Induced Pluripotent Stem Cells Show Metabolomic
Differences to Embryonic Stem Cells in Polyunsaturated
Phosphatidylcholines and Primary Metabolism
John K. Meissen1, Benjamin T. K. Yuen2, Tobias Kind1, John W. Riggs2, Dinesh K. Barupal1,
Paul S. Knoepfler1,2*, Oliver Fiehn1*
1University of California Davis Genome Center, University of California Davis, Davis, California, United States of America, 2Department of Cell Biology and Human
Anatomy, University of California Davis School of Medicine, Davis, California, United States of America
Induced pluripotent stem cells are different from embryonic stem cells as shown by epigenetic and genomics analyses.
Depending on cell types and culture conditions, such genetic alterations can lead to different metabolic phenotypes which
may impact replication rates, membrane properties and cell differentiation. We here applied a comprehensive
metabolomics strategy incorporating nanoelectrospray ion trap mass spectrometry (MS), gas chromatography-time of
flight MS, and hydrophilic interaction- and reversed phase-liquid chromatography-quadrupole time-of-flight MS to examine
the metabolome of induced pluripotent stem cells (iPSCs) compared to parental fibroblasts as well as to reference
embryonic stem cells (ESCs). With over 250 identified metabolites and a range of structurally unknown compounds,
quantitative and statistical metabolome data were mapped onto a metabolite networks describing the metabolic state of
iPSCs relative to other cell types. Overall iPSCs exhibited a striking shift metabolically away from parental fibroblasts and
toward ESCs, suggestive of near complete metabolic reprogramming. Differences between pluripotent cell types were not
observed in carbohydrate or hydroxyl acid metabolism, pentose phosphate pathway metabolites, or free fatty acids.
However, significant differences between iPSCs and ESCs were evident in phosphatidylcholine and phosphatidylethanol-
amine lipid structures, essential and non-essential amino acids, and metabolites involved in polyamine biosynthesis.
Together our findings demonstrate that during cellular reprogramming, the metabolome of fibroblasts is also
reprogrammed to take on an ESC-like profile, but there are select unique differences apparent in iPSCs. The identified
metabolomics signatures of iPSCs and ESCs may have important implications for functional regulation of maintenance and
induction of pluripotency.
Citation: Meissen JK, Yuen BTK, Kind T, Riggs JW, Barupal DK, et al. (2012) Induced Pluripotent Stem Cells Show Metabolomic Differences to Embryonic Stem
Cells in Polyunsaturated Phosphatidylcholines and Primary Metabolism. PLoS ONE 7(10): e46770. doi:10.1371/journal.pone.0046770
Editor: Petras Dzeja, Mayo Clinic, United States of America
Received May 2, 2012; Accepted September 5, 2012; Published October 15, 2012
Copyright: ? 2012 Meissen 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: This research was financially supported by California Institute for Regenerative Medicine grant RN2-00922 and National Institutes of Health grant
1R01GM100782 (both to PSK and National Institutes of Health grant R01 DK078328 and National Science Foundation grant MCB-1139644 (both to OF). The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (PSK); firstname.lastname@example.org (OF)
Embryonic stem cells (ESCs) possess substantial potential for use
in regenerative medicine therapies due to their capacity for
unlimited self-renewal and their pluripotency to differentiate into
all cell types except extraembryonic tissues. However, several
challenges face the potential use of ESCs for therapies, including
their potential immune rejection as allotransplants. Subsequently,
the discovery that introduction of the Oct3/4, Sox2, c-Myc, and
Klf4 transcription factor encoding genes could reprogram somatic
cells to exhibit the morphology, growth properties, and gene
expression of ESCs greatly expanded the possibilities of stem cell-
based treatments and potentially resolved some of the key issues
with ESC [1,2,3,4,5,6]. Since their discovery in 2006, these cells,
termed induced pluripotent stem cells (iPSCs), have been studied
extensively, particularly to examine the degree to which they
resemble ESC [7,8,9].
The high degree of similarity between ESCs and iPSCs has been
confirmed on many levels [8,9]. For example, tetraploid comple-
mentation experiments with iPSCs generating full-term mice
demonstrated similarity in developmental potential and pluripo-
tency [10,11,12]. Gene expression analysis, including broad
profiling and focused analysis of characteristic ESC genes, as well
as epigenetic studies further supported a strikingly high degree of
similarity between iPSCs and ESCs [1,2,3,4,5,6]. However,
several of these reports identified a small range of differences
between the two pluripotent stem cell types, illustrating that while
these cells are similar, they are not identical. Some published
reports suggest iPSCs are their own unique subtype of pluripotent
cell with a distinguishable gene expression profile , and that
iPSCs retain epigenetic properties or ‘‘memory’’ from their
parental cells of origin . A recent publication reported
immune rejection of teratomas formed from iPSCs in contrast to
teratomas formed from mouse ESCs (mESCs) . In addition, in
some cases iPSCs may possess a few mutations of currently
unknown function [16,17,18,19].
