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© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
Influence of node abundance on signaling network
state and dynamics analyzed by mass cytometry
Xiao-Kang Lun1,2, Vito R T Zanotelli1,3, James D Wade1,4, Denis Schapiro1,3, Marco Tognetti1,2,5,
Nadine Dobberstein1 & Bernd Bodenmiller1
Signaling networks are key regulators of cellular function. Although the concentrations of signaling proteins are perturbed
in disease states, such as cancer, and are modulated by drug therapies, our understanding of how such changes shape the
properties of signaling networks is limited. Here we couple mass-cytometry-based single-cell analysis with overexpression of
tagged signaling proteins to study the dependence of signaling relationships and dynamics on protein node abundance. Focusing
on the epidermal growth factor receptor (EGFR) signaling network in HEK293T cells, we analyze 20 signaling proteins during a
1-h EGF stimulation time course using a panel of 35 antibodies. Data analysis with BP-R 2, a measure that quantifies complex
signaling relationships, reveals abundance-dependent network states and identifies novel signaling relationships. Further, we
show that upstream signaling proteins have abundance-dependent effects on downstream signaling dynamics. Our approach
elucidates the influence of node abundance on signal transduction networks and will further our understanding of signaling in
health and disease.
Signaling networks are at the core of cellular information processing
and transform external signals into cellular responses. Signals are
transduced by modulating enzymatic activities mainly via protein
phosphorylation, and cells implement sophisticated mechanisms,
such as feedback loops, pathway crosstalk, and differential enzyme
localization, to integrate signals and drive cellular processes and
physiological outputs. The abundance of individual signaling pathway
components (nodes) is central to the activity and output of a signaling
network1. Changes in node abundance are tightly regulated and control biological programs such as stem cell differentiation and embryogenesis2. Abundance deregulation of particular signaling network
nodes by genomic, transcriptional, or post-transcriptional regulatory
defects3–5 underlies human diseases, the prime example being cancer6. Copy number alterations of genes encoding critical proteins7–9,
independent of mutations that constitutively change enzymatic activity10, drive progression of many cancer types. Genomic instability
in cancer cells causes abnormally broad distributions of signaling
protein abundances in a given tumor11, yet the consequences of the
protein abundance levels on signaling properties is poorly understood
limiting our ability to rationally design therapies.
The EGFR signaling network is affected by gene copy number
alterations that deregulate protein abundances (e.g., of EGFR, HER2,
ERK, and AKT) in a number of cancer types7–9. EGFR signaling controls cell growth, motility, survival, differentiation, and metabolism12.
Many drugs target the activity of the EGFR signaling network13,14.
The receptor tyrosine kinase (RTK) function of EGFR is activated by
its dimerization upon ligand binding. EGFR auto-phosphorylation
recruits adaptor proteins that typically activate the MAPK/ERK and
AKT signaling pathways. The MAPK/ERK branch activates the GTPase
RAS, which triggers a kinase phosphorylation cascade consisting of
RAF, MEK, ERK, and p90RSK. The output of the MAPK/ERK branch
is transcription of genes regulating growth and division15,16. Signal
transduction through the AKT branch starts by PI3K activation, producing PIP3, which recruits AKT and PDK1 to the plasma membrane.
PDK1 phosphorylates AKT15,17, which mediates signaling through
the mTORC1 complex to modulate translation via p70S6K and 4EBP1
(ref. 17). Other AKT targets are GSK3β, PRAS40, and TSC2. The
AKT pathway controls cell survival, proliferation, and migration 17.
STAT proteins and the PKC pathway can also be activated by EGFRmediated signaling18,19. EGFR signaling involves crosstalk and feedback loops both internally (e.g., active ERK attenuates upstream RAF
or MEK signaling via negative feedback)15 and with other signaling
pathways (e.g., WNT and TGF-β pathways)20,21.
