Download Assay of the Multiple Energy-Producing Pathways of Mammalian Cells

Assay of the Multiple Energy-Producing Pathways of
Mammalian Cells
Barry R. Bochner1*, Mark Siri1¤a, Richard H. Huang1¤b, Shawn Noble1, Xiang-He Lei1, Paul A. Clemons2,
Bridget K. Wagner2
1 Biolog, Inc., Hayward, California, United States of America, 2 Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of
America
Abstract
Background: To elucidate metabolic changes that occur in diabetes, obesity, and cancer, it is important to understand
cellular energy metabolism pathways and their alterations in various cells.
Methodology and Principal Findings: Here we describe a technology for simultaneous assessment of cellular energy
metabolism pathways. The technology employs a redox dye chemistry specifically coupled to catabolic energy-producing
pathways. Using this colorimetric assay, we show that human cancer cell lines from different organ tissues produce distinct
profiles of metabolic activity. Further, we show that murine white and brown adipocyte cell lines produce profiles that are
distinct from each other as well as from precursor cells undergoing differentiation.
Conclusions: This technology can be employed as a fundamental tool in genotype-phenotype studies to determine
changes in cells from shared lineages due to differentiation or mutation.
Citation: Bochner BR, Siri M, Huang RH, Noble S, Lei X-H, et al. (2011) Assay of the Multiple Energy-Producing Pathways of Mammalian Cells. PLoS ONE 6(3):
e18147. doi:10.1371/journal.pone.0018147
Editor: Daniel Tomé, Paris Institute of Technology for Life, Food and Environmental Sciences, France
Received August 8, 2010; Accepted February 27, 2011; Published March 24, 2011
Copyright: ß 2011 Bochner et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Principal funding for the research at Biolog was provided by National Institutes of Health (NIH) Grants R43-CA101605 from National Institutes of HealthNational Cancer Institute (NIH-NCI) and grant R44-MH074145 (Small Business Innovation Research) from National Institutes of Health-National Institute of Mental
Health (NIH-NIMH). Additional funding and support was from Biolog, Inc., which employed the authors who designed the studies, collected the data, analyzed the
data, and prepared the manuscript. P.A.C. is supported in part by NCI’s Initiative for Chemical Genetics (N01-CO-12400), NIH Genomics-Based Drug Discovery/
Target ID Project (RL1-HG004671), and NIH Molecular Libraries Network (U54-HG005032). B.K.W. is supported by the Juvenile Diabetes Research Foundation and a
Type 1 Diabetes Pathfinder Award (DP2-DK-083048, NIH-NIDDK). Barry Bochner, CEO and CSO at Biolog, made the decision to publish the manuscript.
Competing Interests: Barry Bochner is the CEO, CSO, and Chairman of the Board of Biolog, Inc., and has ownership of stock. Shawn Noble and Xiang-He Lei are
employees of Biolog, Inc., and Mark Siri and Richard Huang were employees of Biolog, Inc. at the time of data collection. Patents 6,696,239 and 6,727,076 have
been issued to Biolog, Inc., and the Phenotype MicroArrays described in this manuscript are produced and marketed by Biolog, Inc. These relationships do not
alter the authors’ adherence to all the PLoS ONE policies on data sharing.
* E-mail: [email protected]
¤a Current address: Denver School of Pharmacy, University of Colorado, Aurora, Colorado, United States of America
¤b Current address: Agilent Technologies, Santa Clara, California, United States of America
by adding serum or electron carriers; however, we were able to
develop two redox chemistries (Dye Mix MA and MB) that give
very little non-specific dye reduction (see Figure S1 and Figure
S2) and are reduced by a wide range of mammalian cells.
Importantly, the reduction of these dyes is dependent on the
presence of a usable carbon-energy source in the medium, such as
glucose. Therefore, just as in microbial cells, our new redox dye
chemistries measure reductase activity due to energy (NADH)
producing catabolic pathways that use diverse biochemical
substrates.
The extent to which animal cells from various organs and
tissues use different carbon substrates for energy has not been
systematically investigated. It is known that, in addition to
glucose, animal cells can metabolize and grow on other substrates
[11–19]. A survey of nutrient metabolism [12] found 32
carbohydrates that could be metabolized by mammalian cells,
of which 15 could support the growth of at least one of five cell
lines tested. However, as in vitro culture media were developed
using glucose, pyruvate, and glutamine as energy sources, which
were shown to support growth of most cells of interest, the
Introduction
Tetrazolium-based redox assays can be used to measure the
active in vivo energy-producing pathways of a very wide range of
microbial cells [1,2], including eukaryotic microbial cells that
undergo mitochondrial respiration. Tetrazolium reduction is a
ubiquitous property of cells, enabling a simple colorimetric cellular
assay technology to be multiplexed into thousands of different
assays and facilitating detailed cellular analyses in what we term
Phenotype MicroArrays [3–6]. Based on previous success in
profiling metabolic activity of microbial cells (www.biolog.com/
mID_section_13.html), we sought to develop a redox chemistry
that could extend comparable analytical capabilities to mammalian cells.
Redox dyes such as tetrazolium dyes (e.g., MTT, MTS, XTT)
and resazurin-based dyes (e.g., Alamar Blue) measure nonspecific
cellular reductase activities [7–10]. We assayed various mammalian cell lines in RPMI-1640 medium without carbon energy
sources (glucose and glutamine) and found substantial levels of
non-specific background dye reduction, which was exacerbated
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Metabolic Profiling Technology for Mammalian Cells
impetus to study further the scope and diversity of nutrients
metabolized by different cell types waned. Information about all
pathways that contribute to energy production not only underlies
our fundamental understanding of animal nutrition, but also is
likely to help us understand and treat undesirable or aberrant
metabolism, such as in obesity and diabetes. It may also help us
develop new approaches in nutritional therapy to improve
treatment of a wide range of conditions, including aging, cancer,
infectious disease, inflammation, and wound repair [20].
terization as well as a unique profile of the metabolism of an
animal cell.
These results with human cell lines mirror our results with
bacterial and fungal cells [2], in that we observe differential
catabolic activities in different cell types consistent with the
known physiological properties of different cells. Furthermore,
they recapitulate the results of earlier studies [12] that showed
five different cell lines were readily distinguishable by their
profiles of carbohydrate preference. The substrate nutrients in
PM-M1 to –M4 are all present at low millimolar concentrations.