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One important, but relatively less explored area related to
pluripotency and stem cell biology is cellular metabolism.
Metabolomics has significant potential to advance our under-
standing of stem cells and their relationship to other cell types, as it
effectively characterizes the biochemical state of a particular cell
type and delineates relevant changes compared to other experi-
mental conditions . The metabolome consists of an immense
number of components connected by many complex biological
pathways spanning a wide array of structural classes such as lipids,
carbohydrates, amino acids, and nucleotide structures. This
chemical diversity presents a particular challenge as a single
analytical method cannot encompass metabolites with such diverse
chemical properties [21,22]. Differences in metabolic oxidation
states were reported between mESCs and differentiated cells ,
and changes in amino acid and carbohydrate metabolism were
found in mesenchymal stem cells relative to glioma cells .
Recently, the metabolic relationships between iPSCs, parental
fibroblasts, and ESCs demonstrated that iPSC reprogramming
resulted in metabolite profiles with a high degree of similarity to
mESCs . Specific metabolites including free fatty acids and S-
adenosylmethionine cycle members displayed statistically different
levels between pluripotent cell lines; hydroxyl acid levels were
statistically different when comparing pluripotent cell lines to
fibroblasts. Here, we employed a different metabolomics approach
to evaluate and compare the metabolite profiles of iPSCs relative
to parental mouse embryonic fibroblast cells (m15) and mESCs
using a previously characterized iPSC line . Specifically, we
applied four different metabolomics platforms for analysis: direct
infusion nanoelectrospray ionization mass spectrometry (nanoESI-
MS), gas chromatography time-of-flight mass spectrometry (GC-
TOF MS), and hydrophilic interaction (HILIC) and reverse phase
(RP) chromatography quadrupole time-of-flight mass spectrometry
(HILIC/RP-QTOF MS), to cover a greater extent of the stem cell
metabolome. When integrating the data from these platforms
based on chemical and biological similarity, resultant metabolic
interaction network graphs support the notion that iPSCs have
undergone near complete metabolome reprogramming, with some
important metabolic changes between iPSCs and mESCs which
differed from those previously reported .
Direct infusion shotgun lipidomics illustrates similarity of
mESC and iPSC lipid phenotypes
To begin characterizing the metabolome of mESCs and iPSCs,
we employed nanoelectrospray ion trap tandem mass spectrom-
etry (nanoESI-MS) for a first-screen metabolic investigation of the
cell extracts, focusing on a rapid comparison of lipid profiles.
Overall, nanoESI-MS yielded approximately 600 distinguishable
ions per mass spectrum when processed with the GeneData
Expressionist Refiner MS tool; individual ions were selected for
structural investigation by collision induced dissociations. In direct
infusion mass spectrometry, lipids are not separated by chroma-
tography causing some level of ambiguity for annotating isobaric
lipids and some lipid adduct species. Nevertheless, 157 different
lipid structures including phosphatidylcholines (PC), phosphati-
dylethanolamines (PE), triacylglycerols (TG), diacylglycerols (DG),
phosphatidylserines (PS), sphingomyelins (SM), and acylcarnitines
with a diverse assortment of acyl structures were annotated by
matching experimental to predicted mass spectral fragmentations
by the in-house LipidBlast database, based on their major
structural features such as lipid head groups and acyl chains
Ion abundances were normalized to total intensity for each
sample and used to determine which cell types were closest to each
other in overall lipid ratios and diversity. While measurements of
individual lipids in nanoESI-MS might be susceptible to ionization
suppression or be obscured by isobaric interferences, use of direct
infusion full scan mass spectral fingerprints is acceptable for
defining the degree of between-group metabolic differences and
has been shown as a suitable tool in lipid research , specifically
because phospholipids are ideal molecules for electrospray
ionization. Hence, we have used these lipidomic fingerprints as
input for multivariate statistics to determine differences in lipid
phenotypes between stem cells and m15 fibroblasts. Indeed,
unsupervised Principal Component Analysis (PCA) of lipidomic
fingerprints clearly separated m15 fibroblast cells from the lipid
signatures of pluripotent cells (Figure 1). This finding supports the
notion that major metabolic reprogramming events take place on
the level of membrane lipids. In fact, PCA analysis of primary
metabolism data confirmed that metabolic reprogramming in
pluripotent stem cells extends beyond membrane lipids (Figure
S1). As predicted, iPSC lipidomic and metabolomic phenotypes
were so similar to mESCs that supervised techniques had to be
used to distinguish the iPSC from mESC clusters. Partial least
square multivariate analysis (PLS) showed that there are discern-
ible metabolic differences between iPSCs and mESCs for both
lipids and primary metabolites in addition to the dominant
metabolic reprogramming from fibroblast to pluripotent cells
(Figure 1 and Figure S1). iPSCs are known to possess many of the
properties of ESCs, yet still possess distinguishing characteristics
[8,9] which are also reflected at the metabolic level in our results as
the two cell types expressed characteristic differences in overall
lipid phenotypes. This finding supports the underlying hypothesis
that the overall similarity in biological behavior and gene
expression of mESCs and iPSCs is also reflected in overall
metabolic similarities, specifically in the diversity of lipid structures
that define membranes and that are known to exert potent
signaling function in cell biology . While lipid fingerprinting
enabled a rapid and meaningful comparison of overall metabolic
characteristics, the shortcomings of direct infusion mass spectrom-
etry necessitated the application of more detailed quantitative
comparisons by superior, yet more time consuming, chromatog-
raphy-based MS techniques.
