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Archive for the ‘Brain Imaging’ Category

The major input of the basal ganglia, the striatum, contains ~ 6 cell types, primarily: 1) medium spiny neurons ( ~ 96%), 2) Deiter’s neurons (2%), 3) cholinergic interneurons (1%; tonically active but usually stop firing in response to reward). Medium spiny neurons are particularly interesting little buggers.

All medium spiny neurons in the striatum express at least one of DR1 and DR2 receptors, and a minority express both. They receive dopamine-based (“dopaminergic”) input from the substantia nigra that modulates the neurons’ responses to glutamate-based excitatory input from the cortex, hippocampus, amygdala, and etc.

Medium spiny neurons themselves use GABA as their neurotransmitter and thus have an inhibitory effect on the neurons in the globus pallidus that their axons project to.

Dopamine input increases the intrinsic excitability of medium spiny neurons in part by decreasing the inactivation rate of their inhibitory A-type potassium channels. Azdad et al (here) show how rats with pharmacologically-depleted dopamine also have decreased spine density in their medium spiny neurons:

two-photon confocal microscopy in 280 µm thick stratial slices; doi:10.1371/journal.pone.0006908

So, this is possibly a homeostatic regulation system whereby dopamine-based changes in the morphology of medium spiny neurons is accommodated for by channel-based increases in excitability.

Identifying this type of neuron in section slices accurately is a major challenge of circuitry analysis. Matamales et al (here) have shown that one way to identify the types of cells in the striatum is by staining their nuclei with a dsDNA intercalating fluorescent molecule (TO-PRO-3). This allows nuclear diameter, nuclear shape, and heterochromatin distribution to be visualized.

As opposed to traditional antibodies specific to one type of molecule (red in the following figure), the nuclear DNA morphology-based system is useful for classifying cells using just one type of marker:

mouse striatal slices labeled and visualized w confocal microscopy ; doi:10.1371/journal.pone.0004770

The authors note that “similar nuclear appearances were observed for principal neurons in other brain regions such as pyramidal neurons of the cerebral cortex,” suggesting that their method might be broadly useful in other brain regions, too.

Finally, here is a 3d reconstruction of a medium spiny neuron from a mouse collected using multiphoton imaging:

gray = regions of dendrite too thin to trace ; cell centered database image basename ACC1

One day there will probably be a massive database with probabilistic reconstructions of many of these types of neurons arrayed in some sort of physiologically relevant order. How the world will change…

References

Martone, M. E., Gupta, A., Wong, M., Qian, X., Sosinsky, G., Ludaescher, B., and Ellisman, M. H. A cell centered database for electron tomographic data. J. Struct. Biology 138: 145-155, 2002.

Azdad K, Chàvez M, Bischop PD, Wetzelaer P, Marescau B, et al. (2009) Homeostatic Plasticity of Striatal Neurons Intrinsic Excitability following Dopamine Depletion. PLoS ONE 4(9): e6908. doi:10.1371/journal.pone.0006908

Matamales M, Bertran-Gonzalez J, Salomon L, Degos B, Deniau J-M, et al. (2009) Striatal Medium-Sized Spiny Neurons: Identification by Nuclear Staining and Study of Neuronal Subpopulations in BAC Transgenic Mice. PLoS ONE 4(3): e4770. doi:10.1371/journal.pone.0004770

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Variance at the postsynaptic density (PSD) has the potential to account for various differences in human mental characteristics. Hahn et al (here) examined the prefrontal cortex (BA 9) of 12 human brains after death with mean postmortem intervals of 9.6 hours and mean freezing times at – 80°C of 10.2 +/- 1.9 years. They then isolated fractions of the PSDs and visualized them with electron microscopy.

The authors noted that the neuronal ultrastructures were surprisingly well-maintained. Here is a EM image of synaptic membrane fractions, with arrows on right indicating filamentous crossbridges:

scale bars = 1000 / 200 nms; doi: 10.1371/journal.pone.0005251.

Given that these brains were not frozen immediately following death like other animal brains usually are, the sound structure of their PSDs is interesting.

Also of note is that, in their PSD fractions, various protein concentrations (i.e. receptor tyrosine kinase erbB-4; NMDA receptor’s 1 + 2; DLG5/PSD95) are relatively constant between subjects, as shown by western blotting:

doi: 10.1371/journal.pone.0005251.

