Figures and data in Self-organization of modular network architecture by activity-dependent neuronal migration and outgrowth

Version of Record: October 11, 2019
Version of Record: October 8, 2019
Accepted Manuscript: September 17, 2019

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Samora Okujeni

Ulrich Egert

(2019)
Self-organization of modular network architecture by activity-dependent neuronal migration and outgrowth

eLife 8:e47996.

https://doi.org/10.7554/eLife.47996
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Additional files

7 figures, 3 tables and 1 additional file

Figures

Figure 1

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Model of activity-dependent network development.

Neuronal wiring strategies may involve expansion of neurite fields and migration towards other neurons to increase connectivity modeled as neurite field overlap. (A) Transfer function of membrane …
https://doi.org/10.7554/eLife.47996.002

Figure 2 with 6 supplements

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Model of activity-dependent growth and migration.

(A) Activity-dependent growth produced a characteristic overshoot and subsequent pruning of neurite fields. The overall size of developing neurites decreased with increasing migration rates and …
https://doi.org/10.7554/eLife.47996.003

Figure 2—figure supplement 1

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Influence of neuronal clustering on neurite field development.

Increasing migration rates in the simulation promoted neuronal clustering leading to lower final CI and smaller neurite field sizes. At the onset of high network activity, neurite fields were pruned …
https://doi.org/10.7554/eLife.47996.004

Figure 2—figure supplement 2

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Simulation of saturating network growth.

(A) Decreasing the slope of the sigmoidal transfer function mapping membrane potential to firing rate resulted in saturating growth (inset: blue: $a$ = 0.2) instead of an overshoot (inset: black, $a$ …
https://doi.org/10.7554/eLife.47996.005

Figure 2—video 1

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posterframe for video — Simulated network development with migration rate 0 µm/day.
https://doi.org/10.7554/eLife.47996.006

Figure 2—video 2

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Figure 2—video 3

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Figure 2—video 4

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Figure 3 with 2 supplements

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Morphometric analyses of network development.

(A) Dense networks established characteristic mesoscale architectures for the different PKC conditions. PKC⁻ networks had a more homogeneous distribution of axons (red), dendrites (green) and cell …
https://doi.org/10.7554/eLife.47996.010

Figure 3—source data 1 Source data and Matlab script for Figure 3B,C,E,F,H,I.: https://doi.org/10.7554/eLife.47996.013
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Figure 3—figure supplement 1

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Clustering and dendrite development in sparse networks.

Exemplary images with neuronal nuclei (NeuN, red) and dendrites (MAP2, green) stained at 8 and 22 DIV. PKC inhibition diminished and PKC stimulation promoted neuronal migration and clustering of …
https://doi.org/10.7554/eLife.47996.011

Figure 3—figure supplement 2

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Development of synapses in sparse networks.

Immunohistochemical staining of dendrites (MAP2, red), presynaptic compartments (synapsin, green) and cellular nuclei (DAPI, blue). Synapse densities on dendrites increased in all PKC conditions up …
https://doi.org/10.7554/eLife.47996.012

Figure 4 with 1 supplement

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Development of spontaneous network activity.

(A) SBE rates gradually increased during development until 28 DIV, which was accelerated in clustered PKC⁺ networks and decelerated in homogeneous PKC⁻ networks. In result, SBE rates differed …
https://doi.org/10.7554/eLife.47996.017

Figure 4—source data 1 Source data and Matlab script for Figure 4A–C,E.: https://doi.org/10.7554/eLife.47996.019
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Figure 4—figure supplement 1

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Sample MEA recordings from dense networks.

(A) SBE spike activity on a MEA electrode. Spike times (blue) were detected with a voltage threshold on high-pass filtered raw data. (B) Global spike histogram (10 ms bins) and spike activity of a …
https://doi.org/10.7554/eLife.47996.018

Figure 5

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PFR-dependent Ca²⁺ gain.

(A) Spiking raster and firing rate averaged across all electrodes within 40 ms bins (synchronized to the frame times of the Ca²⁺ measurement) and Ca²⁺ signal for one neuronal soma at 19 DIV in a PKC^N…
https://doi.org/10.7554/eLife.47996.021

Figure 5—source data 1 Source data and Matlab script for Figure 5B–E,F.: https://doi.org/10.7554/eLife.47996.022
Download elife-47996-fig5-data1-v2.zip

Figure 6

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Functional aspects of network maturation.

