A Weird Synapse In Our Ears Gets Weirder

So far on The Naive Observer I’ve written mostly about large scale mapping of connections in the brain. While, as you have hopefully read, I think that neuroscience is going to be hugely advanced by untangling the brain on the cellular level, there are other important issues to consider. That neurons are intricately connected and pass information around their circuitry to make computations that give rise to the mind is daunting and amazing to think about, but we can’t forget that, at a more basic level, all that information is transmitted across tens of trillions of individual synaptic connections. Thus, exactly how synapses transmit information is clearly extremely important to understanding how the brain works, and as it turns out, synapses have worlds of complexity unto themselves. A study just published by Nouvian and friends in Germany deals with this finer point of neural computation and connectivity. Their paper looks at the inner hair cell ribbon synapse, a strange synapse necessary for hearing in the mamalian ear (in the paper, the mouse ear) that doesn’t seem to play at all by the rules. This study gives the perfect backdrop in which to frame normal synaptic transmission, and then look at a case where things are very different.

First of all, some basics.

A synapse is made of up three distinct parts:
1) The Presynaptic Terminal is where a neuron sends its signal from. These structures are located at the end of a long cellular appendage called an axon.
2) The Postsynaptic Terminal is where neurons receive signals sent from the presynaptic terminal. These structures are located at on appendages called dendrites, which are the information collecting arms of neurons.
3) The Synaptic Cleft is the very small space between the pre- and postsynaptic terminals(on the order of 10-50 nanometers wide). The signal is transmitted across this space by special chemicals called neurotransmitters that are released by the presynaptic terminal and detected by the postsynaptic terminal.

The vast majority of synapses in the brain, including the one described above, are chemical synapses, meaning that they transmit electrical activity between cells by releasing chemical messengers, or neurotransmitters, from a presynaptic terminal of one cell, to a post synaptic terminal of another. This involves momentarily transforming an electrical signal to a chemical signal and then back again. (There is also another type of synapse – the electrical synapse. They are less numerous, extremely important, but generally don’t get a whole lot of attention paid to them – an unfortunate circumstance that I am going to perpetuate for the time being.) When you get down and look really closely at a chemical synapse (which I will henceforth refer to as a synapse) you see that they are jam packed with machinery required for synaptic transmission. The most notable feature of the presynaptic terminal is a bunch of synaptic vesicles, which are essentially small spheres of bubbles of cellular membrane, filled with neurotransmitter. When a signal reaches a presynaptic terminal, it causes calcium channels in the terminal to open, and then, by a complex process involving a number of important proteins, the calcium that enters the cell through these channels causes a number of vesicles to fuse with the presynaptic membrane, releasing their neurotransmitter contents into the synaptic cleft. Since the cleft is so small, the neurotransmitters diffuse very rapidly to the postsynaptic terminal, where they bind to specialized receptor molecules embedded in the postsynaptic terminal that only recognize the specific type of neurotransmitter used by that synapse. These receptor molecules then trigger a new electical signal to be generated in the postsynaptic cell. Many of these postsynaptic signals, from many synapses, come together in a larger electrical signal and either cause the postsynaptic cell to fire its own signal or not.

There has been a lot of research done on how all of these molecules work, and as a result, the process of chemical synaptic transmission is extremely well characterized. While studying the hair cell inner synapse though, Nouvian and friends came across some peculiarities in the presynaptic machinery that leads to synaptic vesicle fusion and neurotransmitter release, so I am going to go into a bit more detail on this mechanism.

Generally, for a synaptic vesicle to fuse to the presynaptic membrane and release its neurotransmitter, the vesicle must be docked and primed for fusion at the active zone of the synapse. Docking and priming involves attachement of the vesicle to the inside of the membrane with 3 different proteins referred to collectively as SNAREs. When calcium enters the neuron, signalling the arrival of an electrical signal at the presynaptic terminal, another molecule called synaptotagmin detects that calcium and signals to the SNAREs to tighten, somewhat like a zipper, resulting in fusion of the vesicle with the membrane and neurotransmission. Incidentally, these SNAREs are the target of a group of rather notorious neurotoxins called botulinum toxins. These toxins are the most potent neurotoxins known, with lethal doses as low as 1ng of toxin per kg of whatever it is you are trying to kill. The wikipedia article estimates that this means, if distributed properly, 4 kg of this stuff would be enough to kill the entire human race – mind you 4kg of purified protein is a lot to amass. On an equally sinister note, BOtulinum TOXin is also the functional ingredient in Botox, whose mode of action is quite instructive as to the function of these toxins. Botox temporarily paralyzes the muscles in your face to prevent wrinkles, and meanwhile causes some swelling that gives the appearance of taught skin. To cause this paralysis, the toxins in Botox cleave the SNAREs that would otherwise be involved in vesicular fusion, synaptic transmission and hence muscular contraction and movement.