Analyses of blood chemistries from non-fasting normal humans
suggest that the principal circulating carbon energy sources are
glucose (2–10 mM) and fatty acids, both free (0.01–1.0 mM) and
in the form of triglycerides and lipoproteins [23,24]. Glutamine
(0.6 mM), alanine (0.35 mM), and lactic acid (2 mM) also have
significant levels in normal blood [24,25]. A recent metabolomic
study has documented a wider array and higher concentrations of
potentially metabolizeable biochemicals in human plasma after
exercise, including glucose-6-phosphate, inosine, uridine, alanine,
lactate, pyruvate, succinate, malate, fumarate, and glycerol [26].
Furthermore, the cells lining the intestine are exposed to an even
more diverse and variable nutritional environment. Similarly, the
liver is fed directly from the intestine by the portal circulation.
Therefore, it is reasonable to expect that colon and liver cells
would have more diverse capabilities for substrate metabolism
than cells from other tissues.
Results
Metabolic fingerprinting of cancer cells
To demonstrate the application of this platform to human cells,
we profiled seven diverse human cancer cell lines in four
microplates (termed PM-Ms) containing 367 substrate nutrients
(Figure 1; see Table S1 for a complete map of the nutrients). PMM1 contains primarily carbohydrate and carboxylate substrates,
whereas PM-M2, -M3, and -M4 contain individual L-amino acids
and most dipeptide combinations. With the exception of abiotic
color formation in a few wells containing reducing sugars
(palatinose, D-turanose, D-tagatose, and L-sorbose), multiple lines
of evidence presented below suggest that the color formed from
each substrate reflects the energy-producing activity of its catabolic
pathway. This method is simple to perform and reproducible
(Figure S3).
All cell lines tested produced a strong reductive response in wells
containing glucose (Figure 1; green boxes, all panels) and little or
no response in wells lacking any carbon source (Figure 1; red
boxes, all panels). Two leukemic cell lines (CCRF-CEM and HL60) show additional metabolism limited to D-mannose, D-maltose,
and maltotriose (Figure 1A,B; blue boxes). Prostate cancer cells
(PC-3) show additional metabolism of fructose, some nucleosides,
and tricarboxylic acid (TCA) cycle intermediates (Figure 1C; blue
boxes), and lung cancer cells (A549) show additional metabolism of
dextrin, glycogen, and galactose (Figure 1D; blue boxes). In
addition, both prostate and lung cells show metabolism of
glutamine
and
many
glutamine-containing
dipeptides
(Figure 1C,D; purple wells on PM-Ms 2-4). Both colon
(COLO205) and liver (HepG2) cancer cell lines demonstrate a
more diverse catabolic response, with more wells showing a strong
reductive response (Figure 1E–G). For example, colon cells
additionally metabolize lactic acid, butyric acid, and propionic
acid (Figure 1E; blue boxes), and liver cells metabolize alanine,
arginine, and dipeptides containing them (Figure 1F,G; Ala = blue
boxes, Arg = gold boxes). This last result is consistent with an
NMR-based report that alanine is elevated in response to hypoxia,
and is associated with hepatomas [21].
Importantly, we observe that two genetically related liver cell
lines have similar metabolic profiles. The HepG2/C3A cell line
(Figure 1G) was derived as a spontaneous clonal variant of
HepG2 (Figure 1F), selected for its ability to grow on pyruvate
as the principal energy source [22]. Accordingly, this clone
shows stronger metabolism of pyruvate (Figure 1G; magenta
box) relative to its parent. Interestingly, along with this
metabolic change, HepG2/C3A cells have also been reported
to have additional phenotypes more typical of normal liver cells,
such as producing and secreting higher levels of albumin and
other serum proteins [22]. The colon cell line COLO205 has a
similar metabolic profile to HepG2, with a salient difference
being the observed increased metabolism of butyric acid and
propionic acid, which are plentiful in the colonic environment
due to their production by resident anaerobic bacteria. Thus,
the substrate utilization pattern provides a metabolic characPLoS ONE | www.plosone.org
Mechanism of cellular dye reduction
To study further the mechanistic aspects of this dye-reduction
assay, we tested HepG2/C3A cells in PM-M1 panels with varying
concentrations of the mitochondrial inhibitors carbonyl cyanide-ptrifluoromethoxyphenylhydrazone (FCCP) and rotenone. Even at
low concentrations, these inhibitors completely block dye reduction by substrates that would presumably be oxidized mitochondrially, such as lactic acid, a-keto-glutaric acid, succinamic acid,
and acetic acid (Figure 2; red boxes). At higher concentrations,
they partially block reduction of fructose, adenosine, and inosine
(Figure 2; blue boxes), but have little effect on color formation in
wells containing glucose or mannose (Figure 2; green boxes).
Adding back 5 mM glucose after an 18-hour exposure is not
sufficient to rescue the cells when the concentration of the
inhibitor was high enough to block substrate catabolism. This
result is consistent with our interpretation that the assay reflects the
production of NADH from various substrates, and that the
fraction of NADH produced mitochondrially is different for
different energy substrates.
