Metabolic shift in density-dependent stem cell differentiation
Singh et al. Cell Communication and Signaling
Metabolic shift in density-dependent stem cell differentiation
Simar J. Singh 0 1
Drew E. Glaser
Kara E. McCloskey
Fabian V. Filipp 0 1
0 Systems Biology and Cancer Metabolism, Program for Quantitative Systems Biology, University of California Merced , 2500 North Lake Road, Merced, CA 95343 , USA
1 Systems Biology and Cancer Metabolism, Program for Quantitative Systems Biology, University of California Merced , 2500 North Lake Road, Merced, CA , USA
Background: Vascular progenitor cells (VPCs) derived from embryonic stem cells (ESCs) are a valuable source for cell- and tissue-based therapeutic strategies. During the optimization of endothelial cell (EC) inductions from mouse ESCs using our staged and chemically-defined induction methods, we found that cell seeding density but not VEGF treatment between 10 ng/mL and 40 ng/mL was a significant variable directing ESCs into FLK1+ VPCs during stage 1 induction. Here, we examine potential contributions from cell-to-cell signaling or cellular metabolism in the production of VPCs from ESCs seeded at different cell densities. Methods: Using 1D 1H-NMR spectroscopy, transcriptomic arrays, and flow cytometry, we observed that the density-dependent differentiation of ESCs into FLK1+ VPCs positively correlated with a shift in metabolism and cellular growth. Results: Specifically, cell differentiation correlated with an earlier plateauing of exhaustive glycolysis, decreased lactate production, lower metabolite consumption, decreased cellular proliferation and an increase in cell size. In contrast, cells seeded at a lower density of 1,000 cells/cm2 exhibited increased rates of glycolysis, lactate secretion, metabolite utilization, and proliferation over the same induction period. Gene expression analysis indicated that high cell seeding density correlated with up-regulation of several genes including cell adhesion molecules of the notch family (NOTCH1 and NOTCH4) and cadherin family (CDH5) related to vascular development. Conclusions: These results confirm that a distinct metabolic phenotype correlates with cell differentiation of VPCs.
Stem cells; Differentiation; Vascular fate; Cell seeding density; Systems biology; Metabolism; NMR; Metabolomics; Fluorescence-activated cell sorting; Flow cytometry; Cell adhesion; Cell contact; Cell communication; Microenvironment; Cancer stem cells; Embryonic stem cells; Vascular progenitor cells; Endothelial cells
Vascular progenitor cells (VPCs) and endothelial cells
(ECs) are desirable cell sources for cellular therapeutic
and tissue engineering strategies including: peripheral
vascular disease [
], severe ischemic heart disease
] and lining the lumens of small diameter vascular
grafts in order to minimize thrombosis or arteriosclerosis
]. In cancer, the vascular niche promotes cancer stem
cells (CSCs) and is enriched with CSC-derived ECs, which
promote tumor invasion and metastasis . VPCs are
important for maintenance of the stemness of normal
adult stem cells, including self-renewal,
undifferentiated status, and dormancy. However, it is sometimes
difficult to obtain sufficient numbers of proliferating
VPCs and ECs, especially from aged adults and
diseased patients [
]. Alternatively, embryonic stem
cells (ESCs) and induced pluripotent stem cells
(iPSCs) with their unlimited capacity for self-renewal,
are considered excellent cell sources in a variety of
cell-based therapies. In addition to their growing
therapeutic applications, these cell sources in combination
with derived VPCs and ECs can also serve as
representative in vitro models of vascular development.
