GABAergic FatePax3, Pax7, Lim1, Nkx2.2, SLC32A1, Gad1 [6,8,11]

Glutamatergic FateTbr1, Tbr2, VGlut2 [1,7,9]

Pax2

Lbx1Ptf1a

Lmx1bTlx3

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  CellsWilliam  Fisher1,2,  Tanner  Lakin1,3,  Adele  Doyle1,41Neuroscience  Research  Institute,  2College  of  Creative  Studies,  3Molecular  Cellular  &  Developmental  Biology,  4Center  for  BioEngineering,  Univ.  of  California  Santa  Barbara

ReferencesIntroduction: [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,  CerebCort,  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-2004Figure  2.    Validation  of  probabalisticmodel. To  determine  the  accuracy  of  the  rules,  we  compared  published  experimental  observations  versus  the  output  generated  by  our  custom  probabalisticbooleanMatlabsimulation.  (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).

AbstractTo  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  cellsn=104 virtual  cells

A B

C D E

F G H

BackgroundStem  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.

DiscussionGlutamatergic 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

Percen

t  of  C

ells  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