Stem cell metabolomics requires more than one platform
for comprehensive annotation
In order to detail the differences in lipids between stem cell types
observed by nanoESI-MS fingerprinting, we profiled all three cell
lines by hydrophilic interaction chromatography (HILIC) -
accurate mass quadrupole time-of-flight (QTOF) tandem mass
spectrometry (MS/MS). This technique yields more accurate lipid
annotations because each annotated compound must fit the
predicted accurate mass with a less than 1.5 mDa error, in
addition to chromatographic separation of isobaric interferences
and to matching the modeled LipidBlast mass spectral fragmen-
tations (Figure 2). Furthermore, QTOF mass spectrometry is not
limited by the 1/3 mass exclusion rule, unlike ion trap MS/MS,
enabling the direct detection of low m/z fragment masses such as
choline head groups. However, the nanoESI-ion trap MS/MS
enables generation of more comprehensive fragmentations on
many more precursor ions during infusions, and hence, yields
more annotated lipid structures than single injection HILIC-
QTOF MS/MS. Direct comparison of lipids that were analyzed
by LC-QTOF MS or by nanoESI-ion trap MS/MS methods
resulted in high concordance in quantitative levels of lipids
between all three cell types, except for a few lipids that occurred in
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different isobaric or isomeric forms and which hence need LC-
separation prior to detection (Figure S2).
HILIC-QTOF methodology provided data enabling annotation
of many structures beyond lipids, and combined HILIC and
reversed-phase liquid chromatography QTOF data sets, processed
with METLIN, NIST, and LipidBlast MS/MS libraries ,
yielded a total of 55 annotated structures (Table S2). Liquid
chromatography, even when used with particle sizes of less than
2 mm, still provides more selectivity (and less universality) and
lower total peak capacity than classic gas chromatography (GC),
especially for sugars and sugar derivatives. After derivatization and
GC-TOF mass spectrometry data acquisition, the in-house
BinBase data processing system  yielded 111 different
identified and quantified structures including glycolysis, pentose
phosphate pathway, and citric acid cycle metabolites as well as
amino acids, free fatty acids, and sugar alcohols. However,
complex and thermolabile compounds like membrane lipids or
cations like acylcarnitines are not amenable to gas chromatogra-
phy. Therefore, combining data from LC-QTOF MS methods
with different chromatographic separation techniques and GC-
TOF MS technology expanded the structural coverage of the stem
cell metabolomes beyond the capacity of a single system. As
expected, metabolites that were detected by both LC-QTOF MS
and GC-TOF MS based methods provided similar quantitative
results (Table S2), proving that data can be integrated into
comprehensive metabolomic maps without quantitative distortions
caused by one of our validated metabolomic platforms.
Degree of fatty acyl unsaturation in complex lipids is a
defining characteristic for stem cells
In order to reveal differences in metabolic regulation between
the cell types used in this study, we mapped all identified
compounds from LC-QTOF and GC-TOF MS data onto
metabolite network graphs that were generated by the MetaMapp
tool which integrates both biochemical and chemical similarity
(Figures 3,4) [31,32]. Interestingly, the degree of desaturation of
phosphatidylcholine (PC) membrane lipids was different when
comparing mESCs to m15 fibroblast cells (Figure 3A). All seven
detected PC structures with three or more double bonds were
detected at statistically significant elevated levels in mESCs, and all
PCs with one or two double bonds were less abundant in mESC
though only two of six presented a p,0.05. This result correlates
with previously reported observations of higher levels of unsatu-
rated structures, including PCs, in mESCs . Complementing
this report, our results now provide evidence that these elevated
levels of highly unsaturated structures only affect phosphatidyl-
cholines, as phosphatidylethanolamines (PE), the second largest
class of complex membrane lipids revealed in our annotations, did
not display the same compositional difference observed for PCs.