During early stages of vascular development,
signaling from vascular endothelial growth factor
(VEGF = VEGFA, vascular endothelial growth factor
A, GeneBank: 7422) and the VEGF receptor, FLK1
(FLK1 = VEGFR = KDR, kinase insert domain
receptor, GeneBank: 3791) promotes ventral mesoderm
and hematopoietic fate [
] leading to activation
of the mitogen activated protein kinase pathway [
Endothelial, hematopoietic, and smooth muscle cells
have been derived from outgrowths of FLK1+ VPCs,
making this VEGF receptor a hallmark for
identification of VPCs [
]. However, despite our growing
understanding of the critical biochemical factors in
development, the precise timing and quantitative levels
of EC induction/activation for directing vascular fate
from ESCs in vitro have remained confounding. This is
complicated by the inherent variability in kinetic and
autocrine signaling from ESC line-to-ESC line [
example, the optimal time to induce the mouse D3-ESC
line into FLK1+ VPCs has been reported to occur at
day 4 (FLK1+ = FLK1 positive = VEGFR expressing cells)
], while the optimal time for the corresponding
mouse R1-ESC line has been reported at day 2 [
Additionally, while VEGF is the most published growth factor
associated with directing EC differentiation, published
treatment levels vary between 20 ng/mL and 50 ng/mL
12, 15, 18
]. Matrix signaling is also an important signal in
stem cell fate, but studies on this topic have also been
conflicting. For example, it has been reported that collagen
type-IV directs a higher percentage of ECs [
12, 15, 18
However, more recent studies show fibronectin
promotes increased cell adhesion and/or proliferation,
generating greater numbers of VPCs and ECs compared with
collagen-type IV [
]. Moreover, increasing evidence
supports a role for modified cellular metabolism in the
regulation of stem cell self-renewal, specification, and
plasticity in cancer and development [
this growing understanding of cellular metabolism as a
regulator of cell function, the role of cell seeding
density in metabolic alterations supporting vascular fate is
Therefore, using our established staged differentiation
methodology and chemically-defined media formulations
(Fig. 1a), we examined a number of combinatorial
variables (induction time, VEGF treatment, matrix
signaling, and cell seeding density) for directing the
generation of VPCs (stage 1). The results indicated that
cell seeding density was a significant factor in the first
stages of induction of ESCs into VPCs, especially in the
A3-ESC cell line [
] generated by our own laboratory.
Therefore, we set out to further examine the underlying
mechanisms related to density-dependent differentiation
in this ESC line.
Embryonic stem cell culture
Mouse A3-ESCs were extracted, generated, and cultured
at 3,000/cm2 on inactivated mouse embryonic fibroblasts
(MEFs; 20,000/cm2) [
]. Prior to induction, the A3-ESCs
are purified from MEFs by gravity separation followed by
MEF adhesion to tissue culture dishes for 1–2 hours and
passaged onto 0.5% gelatin-coated plates in ESC culture
media containing: Knockout Dulbecco’s Modified Eagle
Medium (KO-DMEM; Invitrogen), 15% Knockout Serum
Replacer (KSR; Invitrogen), 1Χ penicillin-streptomycin
(Invitrogen), 1Χ non-essential amino acids (Invitrogen),
2 mM L-glutamine (Invitrogen), 0.1 mM 2-mercaptoethanol
(Calbiochem), 2000 units/mL of leukemia inhibitory
factor (LIF-ESGRO; Chemicon), and 10 ng/mL of bone
morphogenetic protein 4 (BMP4, GeneBank: 652)
(R&D Systems). Full media changes occurred every other
day and cells were passaged every 4–5 days.
Induction of FLK1+ VPCs
A3-ESCs were harvested and plated at either 1,000,
5,000 or 10,000 cells/cm2 in 12-well cell culture dishes,
coated with 50 ng/mL fibronectin (BD Biosciences), and
fed our induction media: alpha-minimal essential medium
(MEM; Corning), 20% KSR (Invitrogen), 1Χ
penicillinstreptomycin (Invitrogen), 1Χ nonessential amino acids
(Invitrogen), 2 mM L-glutamine (Invitrogen), 0.05 mM
2-mercaptoethanol (Calbiochem), and 5 ng/mL BMP4
(R&D Systems), and 0 to 30 ng/mL of VEGF (R&D
Systems) without media change for 4 days.
Experiments were conducted in triplicate (N = 3) allowing for
analysis of variance. The assessment of stage 1 VPCs,
which are not contact-inhibited, was quantified by the
percentage of FLK1+ cells over time, previously shown
to correlate with down-regulation of the pluripotent stem
cell marker POU class 5 homeobox 1 (POU5F1 = OCT3/4,
GeneBank: 5460) over the same time period [
Characterization of VPCs
Adherent cells were harvested using Cell Dissociation
Buffer (Invitrogen), fixed in 4% paraformaldehyde
(Tousimis), rinsed 2Χ with phosphate buffered saline
(PBS), blocked using 0.5% donkey serum (Fitzgerald) and
1% bovine-serum albumin (Sigma) for 1 h at room
temperature, and stained with Alexa Fluor
647®-conjugated anti-FLK1 antibody (Biolegend) at 1:100 and allowed
to incubate overnight at 4 °C. Cells were rinsed 2Χ with
PBS before being analyzed on an LSR II flow cytometer
(BD Biosciences) and FloJo Software (TreeStar) at 1, 2,
and 3 days post induction of differentiation. Samples were
analyzed in triplicate (N = 3) for each data point.