So generally, botulinum toxins shut down synaptic transmission. This makes these toxins very useful in neuroscience, since one of the best ways to study the function of a specific neuron or type of neuron is to shut it down. However, the synapse that Nouvian and friends study – the hair cell ribbon synapse in the ear – doesn’t seem to play by the rules. To begin with the, when the group tried using the botulinum toxins, they didn’t work; synaptic vesicle fusion, and thus presumably transmission, still occurred. Following this up from many different angles that you can read about in my In Depth critique, Nouvian and friends present strong evidence that hair cell  ribbon synapses do not use the typical SNARE machinery for synaptic transmission. Now, this doesn’t necessarily mean that some strange new way of release has been discovered – there are other sets of vesicular fusion machinery found outside of the nervous system that are insensitive to the specific botulinum toxins that Nouvian and friends used.They did show that non-specific cleavage of protein with trypsin (an enzyme that used by our stomachs) did inhibit fusion, indicating that vesicle release in hair cells does require some type of protein. Most proteins are trypsin sensitive though, so this doesn’t tell us much, although it does rule out magical protein-independent vesicular fusion.

In Depth

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Canadian National Research Council Shifting for the Worse

This article from Nature News reports that the NRC is moving even further away from basic research. The Research Council, which was already leaning more toward the industry side of things, announced that it will be taking an additional 20% of its funding away from institution and bringing it under the control of the Executive Committee, “which will direct it towards research with a focus on economic development, rather than pure science.” All told, 80% of the funding will be set aside for research pertinent to “economic development,” with the paltry remainder of 20% reserved for more basic pursuits. Showing a complete misunderstanding of how the scientific community works, NRC President John McDougall justifies the move thus: “Duplicating the efforts of universities at NRC doesn’t make much sense.” This philosophy is not surprising given his beginnings in the petroleum industry. Accordingly, one of the four flagship projects of the new model is “using algae to soak up carbon dioxide emissions from industry.” If Canada ends up with a Conservative government next week, watch for this project being bandied about as a viable way to reduce emmissions and way justify keeping the Alberta tar sands going ahead full steam.

For an example of a great institute that is likely going to get hit with cuts, check out the Canadian Rivers Institute, which consults with governments world wide on fresh water management.

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Going Backwards on the Road to Freedom of Knowledge?

Kent Anderson, CEO/Publisher of the Journal of Bone & Joint Surgery and Editor in Cheif of the Scholarly Kitchen,  tells me that since abstracts seem to be the main content that reaches the scientific audience these days, the publishing industry needs to start charging for them. He tries to temper his argument by saying that free databases, that allow free abstract reading, like PubMed, allow the majority of scientific reading to be done outside of the publisher’s websites. This, he argues decreases the publishing website’s usage-based value metrics ratings, which means that abstracts are bad for both open source and pay-site publishers. This, however, is a meager attempt at shrouding his real point: that we should be paying for abstracts because they have become more useful in the accelerating information age. He goes on to ask the following questions: “Providing the abstract freely to anyone who wants to use it has become a habit […] and how long can we sustain it?”  And, “Why are abstracts — arguably the most distilled, useful, structurally predictable, and desirable editorial feature of scientific articles — given away freely in an online?” The answer my friend, is that scientific information should be freely available. We aren’t going to go backward on this point, and trying to force us won’t turn out well for you publications. Take your business model to a different industry and let the freedom of knowledge progress.

