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Archive for the ‘Molecular Neuroscience’ Category

The expression of green fluorescent protein in heterologous systems in 1994 spawned for light microscopy what has been called the “green revolution,” allowing researchers to visualize individual protein molecules in cells.

In a recent paper, Shu et al first review attempts to produce similar molecules for electron microscopy, such as horseradish peroxidase, but conclude that they all have serious drawbacks. They then report their engineering of a protein molecule called miniSOG, a fluorescent molecule that is also an efficient oxygen generator. The only cofactor of this protein is flavin mononucleotide, which is necessary for the mitochondrial electron transport chain and is thus present in nearly all cells.

In cultured HeLa cells, they fused miniSOG to cytochrome C to show that expressing this molecule is able to mark mitochondria in both light and electron microscopy:

J = confocal image prior to photooxidation, K = transmitted light image following photooxidation; arrows = cells expressing miniSOG, arrowheads = cells not expressing the marker; L / M = electron microscopy, note the well-preserved morphology of outer and inner membranes and cristae of the mitochondria, indicating a strong signal; doi:10.1371/journal.pbio.1001041

The researchers also fused miniSOG to an isoform of SynCAM, a cell adhesion protein that is expected to localize post-synaptically. They then used serial block face scanning em in mouse tissues to determine the location of their marker in 3d space. They show this 3d reconstruction from 2d image stacks in a 1.5 min supplementary video, which I’ve uploaded here and am embedding for your viewing pleasure:

The only problem I can see is that the “miniSOG revolution” isn’t nearly as catchy a name as the “green revolution.” Any suggestions?

Reference

Shu X, Lev-Ram V, Deerinck TJ, Qi Y, Ramko EB, et al. (2011) A Genetically Encoded Tag for Correlated Light and Electron Microscopy of Intact Cells, Tissues, and Organisms. PLoS Biol 9(4): e1001041. doi:10.1371/journal.pbio.1001041

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Memory consolidation is known to occur when short-term memory traces in the hippocampus are transferred to long-term storage areas in the cortex, over a period of ~ 1 week. Now Lesburguères et al have published a very interesting study looking at the mechanism of this transfer in rats, showing (by inhibiting various processes) that it is dependent upon epigenetic changes (specifically, histone acetylations) in the olfactory cortical neurons that are “tagged” with the memory for that smell. This process is also dependent upon synaptic activation, indicating that there is some sort of way that synaptic signals communicate with the epigenome of the cell, and determining those mechanisms will likely be very enlightening.

This is just one of a slew of recent papers emphasizing the importance of epigenetics in cellular regulation, and I have officially jumped on the bandwagon. For example, John et al’s recent paper shows the importance of chromatin’s accessibility state to the “de novo” DNA binding patterns of the glucocorticoid receptor, a model transcription factor. They found that chromatin’s accesibility state explained much more variance in the transcription factor binding activity than the intergenic DNA binding motifs. Nature Genetics is not OA so I can’t post it here, but do look at their fig 3 showing the correlation between glucocorticoid receptor CHiP-Seq and DNase-seq, which is staggeringly high.

One of the reasons epigenomics holds great promise is that it seems much more “decodable” than the protein, lipid, or RNA landscape of a living cell. For example, see recent technologies to probe the cytosine methylation, nucleosome positioning, or various histone modifications. Of course histones can undergo many post-translational modifications, but one at a time is a start and eventually some sort of multi-antibody system might co-immunoprecipitate many types of them, or some other method entirely could decode the histone modification landscape.

Indeed, one can imagine a future technology that would first determine the position of neurons and glial cells, then characterize the neurons’ post- and pre-synaptic densities, and then “sequence” their epigenomes; such information might be able to reproduce a lot of the function of that network.

References

Lesburguères et al, 2011. Early Tagging of Cortical Networks Is Required for the Formation of Enduring Associative Memory. Science. doi: 10.1126/science.1196164.

John et al, 2011. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nature Genetics doi:10.1038/ng.759.

Lister R et al, 2008. Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis. Cell doi:10.1016/j.cell.2008.03.029.

Zhang Z et al, 2011. High-Resolution Genome-wide Mapping of the Primary Structure of Chromatin. Cell doi: 10.1016/j.cell.2011.01.003.

Maze I et al, 2010. Essential Role of the Histone Methyltransferase G9a in Cocaine-Induced Plasticity. Science doi: 10.1126/science.1179438.

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One of the canonical models for gene regulation involves a regulatory protein recognizing and binding preferentially to a particular sequence of DNA in the promoter region of a gene and thus increasing the affinity of RNA polymerase for that region. Camas et al (here) use the LacI family of transcriptional regulators (which have the helix-turn-helix domain) to search for correlations between the amino acid of transcription factors and the DNA sequences they regulate. Two findings stick out:

1) They found a consensus binding site across the family of LacI transcription regulators, which is here:

doi:10.1371/journal.pcbi.1000989

This is a promising indication that there is some sort of DNA sequence conservation among transcription factors. It is computationally expensive and statistically complex to search for these conserved sequences (and especially to do so combinatorially), so any current findings should in my mind be viewed as validations of more precise and useful findings in the future. (Perhaps I am overly optimistic!)

2) They found sequence correlations between amino acids 15 and 16 of the transcription factors and nucleotides 5 and 4 of their associated DNA binding sites. In particular, transcription factors with the same DNA-contacting amino acids tend to recognize highly similar (“degenerate”) nucleotide sequences:

doi:10.1371/journal.pcbi.1000989; "Recognition degeneracies are represented as unidirectional arrows (asymmetrical intrinsic), bidirectional divergent arrows (symmetrical intrinsic), and bidirectional convergent arrows (extrinsic). Colors for polar (green), basic (blue), acidic (red) and hydrophobic (black) amino acids.