Classically, two approaches are used to characterize the effect of proteins on signal transduction. The first approach analyzes cell populations. Here, western blot analysis, mass spectrometry, RNA microarrays,
and synthetic lethality screens are used to identify signaling relationships22–24. Protein–protein interaction analyses are used to determine
which proteins in a network directly interact23,25. Population-based
methods yield a comprehensive view of signaling but are difficult to
use in analysis of protein abundance dependencies owing to inherent
limitations. Proteins must be expressed at different abundances or cells
must be sorted to yield a non-continuous abundance titration. Such
methods result in a large number of samples, and cell-to-cell protein
1Institute of Molecular Life Sciences, University of Zürich, Zürich, Switzerland. 2Molecular Life Science PhD Program, Life Science Zürich Graduate School, ETH
Zürich and University of Zürich, Zürich, Switzerland. 3Systems Biology PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich,
Zürich, Switzerland. 4Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
5Institute of Biochemistry, ETH Zürich, Zürich, Switzerland. Correspondence should be addressed to B.B. (bernd.bodenmiller@imls.uzh.ch).
Received 28 July 2016; accepted 15 December 2016; published online 16 January 2017; doi:10.1038/nbt.3770
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RESULTS
Analyzing continuous protein abundance dependencies
To systematically identify and characterize protein-abundancedependent signaling relationships, dynamics, and network activation
states, we exploited the variation and large dynamic range of protein
abundance induced by transient transfection, and used mass cytometry to quantify the abundance of the transfected protein of interest
(POI) in conjunction with comprehensive signaling network readouts
in single cells. We cloned genes encoding POIs into vectors containing a cytomegalovirus (CMV) promoter and a GFP-tag sequence31 to
transiently overexpress GFP-tagged POIs in HEK293T cells (Fig. 1a).
The tagged protein abundance was measured by mass cytometry using
an anti-GFP antibody (Fig. 1a). Ordering the measured cells based
on the GFP signal provided a continuous POI titration (Fig. 1b).
Typically, not all cells were transfected, yielding an internal control
for every experiment. To measure the single-cell EGFR signaling
network states, we designed and validated a panel of 35 antibodies that mostly detect phosphorylation sites on signaling proteins
(Supplementary Tables 1–3). These data were used to determine
the abundance dependencies of network activation state and signaling dynamics (Fig. 1b).
To validate our system we confirmed that, first, the GFP tag was
reliably detected by mass cytometry (Supplementary Fig. 1); second,
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a
b
A GFP
Cloning
Transient transfection
A
A
Untransfected
GFP
GFP
A
GFP
A
Mass
Mass
Cell count
Mass
Ion counts
Ion counts
Ion counts
A GFP A GFP
……
Z-GFP
A-GFP
Y-GFP
A GFP
X-GFP
GFP (ProteinA) abundance
GFP
Identify shapes
of relationships
Low expression High expression
ProteinB
ProteinC
ProteinD
Antibody staining
A
A
GFP
GFP
A
GFP
A
GFP
Signaling activity
abundance variations within each sample remain masked. The second
approach studies signaling relationships in single cells. Here, fluorescence microscopy and flow cytometry are used with a variety of reporters and assays, including proximity ligation assay26 or fluorescence
resonance energy transfer27. These approaches allow study of signaling
relationships and dynamics through time and space; however, only a
few signaling nodes can be measured simultaneously.
A recently developed single-cell-analysis technology, called mass
cytometry, allows for the simultaneous measurement of over 40 signaling nodes in single cells using metal-isotope-tagged antibodies28,29.
This capability makes mass cytometry uniquely suited to comprehensively query the function of nodes in signaling networks within
heterogeneous cell populations. Mass cytometry is quantitative and, in
combination with mass-tag cellular barcoding, a powerful screening
tool28. Algorithms to analyze multiplexed single-cell mass cytometry
data allow quantification of signaling relationships, therefore helping
to decipher the highly complex network behaviors that operate even
in simple biological systems30.
Here, we coupled protein overexpression with mass cytometry to
measure the effect of varying node abundance on the activation state
and signaling relationships of an unstimulated EGFR signaling network, as well as the signaling dynamics of the network in response
to EGF stimulation. We exploited the finding that transient protein
overexpression in a cell population typically produces a continuous
abundance range of the target protein over four orders of magnitude. We overexpressed 20 central EGFR signaling network proteins
individually in human embryonic kidney (HEK) 293T cells, sampled
during an EGF stimulation time course over 60 min totaling 360
conditions. An average of 11,000 cells per condition was analyzed
with a panel of 35 antibodies to provide a comprehensive single-cell
proteomic EGFR network analysis. To identify signaling relationships
in this data set, we developed a statistical measure that we call ‘binned
pseudo R-squared’ (BP-R2) that recapitulated known signaling relationships and identified relationships that were—to the best of our
knowledge—not described previously. Thus, our experimental and
computational approach enables the study of how the strength and
dynamics of signal transduction are tuned by node abundances.
