Label-free optical biosensor for probing receptor biology: theory

Label-free optical biosensor for
probing receptor biology: theory,
modeling and applications
Ye Fang, Ann M. Ferrie, Elizabeth Tran, Gary Li,
Florence Verrier, Jun Xi, and Meenal Soni

Abstract
Label-free optical biosensors have migrated from a tool solely for biomolecular
interaction analysis to a universal platform for both biochemical and cell-based
assays. This poster presents the theoretical analysis and experimental data for the
use of resonant waveguide grating (RWG) biosensors to characterize stimulusmediated
cell responses including receptor signaling. The biosensor is capable of
detecting redistribution of cellular contents in both directions that are perpendicular
and parallel to the sensor surface. This capability relies on online monitoring cell
responses with multiple optical output parameters, including the changes in incident
angle and the shape of the resonant peaks. When the changes in the peak shape
are mainly contributed to stimulation-modulated inhomogeneous redistribution of
cellular contents parallel to the sensor surface, the shift in the incident angle
primarily reflects the stimulation-triggered dynamic mass redistribution (DMR)
perpendicular to the sensor surface. The optical signatures are obtained and used
to characterize several cellular processes, particularly receptor signaling. A
mathematical model is developed to link the bradykinin-mediated DMR signals to
the dynamic relocation of intracellular proteins and the receptor internalization
during B2 receptor signaling cycle. Chemical biology and cell biology analysis
provides evidence linking specific cellular events to a ligand-induced DMR signal.
Together with recent advancement in instruments for high throughput screening, the
newly discovered ability of optical biosensors for assaying living cells will accelerate
wide adoption of label-free biosensors in both drug discovery processes and
fundamental research.
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Corning ® Epic ® System
The Corning Epic System is a high-throughput, label-free detection platform that
consists of SBS-standard 384-well microplates with optical sensors inside each well,
an HTS-compatible microplate reader and a set of label-independent assay
protocols. The Epic System is applicable to both biochemical and cell-based assays,
and enables high-throughput screening of “intractable” targets.
8
+
=
Response (pm)
6
4
2
K D = 53 nM
R 2 = 0.9707
0
0 500 1000 1500 2000 2500 3000
[Acetazolamide] (nM)
Microplate
• 384-well format
• Optical biosensor in each well
• Surface chemistry
Plate Reader
• Compatible w/ HTS automation
• ≥ 40,000 wells/8hrs
• Sensitivity of 5pg/mm 2
(300Da drug to 75kDa target)
Binding Data
• Manipulated and
analyzed by
customer
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Corning ® Epic ® system is centered around resonant waveguide
grating (RWG) biosensor
• An optical biosensor comprise an optical transducer for converting a molecular recognition
event or a stimulus-induced cellular response into a quantifiable signal
Resonant waveguide
grating
(RWG)
Y
Y
Y Y Y Y
Y
Waveguide
Glass
Intensity
Broadband light
Reflected light
Wavelength (pm)
Epic Microplate
• 384-well format
• Optical biosensor
in each well
Fang, Y. (2006) Assays Drug Dev. Tech. 4, 583-595
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Corning® Epic® system is applicable to both biochemical and cellbased
assays
• Corning® Epic® system is the first label-free and high throughput biosensor