Even though many of these studies are in bacteria, such regulatory systems play a large role in neural systems, as general regulatory mechanisms are conserved across the phylogenetic tree. It is interesting to see how all of these disciplines are intertwined.

Reference

Camas FM, Alm EJ, Poyatos JF (2010) Local Gene Regulation Details a Recognition Code within the LacI Transcriptional Factor Family. PLoS Comput Biol 6(11): e1000989. doi:10.1371/journal.pcbi.1000989

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Nanoparticles and brain imaging

An interesting article today by Bruce Cohen on the use of “next-generation” molecular light generators can be found here.  These inorganic atoms, which are typically doped with some other material, are designed to have desirable excitation properties. For example, their use minimizes the amount of deleterious photobleachingin the sample. On the other hand, they are often large, making them difficult to use biologically without altering the system under study. However, as their size decreases, these molecules will likely play a large role in neuroscience in the coming years.

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Postsynaptic densities (PSDs) in the forebrain contain ~ 100 – 400 proteins, including receptors (NMDA; AMPA) scaffolding molecules (DLG4, DLG1; HOMER1; SHANK2), kinases, phosphatases, and cytoskeletal components. They are typically 20–30 nm thick and 300 nm long.

In order to build a model of the PSD using DLG4*, Chen et al (here) cultured hippocampal neurons, incubated primary antibodies to DLG4, conjugated the antibody to secondary 1.4-nm gold bodies, and silver enhanced the gold. They then visualized 8 dendritic spines free from ice damage with EM tomography with pixel sizes of 0.48 – 0.75 nm. Finally, they built a model of the PSD in excitatory (glutamate-based) synapses based on imaging each component.

For example, via the following an EM image of 120 nm thick section (A), they were able to create a 1.5 nm thick virtual image without so much overlap (B), and then render the vertical filaments in a 3d reconstruction (C):

scale bar = 20 nm, doi: 10.1073/pnas.0800897105

The vertical filaments are typically 5 nm in diameter (4.9 ± 0.2 nm, n = 22) and 20 nm long (21 ± 3 nm, range 16–25 nm, n = 38). They are also uniformly spaced. On the basis of the immunolabeling, it appears that these filaments are DLG4 proteins, with their N-termini attached to the membrane.

After doing this for each component of the PSD, they propose the following model, where red is vertical filaments of DLG4, cyan is NMDA-like receptors, blue is AMPA-like receptors, purple is horizontal filaments associated with NMDA / AMPA – like receptors, and white cross-links the vertical filament meshwork, especially near NMDA receptors:

doi = 10.1073/pnas.0800897105

It’s possible that some of the vertical filaments are not DLG4 proteins but instead some other type. However, there are ~ 300 DLG4 proteins  and ~ 30 NMDA receptors per PSD (see here) so it is certainly possible that there could be 1 vertical filament for each NMDA receptor with spares left over for AMPA receptors. Regardless, this is a useful mental map for the molecular structure of the PSD, with implications for the mechanisms of AMPA / NMDA receptor-dependent synaptic plasticity.

* DLG4 is AKA PSD-95, but I’m going with the wikipedia definition.

Reference

Chen X, et al. 2008 Organization of the core structure of the postsynaptic density. PNAS doi: 10.1073/pnas.0800897105 .

Chen X, et al. 2005 Mass of the postsynaptic density and enumeration of three key molecules. PNAS doi: 10.1073/pnas.0505359102

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Tadpoles are a model system for neural development and have well-characterized retinal ganglion cells that guide axons out of the retina. This is promoted by gradients of the guidance cue netrin. There is a poorly named protein called “deleted in colectoral cancer” (DCC) that acts as the netrin-1 receptor.

Manitt et al used DCC primary antibodies, IgG secondary antibodies coupled to 1 nm gold particles, and then serial section TEM to visualize this system. The picture below shows that DCC is present on presynaptic vescicles (arrows in J + K), the surface of presynaptic membranes (L), and on the surface of axonal filopedia (M). It also indicates synapses via triangles, and has scale bars of 200 nm:

This is consistent with their model that DCC receptor proteins mediate netrin signaling in axon pathfinding and synaptogenesis. BDNF is another molecule that promotes both increased synaptic density and axon branching. But they have slightly different mechanisms, showing that different cues can lead to the subtly differences in neural circuitry that defines the developing brain.

Reference

Manitt et al, 2009. doi:10.1523/JNEUROSCI.0947-09.2009. Pubmed here.

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The role of the guanine nucleotide exchange factor Tiam1 has long been known to stimulate the outgrowth of neurons (see here) by shifting the Rac / Rho actin polymerization equilibrium (see here) towards Rac. Now Fard et al. have used RNA interference in cell culture to show that Tiam1 is specifically associated with NGF/TrkA-dependent neurite elongation. Here is the mechanism they propose, involving tyrosine kinase receptors:

RNA interference is a useful technique for evaluating mechanisms. As opposed to pure gene knock-out studies, it can allow for the evaluation of a more dose-dependent relationship.

Reference

Shirazi Fard S, Kele J, Vilar M, Paratcha G, Ledda F (2010) Tiam1 as a Signaling Mediator of Nerve Growth Factor-Dependent Neurite Outgrowth. PLoS ONE 5(3): e9647. doi:10.1371/journal.pone.0009647

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