GFP (ProteinA) abundance
Network state
Mass cytometry
Refelectron
TAG
G
TAG
Pusher
Detector
Network status
© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
Articles
A
A
A
B
BP
BP
C
CP
CP
D
DP
DP
E
GFP (ProteinA) abundance
Figure 1 Workflow of abundance-dependent network analysis.
(a) Experimental workflow. Signaling POIs are cloned into vectors
containing a CMV promoter and a GFP-tag sequence to transiently
overexpress GFP-tagged POIs in HEK293T cells. We quantify anti-GFP
antibody as readout of POI–GFP abundance, together with other 35
markers, by mass cytometry. (b) Data analysis workflow. Cells were ordered
based on the GFP signal, providing a continuous POI titration, which
was then coupled to other signaling markers to determine the abundance
dependencies of network activation state and signaling dynamics in
the network after transfection. The network in the illustration does not
represent an actual biological example.
the GFP tag did not affect the localization and activity of the POI
(Supplementary Figs. 2 and 3, Supplementary Table 4, and
Supplementary Dataset 1); third, POI expression levels were linearly
related to GFP abundance, validating GFP as readout of the total POI
abundance (Supplementary Fig. 4a,c); fourth, POI overexpression
for 18 h (i.e., the time point of our experiments) did not alter the
underlying network structure (Supplementary Fig. 4b,c); fifth, the
antibody-based GFP quantification by mass cytometry was comparable to fluorescence-activated cell sorting (FACS; Supplementary
Fig. 5); sixth, the cell culture media and cell detachment did not alter
signaling processing in the EGFR network (Supplementary Figs. 6
and 7); and, seventh, the levels of the GFP-tagged POIs were stable
during the 1-h EGF stimulation time course (Supplementary Fig. 8
and Supplementary Video 1). We also found that the method was
robust and highly reproducible as evidenced by the high concordance
between the three individual experiment replicates (Supplementary
Fig. 9 and Supplementary Dataset 2).
KRASG12V and MEK1DD abundance effect on signaling
We first studied a well-known signaling circuit. Constitutively active
mutants of KRAS and MEK1 (KRASG12V and MEK1DD) lead to
ERK phosphorylation and activate components downstream in the
MAPK/ERK pathway. As expected, we found that overexpression
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4
3
2
1
10
0
p-ERK1/2
10
10
10
0
0 10
2
10
3
10
10
Normalized
medians
-GFP
-GFP
2
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10
0
1
3
10
4
0 10
MEK1
G12V
-GFP
1.0
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
10
1
10
2
3
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p-ERK1/2
p-p90RSK
p-AKT
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2 4 6 8 10
KRASG12V-GFP (bin)
-GFP
MEK1
DD
10
4
10
3
10
2
10
3
10
4
G12V
-GFP
r = 0.74
1
0 10
10
2
3
10
r = 0.32
10
4
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3
10
2
KRAS
-GFP
r = 0.15
101
0
1
f
2
10
3
104
0 10
1
10
2
EGFR
h
STAT1
FAK
PI3K
RAS
MEK1/2
