How cells know where to go? Signal Detection and Processing at the Microorganism Level Herbert...
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How cells know where to go? Signal Detection and Processing at the Microorganism Level Herbert Levine UCSD – Center for Theoretical Biological Physics (NSF) Outline: • Experiments on chemotactic response in Dictyostelium • Signal versus noise in gradient sensing • Nonlinear amplification via signal transduction • Cell motility mechanics *Work also supported by NIGMS P01 * Collaboration between 2 bio labs, 1 exp phys lab, and a theory group
How cells know where to go? Signal Detection and Processing at the Microorganism Level Herbert Levine UCSD – Center for Theoretical Biological Physics
How cells know where to go? Signal Detection and Processing at
the Microorganism Level Herbert Levine UCSD Center for Theoretical
Biological Physics (NSF) Outline: Experiments on chemotactic
response in Dictyostelium Signal versus noise in gradient sensing
Nonlinear amplification via signal transduction Cell motility
mechanics *Work also supported by NIGMS P01 * Collaboration between
2 bio labs, 1 exp phys lab, and a theory group
Slide 2
Cells know where to go Wild-type leukocyte response to fin
wound Matthias et al (2006) Cells can bias their motility machinery
based on detecting signals; Much of the time, these signals are
diffusing chemicals, but cells can also use mechanical cues
Applications to immune response, wound healing, cancer
metastasis
Slide 3
Close-up view of chemotaxis Dictybase Website
http://dictybase.org/index.html Cell moves about one cell diameter
per minute Decision-making maintains flexibility (limited
hysteresis) We work on chemotaxis in a model organism, the
Dictyostelium amoeba simplified signaling availability of genetics
tools ease of experiments chemotaxis is crucial for survival
Slide 4
Questions for theory Can one predict macroscopic measures of
cell motility given the space-time course of an applied signal and
(possibly) the cell history Cell speed, directional persistence,
chemotactic index Not just averages, but also distributions Not
just wild-type, but also mutant strains We will tackle this problem
in stages Sensing of the signal Making the chemotactic decision
Driving the cell s acto-myosin motor
Slide 5
Slide 6
Mutual Information Sensing is done roughly 50K receptors, each
with a binding constant of 30nM. How much information do we get
about a parameter which affects our noisy measurement? Given the on
and off rates for the receptors (measured in single molecule
measurements), we can calculate the mutual information contained in
one measurement snapshot result in bits for small gradients
Slide 7
Microfluidics Revolutionizing measurements Soft lithography
Stable gradients Controlled geometries Rapid switching Brings
experiments to where they can be compared more quantitatively to
theory From A. Groisman lab; Skoge et al (2010)
Slide 8
Cell migration in a gradient cAMP gradient flow rate 640 m/s 1h
real time = 8 sec movie From Song et al
Slide 9
Compare to experiment We can directly compare this number to
the information about the gradient indicated by the actual cell
motion C.I. = 0.556 100 Movement up gradient 5% gradient C.I. =
0.04 100 no gradient Fuller, Chen et al, PNAS (2010)
Slide 10
In shallow gradients the E + I mutual information is limited by
the external mutual information (receptor occupancy). E + I mutual
information decrees at higher concentrations due to an increase in
internal noise. We also analyzed the instantaneous angle of the
cells in the devices. Result: The cell operates at the sensor noise
limit for small gradients and concentrations; eventually limited by
other noise and/or processing losses Distribution allows
calculation of the cell s output information
Slide 11
Cell Decision Model Can we go beyond this phenomenological
approach to discuss actual gradient sensing process We will focus
on a gradient sensing approach, which tries to explain how external
signals can get amplified to the level of decisions
Slide 12
Subcellular polarity markers; front vs back Receptors Occupancy
PI 3 K PTEN RAC Myosin Starting with the work of Parent and
Devreotes, response can be tracked by subcellular markers Uniform
receptor Lipid modifications F-actin at the front Myosin at the
rear These allow for the measurement of the kinetics of the
gradient sensing step
Slide 13
Focus on RAS Ras is activated by a GEF; inactivated by a GAP
Note: Can attach fluorescent marker to RBD; can be used to track
decision
Slide 14
Cell in a microfluidic device with chemoattractant gradient,
variable vertical height. Most of the cell in focal plane: no
bleaching issues Visualize Ras*, an upstream signaling component
Monica Skoge, Loomis lab Ras activations correlates with cell
motility (Hecht et al)
Slide 15
Detailed measure of correlation From Hecht et al PLoS Comp. Bio
(2011) Measure is the cosine of angle between patch and protrusion
Done both in under agar expts and in microfluidic chamber
Slide 16
Models of Gradient sensing Gradient sensing is hard because it
cannot be done by local circuits in the cell Models must postulate
an inhibitory mechanism (either direct or via depletion; direct can
involve either chemical or mechanical coupling) Models make
quantitative predictions that can be used to rule them out or allow
them to survive
Slide 17
Model classification First, gradient determines allowed
protrusion direction (needs comparison mechanism, based on global
inhibitor) Pseudopods are formed independently of previous ones
Pseudopods occur independently of gradient direction, often by
splitting mechanism Gradient biases lifetimes (needs comparison
mechanism, based on global inhibitor) Sensing and pseudopod
dynamics are strongly coupled; both feedforward and feedback is
needed Strong gradient? Weak gradient? Instructive Reality?