The ability of a cell to use a particular nutrient as a source of
energy is necessary but not sufficient to use it as a growth
substrate. To use a chemical as a sole growth substrate, the cell
must also be able to convert the carbon skeleton of that
chemical into all other metabolites that it requires. This assay
format can also be used to determine the survival responses of
cells under different conditions of substrate supply. HepG2/
C3A cells were tested for cell growth or death with PM-M1
substrates over a five-day incubation (Figure 3A). Four
qualitatively distinguishable responses were observed: proliferation (Figure 3; green boxes), including wells containing Dglucose, D-mannose, D-fructose, D-galactose, uridine, and
xylitol; stasis (Figure 3A; blue boxes), including wells containing
D-maltose, D-glucose-6-phosphate, and inosine; slow death
(Figure 3A; red boxes), including wells containing dextrin, Dsorbitol, and pectin; and rapid death, including wells corresponding to no substrate, D-raffinose, and butyrate (Figure 3A;
black boxes). Sodium butyrate, a general inhibitor of histone
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Figure 1. Assay of seven human cell lines in 367 metabolic assays. Suspensions of the following cancer cells were inoculated into Phenotype
MicroArrays PM-M1 through PM-M4 (Biolog, Hayward, CA) which contained 367 biochemical substrates that could potentially be metabolized and
provide energy for cells: (A) CCRF-CEM leukemia, (B) HL-60 leukemia, (C) PC-3 prostate cancer, (D) A549 non-small cell lung cancer, (E) COLO205
colon cancer, (F) HepG2 hepatocellular cancer, (G) HepG2/C3A, a clonal variant of HepG2. Adherent cells were washed twice in Dulbecco’s PBS (DPBS) before being detached with trypsin. Detached cells and non-adherent cells were suspended at 400,000 cells/mL in RPMI-1640 that lacked phenol
red and glucose and was supplemented with PS and reduced levels of glutamine (0.3 mM) and FBS (5%). The cells were dispensed at 50 mL per well
(20,000 cells per well) into the four microplates. Plates were incubated for 40 hr at 37uC under 5% CO2-95% air. This 40-hour incubation allows cells to
use up residual carbon-energy sources in the 5% serum (e.g., 5% serum would contribute about 0.35 mM glucose, plus lipids, and amino acids) and
minimizes the background color in the negative control wells which have no added biochemical substrate. Furthermore, the 40-hour incubation
allows cells to transition their metabolism to use the various substrates provided in the wells. After incubation, we added 10 mL of 6-fold
concentrated Redox Dye Mix MA (Biolog, Hayward, CA) to HepG2/C3A, HepG2, COLO 205, A549, PC-3 cells whereas Redox Dye Mix MB was added to
HL-60 and CCRF-CEM cells. Plates were incubated at 37uC under air to assess dye reduction for 5 hr (Redox Dye Mix MA) or 24 hr (Redox Dye Mix MB)
and then photographed. Negative controls (red boxes) have no substrate in the well. Wells containing D-glucose (green boxes) serve as a positive
control. Additional differentially metabolized substrates (blue boxes) for each cell line are described in the text.
doi:10.1371/journal.pone.0018147.g001
deacetylases, has previously been shown to be quite toxic to
HepG2 cells [27]. The set of substrates assayed as positive for
energy production corresponds with substrates supporting
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prolongation of survival, as corroborated by microscopic
observation of the healthy morphology and increased cell
numbers in these wells (Figure 3D, compared to Figures 3B,
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Figure 2. Effect of mitochondrial inhibitors on energy-producing pathways of HepG2/C3A cells. HepG2/C3A cells were washed twice
with serum-free inoculating medium (RPMI-1640 with no phenol red or glucose but containing PS, 0.3 mM glutamine) and resuspended in the same
medium at a density of 400,000 cells/mL with varying concentrations of either FCCP or rotenone. Control cells were incubated with 0.003% DMSO,
the solvent for both FCCP and rotenone. These cell suspensions were immediately inoculated into PM-M1 at 50 mL per well. After an 18-hour
incubation at 37uC under 5% CO2-95% air, 10 mL of Redox Dye Mix MA was added to each well. After 5 hours with the dye, the plates were
photographed. After the plates were photographed, glucose was added to each well of the plates to a final concentration of 5 mM to assess cell
viability. The plates were then incubated overnight and photographed again.
doi:10.1371/journal.pone.0018147.g002
optical density of each well at 590 nm, normalized to negativecontrol wells. As expected, white and brown preadipocytes and
adipocytes have different metabolic profiles as demonstrated with
this dye-reduction assay (Figure 4). In order to quantify these
comparisons, we also calculated a comparison score (CS) as the
absolute value of the ratio between two states being compared at a
particular time point (Figure S4). Quantitatively, the greatest
difference between brown and white preadipocytes was in their
metabolism of glycogen, with brown preadipocytes showing faster
metabolism (Figure S4 and Figure S5). We also observed much
stronger metabolism of the three sugar phosphates (fructose-6phosphate, glucose-1-phosphate, and glucose-6-phosphate) by
brown preadipocytes (Figure 4). Interestingly, the metabolism of
these four nutrients decreased during brown adipogenesis.
We also sought to identify differences in substrate metabolism
that might occur during adipocyte differentiation (Figure 4).
Surprisingly, all changes that occurred in brown adipocytes after
differentiation were decreases in metabolism. Like 3T3-L1
differentiation, brown adipocyte differentiation resulted in a
decreased metabolism of dextrin and butyric acid. On the other
hand, after differentiation, there were more substrates for which
dye reduction increased in white and not brown adipocytes.
Acetoacetic acid and succinamic acid resulted in some the greatest
increases in dye reduction. Indeed, for adipocytes, acetoacetic acid
C). Further, the quantification of ATP levels in these wells, after
three days in cell culture, shows consistency with our method
(Figure 3E). Thus, the ability to produce energy from a substrate
corresponds with increased survival, even if only temporarily.
One possible exception is pectin which may slow cell death by a
mechanism other than energy production.
Metabolic profiling of adipocytes
We sought to apply this technology to a cellular model system
representing a highly metabolically active state. We reasoned that
adipocyte biology would be well-suited for nutrient-response
profiling, since we could compare white and brown adipocytes,
which have very different physiological roles, as well as analyze the
cell-state changes that must occur during differentiation of
preadipocyte fibroblasts. Therefore, we profiled the mouse cell
line 3T3-L1, which has been used for decades as a model of white
fat [28], and an immortalized brown preadipocyte cell line [29], in
both undifferentiated and differentiated states. This experimental
design enabled us to focus on four major comparisons: brown
preadipocytes with brown adipocytes, white preadipocytes with
white adipocytes, brown with white preadipocytes, and brown
with white adipocytes.