(A) Network synchrony (average spike train correlation for all electrode pairs determined with 30 ms time bins, mean ± SEM) stabilized early in development in all PKC conditions but was …
https://doi.org/10.7554/eLife.47996.023

Figure 6—source data 1 Source data and Matlab script for Figure 6A,B.: https://doi.org/10.7554/eLife.47996.024
Download elife-47996-fig6-data1-v2.zip

Author response image 1

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Tables

Table 1

Morphometric analysis of network development under different PKC conditions.

Results are presented as mean ± standard error of mean (SEM). Significance was determined against PKC^N, or between specified developmental time windows, using independent Student’s t-test. N …

https://doi.org/10.7554/eLife.47996.014

	DIV	PKC^-	PKC^N	PKC⁺	unit
clustering index
	8	0.92 ± 0.01 (1.8*10⁻¹⁰)	0.84 ± 0.01	0.82 ± 0.02 (6.1*10⁻¹)	CI
	15	0.9 ± 0.01 (1.5*10⁻⁶)	0.73 ± 0.01	0.69 ± 0.02 (1.1*10⁻¹)	CI
	22	0.88 ± 0.01 (4.4*10⁻⁴)	0.75 ± 0.03	0.67 ± 0.02 (3.3*10⁻²)	CI
	29	0.89 ± 0.01 (8.0*10⁻⁹)	0.79 ± 0.01	0.67 ± 0.02 (2.1*10⁻⁵)	CI
	8 vs. 22	−4.49 (3.5*10⁻⁴)	−9.65 (3.1*10⁻²)	−18.6 (1.2*10⁻⁵)	% change
	22 vs. 29	1.04 (4.9*10⁻¹)	4.5 (2.7*10⁻¹)	0.01 (1.0)	% change
Dendrite size
	8	476 ± 9 (1.3*10⁻³)	421 ± 12	408 ± 8 (3.4*10⁻¹)	µm
	15	797 ± 22 (1.2*10⁻²)	676 ± 37	610 ± 30 (2.1*10⁻¹)	µm
	22	1413 ± 64 (7.9*10⁻⁵)	1021 ± 41	816 ± 24 (3.6*10⁻⁴)	µm
	29	1380 ± 74 (1.9*10⁻⁴)	962 ± 65	760 ± 37 (1.3*10⁻²)	µm
	8 vs. 22	196.59 (4.0*10⁻²⁰)	142.46 (9.1*10⁻¹²)	100.08 (1.4*10⁻¹⁵)	% change
	22 vs. 29	−2.38 (7.4*10⁻¹)	−5.81 (5.0*10⁻¹)	−6.86 (2.5*10⁻¹)	% change
Synapse density
	8	281 ± 11 (2.2*10⁻¹)	255 ± 18	254 ± 14 (9.4*10⁻¹)	#
	15	1142 ± 44 (2.3*10⁻⁴)	754 ± 39	510 ± 9 (2.4*10⁻⁵)	#
	22	2188 ± 100 (2.1*10⁻⁵)	1427 ± 99	885 ± 27 (3.4*10⁻⁵)	#
	29	2019 ± 110 (4.5*10⁻⁸)	1114 ± 56	669 ± 21 (5.7*10⁻⁸)	#
	8 vs. 22	678.77 (4.7*10⁻²⁴)	458.69 (2.1*10⁻¹⁰)	248.68 (1.2*10⁻¹⁷)	% change
	22 vs. 29	−7.75 (2.7*10⁻¹)	−21.93 (6.7*10⁻³)	−24.35 (1.2*10⁻⁶)	% change
Dendritic occupancy
	8	0.59 ± 0.02 (6.4*10⁻¹)	0.6 ± 0.04	0.62 ± 0.03 (7.5*10⁻¹)	#/µm
	15	1.44 ± 0.05 (1.7*10⁻²)	1.13 ± 0.11	0.85 ± 0.04 (1.7*10⁻²)	#/µm
	22	1.55 ± 0.03 (9.6*10⁻²)	1.4 ± 0.09	1.09 ± 0.04 (5.7*10⁻³)	#/µm
	29	1.47 ± 0.04 (3.0*10⁻⁵)	1.19 ± 0.04	0.9 ± 0.04 (2.3*10⁻⁵)	#/µm
	8 vs. 22	163.83 (7.2*10⁻²⁸)	132.26 (9.0*10⁻⁸)	76.65 (3.7*10⁻¹⁰)	% change
	22 vs. 29	−5.06 (1.5*10⁻¹)	−15.41 (2.3*10⁻²)	−17.45 (3.8*10⁻³)	% change
Neuron density
	8	255 ± 6 (9.6*10⁻⁷)	185 ± 11	168 ± 7 (2.0*10⁻¹)	#/mm²
	15	214 ± 9 (5.9*10⁻⁴)	131 ± 17	158 ± 12 (2.0*10⁻¹)	#/mm²
	22	107 ± 8 (1.9*10⁻¹)	123 ± 6	85 ± 6 (5.7*10⁻⁴)	#/mm²
	29	87 ± 5 (3.0*10⁻¹)	96 ± 7	77 ± 4 (2.6*10⁻²)	#/mm²
	8 vs. 22	−58.03 (7.3*10⁻¹⁷)	−33.66 (1.1*10⁻⁴)	−49.42 (1.0*10⁻⁸)	% change
	22 vs. 29	−18.93 (4.5*10⁻²)	−21.71 (1.3*10⁻²)	−9.79 (2.6*10⁻¹)	% change
Maximum connectivity
	8	0.01 ± 0.001 (3.5*10⁻²)	0.013 ± 0.001	0.014 ± 0.001 (6.3*10⁻¹)	fraction
	15	0.048 ± 0.003 (4.6*10⁻¹)	0.053 ± 0.006	0.029 ± 0.002 (9.2*10⁻⁴)	fraction
	22	0.209 ± 0.031 (8.2*10⁻³)	0.104 ± 0.007	0.098 ± 0.009 (5.9*10⁻¹)	fraction
	29	0.229 ± 0.026 (9.2*10⁻⁴)	0.116 ± 0.014	0.081 ± 0.006 (3.8*10⁻²)	fraction
	8 vs. 22	1987.43 (5.9*10⁻¹⁰)	701.18 (2.6*10⁻¹¹)	604.32 (1.4*10⁻¹⁰)	% change
	22 vs. 29	9.31 (6.3*10⁻¹)	11.48 (5.2*10⁻¹)	−17.23 (1.3*10⁻¹)	% change
N
	8	24	11	15
	15	9	4	7
	22	15	11	11
	29	17	16	15