Triplicate samples of conditioned induction media (N = 3)
were harvested at 1, 2 and 3 days post-induction and
stored at −80 °C. Prior to 1D 1H-NMR spectroscopy
metabolomics analysis, supernatants were extracted using
1:1 cold methanol (BDH 67-56-1) and chloroform
(Amresco 0757) mixture [
]. The extracts were cleared by
centrifugation at 14,000 g and the aqueous phase was
collected. Freeze-dried metabolite samples were
resuspended in 200 μL of H20 with 5% D2O spiked with
0.75% 3-(trimethylsilyl)propanoic-2,2,3,3-d4 acid (TSP)
(Sigma 293040) to a final concentration of 2.409 mM
into 3 mm NMR tubes (Norrell C-S-3-HT-7). Spectra
were recorded using 1D 1H excitation sculpting at
512 scans, d1 = 1 s, 1H pulse 11.0 μs, power level of
shaped pulse 25.55db and an experimental time of
10 min at 300 K at an Avance II 600-MHz spectrometer
fitted with a cryogenic probe operating with TOPSPIN
2.0 (Bruker BioSpin GmbH). All spectra were
automatically phased, baseline corrected and referenced to TSP
(δ 0.00 ppm) using Chenomx NMR spectroscopy suite
8.1 (Chenomx Inc). Metabolite concentrations were
quantified on the basis of matching chemical shifts and
multiplicities to the Chenomx reference compound
library. Exometabolome analysis by NMR spectroscopy
provides direct comparison of absolute metabolite
concentrations of analytes. Not surprisingly, the amount of
metabolites excreted or taken up scales with the initial
seeding density. Therefore, by normalizing each time
point to the first time point post-induction, dynamic
information of the system can be obtained. The total cell count
prior seeding was obtained in triplicate (N = 3) by
analyzing cells accurately using multifocal plane analysis in
the TC20 automated cell counter (Biorad).
Cell size and proliferation
Cell diameter and proliferation rates were measured
over the 3 days of VPC induction using an automated
image-based cytometer. Cells were harvested using
0.25% trypsin-EDTA (Corning) from fibronectin
coated cultures dishes at 1, 2 and 3 days post
induction of differentiation, stained with trypan blue, and
pipetted into disposable counting chambers for
counting and image analysis. Cell diameter measurements
of live differentiating ESCs were obtained in the TC20
automated cell counter (Biorad). Multi-planar
brightfield digital images were automatically collected,
quantified, and assessed for cell number and diameter.
Cell proliferation rates were calculated and densities
validated from the live cells per dish (N = 6) over the
3 days of VPC induction.
Differential gene expression
Total RNA was extracted from undifferentiated ESCs
as well as from cells 3 days post induction of
differentiation using TRIzol (Sigma T9424). At least three
biological replicates of RNA samples were analyzed per
condition. The concentration of RNA was determined
using a Nanodrop spectrophotometer (Thermo Scientific).
Two micrograms of RNA was processed with the RT2
profiler array PAMM-146Z (Qiagen SABiosciences) and
used to synthesize cDNA using the RT2 SYBR green master
mix (Qiagen SABiosciences) in a 7300 real-time (RT)
quantitative polymerase chain reaction (QPCR) System
(Applied Biosystems). Gene expression profiles were
analyzed using the ΔΔCT method. RT QPCR
threshold cycle (CT) values were normalized using five different
housekeeping genes (HKG), ACTB, actin beta, GeneBank:
60, B2M, beta-2-microglobulin, GeneBank: 567, GAPDH,
glyceraldehyde-3-phosphate dehydrogenase, GeneBank:
2597, GUSB, glucuronidase beta GeneBank: 2990, and
HSP90AB1, heat shock protein 90 kDa alpha family class B
member 1, GeneBank: 3326. The difference threshold cycle
value (ΔCT) of any gene of interest (GOI) to the average
housekeeping value was calculated using the formula
ΔCT(GOI) = CT(GOI) — AVERAGE(CT(HKG)) for ESCs,
differentiating cells at seeding density of 1,000 cells/cm2
and 10,000 cells/cm2. In addition, change in gene
expressions of any gene of interest was monitored by calculating
ΔΔCT(GOI) = ΔCT(GOI-10 K) — ΔCT(GOI-1 K).