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In Depth: Untangling Starburst Amacrine Cells

In a bet to unravel one of the longest standing controversies in visual neuroscience, and thus in neuroscience period, Briggman and friends have pioneered a new combination of calcium imaging and electron microscopy. This is the arguably the more impressive sibling of the paper by Bock and friends that I reviewed ealier. The techniques used are in essence the same, but there are a few key differences that helped this paper make some stronger conclusions. Continue reading

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In Depth: Functional Connectomics of the Primary Visual Cortex

Bock and friends have produced a formidable paper, pioneering the new combo of functional calcium imaging and serial recostruction electron microscopy, a technique that will undoubtedly revolutionize the way we study neural circuitry. Numerous sources have called their work (and that in Briggman and friends’ companion paper) a “technical tour de force.” That being said, their findings are rather weak, with low sample sizes leading to a flimsy conclusion. After getting through the somewhat roundabout logic of the experiments, I found myself wanting something more (specifically in the way of n’s) from such a high profile project. Continue reading

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Crash Course In Functional Connectomics

I unabashedly overstate that to understand the brain we need a wiring diagram of it. This is the goal of the burgeoning field of connectomics – to generate an all encompassing brain wiring diagram or connectome. Lately though, after some heated discussions, I’m starting to realize that without information on how the cells in the network function, even the most detailed map of connections might not be all that useful. It was this caveat of connectomics that prompted a pair of papers published in Nature last month. Both papers studied the mouse visual system, one the retina, the other the primary visual cortex (where early visual processing occurs). I review these papers here and here, but to understand the game-changing combo of techniques used in these papers you may need a crash course on one or all of the following items:

1.) Calcium Indicators: Calcium indicators are special dyes that can be used to monitor neuronal activity. Neuronal activity results from ion fluctuations either into or out of the cell. One ion that enters the cell during activity is calcium. When a calcium indicator dye that has been loaded into cells encounters an influx of calcium, the dye glows brighter. Since calcium enters neurons when they are active, calcium dyes glow brighter when neurons are active, and so they allow you to track the activity of neurons.

Here is and example of calcium indicator imaging of waves of neuronal activity propagating through a neural network in a model of epilepsy:

2.) Electron Microscopy: Is an old (1930’s) but powerful type of microscopy that allows for very high resolution micrographs, much higher than traditional light microscopy. Rather than using a beam of light (photons), electron microscopes use a beam of electrons and special techniques for detecting that beam. The result is a microscope that, according to wikipedia, can magnify up to 10,000,000 x, while light microscopy only gets up to 1000’s of times magnification.

3.) Visual Stimulus Selectivity: This is the ability of neurons in the visual nervous system to detect specific visual stimuli, to exclusion of others. The most basic type of selectivity you will hear about is orientation selectivity. A neuron that is orientation selective will only respond when a line of a certain orientation is presented in a certain part of the visual field. This type of selectivity was discovered by David Hubel and Torsten Wiesel, in a rather ironic way that you can hear Hubel talk about below (the popping sound you hear in the background is the readout of neurons becoming excited). Another type of selectivity you’ll read about in the coming posts is direction selectivity, which refers to neurons that fire only when a moving line or dot is present in a specific part of the visual field, and only when it is moving in the right direction.

The general form that both of these papers take runs something like this: Both groups start with calcium indicator imaging in live neurons in either the brain (visual cortex) or the retina. The reason why these groups are working in the visual system is because neurons that deal with visual stimuli respond to very specific stimuli. For instance, in the retina, retinal ganglion cells respond to things like color, or points of light in a specific part of the visual field or lines moving in specific directions. Cells with similar preferred stimuli exist in the primary visual cortex as well. In both papers, the authors chose stimuli to present to the eye, and then using calcium indicator imaging they found neurons that responded to a range of preferred stimuli. They now had information on what role these neurons play – this constituted their functional data.

Once they had their functional data, both groups sliced the pieces of brain or retina that contained their functionally imaged neurons into extremely thin pieces and used electron microscopy to take images of each slice. They then they used the resulting huge data sets to make 3D reconstructions of the cells and circuitry in the volume that contained their functionally imaged cells. The result was a wiring diagram that included their functionally characterized cells as well as the cells received input from and made input into them. Using this diagram both groups – whether successfully or unsuccessfully – made conclusions about how the functionally imaged cells work in the network.

In specific, Bock and friends tried to determine whether orientation selective pyramidal neurons synapse onto inhibitory interneurons in such a way as to make those interneurons orientation selective too?

On the other hand, Briggman and friends kind of do the opposite, asking how input from inhibitory starburst amacrine cells makes direction selective retinal ganglion cells direction selective.