In order to quantitatively compare each state in a rapid and
systematic manner, we developed a scoring system based on the
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Figure 3. Assay of growth, stasis, or death under different nutritional conditions. (A) HepG2/C3A were suspended at 50,000 cells per mL in
serum-free RPMI-1640 medium that lacked phenol red and glucose, but contained PS and 4 mM glutamine. Cells were dispensed into six PM-M1
microplates at 50 mL per well (2,500 cells per well) and incubated at 37uC under 5% CO2-95% air. On six consecutive days, starting on day 0 when the
cells were plated, 10 mL of Redox Dye Mix MA containing 30 mM glucose (a 6-fold concentrate) was added to one microplate, the plate was sealed
with tape (LMT-SEAL-EX, Phenix Research Products, Hayward, CA) to prevent CO2 loss, and incubated at 37uC in an OmniLog instrument (Biolog,
Hayward, CA) for 18 hours to kinetically record formation of purple formazan in the wells on days 0 (red) 1 (yellow) 2 (green) 3, (blue) 4 (purple) and 5
(black). Substrates resulting in distinguishable responses are indicated: cell proliferation (green boxes), cell stasis (blue boxes), slow cell death (red
boxes), rapid cell death (black boxes). In these kinetic graphs, the X-axis is time and the Y-axis is OmniLog color density units. Additional details on the
testing protocol, OmniLog instrument, and kinetic assay reproducibility are provided in Figure S3. (B) Microscopic image of HepG2 in a negativecontrol well after three days in culture. (C) Microscopic image of HepG2 in a butyrate-containing well after three days in culture. (D) Microscopic
image of HepG2 in a D-glucose-containing well after three days in culture. (E) Quantification of ATP levels in HepG2 cells after three days in culture,
using a luciferase-based method. Scale bars = 100 mm.
doi:10.1371/journal.pone.0018147.g003
During periods of exercise, blood concentrations of glycerol (20120 mM), pyruvic acid (40-180 mM), and lactic acid (0.5–5 mM),
rise to significant levels [26,32]. To prevent the accumulation of
toxic levels of acids in muscles, lactic acid is converted via pyruvic
acid to alanine, which is subsequently metabolized, primarily by
the liver [33,34]. The dominant view has been that alanine is
primarily converted to glucose rather than oxidized to CO2. Our
survey of seven cell lines confirms that liver cells are especially
proficient in metabolism of alanine, but also shows that this amino
acid can be used to produce energy. These results are consistent
with metabolomic profiling methods [21], showing that this simple
method provides an independent, flux-based measurement of
cellular metabolism. Under nutritional stress conditions such as
starvation, acetoacetic acid and D-b-hydroxy-butyric acid can
become principal energy sources for cells [35]. Further, under
long-term calorie-restricted diets, pyruvate may play a key role
both as a fuel for mitochondrial energy production and as a
regulator of the SIRT1 protein in hepatocytes [36]. This evidence
suggests the importance of broadening the study of energy
metabolism beyond glucose.
Liver cells, enteroendocrine cells, and adipocytes seem particularly important to study in the context of multiple energymetabolism assays, obesity, and diabetes. The liver plays an
essential physiological role in helping to maintain a constant
supply of glucose and other substrates to the rest of body. Our
method confirms previous reports showing the toxic effect of
sodium butyrate on HepG2 cells [27]. Enteroendocrine cells of
various types are present in the gut epithelium, sensing certain
substrates in the lumen (e.g., methyl-pyruvate, arginine, peptides)
and releasing incretin hormones such as glucose-dependent
insulinotropic peptide, glucagon-like peptide 1, ghrelin, and
consistently produced the strongest dye reduction of any substrate
tested, including D-glucose. Remarkably, all peptides containing
leucine resulted in at least some increase in metabolism after white
adipogenesis, although at a low absolute rate of dye reduction (see
Leu and Leu-Leu, Figure 4). These results indicate that, while
these adipogenic processes may superficially appear to be similar,
each produces a different metabolic response.
Discussion
We have developed a simple colorimetric assay method for
simultaneously measuring multiple energy-producing pathways in cells
and providing a more global perspective of the range and regulation of
multiple energy-producing pathways. This provides a new tool for
researchers wanting to study metabolic pathway activities, with
advantages for measuring in vivo metabolic fluxes. Metabolomic assay
of pool levels are better for detecting the presence of pathways and
detecting the site of pathway blockages, but changes in pathway fluxes
may not result in increases or decreases of pool levels. Another
approach to measuring pathway fluxes using quantitative isotope
tracking, relies on detailed and accurate knowledge of the pathways
present and active. This new colorimetric PM assay is not only
extremely simple to perform, but it does not require any prior
knowledge of the energy pathways present and active in a cell line.
Each well in the PM assay panel provides both qualitative and
quantitative data on the potential of a cell line to metabolize a
biochemical substrate and produce energy, presumably as NADH.
We have provided evidence that glucose is not the only relevant
energy source used by colon cells, liver cells, and adipocytes. This
is likely to be the case for other cells as well. Muscle cells [30] and
brown fat cells [31] are known to actively metabolize fatty acids.
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Figure 4. Comparison of substrate metabolism in brown and white preadipocytes and adipocytes. Normalized optical-density scores
representing nutrient metabolism, showing the greatest differences between white fat (top) and brown fat (bottom) cells in either an
undifferentiated (gray bars) or differentiated (black bars) state. Included are a) wells that cause no change (1, negative control), b) nutrients that are
high in brown preadipocytes and decrease upon differentiation (2, a-D-glucose-1-phosphate; 3, D-glucose-6-phosphate; 4, D-fructose-6-phosphate; 5,
glycogen), c) a similarly behaved nutrient that is high in white fat (6, hexanoic acid), d) nutrients that decrease during differentiation of both cell types
(7, dextrin; 8, butyric acid), and e) nutrients that increase upon white fat differentiation (9, L-leucine; 10, Leu-Leu; 11, succinamic acid, 12. acetoacetic
acid). The normalized scores shown are reprentative of two independent biological experiments. The error bars represent the error for the six alphaD-glucose wells in each experiment, which was taken as the largest and most conservative estimate of the error for other nutrients.
doi:10.1371/journal.pone.0018147.g004
cholecystokinin, which affect appetite, metabolism, and energy
storage by a variety of mechanisms [37,38]. Adipocytes not only
manage the storage of excess energy in the body, but also produce
and release a number of adipokine-signaling hormones [39].