RT2 gene array profiles were normalized, separated
according to differential expression between the two
seeding densities in univariate T-tests with a random
variance model using a p-value cut-off below 0.05, and
ranked with LOG2 fold-change of specimen seeded at
10,000 cells/cm2 in comparison to 1,000 cells/cm2
Differential protein expression analysis
Induced VPCs originally seeded as ESCs at 1,000 cells/cm2
or 10,000 cells/cm2 were harvested 3 days post induction.
Cells were fixed, washed, and blocked in PBS
supplemented with 2% fetal bovine serum (FBS; Corning
35-010CV). Cells were incubated light-protected at 4 °C for 1 h
with the following antibodies and staining reagents: FLK1
PerCP (Biolegend 121915), CDH2 rabbit polyclonal
(Abcam ab12221), CDH5 (CD144) brilliant violet 421
(Biolegend 138013), and Fixable Viability Dye eFluor780
(eBioscience 65-0865-14). After washing, cells were
incubated light protected at 4 °C for 1 h with FITC conjugated
Donkey Anti-Rabbit IgG pre-adsorbed (Abcam ab7079).
Samples were rinsed twice with PBS supplemented with
2% FBS before being analyzed on an LSR II flow
cytometer (BD Biosciences) at a flow rate at least 500
events per second. 100,000 events per sample were
recorded and samples were analyzed in triplicate (N = 3)
for each data point. FloJo Software (TreeStar) was
used for data analysis. Dead cells were gated out from
analysis based on Viability Dye eFlour780 reactivity.
FLK1+ cells were then analyzed for FITC (CDH2) and
Brilliant Violet 421 (CDH5) fluorescence and the
percentage of Flk1+/CDH2+CDH5+ cells were compared between
low density and high density groups.
Characterization of differentiated FLK1+ VPCs
Induction of mouse A3-ESCs [
] into VPCs was
examined over a range of seeding densities, VEGF treatment
levels, and time (Fig. 2a). The greatest number of FLK1+
cells was generated on day 3, with a reduction at day 4.
Although the VEGF treatment levels led to variable
results, the greatest number of FLK1+ VPCs was
consistently and statistically significant in cultures seeded at the
highest seeding density (Fig. 2a-b) while cells initially
seeded at 1,000 cells/cm2 generated significantly fewer
FLK1+ cells. Bright field microscopy revealed that after
three days, the 10,000 cells/cm2 seeding density remained
subconfluent (Fig. 2c).
Metabolic shift during density-dependent differentiation
To identify density-dependent changes in cellular
metabolism during differentiation, we measured metabolite
abundance within conditioned media using 1D 1H-NMR
spectroscopy. This exometabolome analysis provides
insights into metabolite utilization and secretion. A reduction
in metabolite abundance is consistent with cellular uptake
from our chemically defined induction media, whereas an
increase in abundance correlates with active production
and extracellular secretion. Of the metabolites in the
differentiation media profiled, only lactate exhibited an increase
in abundance. Cells seeded at a density of 10,000 cells/cm2
displayed a rapid increase in lactate production between
days 1 and 2, which then slowed between days 2 and 3
(Fig. 3a-b). Conversely, cells grown at a density of 1,000
cells/cm2 produce, on a per cell basis, comparatively more
lactate, and exhibit a significant increase in lactate
abundance between days 1 and 3 (9.0 vs 3.8; p-value < 0.001)
(Fig. 3a-b). The same trend is seen in metabolite utilization.
Cells grown at a density of 10,000 cells/cm2 exhibit higher
rates of metabolite utilization between day 1 and day 2, and
much lower utilization between days 2 and 3 (Fig. 3c-d). In
contrast, cells seeded at lower density increase their
metabolite uptake over time, exhibiting their highest levels of
utilization between days 2 and 3 (Fig. 3c-d).
Differentiation correlates with increased cell size and reduced proliferation
To determine whether the observed shift in metabolite
utilization coincides with a change in cellular proliferation,
we measured the number of live cells present for both
seeding densities following induction of differentiation.
Cells induced at a density of 10,000 cells/cm2 have a
higher proliferation rate between day 1 and day 2 (3.32 vs.