Only one highly unsaturated PE and two plasmenyl-PEs with four
or more double bonds were observed at statistically significant
higher levels, out of a total of nine highly unsaturated PE and
plasmenyl-PE structures. In contrast, seven out of eight more
saturated PE and plasmenyl-PE lipids with one to three double
bonds were less abundant in mESCs compared to the membrane
lipid compositions in fibroblasts (p,0.05). Phosphoethanolamine,
a biosynthetic precursor for PEs , was also elevated in mESCs.
A direct comparison of complex lipids with the same acyl chains
confirmed this differential regulation of classes of membrane lipids:
PC 36:3 (18:2/18:1) was 53% higher in mESCs compared to
fibroblasts, while PE 36:3 (18:2/18:1) was found to be 33% lower
in mESCs. The detected sphingomyelins with one or two double
bonds were not found differentially expressed in these compari-
Figure 1. Lipidomic analysis of pluripotent cell lines (iPSC, mESC) and mouse embryonic fibroblasts (mEF) using nanoelectrospray-
linear ion trap MS/MS. Upper panel: unsupervised Principal Component analysis (left) and supervised Partial Least Square regression analysis
(right). Multivariate vectors with percent total variance explained. Lower panel: examples of differentially regulated membrane lipids.
PC=phosphatidylcholines, PE=phosphatidylethanolamine, with number of carbons followed by number of double bonds. Mean ion intensities
6 standard errors (boxes) and non-outlier ranges (whiskers).
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The effect of metabolic reprogramming of membrane lipids in
mESCs was also seen in in the comparison of iPSCs to m15
fibroblasts (Figure 3B). Highly unsaturated PC and plasmenyl-PC
structures with three or more double bonds increased in iPSCs,
while less unsaturated PCs and plasmenyl-PCs with one or two
double bonds were found at decreased levels. The characteristic
differential reprogramming of PEs and PCs in mESCs was also
confirmed for iPSCs. Highly unsaturated PE structures did not
display the same broad increase observed in PCs, and PE and
plasmenyl-PE structures with one to three double bonds
decreased. Phosphoethanolamine levels increased in iPSCs while
SMs were found largely unchanged.
Down-regulation of amino acid metabolism is
characteristic for pluripotent stem cells
We found that one of the most apparent features of metabolic
reprogramming is the large decrease in amino acid pools in iPSCs
relative to parental fibroblasts such that iPSCs are far more similar
in this regard to mESC (Figure 3A,B). Changes in amino acids
were not restricted to a particular subclass but spanned many
different biosynthetic pathways and transport mechanisms. In
comparison, changes in carbohydrate and hydroxyl acid metab-
olism, including citric acid cycle metabolites, were far less
pronounced. Annotated structures included four glycolysis metab-
olites, two pentose phosphate pathway metabolites, and four citric
acid cycle metabolites. None of these structures presented
significant differences between mESC and m15 fibroblast cell
types, and only succinic acid showed a slight decrease in
concentration in iPSCs compared to m15 fibroblasts. Despite
the large changes in expression of complex lipids, no change was
observed in the status of free fatty acids in the compared cell types.
iPSCs and mESCs differ in lipid profiles, amino acids, and
polyamine biosynthesis metabolites
The iPSCs displayed several differences from the parental m15
fibroblast cells that were not detected in the mESC/m15
comparison (Figure 3A,B). All six lyso-PC and lyso-PE structures,
representing either saturated acyl chains or acyl chains with a
single double bond, were significantly decreased in iPSCs. In
iPSCs, but not in mESCs, further metabolic differences with
fibroblasts were apparent in polyamine biosynthesis by a down
regulation of both putrescine and ornithine, a putrescine
precursor, as well as 59-methylthioadenosine, a product of
polyamine biosynthesis which inhibits spermine biosynthesis
downstream of putrescine in the polyamine biosynthesis pathway
[33,34]. Thirdly, purine metabolism was found to be significantly
changed in iPSCs with a decrease of the nucleotide adenosine-5-
phosphate and its deamination product inosine-5-phosphate which
Figure 2. Annotation of complex lipids by tandem mass spectrometry (MS/MS) and the LipidBlast mass spectral library. LipidBlast
MS/MS spectra were modeled from fragmentation spectra of authentic reference standards using computational scaffolds that altered acyl chain
lengths and degree of unsaturation for each lipid class. Shown here as example is the annotation of the experimental MS/MS spectrum of
arachidonyl-palmitoyl phosphatidylcholine (PC 36:4) by matching major precursor and fragment ions as well as low abundant fragments between m/
z 478–599 using LipidBlast.