system for both biochemical and cell-based assays
Biochemical assays Cell-based assays
Drug
Cell
Protein
150 nm
Pos
DMR
Neg
DMR
Detection Zone
150 nm
Epic ® Biosensor
Epic ® Biosensor
Fang, Y. (2006) Assay Drug Develop. Technol. 4, 583-595
Cooper, M.A. (2006) Drug Discov. Today 11, 1061-1067
Morrow, Jr. K.J. Genetic Engineering & Biotechnology News 36 (March 1, 2008)
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G protein-coupled receptor signaling: temporal and spatial dynamics
Late endosomes
Lysosomes
Degradation
Endocytosis
Early endosomes
Synthesis
k on
Recycling
k off
Golgi
1-5min 5-20min 20-60min >60min
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Evolution of receptor signaling
• Linear signaling cascades
• Network interactions
• Cross-talks
– Receptor dimerization/oligomerization
– Transactivation and transinactivation
– Networks with critical nodes that
participate in the crosstalk between
the signaling networks
• Signaling compartmentalization
– Microdomains
– Organization of signaling complexes
– Restricted collusion and diffusion
– Spatial and temporal gradients
• Signaling integration
• Phenotypic receptor biology
– Cellular context-dependent
– Tissue-specific
C.M. Taniguchi, B. Emanuelli, C. R.Kahn (2006)
Nat. Rev. Mol. Cell Biol. 7, 85 - 96
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RWG biosensor detects stimulus-induced dynamic mass
redistribution (DMR) within cells
The effective refractive index of the sensor system is:
N
n
i
The stimulus-induced change in refractive index (Δn i
) of the homogeneous layer i approximately
form a piece-wise continuous function:
Δn
=
=
f
n
N
o
= αΔ
i
C i
( n , h,
n , n , n , d , λ , m,σ
)
c
+ αC
i
F
S
m
F
A stimulus-induced change in the effective refractive index of the sensor system is:
( Δnc,
d ) = S(
N ΔnC
Δ N = f
)
The refractive index of a given volume within cells is largely determined by the concentrations
of bio-molecules, mainly proteins:
The stimulus-induced change in the effective index of the cell layer is:
− zi
− zi+
1
⎡ ⎤
ΔZC
ΔZC
ΔnC
= α∑(
ΔCi
⎢e
− e ⎥)
Epic® signal = f(C, d, t)
i ⎢⎣
⎥⎦
Penetration
depth ΔZC
n i , z i , c i
n F , d F
n s
Fang, Y., et al. (2006) Biophys. J. 91, 1925-1940
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Dynamic relocation of cellular context in G q -couple receptor signaling:
basis for numerical modeling
• Common to Gq-coupled receptor is the dynamic relocation of cellular targets:
– Protein kinase C isoforms
– GPCR kinases
– Arrestins
– PIP-binding proteins
– Diacylglycerol-binding proteins
Bradykinin stimulation causes relocation of
PKCθ-GFP to cell membrane in A431
Sato, T.K., et al., Science
294, 1881-1885.
PKCθ-GFP
0min
6min
Van Baal, et al. (2005) J. Biol.
Chem. 2005, 280, 9870
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Numerical analysis predicts an optical signal that is similar to those
obtained using RWG biosensor
L
k
+
←
k
R ⎯⎯→
f
r
k
p
LR ⎯⎯→ R
*
kin
⎯⎯→ R
in
kre
⎯⎯→ R
For the net change in protein concentration adjacent to
the cell surface :
dC i
dt
= ak
p
[ LR]
− bk [ R*]
in
Recruitment
Internalization
Theory predicated
Experimental results
Respones (unit)
B
4
3
2
1
0
0 600 1200 1800
Time (s)
128nM
64nM
32nM
16nM
8nM
4nM
2nM
1nM
Response (unit)
3
2
1
0
-1
32nM
8nM
2nM
0.5nM
64nM
0 1000 2000 3000 4000 5000
Time (sec)
Bradykinin in A431
(dual signaling)
Fang, Y., et al. (2006) Biophys. J. 91, 1925-1940
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RWG biosensor differentiates cellular responses mediated through