2.07; p-value < 0.001) and a lower proliferation rate
between day 2 and day 3 (2.01 vs. 3.73; p-value < 0.001)
(Fig. 4a). In contrast, cells grown at low density continue
to increase their proliferation rate over the 3 days of
induction. Notably, while VPCs are not contact-inhibited,
cell cultures at all seeding densities remain subconfluent
after 3 days of culture (Fig. 1D) and continue to
proliferate. A3-ESCs seeded at the highest density contained
fewer cells of a small diameter representative of ESC
size three days post induction compared with cells seeded
at lower density (5–6 μm, 26% vs 36%; p-value < 0.001).
Additionally, proportionately more cells of larger
Fig. 4 Density-dependent shift of proliferation and cell diameter. a Proliferation rate significantly slows at day 3 in cells seeded at 10,000 cells/cm2
(red) but increases in cells seeded at 1,000 cells/cm2 (blue; fold increase of 2.1 vs 3.7; *** p-value < 0.001). b Higher density cells have a greater
percentage of cells with large diameter (9–10 μm, 19.6% vs 8.2%; *** p-value < 0.001) and fewer small diameter cells (5–6 μm, 26.1% vs 36.2%;
*** p-value < 0.001). c Flow cytometric cell scanning contour plot indicating 10,000 cells/cm2 seeding density results in a greater proportion of cells
exhibiting high forward scatter and FLK1 allophycocyanin conjugate (APC) positivity
diameter were found in cultures seeded at a density of
10,000 cells/cm2 compared with lower density (9–10 μm,
20% vs 8%; p-value < 0.001) (Fig. 4b). The forward scatter
measurements from fluorescence-activated cell sorting in
flow cytometry, another indication of cell size, show that
the early A3-ESCs are smaller compared with the larger
differentiated FLK1+ VPCs (Fig. 4c).
Differential gene expression of cell-to-cell signaling molecules during density-dependent differentiation
In order to further investigate the density-dependent
signaling directing FLK1+ VPCs, a number of cell-to-cell
signaling molecules were examined using a targeted PCR
array. The expression pattern of cells seeded at densities
of 10,000 cells/cm2 or 1,000 cell/cm2 revealed significant
differential expression with p-values below 0.05 and a
fold change of 2.0 or higher (Fig. 5a). Gene expression
pattern included significant up-regulation of NOTCH1
(GeneBank: 4851), NOTCH4 (GeneBank: 4855), CDH4,
cadherin 4, retinal, R-cadherin (GeneBank: 1002), CDH5,
cadherin 5, vascular endothelium, VE-cadherin (GeneBank:
1003), DSG1B, desmoglein 1 (GeneBank: 1828), DSG2,
desmoglein 2 (GeneBank: 1829), PKP1, plakophilin 1
(GeneBank: 5317), CTNNA2, catenin cadherin-associated
protein alpha 2 (GeneBank: 1496), WAS, Wiskott-Aldrich
syndrome (GeneBank: 7454), and WASF1, WAS protein
family, member 1 (GeneBank: 8936) as well as significant
down-regulation of NOTCH3 (GeneBank: 4854), and
PKP2, plakophilin 2 (GeneBank: 5318).
Differential protein expression of cell-to-cell signaling molecules during density-dependent differentiation
Protein level differences in cell-to-cell signaling molecule
expression were quantified by flow cytometry (Fig. 6).
Induced VPCs originally seeded at 1,000 cell/cm2 or
10,000 cells/cm2 were stained and analyzed for FLK1,
CDH2 (cadherin 2, neuronal, N-cadherin, GeneBank: 1000)
and CDH5 expression. Importantly, the cell adhesion
molecule CDH5, VE-cadherin, is indicative of vascular
endothelial differentiation. The percentage of cells staining
positive for FLK1, CDH2, and CDH5, FLK+/CDH2+CDH5+,
quadrant 2, (Fig. 6a-b) was higher for cells originally seeded
at 10,000 cells/cm2 than those seeded at 1,000 cells/cm2
(1.51% vs 0.70%, p < 0.01) (Fig. 6c).
