Li WC et al. (2007), Axon and dendrite geography predict the specifi...

XB-ART-38958

Neural Dev 2007 Sep 10;2:17. doi: 10.1186/1749-8104-2-17.

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Axon and dendrite geography predict the specificity of synaptic connections in a functioning spinal cord network.

Li WC , Cooke T , Sautois B , Soffe SR , Borisyuk R , Roberts A .

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BACKGROUND: How specific are the synaptic connections formed as neuronal networks develop and can simple rules account for the formation of functioning circuits? These questions are assessed in the spinal circuits controlling swimming in hatchling frog tadpoles. This is possible because detailed information is now available on the identity and synaptic connections of the main types of neuron. RESULTS: The probabilities of synapses between 7 types of identified spinal neuron were measured directly by making electrical recordings from 500 pairs of neurons. For the same neuron types, the dorso-ventral distributions of axons and dendrites were measured and then used to calculate the probabilities that axons would encounter particular dendrites and so potentially form synaptic connections. Surprisingly, synapses were found between all types of neuron but contact probabilities could be predicted simply by the anatomical overlap of their axons and dendrites. These results suggested that synapse formation may not require axons to recognise specific, correct dendrites. To test the plausibility of simpler hypotheses, we first made computational models that were able to generate longitudinal axon growth paths and reproduce the axon distribution patterns and synaptic contact probabilities found in the spinal cord. To test if probabilistic rules could produce functioning spinal networks, we then made realistic computational models of spinal cord neurons, giving them established cell-specific properties and connecting them into networks using the contact probabilities we had determined. A majority of these networks produced robust swimming activity. CONCLUSION: Simple factors such as morphogen gradients controlling dorso-ventral soma, dendrite and axon positions may sufficiently constrain the synaptic connections made between different types of neuron as the spinal cord first develops and allow functional networks to form. Our analysis implies that detailed cellular recognition between spinal neuron types may not be necessary for the reliable formation of functional networks to generate early behaviour like swimming.

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Species referenced: Xenopus laevis

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	Figure 1. Hatchling Xenopus tadpole, nervous system and neurons. (a) Photograph of tadpole at stage 37/38. (b) The main parts of the CNS with arrowhead at hindbrain/spinal cord border. (c) Transverse section of the spinal cord with the left side stained to show glycine immunoreactive cell bodies (arrows) and axons (in the marginal zone). Diagrammatic right side shows the main regions: neural canal (c) bounded by ventral floor plate (f) and ependymal cell layer (e); lateral marginal zone of axons (mauve), layer of differentiated neuron cell bodies arranged in longitudinal columns (coloured circles) lying inside the marginal zone except in dorso-lateral (dl) and dorsal positions. (d) Diagrammatic view of the spinal cord seen from the left side, showing characteristic position and features of seven different neuron types. Each has a soma (solid ellipse), dendrites (thick lines) and axon(s) (thin lines). Commissural axons projecting on the opposite right side are dashed. See the text for details.
	Figure 2. Recording synaptic connections. (a-c) RB sensory neuron excites an aIN: (a) side view of the isolated brain and spinal cord to show the location of both neurons and their axons; (b) anatomy of recorded neurons with possible synaptic contacts from RB axons onto aIN (arrowheads); (c) injection of current into RB evokes an action potential that leads to a short latency EPSP in the aIN (five traces overlapped). (d-f) RB excites a cIN: (d) location and (e) anatomy of RB and cIN pair; (f) current evoked RB action potentials lead to EPSPs (five traces overlapped) blocked reversibly by glutamate antagonists D-AP5 (25 Î¼M) + NBQX (2.5 Î¼M). (g) Current evoking an action potential in a dla produces short latency excitation (EPSPs) in a dlc (four traces overlapped).
	Figure 3. Unexpected synaptic connections. (a) In a left dlc (l-dlc) interneuron excitation is seen after variable delays as skin stimulation strength to the opposite right side increases (asterisk). The inset shows the probable pathway. (b) In a RB neuron, IPSPs (depolarising at resting membrane potential) occur during swimming, shown in a motor nerve recording (vr). Some IPSPs are mid-cycle (open arrowheads) and others are early-cycle (filled arrowhead). The histogram shows the phase distribution of 148 IPSPs in the swimming cycle. (c) Stimulating an aIN to fire an action potential leads directly to depolarising IPSPs at short latency in a RB neuron. (d) In a dla, fast on-cycle EPSPs, presumed to come from dINs, are seen on 77% of cycles during swimming.
	Figure 4. Dorso-ventral distribution of axons and dendrites. (a) Examples of neurobiotin filled neurons traced in lateral views of the spinal cord to show the dorso-ventral positions of the soma, dendrites and part of the axons. Dendrites emerge from the black soma, with the most ventral dendrite (open arrowhead) and most dorsal (black arrowhead) marked. Axons are on the same side as the soma except for dlcs where they cross ventrally then branch. Rostral to left, dorsal up. (b) Examples of axon trajectories of individual aINs (measured at 0.05 mm steps from the soma at 0 mm) and dorso-ventral extent of their dendrites (vertical lines at right).
	Figure 5. Axons, dendrites and synapse probabilities. (a) Histograms summarise dorso-ventral distribution of cell bodies, dendrites and axons of different neuron types in 10% bins where 0% is ventral and 100% dorsal edge of spinal cord. Distributions are expressed as the probability that a neuron will have a soma or dendrite in a particular dorso-ventral position. Axon distributions are expressed as the probability that a 50 Î¼m segment of the axon from each type of neuron will lie in a particular dorso-ventral position. (b) Plot of synapse probability from recordings versus contact probability from anatomy for cases in bold in Table 2. Highest point (RB-dlc) was omitted in calculating regression.
	Figure 6. Modelling aIN axon growth and positional effects on axon turning angles. (a) aIN descending axons generated by a simple random growth model (red) fit the distribution of real descending axons (blue, to right) but model ascending axons do not match real ascending axons. (b,c) Real aIN ascending axon turning angles depend on the current growth angle and dorso-ventral (d-v) position. (d) In a model where growth angle depends on dorso-ventral position, generated aIN axons (red) match real axons (blue) closely. (e,f) Turning angles of modelled axons significantly match dependence of real axons on current angle and dorso-ventral position.
	Figure 7. Model networks with probabilistic connectivity. (a) The network has a single sensory RB neuron exciting neurons in the right half-centre, which also has sensory pathway dlc and dla interneurons. There are ten of each neuron type in each half-centre. The broad pattern of connections is shown by the axons from groups of neurons onto the half-centres (triangles are excitatory and circles are inhibitory synapses). The actual synaptic connections are determined probabilistically for each neuron. Resistor symbols show electrically coupled neuron groups. (b-d) Examples of activity of selected neurons in response to a single stimulus to the sensory RB neuron for networks with connection probabilities based on experiments: (b) sustained swimming; (c) synchronous activity on each side; and (d) no long-lasting response.

References [+] :

Bourikas, Sonic hedgehog guides commissural axons along the longitudinal axis of the spinal cord. 2005, Pubmed