This suggests consistency in PSD samples between individuals postmortem. Moreover, it suggests that neither the postmortem intervals of between 3 – 24 hrs nor the fractionation procedure disturbs protein-protein interactions.

Reference

Hahn CG, et al. 2009 The Post-Synaptic Density of Human Postmortem Brain Tissues: An Experimental Study Paradigm for Neuropsychiatric Illnesses. doi: 10.1371/journal.pone.0005251

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In order to map synaptic connections in large volumes such as the retina, Anderson et al have argued (see here) that molecular profiling needs to be correlated with electron microscope (EM) images. More on their paper later.

For now, here’s an example of how a molecular profiler can be put to good use. Li et al electroporated expression constructs with genes for horseradish peroxidase targeted to the plasma membrane. Horseradish peroxidase emits amplifiable light at a wavelength of 428 nm. These researchers used it as an anatomical label to correlate spatially with the serial section EM of their tadpole neurons.

One of the advantages of horseradish peroxidase is a uniform plasma membrane distribution including mitochondria / vesicles. It also helps identify long axons / dendrites with small diameters. But on the other hand it has to be electroporated while the animal is still alive to have an effect, unlike some other markers.

Here’s a series of EM images of a distal dendritic branch (blue) that synapses with axon terminals (pink) at white arrow heads:

scale bar = 1 micrometer

You can see how the dendrite recedes as you look to the right in the series of images, which the researchers can reconstruct in their model of the microcircuit.

In order to identify synapses, these researchers used two main criteria:

1) At least two serial sections in which docked and clustered synaptic vesicles oppose the plasma membrane of the next neuron.

2) The distance from the intracellular portion of the presynaptic membrane to the cytosol of the postsynaptic membrane should be significantly longer at synaptic sites than non-synaptic sites. Consistent with this, the presynaptic membrane and the synaptic cleft should be much thicker in electron dense material at synaptic sites than non-synaptic sites.

The following chart quantifies the average membrane thickness at synaptic and non-synaptic sites, although these differences are actually slightly less distinct in neurons expressing horseradish peroxidase:

Reference

Li J, Wang Y, Chiu S and Cline HT (2010) Membrane targeted horseradish peroxidase as a marker for correlative fluorescence and electron microscopy studies. Front. Neural Circuits doi:10.3389/neuro.04.006.2010. Link here.

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Haberthur et al wanted to image gold particles (200 and 700 nm) in rat lungs. In order to do so they performed vascular perfusion on the tissue samples and shaped them with a watchmakers lathe. They then did x-ray tomographic microscopy at a wavelength of 11.5 kilo electron volts, yielding the pixel resolution of 350 nm by 350 nm by 350 nm. This allows for volumetric analysis so that a computer program can reconstruct a 3D image of the tissue sample.

But since the 200 nm gold particles are smaller than the resolution of x-ray tomographic microscopy, the researchers then used transmission electron microscopy to determine the precise location of these molecules. In order to do so, they chopped up their perfused tissue samples with serial sectioning. After correcting for rotation (which tissue samples are liable to do in the microtome), they found a high degree of correlation between their recounstructed image stacks and the real slices. They were also able to track the 200 nm gold particles over a series of TEM images, as shown via the arrow in these two slices 80 nm apart:

Haberthur et al 2009

One might imagine neuro investigations using tomographic microscopy to build 3D models of a given tissue region and then confirming the precise location of individual structures (like synaptic ribbons) within the 3D space via electron microscopy.

Reference

Haberthur D, et al. 2009 Multimodal imaging for the detection of sub-micron particles in the gas-exchange region of the mammalian lung. Journal of Physics: Conference Series 186. doi:10.1088/1742-6596/186/1/012040. Link.

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1) They’re usually too big (~30 nm) and thus may not be able to fit in very small morphological regions such as the synaptic cleft, which are usually about 20 nm wide. One possible way to deal with this is to make the QD smaller! This may be possible to do if researchers switch from CdSe to InP as the crystal core. It is a common mistake to assume that QD’s are smaller than conventional fluorescent dyes–they are in fact 10 to 20 times larger than fluorescein isothiocyanate fluorophores.