The generation of FLK1+ VPCs from ESCs peaks on day
3, followed by a reduction in FLK1+ numbers (Fig. 2ab)
within the range of reported days (2–5) during mesoderm
induction from ESCs [
15, 17, 22–25
]. The other significant
variable in the efficient induction of VPCs was a high cell
seeding density (Fig. 2a-b), while VEGF treatment level
was not significant. The higher density and robustly
differentiating VPC cultures also correlated with reduced
proliferation rates and greater cell diameters, both indicative of
differentiation (Fig. 4). Although these cells are not
contact inhibited nor were they confluent cultures, hypoxia is
known to drive mesoderm commitment [
endothelial fate [
] from ESCs. To determine whether
hypoxia could drive ESC differentiation, we calculated the
molar fraction of oxygen at the cell surface of our high
density cell dishes cultures. Using our experimental cell
proliferation rates, estimated oxygen solubility in saline
solution, and oxygen consumption rates reported for both
ESCs = 27.5x10−18 [
] and ECs = 50x10−18 mol/cell/s
], it was determined that, although oxygen would be
reduced at higher cell seeding densities, none of the
conditions would generate a hypoxic environment (defined as
Interestingly, VEGF treatment was not a determining or
statistically significant variable in directing VPC fate. A
large body of data implicates VEGF signaling in mesoderm
and endothelial cell fate and that the FLK1/VEGF receptor
is one of the key markers defining the angioblast cell
12, 14, 29, 30
]. However, since BMP4 signaling can also
activate the VEGF/VEGFR signaling [
], it is sufficient
in the inductions shown. Moreover, the two distinct VEGF
binding domains in the fibronectin matrix [
stabilize and protect autocrine VEGF production from
], as well as, aid in cell presentation. The
presumptive requirement of VEGF treatment in
chemicallydefined media for EC fate has been most rigorously
examined using single cells cultured in collagen-type IV coated
96-well plates [
12, 30, 34
]. Without fibronectin matrix to
sequester and protect the VEGF generated by the cells, one
might expect that VEGF treatment would be required in
these cultures. However, our results suggest that the
utilization of fibronectin matrix mitigates the need for VEGF
supplementation in VPC induction cultures.
Differential gene expression array analysis identified a
number of cell-to-cell signaling molecules that were
upregulated in the higher density cultures containing more
VPCs. Differential expression of cell surface receptors,
desmosome, catenins, and cytoskeleton regulators could
be required for, or facilitate, the density-dependent
differentiation of ESCs (Fig. 5b-d). Vascular cells take
advantage of many different cell adhesion contacts
demonstrated by the global up-regulation of cadherins,
desmosomal and desmoglein components. Since cell
surface molecules have the ability to communicate
extracellular changes into the cytosol, such as contact formation
with neighboring cells, the gene expression data suggests
a positive feedback reinforcing cellular contacts (Fig. 5d).
Initial cell-cell contacts mediate signals into the nucleus,
where transcriptional changes create positive feedback
promoting and strengthening cell surface contacts and
stimulating regulators of adherens junctions and
desmosomes. Positive feedback circuits have the ability to
create threshold densities for successful differentiation.
Once established and supported by the cell type specific
cell surface contacts molecules, signals of differentiation
can lead to lineage committed cell fates and organized
Amongst genes exhibiting the strongest change in
the context of seeding density-dependent
differentiation of VPCs were the cytoskeleton regulators WAS
and CTNNA2. The expression pattern for both genes
is unaffected in the lower density cohort but was
consistently up-regulated in the higher density cohort.
Wiskott-Aldrich syndrome protein (WASP) is a key
regulator of endothelial cell-cell junctions and
cytoskeleton dynamics and helps form and maintain the
integrity and function of EC monolayers [
Moreover, WASP organizes actin and vascular
epitheliumcadherins at EC junctions, and hence is vital for the
assembly of vascular structures [
along with FLK1 expression, WAS and CDH5 are also
indicators of vascular differentiation [
]. Other studies
have identified members of the E-twenty six (ETS)
transcription factor family associating with FLK1 and
CDH5 promoters in vascular epithelia to regulate vascular
specification from primitive mesoderm [
murine and amphibian model organisms, plakophilins
have been found localized to the nucleus of ESCs
and form complexes with members of the ETS family
of transcription factors to direct development related
gene transcription events .