We observed several significant differences in dye-reduction
profiles between white and brown adipocytes. For example, the
metabolism of glycogen was high in brown versus white preadipocytes, but decreased during cellular differentiation. This observation
is consistent with a report that murine brown preadipocytes show a
myogenic transcriptional profile, suggesting that brown and white
fat arise from separate cell lineages [40]. More recently, the
transcription factor PRDM16 has been shown to control cell fate in
mice; overexpression in myoblasts results in a brown fat state, while
loss of expression induces muscle differentiation [41]. Another
consistent difference between brown and white preadipocytes was
the elevated metabolism of various sugar phosphates. These results
support previous literature reports, in which brown adipose tissue
was shown to have a 15-fold increase in glucose 6-phosphate
dehydrogenase activity [42]. Our results provide additional
evidence for the relationship between brown preadipocytes and
muscle, and show that this assay technology can be used to examine
similarities between distinct cell types, and to dissect subtle
differences between cell types thought to be similar.
In addition to using this assay technology to examine and
compare metabolic differences in cells from various tissues, it is
also productive to compare isogenic cell lines with specificallyengineered mutations. One of the most significant aspects of this
work is the metabolic comparison of the nearly isogenic cell lines
HepG2 and HepG2/C3A. The C3A line was selected by its ability
to grow on pyruvate, and our assay showed that increased
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pyruvate metabolism was the salient detectable metabolic
phenotype. With this tool, it would be illuminating to examine
changes in global energy-producing metabolism in mammalian
cells with knockouts or activations of key genes, for example those
encoding p53, Foxo3a, SIRT1 [43], ChREBP [44], BAD [45],
LXR [46], PPARd [47], GCN 5, or PGC-1a [48]. The assay
technology could also be used in conjunction with RNAi gene
silencing studies in cell lines or primary cells.
Other areas where metabolic profiling analysis of multiple
energy-producing pathways may lead to advancement are cancer,
nutrition, and aging. For example, there are potential applications
in studying the energy metabolism of non-glucose compounds in
PET imaging in neurology and cancer diagnostics. There is
renewed interest in the Warburg effect [49] and in using it in novel
approaches to killing cancer cells with energy antagonists such as
2-deoxyglucose [50] or Akt inhibitors [51]. Energy metabolism is
also relevant in calorie-restricted longevity and cachexia. This
method provides biologists with a simple tool for precise and
detailed phenotypic analysis of animal cells and, as we have
previously shown with microbial cells [6], enables a wide range of
studies relating genotype to phenotype.
Materials and Methods
Cell culture
All cell lines were obtained from ATCC (Manassas, VA). Cells
were cultured in an RPMI-1640-based media (without phenol red,
with penicillin/streptomycin and 10% FCS) and incubated at 37uC
with 6.5% CO2. Cells were washed in Dulbecco’s PBS and
resuspended at a density of 400,000 cells/mL in matched medium
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dium without phenol red, and 50 mL of cell suspension
was added to each column of wells at 2-fold dilutions
(right to left) in the same medium. After 4 hours, 10 mL of 4
different redox dye chemistries were added to a final tetrazolium
concentration of 500 mM.
(TIF)
depleted of carbon-energy sources (no glucose, low glutamine
(0.3 mM), and low FCS (5%)). 50 mL per well of this suspension
(20,000 cells per well) is dispensed into four microplates containing
367 biochemicals that could potentially be metabolized and provide
energy for cells. We found that a 40-hour incubation is optimal for
decreasing the background color in the negative control wells and
increasing the color in various wells with metabolized substrates, to
allow cells to use any remaining carbon-energy sources (e.g., from the
glucose (5% serum would contribute about 0.35 mM glucose), lipids,
glutamine, and other amino acids in the serum), and to adapt the cells
to using the substrate provided in the well. After this incubation,
10 mL of Redox Dye Mix MA (HepG2/C3A, HepG2, COLO 205,
A549, PC-3) or MB (HL-60 and CCRF-CEM) was added, and cells
were incubated to assess dye reduction for 5 hours (Redox Dye Mix
MA) or 24 hours (Redox Dye Mix MB) and photographed.
Figure S2 HepG2/C3A liver cells were cultured in
Phenotype MicroArray PM-M1 for 40 hours and assayed
for dye reduction, as described in Materials and
Methods. Four different redox dye chemistries were used but
in all cases, the concentration of the tetrazolium dye was 500 mM.
(TIF)
Figure S3 PM Assay with kinetics of dye reduction
recorded by the OmniLog instrument. This figure shows the
steps in running a PMM assay. Assays are initiated by adding a cell
suspension to the wells, followed by addition of a redox dye. The
PMM microplates are placed inside of the OmniLog instrument,
which incubates the microplates and reads the color formation in
wells with an internal color video camera every 15 minutes. The
OmniLog software then generates kinetic graphs of color versus time
for all wells. These assays have very high reproducibility. In the
example shown, HME human breast cells (a generous gift of Dr.
Chris Torrance, Horizon Discovery Ltd., Cambridge, UK) were
tested using the standard protocol, but without serum and with the
glutamine concentration increased to 2 mM. The graph shows a
triplicate repeat of the assay with runs shown in green, red, and black.
(TIF)
Effect of mitochondrial inhibitors in HepG2/C3A cells
HepG2/C3A cells were washed twice with serum-free inoculating medium (modified RPMI 1640, with 1X vitamins, 1X salts,
1X amino acids, 0.3 mM glutamine, and no glucose) and
resuspended in the same medium at a density of 400,000 cells/
mL with varying concentrations of either FCCP or rotenone.
Control cells were incubated with 0.003% DMSO, the solvent for
both FCCP and rotenone. These cell suspensions were immediately inoculated into PM-M1. After an 18-hour incubation, 10 mL
of Redox Dye Mix MA was added to each well. After 5 hours with
the dye, the plates were photographed. After the plates were
photographed, glucose was added to each well of the plates to a
final concentration of 5 mM to assess cell viability. The plates were
then incubated overnight and photographed again.
Figure S4 Comparison of substrate metabolism in brown
and white preadipocytes and adipocytes. All adipoctye cell
lines were assayed with Redox Dye Mix MB. (A) Substrates resulting
in the greatest difference in comparison score (CS) at 24 hours
between immortalized brown preadipocytes (blue lines) and 3T3-L1
white preadipocytes (green lines). To simplify the analysis, we only
considered the CS at the 24-hour time point measurement,
reasoning that since the accumulation of signal only increases over
time, we would detect the most stable differences at this time, rather
than only detecting kinetic changes in absorbance. (B) Substrates
resulting in the greatest difference in CS at 24 hours between fully
differentiated brown (blue lines) and 3T3-L1 white adipocytes (green
lines). Graphs are depicted as the normalized change in absorbance
(DDAbs), using the zero-hour time point as baseline.