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led to an increase in xanthine, a metabolite in the purine salvage
pathway . Together with the more pronounced decreases in
amino acid metabolism, metabolic reprogramming in iPSCs had
other off-target effects on pathways involved in nitrogen metab-
olism, namely polyamine and purine biosynthetic pathways.
We compared the iPSCs and mESCs directly to each other to
obtain a better overview of changes in metabolite profiles
(Figure 4). This comparison revealed substantially fewer differ-
ences than comparisons of either pluripotent stem cell line to the
m15 fibroblast cell line, confirming the clustering of lipid
phenotypes in the direct infusion partial least square analysis
(Figure 1). Despite their overall similarity, there were some clear
differences between the two pluripotent stem cell lines. While both
iPSCs and mESCs displayed similar lipid profiles, the magnitude
of those differences varied significantly by cell type. All eight PC
and plasmenyl-PC structures with three or more double bonds
were elevated in mESCs relative to iPSCs, while shorter chain,
more saturated PC and plasmenyl-PC structures were present at
similar levels. All PEs with one or two double bonds were less
abundant in the mESCs, with more than half displaying statistical
significance, while more unsaturated PEs did not display
substantial differences. All reported lyso-PC structures were
present at higher levels in mESCs versus iPSCs, but this effect
was most pronounced and only statistically relevant in saturated
lyso-PCs. Again, the appearance of 5-methylthioadenosine and
putrescine implicated an effect on the polyamine biosynthesis
Figure 3. MetaMapp visualization of metabolic changes in stem cells relative to m15 fibroblast cells. Red nodes represent metabolites
with increased signal intensity in stem cells; blue nodes represent metabolites with decreased signal intensity in stem cells (p,0.05). White nodes
represent detected metabolites without statistically significant changes. Node sizes scale with fold change. Blue edges connect metabolites with
Tanimoto chemical similarity .700; red edges connect reaction-pair metabolites from the KEGG RPAIR database. (A): MetaMapp network comparing
mESCs to m15 fibroblasts. (B): MetaMapp network comparing mESCs to m15 fibroblasts.
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pathway. Numerous amino acids displayed differences between
the pluripotent cell types. Interestingly, the amino acids that
displayed the greatest differences were those that were uncharged
yet polar in nature.
These data indicate that some of the major differences in lipid
profiles between pluripotent stem cells and the fibroblast cells are
more pronounced in mESCs compared to iPSCs. In addition,
elevated levels of 59-methylthioadenosine and lower levels of
amino acids and putrescine in iPSCs relative to mESCs suggest
that while the iPSC metabolome is shifted during cellular
reprogramming to be remarkably similar to that of mESCs,
reprogramming primary metabolism may not be fully complete in
We here show that metabolomics can discern the effects of
genetic differences of cell lines by highlighting metabolic
similarities and differences of pluripotent cell types compared to
somatic cells. We employed multiple mass spectrometry-based
techniques to overcome analytical challenges, such as efficient
throughput and metabolite coverage as all analytical methods
possess coverage limitations [21,22]. Direct infusion nanoESI-mass
spectrometry enabled a rapid analysis of different lipid structures
and confirmed that the pluripotent cells are strikingly similar in
terms of lipid metabolite signatures relative to fibroblasts
indicating near complete metabolic reprogramming of iPSCs.
However, it also revealed some potentially important differences
between iPSCs and the parental fibroblasts and more subtle
distinctions between mESCs and iPSCs.
While the nanoESI-MS method is an effective preliminary
approach to observe variation between sample groups due to
speed and MS/MS coverage, it retains some limitations. The lack
of chromatographic separation and low mass spectrometric
resolution causes isobaric and isomeric structures to form single
peaks in the mass spectra, complicating correlation of ion
intensities to a single structure. Direct infusion analysis is also
particularly susceptible to ion suppression, potentially compromis-
ing appropriate reflection of compound concentration [22,36].