Table 1—source data 1 Source data and Matlab script.: https://doi.org/10.7554/eLife.47996.015
Download elife-47996-table1-data1-v2.zip
Table 1—source data 2 Source data and Matlab script.: https://doi.org/10.7554/eLife.47996.016
Download elife-47996-table1-data2-v2.zip

Table 2

Electrophysiological characterization of network activity during development.

Data were pooled within defined developmental time windows. Significance was determined against PKC^N using independent Student’s t-test. N specifies the number of recorded networks per PKC condition …

https://doi.org/10.7554/eLife.47996.020

	DIV	PKC⁻	PKC^N	PKC⁺
AFR
	3–5	0.03 ± 0.01 (1.0*10⁻¹)	0.05 ± 0.01	0.09 (1.4*10⁻¹)
	6–9	0.08 ± 0.01 (2.6*10⁻³)	0.18 ± 0.03	0.1 ± 0.02 (4.0*10⁻²)
	10–14	0.36 ± 0.04 (1.8*10⁻²)	0.49 ± 0.03	0.42 ± 0.04 (2.0*10⁻¹)
	15–20	0.53 ± 0.06 (3.6*10⁻¹)	0.61 ± 0.06	0.76 ± 0.19 (3.6*10⁻¹)
	21–27	0.69 ± 0.08 (2.5*10-¹)	0.8 ± 0.06	1.21 ± 0.12 (7.3*10⁻⁴)
	28–35	0.6 ± 0.07 (2.9*10⁻³)	0.96 ± 0.09	1.41 ± 0.15 (6.9*10⁻³)
	36–44	0.42 ± 0.08 (2.4*10⁻⁷)	1.18 ± 0.1	2.04 ± 0.83 (4.3*10⁻²)
	45+	0.24 ± 0.02 (9.9*10⁻⁸)	1.06 ± 0.14	1.2 ± 0.63 (8.3*10⁻¹)
SBE rate (SBE/min)
	3–5	0.17 ± 0.04 (2.9*10⁻¹)	0.11 ± 0.03	0.54 (1.5*10⁻²)
	6–9	0.18 ± 0.03 (5.4*10⁻⁶)	1.02 ± 0.15	1.46 ± 0.19 (7.4*10⁻²)
	10–14	1.21 ± 0.13 (2.0*10⁻⁸)	4.26 ± 0.44	11.69 ± 1.27 (3.4*10⁻¹⁰)
	15–20	2.83 ± 0.35 (1.4*10⁻⁸)	6.36 ± 0.43	14.31 ± 2.03 (4.7*10⁻⁷)
	21–27	4.58 ± 0.44 (2.4*10⁻¹⁰)	10.3 ± 0.62	26.82 ± 3 (5.5*10⁻¹²)
	28–35	4.98 ± 0.81 (2.2*10⁻¹³)	16.97 ± 1.07	41.1 ± 5.07 (7.9*10-⁹)
	36–44	4.08 ± 0.75 (9.0*10⁻¹²)	17.25 ± 1.28	26.21 ± 7.05 (8.7*10⁻²)
	45+	3.74 ± 0.56 (2.2*10⁻¹³)	18.85 ± 1.62	45.76 ± 23.86 (2.0*10⁻³)
