.W. O’Malley), P30 DK-56338 (to M.K. Estes), P30 CA-125123 (to C.K.
Osborne), and the Dan L. Duncan Cancer Center of Baylor College of Medicine.
FRET imaging was performed as described previously (Trinkle-
Mulcahy et al., 2001; Chusainow et al., 2006) with the DeltaVision Core
Image Restoration Microscope (Applied Precision). Z stacks were imaged at
0.2 µm separation and a frame size of 1,024 × 1,024 pixels at 1 × 1 bin-
ning with a microscope (IX71; Olympus) using a 60×, 1.42 NA Plan Achro-
mat objective (Olympus) and a charge-coupled device camera (CoolSnap
HQ2; Photometrics). Filter sets were as follows, with a dichroic to split CFP
and YFP: excitation 430 nm/emission 470 nm (CFP), excitation 500 nm/
emission 535 nm (YFP), and excitation 430 nm/emission 535 nm (FRET).
Z series stacks were deconvolved with the DeltaVision constrained iterative
algorithm. After deconvolution (softWoRx; Applied Precision), FRET calcula-
tions were performed using the Applied Precision FRET user interface. FRET
measurements on individual nuclei were acquired on maximum intensity pro-
jections of the derived FRET image. Spectral bleed-through was corrected for
by acquiring specimens containing only CFP-PPAR2 and YFP-p160. Stan-
dard values for and coefﬁcients were 0.6 (CFP) and 0.12 (YFP) acquired
from single donor/acceptor plasmid expression experiments.
siRNA to SRC-2 and SRC-3 oligomers was provided by E. Lader (QIAGEN,
Germantown, MD). Before transfection, optical quality 384-well plates (Senso-
Plate Plus; Greiner) were coated with 20 µl of FBS (Gemini Bio-Products)
overnight at 37°C. Cells were reverse-transfected with siRNA or mismatch
control at a ﬁnal concentration of 10 nM using HiPerFect transfection reagent
(QIAGEN). siRNA and transfection reagents were mixed in OptiMEM I reduced
serum media (Invitrogen) and allowed to complex at room temperature for
20 min. Preadipocytes, diluted to a ﬁnal density of 5,000 cells/well, were
then added to the HiPerFect–siRNA complexes followed by immediate seed-
ing to plates. The total volume of cells and transfection reagent was 30 µl/
well. Final volume of each condition upon seeding to the 20 µl FBS coat was
50 µl/well. After reverse transfection, cells were incubated for 48 h at 5%
CO2/37°C before induction of differentiation for up to 96 h.
Production of lentiviral particles
SRC-1, SRC-2, and SRC-3 cDNAs were cloned into the lentiviral expres-
sion vector pCDH–CMV-MCS-EF1-Puro (System Biosciences) by XbaI–NheI
digestion. Pseudolentiviruses were produced in 293TN cells by cotrans-
fecting lentiviral expression constructs and the pPACK packaging plasmid
mix (System Biosciences). Pseudoviral particles were harvested 48 h after
transfection and were concentrated using a PEG-it virus precipitation solu-
tion kit (System Biosciences).
RNA extraction and qPCR analysis
Total RNA was extracted from cells using TRIzol reagent (Invitrogen) ac-
cording to the manufacturer’s instructions. To measure the relative mRNA
levels of SRC-2, SRC-3, PPAR, and adipocentric genes (ADFP, SREBP1c,
FASN, and C/EBP), quantitative real-time RT-PCR was performed using
the Taqman RT-PCR one-step master mix in conjunction with an ABI 7500
real-time PCR system (Applied Biosystems). Each sample was tested in du-
plicate in three independent experiments. -actin was used as the invariant
control. For C/EBP, relative mRNA was evaluated using the TaqMan
Gene Expression Assay (Assay ID Hs00269972_s1). The following primer
and probe sets were used to detect SREBP1c, FASN, SRC-2, SRC-3,
PPAR2, and ADFP. SREBP1c: 5-TGCGCAAGACAGCAGATTTA-3 (for-
ward), 5-CGCTCCTCCATCAATGACA-3 (reverse), and Roche Universal
ProbeLibrary probe No. 77 (probe); FASN: 5-CGGAGTGAATCTGGG-
TTGAT-3 (forward), 5-CAGGCACACACGATGGAC-3 (reverse), and
Roche Universal Probe Library probe No. 11 (probe); SRC-2: 5-GGAC-
CTGGTAAGAAGGTGTATTCAG-3 (forward), 5-TGCCTCTTAGCATAG-
GACACAGA-3 (reverse), and 5-TCCATGCGCAGCATGAAGGAGA-3
(probe); SRC-3: 5-TTCAGGAAAGGTTGTCAATATAGATACA-3 (forward),
5-AATACACCTTCGGATTATATCTTCAAA-3 (reverse), and 5-TTCACTGA-
GATCCTCCATGAGGCCTG-3 (probe); ADFP: 5-GTGACTGGCAGTGT-
GGAGAAG-3 (forward), 5-TCCGACTCCCCAAGACTGT-3 (reverse),
and 5-CCAAGTCTGTGGTCAGTGGCAGCA-3 (probe); PPAR2: 5-ATGC-
TGGCCTCCTTGATGA-3 (forward), 5-GCTTTCGCAGGCTCTTTAGAA-3
(reverse), and 5-TCTCATATCCGAGGGCCAAGGCTTC-3 (probe); SRC-1:
5-AACCCAGCAGGTGCAACAG-3 (forward), 5-GTCCCCGCCTA-
CCAGATTC-3 (reverse); and 5-CAGGTGTTTGCTGACGTCCAGTG-
Imaging and microscopy
For high-speed image acquisition and subsequent analysis, cells were
imaged using the Cell Laboratory IC-100 Image Cytometer (IC-100; Beck-
man Coulter) controlled by CytoShop 2.0 (Beckman Coulter). The IC-100
platform was equipped with an inverted microscope (Nikon; Eclipse
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