1. GABAergic Fate
Pax3, Pax7, Lim1, Nkx2.2, SLC32A1, Gad1 [6,8,11]
Glutamatergic Fate
Tbr1, Tbr2, VGlut2 [1,7,9]
Pax2
Lbx1Ptf1a
Lmx1b
Tlx3
98.3%
71%
30%
80%
80%
B
Atoh1
90%
90%
97.8%
95%
96.4%
73.5%
79.9%
77.1%
BA
Modeling
Neurotransmitter
Specification
in
Neural
Progenitor
Cells
William
Fisher1,2,
Tanner
Lakin1,3,
Adele
Doyle1,4
1Neuroscience
Research
Institute,
2College
of
Creative
Studies,
3Molecular
Cellular
&
Developmental
Biology,
4Center
for
BioEngineering,
Univ.
of
California
Santa
Barbara
References
Introduction: [1] Lindvall, Nature, 2004 [2] Shi, Nature, 2012 [3] Hori, Neural Plast, 2012 GABAergic/Glutamatergic Fate Switch: [1] Hori, Neuroplast, 2012 [2] Yamada, The Journ of Neurosci, 2014 [3] Puelles, The Journ of Neurosci, 2006 [4] Nakatani, Dev, 2007
2012 [5] Roybon, Cereb Cort, 2009 [6] Pillai, Dev, 2007 [7] Xiang, Somat and Motor Res, 2012 [8] Batista, Dev Bio, 2008 [9] Cheng, Nat Neurosci, 2005 [10] Pozas, Neuron, 2005 [11] Canty, the Journ of Neurosci, 2009 [12] Kwon, Stem Cell Res [13] Blum, Cereb
Cort, 2011 [14] Chen, PLoS One, 2012 [15] Poitras, Dev, 2007 [16] Hoshino, Neuron, 2005, [17] Gaspard, Nature, 2008. [18] Gaspard, Nature Protocols, 2009.
Edge Probability Reason Reference
Atoh1-Glut 79.9% Model Prediction Yamada-The Journ of Neurosci-2014
Atoh1-Ptf1a 90% Co – immunostaining Yamada-The Journ of Neurosci-2014
Ptf1a-Atoh1 90% Co – immunostaining Yamada-The Journ of Neurosci-2014
Ptf1a-Pax2 80% Ptf1a Knock out study Glasgow-Develop-2005
Ptf1a-GABA 73.5% Ptf1a Knock in study Hoshino-Neuron-2005
Ptf1a-Tlx3 30% Ptf1a Knock in study Hori-Develop-2005
Tlx3-Lbx1 71% Tlx3 and Lbx1 knock outs Cheng-Nat Neurosci-2005
Tlx3-Glut 96.4% Co-immunostaining Cheng-Nat Neurosci-2004
Lbx1-GABA 77.1% Model Prediction Cheng-Nat Neurosci-2005
Lbx1-Pax2 80% Lbx1 Knock out study Cheng-Nat Neurosci-2005
Lmx1b-Glut 95% Co-immunostaining Xiang-Somato & Motor Res-2012
Lmx1b-Pax2 98.3% Co-immunostaining Cheng-Nat Neurosci-2004
Pax2-GABA 97.8% Co-immunostaining Cheng-Nat Neurosci-2004
Figure
2.
Validation
of
probabalistic
model. To
determine
the
accuracy
of
the
rules,
we
compared
published
experimental
observations
versus
the
output
generated
by
our
custom
probabalisticbooleanMatlab
simulation.
(A)
Specifically,
we
compared
the
likelihood
of
Ptf1a-­‐
expressing
cells
to
also
express
Pax2
calculated
from
experimental
data
(Glasgow,
2005)
and
as
a
result
of
network
simulation
(n=10,000
cells;
t=20
iterations).
(B)
Likelihood
of
Lmx1b-­‐expressing
cells
to
also
express
Pax2
experimentally
(Cheng,
2004)
versus
in
silico (n=10,000
cells,
t=20
iterations).
Abstract
To
better
understand
the
origin
of
excitatory
and
inhibitory
neurons
in
the
brain,
we
identified
a
transcription
factor-­‐based
molecular
switch
governing
excitatory
(glutamatergic)
versus
inhibitory
(GABAergic)
neuron
differentiation.
We
formalized
this
switch
as
a
set
of
probabalistic rules
and
simulated
the
resulting
timecourse of
gene
expression
during
differentiation
of
virtual
neural
progenitor
cells.
These
gene
expression
dynamics
predict
the
sequence
and
co-­‐
expression
of
six
transcription
factors
known
to
be
important
for
GABAergic and
glutamatergic differentation.
Ongoing
studies
are
testing
the
predictions
of
this
model
in
mouse
pluripotent
stem
cells
differentiated
to
VGlut1/2+ or
GAD1+ cells.
This
quantitative
approach
to
understand
how
essential
brain
cell
types
arise
will
contribute
to
our
understanding
of
nervous
system
development
and
design
of
neuronal
therapies.
Materials
and
Methods
• We
extracted
qualitative
and
quantitative
evidence
regarding
the
regulation
of
GABAergic and
Glutamatergic differentiation
from
literature
using
PubMed
and
Web
of
Science.
