Immunohistochemical analysis of neuron types in the mouse small intestine

The definition of the nerve cell types of the myenteric plexus of the mouse small intestine has become important, as more researchers turn to the use of mice with genetic mutations to analyze roles of specific genes and their products in enteric nervous system function and to investigate animal models of disease. We have used a suite of antibodies to define neurons by their shapes, sizes, and neurochemistry in the myenteric plexus. Anti-Hu antibodies were used to reveal all nerve cells, and the major subpopulations were defined in relation to the Hu-positive neurons. Morphological Type II neurons, revealed by anti-neurofilament and anti-calcitonin gene-related peptide antibodies, represented 26% of neurons. The axons of the Type II neurons projected through the circular muscle and submucosa to the mucosa. The cell bodies were immunoreactive for choline acetyltransferase (ChAT), and their terminals were immunoreactive for vesicular acetylcholine transporter (VAChT). Nitric oxide synthase (NOS) occurred in 29% of nerve cells. Most were also immunoreactive for vasoactive intestinal peptide, but they were not tachykinin (TK)-immunoreactive, and only 10% were ChAT-immunoreactive. Numerous NOS terminals occurred in the circular muscle. We deduced that 90% of NOS neurons were inhibitory motor neurons to the muscle (26% of all neurons) and 10% (3% of all neurons) were interneurons. Calretinin immunoreactivity was found in a high proportion of neurons (52%). Many of these had TK immunoreactivity. Small calretinin neurons were identified as excitatory neurons to the longitudinal muscle (about 20% of neurons, with ChAT/calretinin/+/- TK chemical coding). Excitatory neurons to the circular muscle (about 10% of neurons) had the same coding. Calretinin immunoreactivity also occurred in a proportion of Type II neurons. Thus, over 90% of neurons in the myenteric plexus of the mouse small intestine can be currently identified by their neurochemistry and shape.

Recording from Two Neurons: Second-Order Stimulus Reconstruction from Spike Trains and Population Coding

We study the reconstruction of visual stimuli from spike trains, representing the reconstructed stimulus by a Volterra series up to second order. We illustrate this procedure in a prominent example of spiking neurons, recording simultaneously from the two H1 neurons located in the lobula plate of the fly Chrysomya megacephala. The fly views two types of stimuli, corresponding to rotational and translational displacements. Second-order reconstructions require the manipulation of potentially very large matrices, which obstructs the use of this approach when there are many neurons. We avoid the computation and inversion of these matrices using a convenient set of basis functions to expand our variables in. This requires approximating the spike train four-point functions by combinations of two-point functions similar to relations, which would be true for gaussian stochastic processes. In our test case, this approximation does not reduce the quality of the reconstruction. The overall contribution to stimulus reconstruction of the second-order kernels, measured by the mean squared error, is only about 5% of the first-order contribution. Yet at specific stimulus-dependent instants, the addition of second-order kernels represents up to 100% improvement, but only for rotational stimuli. We present a perturbative scheme to facilitate the application of our method to weakly correlated neurons.

Automatic contour extraction from 2D neuron images

This work describes a novel methodology for automatic contour extraction from 2D images of 3D neurons (e.g. camera lucida images and other types of 2D microscopy). Most contour-based shape analysis methods cannot be used to characterize such cells because of overlaps between neuronal processes. The proposed framework is specifically aimed at the problem of contour following even in presence of multiple overlaps. First, the input image is preprocessed in order to obtain an 8-connected skeleton with one-pixel-wide branches, as well as a set of critical regions (i.e., bifurcations and crossings). Next, for each subtree, the tracking stage iteratively labels all valid pixel of branches, tip to a critical region, where it determines the suitable direction to proceed. Finally, the labeled skeleton segments are followed in order to yield the parametric contour of the neuronal shape under analysis. The reported system was successfully tested with respect to several images and the results from a set of three neuron images are presented here, each pertaining to a different class, i.e. alpha, delta and epsilon ganglion cells, containing a total of 34 crossings. The algorithms successfully got across all these overlaps. The method has also been found to exhibit robustness even for images with close parallel segments. The proposed method is robust and may be implemented in an efficient manner. The introduction of this approach should pave the way for more systematic application of contour-based shape analysis methods in neuronal morphology. (C) 2008 Elsevier B.V. All rights reserved.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Canine spinal cord neuron and axon myelin sheath morphometry

This inedited morphometric study has been developed from healthy canine spinal cord neuron cytoplasm and nucleus, and white matter axonal myelin sheath, from cervical, thoracic and lumbar regions. For the morphometric study, the parameters were area, perimeter, maximum and minimum diameters and roundness for neurons and myelin thickness for axon. For each parameter, 300 neurons were analysed. The results revealed that lumbar neurons had the highest mean values for the analysed parameters, indicating the presence of large neurons in this region, with large axons as a result of myelin thickness, which is proportional to axon calibre. We conclude that these morphometric results can contribute for the establishment of normal patterns, for canine spinal cord cervical, thoracic and lumbar segments.

Hypertrophy and neuron loss: structural changes in sheep SCG induced by unilateral sympathectomy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Delimiting genetic units in Neotropical toads under incomplete lineage sorting and hybridization

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

On the role of synchrony for neuron-astrocyte interactions and perceptual conscious processing

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Susceptibility of neuron-like cells derived from bovine Wharton's jelly to bovine herpesvirus type 5 infections

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effects of sublethal dose of fipronil on neuron metabolic activity of africanized honeybees

Fipronil is a neurotoxic insecticide that inhibits the gamma-aminobutyric acid receptor and can affect gustative perception, olfactory learning, and motor activity of the honeybee Apis mellifera. This study determined the lethal dose (LD50) and the lethal concentration (LC50) for Africanized honeybee and evaluated the toxicity of a sublethal dose of fipronil on neuron metabolic activity by way of histochemical analysis using cytochrome oxidase detection in brains from worker bees of different ages. In addition, the present study investigated the recovery mechanism by discontinuing the oral exposure to fipronil. The results showed that mushroom bodies of aged Africanized honeybees are affected by fipronil, which causes changes in metabolism by increasing the respiratory activity of mitochondria. In antennal lobes, the sublethal dose of fipronil did not cause an increase in metabolic activity. The recovery experiments showed that discontinued exposure to a diet contaminated with fipronil did not lead to recovery of neural activity. Our results show that even at very low concentrations, fipronil is harmful to honeybees and can induce several types of injuries to honeybee physiology. © 2012 Springer Science+Business Media New York.

Flow sorting and exome sequencing reveal the oncogenome of primary Hodgkin and Reed-Sternberg cells

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)