(TIF)
Assessment of cell proliferation, stasis, or death under
different nutritional conditions
HepG2/C3A cells were suspended in inoculating medium with
4 mM glutamine and 1X penicillin/streptomycin, seeded at 2500
cells per well into six PM-M1 microplates, and incubated at 37 C
with CO2. Each day, one microplate was removed and Biolog
Redox Dye MA plus 5 mM glucose was added. The microplate
was then incubated in an OmniLog for 18 hours with purple color
formation data recorded. Cellular ATP levels were measured using
the luciferase-based CellTiterGlo kit (Promega).
Adipocyte cell culture
3T3-L1 preadipocytes were grown in Dulbecco’s Modified
Eagle’s Medium (DMEM) supplemented with 10% donor calf
serum and 100 mg/mL penicillin/streptomycin mix in a humidified
atmosphere at 37C with 5% CO2. Immortalized brown preadipocytes were cultured similarly, in media containing 10% fetal bovine
serum (FBS). Adipocytes were differentiated prior to incubation in
PM-M microplates, such that .90% of cells contained lipid
droplets. 3T3-L1 cells were differentiated by culturing 48 hours in
DMEM with 10% FBS, 170 nM insulin, 2 mg/mL dexamethasone,
and 250 mM isobutylmethylxanthine (IBMX), followed by changing
the media every 48 hours to DMEM containing FBS and insulin.
Brown preadipocytes were differentiated by culturing 48 hours in
DMEM with 10% FBS, 20 nM insulin, 250 mM indomethacin,
1 nM triiodothyronine (T3), 2 mg/mL dexamethasone, and
250 mM IBMX, followed by changing the media every 48 hours
to DMEM containing FBS, insulin, and T3.
Figure S5 Analysis of effects of adipocyte differentiation
on substrate metabolism. (A) Substrates resulting in the
greatest difference in comparison score (CS) at 24 hours between
undifferentiated 3T3-L1 preadipocytes (blue lines) and 3T3-L1
white adipocytes (green lines). (B) Substrates resulting in the
greatest difference in CS at 24 hours between undifferentiated
brown preadipocytes (blue lines) and brown adipocytes (green
lines). Graphs are depicted as the normalized change in
absorbance.
(TIF)
Table S1 Plate maps of Phenotype MicroArray MicroPlates PM-M1 through -M4.
(DOC)
Author Contributions
Supporting Information
Conceived and designed the experiments: BB XHL PAC BKW. Performed
the experiments: MS RHH SN XHL BKW. Analyzed the data: MS RHH
SN XHL PAC BKW. Wrote the paper: BB PAC BKW.
Figure S1 A549 lung cells were cultured as described in
Materials and Methods, suspended in RPMI-1640 me-
PLoS ONE | www.plosone.org
7
March 2011 | Volume 6 | Issue 3 | e18147
Metabolic Profiling Technology for Mammalian Cells
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Bochner et al.
Supporting Information
Assay of the multiple energy-producing pathways of
mammalian cells
Barry R. Bochner1, Mark Siri1†, Richard H. Huang1‡, Shawn Noble1, Xiang-He Lei1, Paul A.
Clemons2, Bridget K. Wagner2
1
2
Biolog, Inc., 21124 Cabot Boulevard, Hayward, CA 94545
Chemical Biology Program, Broad Institute of Harvard and MIT, 7 Cambridge Center,
Cambridge, MA 02142
†
Current address: University of Colorado, Denver School of Pharmacy, 12631 E. 17th Avenue,
Aurora, CO 80045
‡
Current address: Agilent Technologies, 5301 Stevens Creek Blvd, Santa Clara, CA 95051
* Corresponding author email: [email protected].
25
Bochner et al.
Figure S1. A549 lung cells were cultured as described in Materials and Methods, suspended in
RPMI-1640 medium without phenol red, and 50 μL of cell suspension was added to each
column of wells at 2-fold dilutions (right to left) in the same medium. After 4 hours, 10 μL of 4
different redox dye chemistries were added to a final tetrazolium concentration of 500 μM.
26
Bochner et al.
Figure S2. HepG2/C3A liver cells were cultured in Phenotype MicroArray PM-M1 for 40 hours
and assayed for dye reduction, as described in Materials and Methods. Four different redox dye
chemistries were used but in all cases, the concentration of the tetrazolium dye was 500 μM.
27
Bochner et al.
Figure S3. PM Assay with kinetics of dye reduction recorded by the OmniLog instrument.
This figure shows the steps in running a PMM assay. Assays are initiated by adding a cell
suspension to the wells, followed by addition of a redox dye. The PMM microplates are placed
inside of the OmniLog instrument, which incubates the microplates and reads the color
formation in wells with an internal color video camera every 15 minutes. The OmniLog software
then generates kinetic graphs of color versus time for all wells. These assays have very high
reproducibility. In the example shown, HME human breast cells (a generous gift of Dr. Chris
Torrance, Horizon Discovery Ltd., Cambridge, UK) were tested using the standard protocol, but
without serum and with the glutamine concentration increased to 2 mM. The graph shows a
triplicate repeat of the assay with runs shown in green, red, and black.
PM assays initiated by
adding cells to wells
Add 50 μl of cells
Add 10 μl of redox dye
OmniLog PM System
Holds 50 microplates at a
set temperature
and measures color formation
at 15-minute intervals
28
Kinetic assay readout
for up to 4,800 wells in a
single experiment
Highly reproducible kinetics
CVs typically < 10%
Bochner et al.