Similar to PKP1, CTNNA1, catenin cadherin-associated
protein, alpha 1 (GeneBank: 1495) and AJAP1, adherens
junctions associated protein 1 (GeneBank: 55966)
expression levels are correlated with advancing tumor stage and
inversely related to cell proliferation [
32, 40, 41
CTNNA2 has been found as hub for extracellular matrix
organization, loss of CTNNA1 is exhibited by multiple
cancer types, and restoration of CTNNA1 expression in
acute myeloid leukemia cells led to lower proliferation
]. Additionally CTNNA1 regulates differentiation
events in the developing nervous system by maintaining
beta-catenin signaling . It is possible that the higher
levels of PKP1 and CTNNA1 seen in the 10,000 cells/cm2
density group causes these cells to slow their
proliferation in favor of differentiation and growth. The
regulation of desmosomal assembly by DSG1B, DSG2,
and PKP1 not only enforces cell surface adhesion
contacts between ECs but also regulates the cell
signaling events in the cytoplasm and nucleus. DSG2
regulates actin assembly in ECs and affects
proliferation via modulation of EGFR signaling [
associates with eukaryotic translation initiation factor
4A1 to stimulate protein translation  and loss of
PKP1 is linked to prostate cancer proliferation [
Nuclear PKP1 complexes with catenin and is found
bound to single stranded DNA [
]. PKP2, which is
more abundant in the lower density group, binds to
catenin and complexes with the RNA polymerase III
The cadherin family uniformly responds to
densitydependent differentiation [
]. All cadherins
assayed show up-regulation in the higher density
cohort. CDH4 shows the highest density-dependent
fold-change of the cadherin family. In addition to
significant density-dependent up-regulation, vascular
endothelial CDH5 is also significantly different
between undifferentiated and induced ESCs. Of the
desmosomal, desmocollin, and desmoglein
components, DSG1B, DSG2, and PKP1 stand out as positive
responders to density-dependent differentiation
supporting formation of cell surface adhesion contacts in
endothelial formation. For the majority of cell surface,
cell junction and desmosomal components, a global
increase in gene expression in response to
densitydependent seeding is observed.
Among the differentiation and density-dependent
effects on gene expression, perhaps the most
profound is differential expression of the NOTCH
receptor family. NOTCH signaling is a highly conserved
intercellular signaling mechanism essential for proper
cell fate choices during development [
NOTCH1 and NOTCH4 have both been implicated
in vascular morphogenesis [
]. Moreover, NOTCH1
is found expressed in both endothelial and hematopoietic
progenitor cells [
], while NOTCH4 is expressed in
ECs, but not in hematopoietic progenitor cells [
NOTCH3 signaling is highest in late stage smooth
muscle cell differentiation [
] and neural differentiation
]. In the high cell density cultures, NOTCH1 and
NOTCH4 were significantly up-regulated, while NOTCH3
is significantly down-regulated at induction conditions
of 10,000 cells/cm2 compared with the lower density
It is expected that differential expression of NOTCH
components within ESCs seeded at higher density is, at
least indirectly, responsible for the shift in metabolite
utilization observed during the differentiation process.
Specifically, NOTCH signaling has recently been linked
to the regulation of cellular metabolism [
inducing glutamate uptake during the terminal differentiation
of astrocytes . Furthermore, NOTCH inhibition in
glioma stem cells led to reductions in intracellular
glutamate and glutamine, and increased lactate and
]. In the same study, it was noted that NOTCH
blockade modulated the expression of multiple genes
regulating glutamate metabolism, including glutaminase
and several glutamate transporters [
]. Tight regulation
of glutaminase activity and glutamate metabolism are
vital features of both stem cell function and tumor
11, 30, 44
]. Interestingly, glutamine metabolism
also regulates chromatin structure and pluripotency
related transcription factors, such as OCT4, and
therefore may play a pivotal role in vascular development
]. Additional studies examining the role of cell-cell
signaling components, particularly NOTCH, in the
regulation of glutaminergic and other metabolic pathways
could help optimize strategies for ESC differentiation and
understand NOTCH-mediated cancer progression.
An increase in cell size correlating with stem cell
differentiation is intimately coupled to loss of
“stemness”. Moreover, larger cells proliferate more slowly
compared to smaller cells [
]. While in cancer cells,
a positive feedback is used to rapidly ramp up a
distinct metabolic program [
differentiation is accompanied by a switch in metabolism from
an exponential proliferative mode into a differentiated
phenotype. During the time course of differentiation,
ESCs start out as small, rapidly dividing cells, but
rapidly shift away from exhaustive glycolysis and high
metabolite consumption to a reduced metabolic
demand per cell. This observed switch in metabolism
also supports the changing demands of larger, more
differentiated VPCs. Our data shows that the gene
expression program of these differentiating ESCs also
dynamically responds to the culture conditions at
higher cellular density, and actively reinforces cell
surface signaling components leading to up-regulation
of genes associated with VPC fate. This strengthening
of cellular communication may help regulate the
concurrent switch of metabolism from an exponential,
proliferative mode to a differentiated, growth
In summary, we have identified a density-dependent
metabolic shift correlating with increased
differentiation of VPCs from ESCs. This density-dependent
differentiation model is associated with reduced
cellular metabolism, highlighted by a decrease in
exhaustive glycolysis, by a decrease in proliferation, and by
an increase in cell size. Concomitant is enhanced
expression of cell-cell signaling components, including
those known to regulate the differentiation and
feedback circuits. In the future, efficient tissue engineering
approaches may take advantage of such
densitydependent switches and control crosstalk between
cellcell signaling and cellular metabolism.