NFκB
MAPKAPK2
p70S6K
4EBP1
p38
p90RSK
p70S6K
AMPKα
MKK3/6
S6
4EBP1
S6
p38
p90RSK
MKK3
Negative correlation
JNK
CREB/
ATF1
SMAD2/3
SMAD2/3
Positive correlation
MKK4/7
MKK4/7
SMAD1/5
Bin Spearman correlation
MKK3/6
MKK3
JNK
CREB/
ATF1
Relationship strength
ERK1/2
mTOR
MAPKAPK2
AMPKα
MARCKS
(PKC)
MEK1/2
RAF
GSK3β
ERK1/2
BP-R2
BTK/ITK
PLCγ2
PDK1
AKT
β-catenin
NFκB
GSK3β
mTOR
STAT5
RAS
MARCKS
(PKC)
STAT3
SRC
SHP2
BTK/ITK
RAF
STAT1
FAK
PLCγ2
PDK1
AKT
EGFR
PI3K
STAT5
SHP2
β-catenin
–0.6 –0.3 0.0 0.3
0.6
∆Spearman correlation coefficient
MEK1DD-GFP
104
p-FAK
p-MEK1/2
p-SMAD2/3
p-MARCKS
p-p53
p-CREB/ATF1
p-SHP2
Cyclin B1
p-AKT
p-PDK1
p-RB
p-SMAD1/5
p-BTK/ITK
p-GSK3β
p-NFκB
p-JNK
β-catenin
p-4EBP1
p-PLCγ2
E-cadherin
p-STAT3
p-mTOR
p-ERK1/2
GFP
p-S6
p-p90RSK
p-AMPKα
p-MKK3
p-MKK3/6
p-STAT5
Cleaved PARP/caspase3
p-HH3
p-STAT1
p-p38
p-MAPKAPK2
p-p70S6K
STAT3
SRC
3
p-FAK
p-MEK1/2
p-SMAD2/3
p-MARCKS
p-p53
p-CREB/ATF1
p-SHP2
cyclin B1
p-AKT
p-PDK1
p-RB
p-SMAD1/5
p-BTK/ITK
p-GSK3β
p-NFκB
p-JNK
β-catenin
p-4EBP1
p-PLCγ2
E-cadherin
p-STAT3
p-mTOR
p-ERK1/2
GFP
p-S6
p-p90RSK
p-AMPKα
p-MKK3
p-MKK3/6
p-STAT5
Cleaved PARP/caspase3
p-HH3
p-STAT1
p-p38
p-MAPKAPK2
p-p70S6K
p-GSK3β
p-4EBP1
p-AKT
p-RB
p-STAT3
p-NFκB
p-SHP2
p-p53
β-catenin
p-FAK
p-mTOR
p-PLCγ2
E-cadherin
p-CREB/ATF1
Cleaved PARP/caspase3
p-PDK1
p-SMAD1/5
p-MARCKS
p-BTK/ITK
p-SMAD2/3
Cyclin B1
p-HH3
p-MKK3/6
p-STAT1
p-MEK1/2
p-MKK3
p-S6
p-ERK1/2
p-p90RSK
GFP
p-p38
p-MAPKAPK2
p-p70S6K
p-AMPKα
p-JNK
p-STAT5
KRASG12V-GFP
10
MEK1DD-GFP
p-GSK3β
p-4EBP1
p-AKT
p-RB
p-STAT3
p-NFκB
p-SHP2
p-p53
β-catenin
p-FAK
p-mTOR
p-PLCγ2
E-cadherin
p-CREB/ATF1
Cleaved PARP/caspase3
p-PDK1
p-SMAD1/5
p-MARCKS
p-BTK/ITK
p-SMAD2/3
Cyclin B1
p-HH3
p-MKK3/6
p-STAT1
p-MEK1/2
p-MKK3
p-S6
p-ERK1/2
p-p90RSK
GFP
p-p38
p-MAPKAPK2
p-p70S6K
p-AMPKα
p-JNK
p-STAT5
g
4
10
G12V
FLAG-GFP
0 10 10
p-ERK1/2
-GFP
2
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MEK1DD-GFP (bin)
G12V
KRAS
0 10 10
p-ERK1/2
d
KRAS
1
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p-JNK
p-p38
p-STAT5
-GFP
r = 0.24
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p-GSK3β
DD
-GFP
KRAS
1.0
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FLAG-GFP (bin)
MEK1
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FLAG-GFP
1.0
G12V
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FLAG-GFP
b
© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
1
KRAS
p-p90RSK
FLAG-GFP
4
10
p-BTK/ITK
a
SMAD1/5
Figure 2 MAPK/ERK pathway mutants induce oncogenic signaling. (a) Biaxial plots of GFP, representing the abundance of the overexpressed mutant POIs,
versus abundance of phosphorylation on Thr202/Tyr204 on ERK1/2. Constitutively active KRAS G12V-GFP shows an absence of induction on Thr202/Tyr204
on ERK1/2 at the highest levels of KRASG12V-GFP. Constitutively active MEK1DD-GFP directly phosphorylates Thr202/Tyr204 on ERK1/2, and the abundance
of the POI-GFP is correlated with the amount of ERK1/2 phosphorylated at these sites. The FLAG-GFP control does not affect ERK phosphorylation sites.