Application of (lower throughput) GC-TOF MS and LC-QTOF
MS chromatography-based analytical methods minimized the
potential impact of these limitations and expanded metabolite
coverage beyond the capabilities of either individual system. The
GC-TOF MS method provided the greatest number of identified
compounds, which is a reflection of a highly developed
infrastructure designed specifically for the applied system and
method . While MS/MS libraries are available for use with
the LC-QTOF MS system, identification was greatly hampered by
both library coverage and acquisition of MS/MS spectra. The LC-
QTOF MS uses a quadrupole to isolate a particular ion for
generation of MS/MS data, and if a particular ion does not
achieve the relative intensity necessary to trigger isolation in a
data-dependent MS/MS acquisition mode, the MS/MS data
necessary for annotation will not be available. Subsequently, while
the current analysis covers many structures, there are still many
other metabolites that were not annotated and may provide
additional insight into the metabolic state of iPSC, mESC, and
m15 fibroblast cell types.
Several reports manifest large metabolic differences between
fibroblast or other somatic cells and pluripotent cells, for example
with respect to energy metabolism [25,37,38]. Metabolomic
research groups have stated a range of other significant differences
after nuclear reprogramming, most notably on the level of
nucleotides and purines  and membrane lipids , but
neither group identified metabolic differences in lactate or glucose
metabolism. In accordance with these reports, we have observed
very large differences in metabolic phenotypes between mouse
embryonic fibroblasts and embryonic stem cells (Figure 1 and
Figure 4. MetaMapp visualization of metabolic changes in mouse embryonic stem cells relative to metabolite levels in induced
pluripotent stem cells. Red nodes represent metabolites with increased signal intensity in stem cells; blue nodes represent metabolites with
decreased signal intensity in stem cells (p,0.05). White nodes represent detected metabolites without statistically significant changes. Node sizes
scale with fold change. Blue edges connect metabolites with Tanimoto chemical similarity .700; red edges connect reaction-pair metabolites from
the KEGG RPAIR database.
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Figure S1) which mostly conferred metabolic reprogramming in
amino acid and lipid metabolism, but not in compounds such as
taurine, reported as being different in extracellular footprints 5–7
days after nuclear reprogramming by Folmes et al. . We
detected intracellular taurine levels with two independent
analytical techniques, LC-QTOF MS and GC-TOF MS. Neither
of these methods found quantitative metabolic differences for
taurine or energy-related metabolism. Authors have noted the
similarity between energy metabolism in stem cells and cancer cells
with respect to the use of anaerobic glycolysis for generating ATP
rather than by mitochondrial oxidation , similar to the well-
known Warburg effect in cancer cells . It has been pointed out
to be of critical importance to control for available oxygen levels in
cell cultures if hypotheses about hypoxic metabolism are to be
tested, for example in cancer cells . To our knowledge,
however, none of the reports in stem cell metabolism actually
monitored and controlled the level of oxygen available to cells,
including results reported here.
This work detailed many metabolic similarities and differences
between iPSCs, mESCs, and embryonic fibroblasts beyond those
metabolites covered recently  by encompassing additional
structures including lipids, polyamines and amino acids. While
parts of the metabolome coverage were overlapping, quantitative
results revealed conflicting trends to this previous publication .
For example, in the study presented here we did not find any
increase in free fatty acids, unlike a seven-fold difference reported
before between different pluripotent cell lines , and we also
could not confirm broad changes in hydroxyl acid levels when
comparing pluripotent cell lines to fibroblasts. While we did
observe statistically significant changes in 59-methylthioadenosine
levels in iPSCs relative to ESCs, the effect was less than two-fold
and in the opposite direction from the previously published
It is unlikely that conflicting results between our data and the
recently published study  are due to the analytical method-
ologies used in our research, because we have utilized the broadest
line of technologies that were yet applied to stem cell metabolism.
NanoESI-MS, GC-TOF MS, and HILIC- and RP liquid
chromatography-QTOF MS based technologies facilitated expan-
sion of metabolite profiling to encompass a chemically diverse
array of cellular metabolites and enabled an in-depth investigation
and comparison of iPSC, m15 fibroblast, and mESC metabolite
profiles. Application of this global approach delineated some of the
features which, despite their similarity, distinguish iPSCs from
mESCs and identified candidate mechanisms that may be subject
to more targeted, focused methods to attain a more clear
understanding of potential cause and biological impact. Additional
studies of metabolic phenotypes and genomic differences of iPSCs,
ESCs, and embryonic fibroblasts are critical to further advances in
our understanding of pluripotent cell metabolism.
Differences in the degree and range of detected metabolic
changes in stem cell reprogramming may not only be founded in
differences in cell culture conditions, but may also relate to the
differences in cell lines investigated. For example, line-to-line
variability in induced pluripotent stem cells has also been
demonstrated repeatedly; for example, different ways to derive
iPS-cells directly influence x-inactivation and epigenetic variability
[41,42]. In analogy to the apparently conflicting results on stem
cell metabolism, proteomics analyses also showed substantial
differences between each iPS cell line studied, in addition to
differences between individual embryonic stem cell lines .
