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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