distinct classes of GPCRs
thrombin
epinephrine
Lysophosphatidic acid
G q
β2AR
G s
LPA 1
G i
PAR 1
Ca 2+ , Ca 2+
DAG
PKC
PLC
IP 3
AC
cAMP
AC
cAMP
3.0
1.0
1.5
Response (unit)
2.0
1.0
0.0
Response (unit)
0.5
0.0
-0.5
Response (unit)
1.0
0.5
0.0
-1.0
0 600 1200 1800 2400 3000 3600
Time (sec)
-1.0
0 600 1200 1800 2400 3000 3600
Time (sec)
-0.5
0 600 1200 1800 2400 3000 3600
Time (sec)
Fang, Y., et al. (2007) J. Pharmacol. Tox. Methods 55, 314-322
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RWG biosensor assays detect complex GPCR signaling:
Dual signaling pathways of endogenous bradykinin B 2 receptor in A431
bradykinin
G q
G s
B 2
receptor
Response (unit)
4
3
2
1
0
G q pathway modulators
HBSS
500nM GF109203x
Ca 2+ , Ca 2+
cAMP
Response (unit)
5
4
3
2
1
0
G s pathway modulators
1μM KT5720
HBSS
-1
0 1000 2000 3000 4000 5000 6000
Time (sec)
-1
0 1000 2000 3000 4000 5000 6000
Time (sec)
Fang, Y., et al. (2005) FEBS Lett. 579, 6365-6374
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RWG biosensor cell assays differentiate ligand-directed functional
selectivity acting on β 2 AR
250
200
-logEC 50 ±S.E. P-DMR(pm) N-DMR(pm) τ (s) t 1/2 (s)
Weak partial agonist
150
100
50
0
-50
catechol
HO
HO
3.30±0.07 152±13 0±3 130±15 289±42
250
200
Partial agonist
150
100
50
0
HO
dopamine
5.96±0.06 214±32 31±4 252±15 480±23
-50
HO NH 2
250
Strong partial agonist
200
150
100
50
norepinephrine
HO
7.99±0.07 209±16 29±5 180±17 559±15
0
-50
HO NH 2
HO
Full agonist
250
200
150
100
50
0
-50
HO
HO
(-)-epinephrine
HO
N
H
10.13±0.06 232±12 37±7 132±20 521±25
Potency
Efficacy Ability to cause
receptor internalization
60min
Fang, Y. & Ferrie, A.M. (2008) FEBS Lett. 582, 558-564
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RWG biosensor cell assays enable systems cell biology analysis of
receptor signaling
EGFR
EGF
The MAPK pathway
determines the EGFinduced
DMR signal
MEKK-1
JNKK1
JNK
Ras
Raf
MEK
ERK
Sos-1
Grb2
Shc
p
p
p
p
p
p
p
STAT3
p
PIP2
p
p
p
DAG
p
PLCγ
p
p
PI3K IP3
PKC
JAK1
Ca 2+ , Ca 2+
STAT1
p
p
p
p
p
p
p
p
p
p
p
p
p
p
Dynamin
Inhibitor
AG1478
Cytochalasin B
DIPC
GF109203x
KN62
KT5720
KT5823
Latrunculin A
Milrinone
Nocodazole
PD98059
Phalloidin
PP1
Ro 20-1724
R(-)rolipram
SB203580
SB202190
SP600125
U0126
Vinblastine
Wortmainnin
Zardaverine
Target
EGFR
Actin
Dynamin
Protein Kinase C
CaM Kinase II
Protein Kinase A
Protein kinase G
Actin
Phosphodiesterases
Microtubule
MEK
Actin
Src
Phosphodiesterases
Phosphodiesterases
p38 MAPK
p38 MAPK
JNK
MEK1/2
Microtubule
PI3K
Phosphodiesterases
c-Jun
STAT3
ELK-1 FAK
Cell detachment
STAT1
Response (unit)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
HBSS
ELK-1
AG1478
U0126
SB203580
SB202190
SP600125
GF109203x
KT5720
KN62
KT5823
Wortmannin
DIPC
Cytochalasin B
Latrunculin A
Milrinone
Zardaverine
Ro20-1724
Rolipram
Compound
Modulation Profile
Fang, Y., et al. (2005) Anal. Chem. 77, 5720-6725
Fang, Y., et al. (2006) Biochem. Biophys. Acta 1763, 254-261
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Summary
• Epic ® cell assays employ label-free optical biosensor to measure stimulusinduced
dynamic mass redistribution in cells within the detection zone of the
biosensor
• The DMR signal is a novel physiologically relevant and kinetic response of living
cells
• The DMR signal is a global representation of receptor biology and ligand
pharmacology
• Epic cell assays have broad applicability in many classes of targets, and have
potentials in drug discovery and development process as well as others
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