Figure S4. Comparison of substrate metabolism in brown and white preadipocytes and
adipocytes. All adipoctye cell lines were assayed with Redox Dye Mix MB. (A) Substrates
resulting in the greatest difference in comparison score (CS) at 24 hours between immortalized
brown preadipocytes (blue lines) and 3T3-L1 white preadipocytes (green lines). To simplify the
analysis, we only considered the CS at the 24-hour time point measurement, reasoning that
since the accumulation of signal only increases over time, we would detect the most stable
differences at this time, rather than only detecting kinetic changes in absorbance. (B)
Substrates resulting in the greatest difference in CS at 24 hours between fully differentiated
brown (blue lines) and 3T3-L1 white adipocytes (green lines). Graphs are depicted as the
normalized change in absorbance (ΔΔAbs), using the zero-hour time point as baseline.
29
Bochner et al.
Figure S5. Analysis of effects of adipocyte differentiation on substrate metabolism. (A)
Substrates resulting in the greatest difference in comparison score (CS) at 24 hours between
undifferentiated 3T3-L1 preadipocytes (blue lines) and 3T3-L1 white adipocytes (green lines).
(B) Substrates resulting in the greatest difference in CS at 24 hours between undifferentiated
brown preadipocytes (blue lines) and brown adipocytes (green lines). Graphs are depicted as
the normalized change in absorbance.
30
Bochner et al.
Table S1. Plate maps of Phenotype MicroArray MicroPlates PM-M1 through -M4
PM-M1 MicroPlate™ - carbon-energy source assays
A1
Negative
Control
A2
Negative
Control
A3
Negative
Control
A4
-Cyclodextrin
A5
Dextrin
A6
Glycogen
A7
Maltitol
A8
Maltotriose
A9
D-Maltose
A10
D-Trehalose
A11
D-Cellobiose
A12
-Gentiobiose
B1
D-Glucose-6Phosphate
B2
-D-Glucose-1Phosphate
B3
L-Glucose
B4
-D-Glucose
B5
-D-Glucose
B6
-D-Glucose
B7
3-O-Methyl-DGlucose
B8
-Methyl-DGlucoside
B9
-Methyl-DGlucoside
B10
D-Salicin
B11
D-Sorbitol
B12
N-Acetyl-DGlucosamine
C1
C2
D-Glucosaminic D-Glucuronic
Acid
Acid
C3
Chondroitin-6Sulfate
C4
Mannan
C5
D-Mannose
C6
-Methyl-DMannoside
C7
D-Mannitol
C8
N-Acetyl--DMannosamine
C9
D-Melezitose
C10
Sucrose
C11
Palatinose
C12
D-Turanose
D1
D-Tagatose
D2
L-Sorbose
D3
L-Rhamnose
D4
L-Fucose
D5
D-Fucose
D6
D-Fructose-6Phosphate
D7
D-Fructose
D8
Stachyose
D9
D-Raffinose
D10
D-Lactitol
D11
Lactulose
D12
-D-Lactose
E1
Melibionic Acid
E2
D-Melibiose
E3
D-Galactose
E4
-Methyl-DGalactoside
E5
-Methyl-DGalactoside
E6
N-AcetylNeuraminic
Acid
E7
Pectin
E8
Sedoheptulosan
E9
Thymidine
E10
Uridine
E11
Adenosine
E12
Inosine
F1
Adonitol
F2
L- Arabinose
F3
D-Arabinose
F4
F5
Xylitol
-Methyl-DXylopyranoside
F6
Myo-Inositol
F7
Meso-Erythritol
F8
Propylene
glycol
F9
Ethanolamine
F10
F11
D,L- -Glycerol- Glycerol
Phosphate
F12
Citric Acid
G1
Tricarballylic
Acid
G2
D,L-Lactic Acid
G3
G4
G5
Methyl D-lactate Methyl pyruvate Pyruvic Acid
G6
-Keto-Glutaric
Acid
G7
Succinamic
Acid
G8
Succinic Acid
G9
Mono-Methyl
Succinate
G10
L-Malic Acid
G11
D-Malic Acid
G12
Meso-Tartaric
Acid
H1
Acetoacetic
Acid (a)
H2
-Amino-NButyric Acid
H3
-KetoButyric Acid
H6
-HydroxyButyric Acid
H7
Butyric Acid
H8
2,3-Butanediol
H9
3-Hydroxy-2Butanone
H10
Propionic Acid
H11
Acetic Acid
H12
Hexanoic Acid
H4
-HydroxyButyric Acid
H5
D,L--HydroxyButyric Acid
PM-M2 MicroPlate™ - carbon-energy and nitrogen source assays
A1
Negative
Control
A2
Negative
Control
A3
Negative
Control
A4
Tween 20
A5
Tween 40
A6
Tween 80
A7
Gelatin
A8
L-Alaninamide
A9
L-Alanine
A10
D-Alanine
A11
L-Arginine
A12
L-Asparagine
B1
B2
B3
B4
L-Aspartic Acid D-Aspartic Acid L-Glutamic Acid D-Glutamic
Acid
B5
L-Glutamine
B6
Glycine
B7
L-Histidine
B8
L-Homoserine
B9
Hydroxy-LProline
B10
L-Isoleucine
B11
L-Leucine
B12
L-Lysine
C1
L-Methionine
C2
L-Ornithine
C3
C4
L-Phenylalanine L-Proline
C5
L-Serine
C6
D-Serine
C7
L-Threonine
C8
D-Threonine
C9
L-Tryptophan
C10
L-Tyrosine
C11
L-Valine
C12
Ala-Ala
D1
Ala-Arg
D2
Ala-Asn
D3
Ala-Asp
D4
Ala-Glu
D5
Ala-Gln
D6
Ala-Gly
D7
Ala-His
D8
Ala-Ile
D9
Ala-Leu
D10
Ala-Lys
D11
Ala-Met
D12
Ala-Phe
E1
Ala-Pro
E2
Ala-Ser
E3
Ala-Thr
E4
Ala-Trp
E5
Ala-Tyr
E6
Ala-Val
E7
Arg-Ala
(b)
E8
Arg-Arg
(b)
E9
Arg-Asp
E10
Arg-Gln
E11
Arg-Glu
E12
Arg-Ile
(b)
F1
Arg-Leu
(b)
F2
Arg-Lys
(b)
F3
Arg-Met
(b)
F4
Arg-Phe
(b)
F5
Arg-Ser
(b)
F6
Arg-Trp
F7
Arg-Tyr
(b)
F8
Arg-Val
(b)
F9
Asn-Glu
F10
Asn-Val
F11
Asp-Ala
F12
Asp-Asp
G1
Asp-Glu
G2
Asp-Gln
G3
Asp-Gly
G4
Asp-Leu
G5
Asp-Lys
G6
Asp-Phe
G7
Asp-Trp
G8
Asp-Val
G9
Glu-Ala
G10
Glu-Asp
G11
Glu-Glu
G12
Glu-Gly
H1
Glu-Ser
H2
Glu-Trp
H3
Glu-Tyr
H4
Glu-Val
H5
Gln-Glu
H6
Gln-Gln
H7
Gln-Gly
H8
Gly-Ala
H9
Gly-Arg
H10
Gly-Asn
H11
Gly-Asp
H12
-D-Glucose
31
Bochner et al.