• We
combined
these
data
into
a
consensus
regulatory
model
for
GABA
and
glutamatergic differentiation,
leading
to
identification
of
a
Pax2-­‐related
putative
fate
switch.
• We
translated
the
fate
switch
diagram
into
a
mathematical
description
using
probabilities.
We
simulated
gene
expression
dynamics
in
different
sizes
of
virtual
cell
populations
and
different
differentiation
times
to
determine
if
this
novel
inferred
regulatory
switch
is
sufficient
to
explain
experimental
data.
• To
test
predictions
from
the
consensus
model
experimentally,
we
are
differentiating
mouse
embryonic
stem
cells
in
Defined
Default
Medium
for
28
days
to
yield
VGlut1/2+
and
VGAT+ cells
[17-­‐18].
Percentage
of
Cells
of
Each
Subtype
Across
Multiple
Simulations
Glutamatergic
Cells
Legend
Figure
3. Simulation
of
glutamatergic and
GABAergic differentiation
of
neural
progenitor
cells.
(A)
Final
predicted
cell
type
as
a
function
of
time
shown
for
virtual
cell
populations
of
varying
sizes
(102-­‐106 cells).
(B)
Bar
graph
of
relative
numbers
of
cell
fates,
including
neural
progenitor
cell
(NPC;
cyan),
GABAergic
(blue),
glutamatergic(red),
and
unknown
(white/grey).
(C-­‐H)
Gene
expression
of
fate
switch
molecules
as
a
function
of
final
cell
state
for
104
simulated
cells
during
20
time
steps
(rule
iterations).
(C)
Ptf1a,
(D)
Lmx1b,
(E)
Lbx1,
(F)
Tlx3,
(G)
Atoh1,
and
(H)
Pax2.
Neural
Progenitor
Cells
GABAergic
Cells
Unknown
Cells
n=106 virtual
cells
n=102 virtual
cells
n=104 virtual
cells
A B
C D E
F G H
Background
Stem
cell
therapies
have
the
potential
to
drastically
improve
the
treatment
of
neurodegenerative
diseases
[1].
Numerous
protocols
have
been
developed
which
allow
for
the
differentiation
of
neural
progenitor
cells
into
neurons
[2]
as
well
as
some
that
describe
the
molecules
needed
to
specify
individual
neurotransmitter
expressing
subtypes
[3].
However,
the
regulatory
networks
governing
subtype
differentiation
are
not
well
known.
In
this
study,
we
have
integrated
both
qualitative
and
quantitative
data
on
GABAergic and
Glutamatergic differentiation
from
previous
studies
to
develop
an
integrated
molecular
fate
switch
motif
which
revealed
a
Pax2
dependent
fate
switch
submodule.
We
also
created
a
mathematical
model
that
simulates
the
putative
molecular
GABA-­‐Glut
fate
switch
network
dynamics.
Results
Figure
1.
Transcription
factors
affect
the
decision
of
neural
progenitor
cells
to
choose
GABAergic or
Glutamatergic neuron
identity.
(A)
Probabilities
of
molecule
co-­‐expression
extracted
from
literature
and
used
for
Matlab simulation
rules.
(B)
Summary
diagram
showing
interactions
of
predicted
fate
switch
transcription
factors.
Proteins
appear
to
be
either
pro-­‐glutamatergic(red)
or
pro-­‐GABAergic(blue).
Edge
between
genes
represent
rules
encoded
in
the
simulation.
The
probability
of
each
event
occuring (see
Table,
part
A)
is
shown
next
to
each
edge.
Discussion
Glutamatergic and
GABAergic neuron
substypes are
generally
consisidered to
be
mutually
exclusive.
We
have
identified
a
molecular
network
that
may
enable
and
reinforce
this
switch-­‐
like
behavior
(Fig.
1).
Results
from
the
probabalistic formalization
of
this
network
agree
with
experimental
validation
(Fig.
2).
Gene
expression
dynamics
accurately
predict
the
convergence
of
molecules
Pax2
and
Lmx1b
with
their
associated
fate
choice.
They
also
reveal
potential
high
variability
in
expression
levels
of
some
genes
in
particular
cell
states
(e.g.,
Atoh1
in
unknown
and
Glut.
cells,
but
not
GABA
cells)
and
the
theoretical
possibility
of
steady
state
oscillating
states
(Fig.
3).
Ongoing
cell
culture
studies
(Fig.
4)
will
enable
us
to
test
these
predictions
in
a
single
system
simultaneously
and
refine
our
understanding
of
essential
neuron
subtype
origins.
Figure
4
(below).
Phase
images
of
neuronal
differentiation.
In
vitro
cell
culture
of
mouse
embryonic
stem
cells
differentiating
towards
glutamatergicand
GABAergicneurons,
based
on
[17-­‐18].
The
Sonic
Hedgehog
antagonist,
cyclopamine,
enhances
glutamatergicdifferentiation
(B),
instead
of
equal
amounts
GABAergic
(VGAT+)
and
Glutamatergic(VGlut1/2+)
neurons
(Tuj1+)
in
DDM
media
only
(A).
Experiment Simulation
Percent
of
Cells
Co-­‐expressing
Pax2
Lmx1b+
Ptf1a+
A
B
A
B
100 microns
Day 0 Day 8 Day 14
Day 19 Day 28
Day 0 Day 8 Day 14
Day 19 Day 28
100 microns