Archive for the ‘Molecular Neuroscience’ Category

Cells can die for a variety of reasons. Some of them are intentional (“programmed”) in response to exposure to external stressors (like viral or toxic molecules) or internal problems (like DNA damage). And some of them are unintentional (“non-programmed”), which often involves the premature breakdown of cell membranes and loss of cell contents.


The totally simple necroptosis cell death pathway, from the NIH via Wikipedia

Caccamo et al recently published a paper suggesting that one specific type of programmed cell death, necroptosis, might be a key part of what mediates neuron death in Alzheimer’s disease (AD).

Their evidence spanned multiple human data sets of postmortem AD brains and mouse models (5XFAD), and showed that markers for necroptosis (MLKL, RIP1, RIP3) were often significantly correlated with the degree of AD neuropathology seen in those brains.

Notably, their data didn’t provide strong evidence to exclude apoptosis and non-necroptosis necrotic cell death pathways as also contributory to cell death in AD.

So, another study that would also be interesting would be to see a more global comparison of all different types of cell death, to see which markers correlate the strongest with AD neuropathologic changes.

On the other hand, the authors note in their discussion that there is a lot of cross-talk between necrosis and apoptosis, which means that it may be difficult or not make sense to distinguish between them in this way.

Even if necroptosis is the mechanism of cell death in AD, that doesn’t mean that we can just turn off this cell death pathway and rescue neurons and memory. If anything, it suggests that the neurodegeneration itself is intentional, likely helpful to mitigate even more damage, and that changes to stop AD will have to occur much farther up to pathogenic cascade.

Still, it’s critical to understand exactly what is the pathway of degeneration in AD so that we can figure out what to target, and this study might be an important part of that.

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A really nice article from Tyssowski et al. The authors did RNAseq on neurons that either were or were not stimulated with neural activity. They found that a subset of proteins (251) that have been previously described as “[neuronal] activity regulated genes” were able to predict the stimulation state of those neurons well above chance. Specifically: 92% of the time using nearest neighbor classification as measured by leave one out cross validation.

I’m interested in the broad question of “which RNAs/proteins are important for neuronal activity” and this set of activity regulated genes is pretty clearly within that set. Interestingly, it seems that the expression of these genes is pretty highly correlated (very similar chromatin states, transcription factors, etc), so I don’t think you would have to perfectly preserve ALL of them in order to allow for a high-fidelity preservation of information.

On that note, it’d be interesting if someone were to use this data to try to predict neuron stimulation state using the smallest set of activity regulated genes as necessary. For example, the 19 rapidly-induced activity regulated genes, including the non-transcription factors Arc and Amigo3, seem like they would punch above their weight in terms of predicting neuronal activation state.

Figure 6 from the paper

Arc expression and enhancer acetylation is stimulated after only 10 minutes of neuronal activity; http://biorxiv.org/content/early/2017/06/05/146282.full.pdf+html

It also suggests an experiment for any brain preservation procedure that purports to preserve gene expression important for neural activity: stimulate neural activity on a subset of neurons (probably in vitro, since it’s easier and should yield the same result), perform your brain preservation processing steps, attempt to measure the expression of these genes, and then see if you can distinguish between which neurons were stimulated or not on the basis of those measurements.

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What is the mechanism by which dendritic spines can change structure over a rapid time course? Though this may seem esoteric, it is probably how memories form and is thus utterly essential to neuroscience. Two new papers present some relevant data.


Two-photon imaging data of dendritic spines, from Wikipedia User:Tmhoogland

First, as has been shown several times, Harward et al show that glutamate uncaging at single dendritic spines leads to a rapid increase in spine volume after only ~ 1 minute that degrades over a period of several more minutes:


Harward et al; doi:10.1038/nature19766

Along the same time course as the dendritic spine volume increase, these authors also detected TrkB activation (using their amazing new FRET sensor), which was largely in the activated spine but also traveled to nearby spines and the dendrite itself:


Harward et al; doi:10.1038/nature19766


In what is to me probably their most compelling experiment, they show that hippocampal slices without BDNF have highly impaired volume changes in response to glutamate, and that this can be rescued by the addition of BDNF:


Harward et al; doi:10.1038/nature19766

They also present several lines of evidence that this is an autocrine mechanism, with BDNF released from spines by exosomes and binding to TrkB receptors on the same spine.

In a separate article in which most of the same authors contributed, they show that another protein, Rac1, is activated (ie, GTP-bound, leading to fluorescence) very quickly following glutamate uncaging at single spines:



Hedrick et al; doi:10.1038/nature19784

They also show that a similar rapid course of activation following glutamate uncaging occurs for the other Rho GTPases Cdc42 and RhoA.

Interestingly, they also show that these proteins mediate synaptic crosstalk, whereby the activation of one dendritic spines causes nearby dendritic spines to increase in strength. After several more experiments, here is their diagram explaining this mechanism:


Hedrick et al; doi:10.1038/nature19784

Overall I find their data trustworthy and important. The most interesting subsequent question for me is whether endogenous amounts of CaMKII, BDNF, TrkB, and Rho GTPase signaling components (e.g., Cdc42, RhoA, Rac1) vary across dendritic spines, and whether this helps mediate variability in spine-specific and spine neighbor-specific degrees of plasticity. My guess is that they do, but AFAICT it remains to be shown.

If it is true that spines, dendrites, and neurons vary in the expression and distribution of these proteins, then any attempt to build models of the brain, as well as models of individual brains that have any sort of dynamic component, probably need to measure and model the local densities of these protein mediators of plasticity.