Overall, all reports so far concur that metabolic reprogramming is
a key feature in reprogramming cells into a pluripotent state which
may involve a wide range of metabolic pathways. The extent of
which pathways are mostly reprogrammed appear to be depen-
dent on the actual culture conditions, time points and cell types
being studied. Indeed, taking together these different reports,
metabolomics may be a tool to determine the phenotypic
proximity of different iPSC lines to embryonic stem cells, in
addition to informing about metabolic reprogramming events
from fibroblast cells.
An area of intense interest in the stem cell field has been
defining the properties of iPSCs relative to ESCs. Just how similar
are iPSCs and ESCs? Based on previous work at the cell biological,
gene expression, and epigenetic levels, the two pluripotent stem
cell types are remarkably similar, although not identical. We
detected higher levels of phosphatidylcholines comprised of three
or more double bonds in mESCs relative to m15 fibroblasts,
confirming recently reported results . Such increase in
unsaturation levels of polyunsaturated PCs may alter membrane
fluidity and hence can contribute to physiological changes that are
relevant for stem-cell phenotypes. However, the more detailed
investigation presented here also reveals that this up-regulation of
membrane lipids did not extent to phosphatidylethanolamines in
either pluripotent cell type. Indeed, sometimes just the opposite
regulation was found as for the 18:1/18:2 PC and 18:1/18:2 PE
lipids, especially when comparing mESCs to fibroblasts. iPSCs
displayed the same tendency, supporting the concept that the iPSC
model reflects ESCs in terms of regulating membrane composi-
tion. Such differential changes may also be due to different activity
levels of methylating enzymes, because PCs may originate from
PEs by methylation steps catalysed by the enzyme phosphatidyl-
ethanolamine N-methyltransferase, a ,20 kDa transmembrane-
spanning enzyme located mainly in the endoplasmic reticulum.
PCs may otherwise originate de novo from CDP-choline and
diacylglycerols, but the exact origin of PCs in these cell types has
not been determined yet. DNA- and Histone-methyltransferase
activities are well known to be involved in stem cell developmental
phenotypes . It is interesting that the magnitude of PE-
dependent methyltransferase activity was less pronounced in
iPSCs than in mESCs, suggesting the genetic reprogramming of
the iPSC line was not entirely adequate to yield identical mESC
Metabolic differences between the iPSC and mESC lines were
also evident in primary metabolism, specifically for amino acids.
Both cell types yielded lower amounts of free amino acids
compared to the m15 fibroblasts, but several amino acids were
present at even lower levels in iPSCs relative to mESCs. While this
dissimilarity might point to differences in protein synthesis (i.e.
differences in amino acid consumption), differences in amino acid
transporter activities would be another potential explanation given
that both essential and non-essential polar uncharged amino acids
displayed the most substantial differences. Statistically significant
changes in both putrescine and 5-methylthioadenosine indicate
that the polyamine biosynthesis pathway is another deviation
between mESCs and iPSCs. This effect is notable due to
polyamine biosynthesis involvement in cell proliferation and
The body of literature in stem cell metabolism is growing.
However, at this point mechanistic interpretations have not
established which parts of metabolic reprogramming are a
necessary condition for pluripotency and which metabolic
differences are consequence rather than causal factor in cellular
de-differentiation. Detailed investigations on mechanistic aspects
of stem cell metabolism, including flux studies and studies under
hypoxia versus normoxic conditions, may be needed to reveal the
underlying metabolic prerequisites for pluripotency.
Stem Cell Metabolomics
PLOS ONE | www.plosone.org7 October 2012 | Volume 7 | Issue 10 | e46770
Materials and Methods
Cell material was developed, cultured, and characterized as
described previously . Mouse embryonic fibroblast and
pluripotent cells were maintained with identical culture media
(ES cell media). The induced pluripotent stem cells showed both
cellular phenotypic and gene expression profiles similar to levels
observed in mouse embryonic stem cells. To prepare cell pellets for
metabolite extraction, all cells were harvested by trypsinization,
washed three times with cold PBS, divided into one million cell
aliquots, and flash frozen in liquid N2. Cell pellets were stored at
280uC prior to extraction.
Three replicates from each cell type were used for nanoESI-MS
analysis. Cellular metabolites were extracted with 225 mL of
methanol, 750 mL of t-butyl methyl ether, and 187.5 mL of H2O.