CSCs: Cancer stem cells; CT: Threshold cycle; ECs: Endothelial cells;
ESCs: Embryonic stem cells; ETS: E-twenty six; FBS: Fetal bovine serum;
GOI: Gene of interest; HKG: Housekeeping genes; iPSCs: Induced pluripotent
stem cells; KO-DMEM: Knockout Dulbecco’s Modified Eagle Medium;
KSR: Knockout serum replacer; LIF: Leukemia inhibitory factor; MEFs: Mouse
embryonic fibroblasts; MEM: Minimal essential medium; PBS: Phosphate
buffered saline; QPCR: Quantitative polymerase chain reaction; RT: Real-time;
TSP: (trimethylsilyl)propanoic-2,2,3,3-d4 acid; VPCs: Vascular progenitor
cells; WASP: Wiskott-Aldrich syndrome protein; ΔCT: Difference threshold
Used gene symbols
ACTB: actin beta, GeneBank: 60; AJAP1: adherens junctions associated protein
1, GeneBank: 55966; B2M: beta-2-microglobulin, GeneBank: 567; BMP4: bone
morphogenetic protein 4, GeneBank: 652; CDH2: cadherin 2, type 1,
neuronal, N-cadherin, GeneBank: 1000; CDH4: cadherin 4, type 1, retinal,
R-cadherin, GeneBank: 1002; CDH5: cadherin 5, type 2, vascular endothelium,
VE-cadherin, GeneBank: 1003; CTNNA1: catenin (cadherin-associated protein),
alpha 1, GeneBank: 1495; CTNNA2: catenin (cadherin-associated protein),
alpha 2, GeneBank: 1496; DSG1B = DSG1: desmoglein 1, GeneBank: 1828;
DSG2: desmoglein 2, GeneBank: 1829; FLK1 = VEGFR = KDR: kinase insert
domain receptor, GeneBank: 3791; GAPDH: glyceraldehyde-3-phosphate
dehydrogenase, GeneBank: 2597; GUSB: glucuronidase beta, GeneBank: 2990;
HSP90AB1: heat shock protein 90kDa alpha class B member 1, GeneBank: 3326;
NOTCH1: notch1, GeneBank: 4851; NOTCH3: notch3, GeneBank: 4854;
NOTCH4: notch4, GeneBank: 4855; PKP1: plakophilin 1, GeneBank: 5317;
PKP2: plakophilin 2, GeneBank: 5318; POU5F1: OCT3/4, POU class 5
homeobox 1, GeneBank: 5460; VEGFA = VEGF: vascular endothelial
growth factor A, GeneBank: 7422; WAS: Wiskott-Aldrich syndrome,
GeneBank: 7454; WASF1: WAS protein family, member 1, GeneBank: 8936.
F.V.F. is grateful for the support of grant CA154887 from the National
Institutes of Health, National Cancer Institute. The cell differentiation studies
were funded through an NSF Integrative Graduate Education and Research
Traineeship Award, an National Science Foundation Science and Technology
Center for the Emergent Behavior of Integrated Biological Systems Award,
and California Institute for Regenerative Medicine Award. This collaborative
endeavor was supported by grants from the University of California Cancer
Research Coordinating Committee CRCC CRN-17-427258, University of
California Academic Senate Graduate and Research Council and the Health
Sciences Research Institute.
Availability data and materials
Not applicable to this article as all data generated is included in this
SJS, WT, FVF carried out cell density experiments, FVF conducted
metabolomics experiments, SJS quantified metabolite levels and cell
proliferation rates, DEG, SJS, FVF carried out flow cytometry analysis and
image processing. FVF, KEM conceived and designed the study. FVF, SJS
wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Ethics approval and consent to participate
All experimental protocols were approved by the Institutional Review Board at
the University of California Merced. The study was carried out as part of IRB
UCM13-0025 of the University of California Merced as part of dbGap ID 5094.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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