Selective X
Slide 18
LEGI Local activation and global inhibition explains adaptation
to global stimulus versus steady gradient response Membrane-bound
activator Diffusing inhibitor We will assume that RAS is the
effector molecule Ras* Parent+Devreotes; Iglesias +Levchenko
Ph-domain
Slide 19
LEGI as applied to RAS activation Since A and I are both
proportional to S in steady-state, uniform S results in a transient
activation of E but eventual perfect adaptation. With a non-uniform
S, I gives average value and A remains local - pattern in the
effector E
Slide 20
LEGI Local activation and global inhibition explains adaptation
to global stimulus versus steady gradient response Membrane-bound
activator Diffusing inhibitor We will assume that RAS is the
effector molecule Ras* Parent+Devreotes; Iglesias +Levchenko
Ph-domain
Slide 21
Perfect Adaptation Only two simple ways to obtain perfect
adaptation in signaling; integral feedback (B) versus incoherent
feed-forward (A) Integral feedback relevant for E. Coli chemotaxis;
here, other strategy is used Can be directly investigated using
microfluidic device (~ 1 sec switching time) and fluorescent marker
with Ras binding domain K. Takeda, Firtel lab
Slide 22
Adaptation kinetics Takeda et al, Science Sign., (2012) X
Slide 23
Amplifying by Ultrasensitivity Assume that the K s are very
small and the baseline rates are balanced Loomis, Levine, Rappel
(2009) Janetopoulos et al 2004
Slide 24
Phase-field simulation of Reaction-diffusion equations Use
Monte Carlo calculation to obtain noise signal at the receptor
level Use this receptor noise signal as input in a 2d calculation
of the internal signalng pathways Mathematical methods can be used
to calculate effect of receptor noise on internal pathways
Slide 25
Snapshots With physiological surface diffusion (for lipids),
mean concentration of 1nM with K d =30nM, N=70K 2% 5% 10%
Slide 26
Mutual Information after processing Preliminary results Model
captures high concentration saturation Possible need for new
mechanism at low C Can be compared to other gradient sensing models
Background c = 5nm
Slide 27
Back to the gradient problem In the presence of a gradient,
activated Ras becomes localized to the front but in a stochastic,
patchy fashion May be due to feedback from the actin cytoskeleton
Models which couple compass systems (LEGI) to excitable ( actin )
dynamics (see Hecht et al, PRL (2010); Xiong et al PNAS (2010) As
we have seen, these patches are very highly correlated with sites
of actin polymerization and membrane protrusion Models for Ras
patches can be used to create simple models of cell
morphodynamics
Slide 28
Actin-Myosin dynamics
Slide 29
Back to the gradient problem In the presence of a gradient,
activated Ras becomes localized to the front but in a stochastic,
patchy fashion May be due to feedback from the actin cytoskeleton
Models which couple compass systems (LEGI) to excitable ( actin )
dynamics (see Hecht et al, PRL (2010); Xiong et al PNAS (2010) As
we have seen, these patches are very highly correlated with sites
of actin polymerization and membrane protrusion Models for Ras
patches can be used to create simple models of cell
morphodynamics
Slide 30
Pseudopod distributions We can then choose patches completely
at random from this distribution We then embed this in simple
motility model
Slide 31
Fun and Games Note: Mechanical model contains just nodes
connected by springs, together with an overall area constraint and
patch forcing
Slide 32
Left-right temporal ordering? Even in a random pseudopod
simulation, there is automatically left- right bias for chemotaxing
cells Alternative - Otsugi (2010)
Slide 33
A word about applications Amoeboid motion is one of the ways
that tumor cells can spread This capability limits current
approaches to metastatic disease We have begun studying the
interplay of noise with more complex 3d geometries for amoeboid
motion Friedl and Wolf, 2003 (Hecht et al PloS One (2011))
Slide 34
Summary Chemotactic response requires a sophisticated
biophysics approach Models are necessarily spatially-extended Noise
is not negligible Cell geometry is complex and changeable We have
used a variety of methods to look initially at the signaling and
more recently at the mechanics Eventually, we will understand how
it really works!