Mass spectrometry analysis was performed with a LTQ linear ion
trap mass spectrometer (ThermoFisher Scientific, San Jose, CA)
coupled to an Advion NanoMate chip based nanoelectrospray
ionization source (Advion Biosciences Inc., Ithaca, NY). Sample
material was analyzed in positive ion mode and MS/MS
acquisition was applied to pooled samples representative of each
experimental condition. Data files were processed with GeneData
Expressionist Refiner MS v6.2.0 software (GeneData, Basel,
Switzerland), available at http://www.genedata.com/products/
expressionist.html) and processed with Statistica 9.0 software
(StatSoft, Tulsa, OK) for PLS analysis. The NIST MS Search
program (National Institutes of Standards and Technology,
Gaithersburg, MD) was used to compare MS/MS data to
LipidBlast, an in-house library of lipid structure MS/MS spectra.
Six replicates of each sample condition were extracted,
derivatized, and analyzed as reported previously [30,46] for GC-
TOF analysis. Sample materials were analyzed with a Leco
Pegasus IV time of flight mass spectrometer (Leco Corporation, St.
Joseph, MI) coupled to an Agilent 6890 gas chromatograph
(Agilent Technologies, Santa Clara, CA) equipped with a 30 m
long 0.25 mm i.d. Rtx5Sil-MS column and a Gerstel MPS2
automatic liner exchange system (Gerstel GMBH & Co.KG,
Mu ¨lheim an der Ruhr, Germany). Result files were exported to
our servers and further processed by our metabolomics BinBase
Six replicates of each sample condition were extracted with 3:1
methanol:H2O for LC-QTOF MS analysis. An Agilent 1200
Series HPLC system equipped with either a Waters 1.7 mm
Acquity BEH HILIC 2.16150 mm column (Waters Corporation,
Milford, MA) or an Agilent 1.8 mm Zorbax Eclipse Plus C18
2.16150 mm column were used for chromatographic separations.
LC eluents were analyzed with an Agilent 6530 accurate-mass Q-
TOF mass spectrometer equipped with an Agilent Jet Stream ESI
source in positive ion mode. MS and MS/MS data was collected
and mass calibration was maintained by constant infusion of
reference ions. For MS/MS library annotation, raw data files were
compared to METLIN, LipidBlast, and NIST MS/MS libraries.
All MS/MS library matches were manually confirmed.
Annotated structures were clustered with a web-based Pub-
Chem structural clustering tool and were used as an input in
MetaMapp software (available at: http://metamapp.fiehnlab.
ucdavis.edu) with CID-KEGG ID pairs for generation of
Cytoscape network files . Results of differential statistics
generated using Statistica 9.0 software were converted into
Cytoscape node attribute files and were imported into Cytoscape.
The graph was visualized using an organic layout algorithm in
A more detailed summary of procedures is available as Table
Partial Least Square (PLS) multivariate analysis on all
three metabolomic platforms combined (left panels) or
excluding the nanoelectrospray-ion trap MS data (right
Principal Component Analysis (PCA) and
select compounds in m15 progenitor cells, induced
pluripotent stem cells and embryonic stem cells,
detected in more than one metabolomics platform.
Upper panel: unsaturated phosphatidyl-lipids detected by HI-
LIC-QTOF MS and by nanoelectrospray-linear ion trap mass
spectrometry. All data are given as normalized intensities. For
method details, see Supplement Methods. Lower panel: quantification
of pantothenic acid by gas chromtography (GC)-time of flight mass
spectrometry (TOF), hydrophilic interaction chromaography/
quadrupole time pf flight mass spectrometry (QTOF) and reversed
phase liquid chromatography-QTOF.
Method comparison for quantification of
direct infusion nanoelectrospray/linear ion trap tan-
dem mass spectrometry and LipidBlast annotations
with manual quality checks.
Identified lipids by shotgun lipidomics using
cultures using gas chromatography-time of flight mass
spectrometry and BinBase annotations, and liquid
chromatography-quadrupole time of flight mass spec-
trometry and Metlin and LipidBlast annotations. Signif-
icance levels are given as one-way ANOVA p-values.
Identified metabolites in m15 and stem cell
Methods: Cell Culture and Preparation; NanoESI-MS
Extraction and Analysis; GC-TOF MS Extraction and
Analysis; LC-QTOF MS Extraction and Analysis; Meta-
Mapp Network Visualization.
Extended information on Materials and
We thank Wan Tan, Dawei Yang, and Gert Wohlgemuth for their kind
assistance with GC-TOF and NanoESI-MS methodologies.
Conceived and designed the experiments: PSK OF. Performed the
experiments: JKM BY JWR. Analyzed the data: JKM TK DKB OF.
Contributed reagents/materials/analysis tools: TK DKB. Wrote the paper:
JKM PSK OF.
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