SBE strength (APs per burst)
	3–5	6.2 ± 3 (1.0*10⁰)	6.2 ± 3.8	3.5 (7.6*10⁻¹)
	6–9	21.6 ± 2.1 (9.4*10⁻⁷)	9.5 ± 1	4.6 ± 0.6 (1.2*10⁻³)
	10–14	21.2 ± 2.1 (5.7*10⁻⁹)	9 ± 0.8	2.7 ± 0.5 (1.5*10⁻⁷)
	15–20	15.2 ± 1.9 (2.1*10⁻³)	8 ± 1.3	2.7 ± 0.3 (1.2*10⁻²)
	21–27	10.1 ± 1 (6.1*10⁻⁸)	4.9 ± 0.4	4.4 ± 0.9 (5.4*10⁻¹)
	28–35	8.9 ± 0.9 (2.9*10⁻⁶)	4 ± 0.5	2.3 ± 0.3 (1.7*10⁻²)
	36–44	6.2 ± 0.6 (2.2*10⁻¹)	5.1 ± 0.6	5.1 ± 1.6 (9.8*10⁻¹)
	45+	5.6 ± 0.8 (4.2*10⁻²)	3.8 ± 0.5	1.1 ± 0.4 (2.2*10⁻¹)
PFR (Hz)
	3–5	12.3 ± 3.8 (9.0*10⁻¹)	13.2 ± 7.3	11.5 (9.2*10⁻¹)
	6–9	50.8 ± 4.8 (6.4*10⁻⁵)	28.3 ± 2.7	17.4 ± 1.7 (4.9*10⁻³)
	10–14	76.6 ± 5.4 (6.7*10⁻¹⁴)	32.1 ± 2.3	10.3 ± 1.4 (2.1*10⁻⁹)
	15–20	59.1 ± 6.5 (5.3*10⁻⁵)	29.8 ± 3.4	10.9 ± 1.3 (6.4*10⁻⁴)
	21–27	43.3 ± 4.1 (7.4*10⁻¹⁰)	18.9 ± 1.5	13.7 ± 2 (4.4*10⁻²)
	28–35	42.3 ± 4.4 (2.4*10⁻⁸)	15.5 ± 2	8.1 ± 1.2 (1.8*10⁻²)
	36–44	30.5 ± 3.1 (2.7*10⁻²)	21.4 ± 2.6	6.1 ± 0.4 (1.3*10⁻¹)
	45+	27.5 ± 3.8 (1.5*10⁻²)	16.4 ± 2.3	5.6 ± 1.7 (2.8*10⁻¹)
Network synchrony
	3–5	0.1 ± 0.03 (2.6*10⁻¹)	0.04 ± 0.02	0.08 (3.4*10⁻¹)
	6–9	0.39 ± 0.02 (3.3*10⁻³)	0.29 ± 0.02	0.15 ± 0.02 (3.1*10⁻⁴)
	10–14	0.52 ± 0.03 (2.5*10⁻¹⁰)	0.31 ± 0.02	0.12 ± 0.02 (1.4*10⁻¹⁰)
	15–20	0.53 ± 0.04 (4.6*10⁻⁵)	0.35 ± 0.02	0.16 ± 0.03 (1.7*10⁻⁵)
	21–27	0.51 ± 0.03 (5.8*10⁻¹³)	0.26 ± 0.02	0.2 ± 0.02 (4.8*10⁻²)
	28–35	0.57 ± 0.04 (2.0*10⁻¹⁰)	0.24 ± 0.03	0.15 ± 0.03 (7.6*10⁻²)
	36–44	0.53 ± 0.04 (1.3*10⁻⁵)	0.3 ± 0.03	0.11 ± 0.03 (1.3*10⁻¹)
	45+	0.45 ± 0.05 (2.5*10⁻³)	0.26 ± 0.03	0.14 ± 0.11 (3.8*10⁻¹)
N
	3–5	7	3	1
	6–9	33	40	24
	10–14	70	92	47
	15–20	53	65	27
	21–27	77	121	56
	28–35	47	62	29
	36–44	38	57	4
	45+	38	36	2