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CSF- and serum-borne autoantibodies against brain proteins are known to cause a wide range of cognitive sequelae due autoimmune attack. For example, when antibodies are raised against the protein LGI1, which is thought to act as a voltage-gated K+ channel, a common result is encephalopathy.

As a result, LGI1 is often included in autoimmune panels, along with several other proteins including CASPR2, NMDA and AMPA subunits, GABA-B receptors, GAD65, CRMp-5, ANNA-1, and ANNA-2.

Recently, Ariño et al presented a summary of 76 patients with LGI1-associated cognitive deterioration, 13% of which had forms of cognitive deterioration distinct from limbic encephalitis. At 2 years their major outcomes were:

  • 35% fully recovered
  • 35% regained independence but to baseline levels
  • 23% required assistance due to cognitive defects
  • 6% died

In mice, LGI1 is primary expressed at the RNA level in neurons at the RNA level, while in humans its expressed in both mature astrocytes and neurons (data from here and here), eg in the Darmanis et al 2015 human data set its actually expressed higher in astrocytes:


It might be interesting to see whether encephalopathies are generally only caused by autoantibodies against proteins expressed in neurons, or whether or cell type-expressed proteins can also lead to a similar clinical outcome.


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A cool study shows that knocking out the regulatory protein Epac2 in mice has large effects on the structural stability of basal dendrites in pyramidal neurons.

This is not the first study to have demonstrated this type of selective regulation, but it’s still surprising. The apical dendrites are so similar and close to the basal ones; why wouldn’t a regulatory molecule affect both classes?

The fact that they are regulated differentially shows that each structural component of neurons is finely tuned. This is weak evidence in favor of the theory that the neuronal morphology carries lots of information.


Srivastava DP, Woolfrey KM, Jones KA, Anderson CT, Smith KR, et al. (2012) An Autism-Associated Variant of Epac2 Reveals a Role for Ras/Epac2 Signaling in Controlling Basal Dendrite Maintenance in Mice. PLoS Biol 10(6): e1001350. doi:10.1371/journal.pbio.1001350

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What has been the growth rate of computing power, multi-neuron recording, and DNA sequencing over the past decade? Konrad Kording provides an illuminating chart pertaining to this question:

neurons recorded = the number of neurons that can be recorded from simultaneously; the neuron and computer scales are exponential fits to data; doi:10.1371/journal.pcbi.1002291

Given the above DNA sequencing trends, it’s no surprise that groups in many different fields are developing strategies to turn the problem they are trying to study into a sequencing problem.

See, for example, Jonathan Weissman’s talk on ribosome profiling, which is an elegant way to use DNA sequencing of mRNA molecules tethered to the ribosome as a way to study translation.

In his article, Kording touches on a couple of intriguing sequencing technologies that might help make the “data-out” step of a given neuroscience experiment more high-throughput.

The method for connectomics he describes is particularly fascinating. The idea is to assign neurons a unique DNA barcode that is spread to each of its synaptic partners via a transsynaptic virus, and then sequence the set of barcodes from a given group of cells.

One aspect that I think Kording might have underemphasized is that these technologies would improve greatly if we improved our ability to sequence the DNA of individual neurons.

For example, typical protocols for probing the expression of intermediate early genes rely on harvesting cells from mass culture or coarse brain regions before sequencing. This is powerful, but it would be much more so if we could analyze the distribution of gene expression between cells rather than across them.

Single-cell genomics is advancing, but it is not yet at the point of routine laboratory use for a typical sequencing experiment. And in order to really take advantage of DNA sequencing technology in understanding how networks of neurons work together, it will presumably need to reach that point.


Kording KP (2011) Of Toasters and Molecular Ticker Tapes. PLoS Comput Biol 7(12): e1002291. doi:10.1371/journal.pcbi.1002291

Link to Jonathan Weissman’s 11/16/11 talk.

Oyibo H, et al. 2011 Probing the connectivity of neural circuits at single-neuron resolution using high-throughput DNA sequencing. Presentation at Computational and Systems Neuroscience Meeeting, pdf.

Saha RN, et al. 2011 Rapid activity-induced transcription of arc and other IEGs relies on poised RNA polymerase II. doi: 10.1038/nn.2839.

Kalisky T, et al. 2011 Single-cell genomics. doi:10.1038/nmeth0411-311

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In their review of the “neuroproteome” associated with aging and cognitive decline, VanGuilder and Freeman discuss some of the technical approaches and findings in the field.

This illustrative figure shows some of the major cellular players involved and lists some example proteins involved in four important pathways:

"numerous cell types (microglia (green), astrocytes (orange), oligodendrocytes (blue), and neurons (violet)) and subcellular components (mitochondria (brown), endoplasmic reticulum (green), cytoskeleton (orange/red), and synaptic machinery) are affected by brain aging"; doi: 10.3389/fnagi.2011.00008

As you can see, many proteins have been implicated, although the degree of up-/down-regulation of these proteins is not fully elucidated.

The authors mention the value of standardizing efforts to profile the proteome in important brain regions across the lifespan of rodent models. This step would make these results more robustly quantitative and help iterate towards a consensus.


VanGuilder H. D. and Freeman W. M (2011) The hippocampal neuroproteome with aging and cognitive decline: past progress and future directions. Front. Ag. Neurosci. 3:8. doi: 10.3389/fnagi.2011.00008

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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?


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.


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:


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.


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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