PM-M3 MicroPlate™ - carbon-energy and nitrogen source assays
A1
Negative
Control
A2
Negative
Control
A3
Negative
Control
A4
Gly-Gly
A5
Gly-His
A6
Gly-Ile
A7
Gly-Leu
A8
Gly-Lys
A9
Gly-Met
A10
Gly-Phe
A11
Gly-Pro
A12
Gly-Ser
B1
Gly-Thr
B2
Gly-Trp
B3
Gly-Tyr
B4
Gly-Val
B5
His-Ala
B6
His-Asp
B7
His-Glu
B8
His-Gly
B9
His-His
(c)
B10
His-Leu
B11
His-Lys
(d)
B12
His-Met
C1
His-Pro
C2
His-Ser
C3
His-Trp
C4
His-Tyr
C5
His-Val
C6
Ile-Ala
C7
Ile-Arg
(b)
C8
Ile-Asn
C9
Ile-Gln
C10
Ile-Gly
C11
Ile-His
C12
Ile-Ile
D1
Ile-Leu
D2
Ile-Met
D3
Ile-Phe
D4
Ile-Pro
D5
Ile-Ser
D6
Ile-Trp
D7
Ile-Tyr
D8
Ile-Val
D9
Leu-Ala
D10
Leu-Arg
(b)
D11
Leu-Asn
D12
Leu-Asp
E1
Leu-Glu
E2
Leu-Gly
E3
Leu-His
E4
Leu-Ile
E5
Leu-Leu
E6
Leu-Met
E7
Leu-Phe
E8
Leu-Pro
E9
Leu-Ser
E10
Leu-Trp
E11
Leu-Tyr
E12
Leu-Val
F1
Lys-Ala
(d)
F2
Lys-Arg
(b)
F3
Lys-Asp
F4
Lys-Glu
F5
Lys-Gly
F6
Lys-Ile
(b)
F7
Lys-Leu
(b)
F8
Lys-Lys
F9
Lys-Met
(e)
F10
Lys-Phe
F11
Lys-Pro
F12
Lys-Ser
G1
Lys-Thr
G2
Lys-Trp
(b)
G3
Lys-Tyr
(b)
G4
Lys-Val
(d)
G5
Met-Arg
(b)
G6
Met-Asp
G7
Met-Gln
G8
Met-Glu
G9
Met-Gly
G10
Met-His
G11
Met-Ile
G12
Met-Leu
H1
Met-Lys
(e)
H2
Met-Met
H3
Met-Phe
H4
Met-Pro
H5
Met-Thr
H6
Met-Trp
H7
Met-Tyr
H8
Met-Val
H9
Phe-Ala
H10
Phe-Asp
H11
Phe-Glu
H12
-D-Glucose
PM-M4 MicroPlate™ - carbon-energy and nitrogen source assays
A1
Negative
Control
A2
Negative
Control
A3
Negative
Control
A4
Phe-Gly
A5
Phe-Ile
A6
Phe-Met
A7
Phe-Phe
A8
Phe-Pro
A9
Phe-Ser
A10
Phe-Trp
A11
Phe-Tyr
A12
Phe-Val
B1
Pro-Ala
B2
Pro-Arg
(b)
B3
Pro-Asn
B4
Pro-Asp
B5
Pro-Glu
B6
Pro-Gln
B7
Pro-Gly
B8
Pro-Hyp
B9
Pro-Ile
B10
Pro-Leu
B11
Pro-Lys
(b)
B12
Pro-Phe
C1
Pro-Pro
C2
Pro-Ser
C3
Pro-Trp
C4
Pro-Tyr
C5
Pro-Val
C6
Ser-Ala
C7
Ser-Asn
C8
Ser-Asp
C9
Ser-Glu
C10
Ser-Gln
C11
Ser-Gly
C12
Ser-His
(b)
D1
Ser-Leu
D2
Ser-Met
D3
Ser-Phe
D4
Ser-Pro
D5
Ser-Ser
D6
Ser-Tyr
D7
Ser-Val
D8
Thr-Ala
D9
Thr-Arg
(f)
D10
Thr-Asp
D11
Thr-Glu
D12
Thr-Gln
E1
Thr-Gly
E2
Thr-Leu
E3
Thr-Met
E4
Thr-Phe
E5
Thr-Pro
E6
Thr-Ser
E7
Trp-Ala
E8
Trp-Arg
E9
Trp-Asp
E10
Trp-Glu
E11
Trp-Gly
E12
Trp-Leu
F1
Trp-Lys
(e)
F2
Trp-Phe
F3
Trp-Ser
F4
Trp-Trp
F5
Trp-Tyr
F6
Trp-Val
F7
Tyr-Ala
F8
Tyr-Gln
F9
Tyr-Glu
F10
Tyr-Gly
F11
Tyr-His
F12
Tyr-Ile
G1
Tyr-Leu
G2
Tyr-Lys
G3
Tyr-Phe
G4
Tyr-Trp
G5
Tyr-Tyr
G6
Tyr-Val
G7
Val-Ala
G8
Val-Arg
G9
Val-Asn
G10
Val-Asp
G11
Val-Glu
G12
Val-Gln
H1
Val-Gly
H2
Val-His
H3
Val-Ile
H4
Val-Leu
H5
Val-Lys
H6
Val-Met
H7
Val-Phe
H8
Val-Pro
H9
Val-Ser
H10
Val-Tyr
H11
Val-Val
H12
-D-Glucose
32