(b) The abundances of measured phosphorylation sites are plotted over the range of the KRAS G12V-GFP and MEK1DD-GFP expression. Phosphorylation
sites of the same pathway (e.g., on ERK1/2 and p90RSK, AKT and GSK3β, or p38 and JNK) show similar trends. An individual experiment is shown
here. Plots for three replicates are shown in Supplementary Figure 9b–e. (c) Strong single-cell correlations within biaxial plots indicate co-regulated
phosphorylation sites. (d) Unchanged and reduced correlations indicate unrelated phosphorylation sites. For c and d, a representative individual experiment
from three replicates is shown. (e,f) Heat maps showing for all pairs of measured markers the change in Fisher-transformed Spearman correlation values
for overexpression of KRASG12V-GFP (e) and MEK1DD-GFP (f) when compared to the FLAG-GFP overexpression control. (g,h) BP-R2 scores and Spearman
correlations of bin medians for all measured markers in cells where KRASG12V-GFP (g) or MEK1DD-GFP (h) was overexpressed overlaid on a literature-based
graph of canonical signaling pathways14,15,21,23,35,44–48. Strong relationships identified from the BP-R2 analysis are plotted on the signaling maps as colored
circles. The sizes of circles indicate relationship strengths quantified by BP-R2. The directionalities of relationships, as judged by Spearman correlation of
bin medians, are shown by the color of the circles (positive correlation indicates that cells show generally increasing marker levels, and a negative correlation
indicates decreasing marker levels as POI-GFP levels increase). For e to h, data from three individual experiment replicates were used.
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Articles
of KRASG12V-GFP or MEK1DD-GFP increased phosphorylation on
Thr202 and Tyr204 of ERK1/2 (Fig. 2a). Our approach also elucidated
the abundance-dependent effects on these signaling relationships. The
relationship between KRASG12V-GFP and p-ERK1/2 was bow-like, as
high levels of KRASG12V-GFP corresponded to reduced phosphorylation of ERK1/2. By contrast, the MEK1DD-GFP abundance relationship with p-ERK1/2 was monotonic as p-ERK1/2 increased with
MEK1DD-GFP expression (Fig. 2a). These results verified the oncogenic activation of p-ERK1/2 induced by KRASG12V and MEK1DD.
Next, we analyzed the impact of KRASG12V-GFP and MEK1DDGFP abundance on all measured phosphorylation sites. We divided
the measured cells into ten bins according to the GFP signals and
plotted the bin medians (Fig. 2b and Supplementary Fig. 9b–e). This
analysis revealed that the phosphorylation site abundances on ERK1/2
and its direct downstream target Ser380 of p90RSK had similar relationships to the abundances of KRASG12V-GFP and MEK1DD-GFP.
Phosphorylation of AKT on Ser473 and its direct target Ser9 of
GSK3β also had parallel trends and showed reduced levels when the
MAPK/ERK signal peaked, suggesting interpathway regulation. We
also observed increased JNK phosphorylation on Thr183/Tyr185
induced by the KRASG12V mutant (Fig. 2b), as reported previously32.
This shows that our approach recapitulates known signaling relationships and identifies abundance-determined signaling responses.
We then systematically evaluated signaling relationships between
all pairs of measured markers modulated by KRAS G12V-GFP or
MEK1DD-GFP overexpression. We exploited the fact that overexpression of one protein increases signaling (i.e., phosphorylation levels)
and thus expands the dynamic range of many measured markers
(Fig. 2c). This enabled the use of correlation analysis to distinguish signaling relationships (high correlation) from biological and
technical noise (low correlation). For example, overexpression of
KRASG12V-GFP resulted in an increased Spearm …
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