Key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Strain, strain background (Rattus norvegicus domestica)	wildtype wistar rat pups	CEMT, University, Freiburg
Genetic reagent	AAV9.CAG.GCaMP6s.WPRE.SV40	Penn Vector Core, University of Pennsylvania	V3296TI-R	titer 1e11
Antibody	anti-MAP2 (chicken polyclonal)	Abcam, Cambridge, UK	ab92434 RRID:AB_2138147	1:500
Antibody	anti-NeuN (rabbit polyclonal)	Abcam, Cambridge, UK	ab128886 RRID:AB_2744676	1:500
Antibody	anti-Neurofilament (mouse monoclonal)	Abcam, Cambridge, UK	ab24571 RRID:AB_448148	1:10
Antibody	anti-Synapsin (mouse monoclonal)	Synaptic Systems GmbH, Germany	106001 RRID:AB_887805	1:200
Antibody	anti-chicken-Cy2 (goat polyclonal)	Abcam, Cambridge, UK	ab6960 RRID:AB_955003	1:200
Antibody	anti-rabbit-Cy3 (goat polyclonal)	Abcam, Cambridge, UK	ab6939 RRID:AB_955021	1:200
Antibody	anti-mouse-Cy5 (goat polyclonal)	Abcam, Cambridge, UK	ab6563 RRID:AB_955068	1:200
Chemical compound, drug	4,6-diamidino-2-phenyindole, diclactate (DAPI)	Sigma-Aldrich, Germany	D9562	1:5000
Chemical compound, drug	Gödecke6976	Tocris Bioscience, Bristol, UK	2253	1 µM
Chemical compound, drug	Phorbol-12-Myristate-13-Acetate (PMA)	Sigma-Aldrich, Munich, Germany	P1585	1 µM
Chemical compound, drug	Picrotoxin	Tocris Bioscience, Bristol, UK	1128	10 µM
Chemical compound, drug	DMSO	Sigma-Aldrich, Munich, Germany	D8418	0.1%
Chemical compound, drug	DNase (type IV)	Sigma-Aldrich, Munich, Germany	D5025	50 g/ml
Chemical compound, drug	minimal essential medium	Invitrogen, Karlsruhe, Germany	21090055
Chemical compound, drug	horse serum (heat-inactivated)	Invitrogen, Karlsruhe, Germany	26050088	20%
Chemical compound, drug	phosphate buffered saline (PBS)	Invitrogen, Karlsruhe, Germany	21600010
Chemical compound, drug	glucose	Sigma-Aldrich, Munich, Germany	G7528	20 mM
Chemical compound, drug	L-glutamine	Invitrogen, Karlsruhe, Germany	25030024	0.5 mM
Chemical compound, drug	gentamycin	Invitrogen, Karlsruhe, Germany	15750060	20 µg/ml
Chemical compound, drug	potassiumD-gluconate	Sigma-Aldrich, Munich, Germany	G4500	125 mM
Chemical compound, drug	EGTA	Carl Roth, Karlsruhe, Germany	3054	5 mM
Chemical compound, drug	KCl	Sigma-Aldrich, Munich, Germany	P4504	20 mM
Chemical compound, drug	Na₂-ATP	Carl Roth, Karlsruhe, Germany	K054	2 mM
Chemical compound, drug	Hepes	Carl Roth, Karlsruhe, Germany	9105	10 mM
Chemical compound, drug	CaCl₂	Sigma-Aldrich, Munich, Germany	C3881	0.5 mM
Chemical compound, drug	KOH	Sigma-Aldrich, Munich, Germany	P4504
Chemical compound, drug	MgCl₂	Sigma-Aldrich, Munich, Germany	MO250	2 mM
Software	MC Rack software	Multi Channel Systems, Germany	versions 3.3–4.5 RRID:SCR_014955
Software	Spike2 software	Cambridge Electronics Design Ltd., Cambridge, UK.	RRID:SCR_000903
Software	Zen	Carl Zeiss, Jena, Germany	RRID:SCR_013672
Software	MEA-Tools	Egert et al., 2002 (PMID 12084562)	version 2.8
Software	FIND toolbox	Meier et al., 2008 (PMID 18692360)
Software	ImageJ	Schneider et al., 2012 (PMID 22930834)	RRID:SCR_003070
Software	Matlab	Mathworks, Natick, MA, USA	versions 2014a – 2017a