2) Since QD’s blink, it can be difficult to track multiple molecules bound to them in a given region as they might cross over undetected. Thus sophisticated algorithms must distinguish between various QD’s. However, it is possible for each QD to emit a different fluorescent wavelength if their sizes are varied slightly, due to variations in the effects of quantum confinement. Note also that the material of the outer surface plays a large role in determining the fluorescence emitted by the same crystal core, which could possibly also be exploited to yield more variation in QD emissions.

3) The QD’s often affect the ligand characteristics of the bound antibody. If one is hoping to detect the function of some protein in typical cellular processes it will be difficult to do so if the QD-bound molecule has different activity–for example, less preferential binding to another protein–than non-QD-bound endogenous molecules.  The possibility of this needs to be carefully quantified before an experimental design assumes that it is not the case.

Despite these problems, there are some ways that QD’s could be used in vivo to detect action potentials. If they were bound to synaptically-important proteins in multiple adjacent neurons, it might be possible to track the spike trains of each neuron and how they interact after exposure to various chemical manipulations. One of the most important benefits of QD’s to this type of design is their high photostability and long lifetime in the aqueous solution of cells.

References

Alcor D, et al. 2009 Single-particle tracking methods for the study of membrane receptors dynamics. doi: 10.1111/j.1460-9568.2009.06927.

Cao YW, et al. 1999 Synthesis and Characterization of InAs/InP and InAs/CdSe Core/Shell Nanocrystals. Abstract.

Pathak, et al. 2009 Quantum Dot Labeling and Imaging of Glial Fibrillary Acidic Protein Intermediate Filaments and Gliosis in the Rat Neural Retina and Dissociated Astrocytes. doi:10.1166/jnn.2009.GR08

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The NYT on the project to dissect and analyze H.M.’s brain:

“We’re going to get the kind of resolution, all the way down to the level of single cells, that we have not had widely available before,” said Donna Simmons… The thin whole-brain slicing “will allow much better opportunities to study the connection between cells, the circuits themselves, which we have so much more to learn about.”… “Ideally, anyone with the technology could do the same with their own specimens.”

Ho hum. What happened to the apparent controversy over the feasibility of this a few months ago? It seems that we will indeed soon have neuron by neuron maps. The question is, at which level  do we achieve scale separation? Surely we will need to go lower than the level of the neuron to capture memories that are encoded via the strength of NMDA receptors. But how much further down?

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In anisotropic tissue something interferes with the free diffusion of water molecules, for instance cell membranes or microtubles. This means that diffusion will be faster parallel to an axon and slower perpendicular to it. In DTI, the diffusion coefficient will miss these local effects and thus the diffusion coefficient will vary depending upon the orientation in which the tissue is measured. By measuring a given area of tissue (i.e., a voxel) from 6+ directions, you can describe the orientation of average axons in a vector. Following some fancy math, you can determine white matter pathways between voxels as well as connectivity probabilities.

Gong et al recently used diffusion tensor imaging on the whole brains of 80 right-handed young adults in 3-mm slice thickness (no gaps) for 40 overall slices from 6 diffusion directions with a b value of 1000 s/mm^2. Note higher b values lead to greater image contrast. They then interpolated the diffusion-weighted images into 1-mm isotropic dimensions. They partitioned the cerebral cortex into 78 cortical regions and restricted the trajectory of fiber bundles to white matter voxels to evaluate their connectivity to the adjacent cortical region. They then counted number of fiber bundles connecting each pair of regions and focused on the connections consistent across their subjects, to account for the variability in brain anatomy between individuals.

The researchers found 329 statistically significant anatomical connections between cortical regions out of 3003 potential between-region connections, a “sparsity” of 11%. They also identified the nodes and edges in their network that have betweeness values 1 SD above the mean. Betweeness is a measure of how often that vertex occurs on the shortest path between other vertices, and its relative importance to the network. Kind of cool because they can evaluate whether those vertices have also been shown to be important in previous non-DTI studies.

DTI will be a big part of the forthcoming human connectome project of the NIH. Resolution on the individual neuron scale is considered unrealistic by many, as Gong et al noted in their paper, and DTI is a viable alternative. Next steps would be connecting structure more with function, determining changes to the anatomy as a result of neurodegenerative diseases, and fixing methodological snags. DTI in the brain is poised to be very useful in the coming decades.

Reference

Gong G, et al. 2009 Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex 19:524-536.

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