Both of these matters are rather complex, so if you are interested in how these groups undertook their endeavours, have a look at the In Depth critiques (linked directly above).

Regardless of the particulars though, both of these papers involved heroic efforts and a result, for the first time, we get to see functional data combined with electron microscopy in the central nervous system. Keeping in mind that solving the brain will rely on reconstructing cellular circuitry and integrating the resulting network diagram with knowledge of how the cells function in that circuitry, this new step toward functional connectomics is an extremely important one. You can count on seeing studies like this pop up more often in the near future.

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Neuroskeptic on todays scientific process

I can’t decide whether this is insensitive or just extremely accurate, but regardless it’s a fitting allegory. Point number 1 is more accurate than you think, lots of great ideas come out of drunken nights and caffeine-jittered minds.

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In Depth: A Map of 16,000 Fruit Fly Neurons

Hopefully as you see in a bit more depth how the paper unfolds, it will become more apparent that Chiang and friends make few real findings or even predictions. However, aside from the transparency and respect for freedom of knowledge that I mentioned in the Meat and Potatoes, the strongest analytical value of the paper is the predictive power of the “FlyCircuit” database they are introducing.

Continue reading

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A Map of 16,000 Fruit Fly Neurons

To understand how the brain works we need a wiring diagram of it. If we ever get there, this diagram will give us hints about how information is processed, synthesized and perhaps even stored, and eventually will bring us closer to understanding things like memory, emotion and consciousness. So whole brain connectivity maps – aka connectomes – are a hot pursuit right now. We are just on the cusp of starting to have the capability to reconstruct wiring diagrams of neural circuitry on a relatively large scale (read: volumes of a fraction of a cubic millimetre). Knowing that replicating a very exact wiring diagram of a single brain is currently nearly impossible, some groups are opting to generate models of connectivity, which involves taking small pieces of information and extrapolating from them to make a best guess of what the connectivity likely looks like.

How best to construct a model like this? Chiang and friends in Taiwan decided to look at fruit fly brains, take heaps of pictures of individual neurons inside those brains, throw the photos together at the end and see what they got. They chose the fruit fly (Drosophila melanogaster) because the fly is a longstanding and extremely useful model organism. The fruit fly is genetically very malleable and as a result there are many techniques around that allow experimenters to probe their cellular biology.   Using one of these genetic techniques unique to flies, the group forced small ensembles of neurons or even single neurons to make fluorescent proteins. They could then remove the tiny brain, take a picture of the neuron(s) and determine their 3D position relative to a structure at the centre point of the brain. Using the resulting 3D coordinates, and a complex algorithm that is beyond my comprehension and apparently beyond the scope of the paper, the 3D images of neurons were transformed into the space of a “template brain,” which is just a model based on the average of many brains. By labelling different populations of neurons in different brains with their fluorescence tool, Chiang and friends generated a bank of 3D neurons that they then populated their average brain with and they ended up getting something (a model) that looks like this:

Wiring of Female Drosophila Brain

Noteworthy developments in the paper include the group’s working definition of a “Local Processing Unit” – an ensemble of neurons that is presumably making a concerted effort in some kind of neural computation. This definition and their algorithm for pulling Local Processing Units out of their model indicates that an obscure group of neurons called the ventrolateral protocerebrum (VLP) may in fact contain, not one, but two separate Local Processing Units! Not the big conclusion you expect from a Cell Press mag. What’s more, this is just a prediction, actually testing it is a whole other kettle of fish, and since we apparently don’t know much about the VLP it will be even harder. Something a bit more exciting is that the final wiring diagram that the group constructs may be able to predict the direction of information flow. They do give a single example of this (without context), but other than that they are pretty close-chested with their predictions of info flow. All in all, the findings and non-findings of the paper amount to rather mundane predictions. Which leads to an important point: the study doesn’t really show us anything new about the fly brain. While their process is interesting, the images are beautiful and the wiring diagrams they spit out at the end are proof of a tonne of work, that impressiveness doesn’t really give rise to tangible findings.

For me, the true value of this study shines through in the transparency and scientific goodwill apparent in the work. From the beginning of the article Chiang and friends admit openly that their approach doesn’t extend past generating a map of “brain-wide interregoinal connectivity.” Indeed, their process can’t give any real insights into fine neural circuitry because they do not consider synapses. But the really impressive part is this: “Raw data for each of 16,000 sample images and the two template brains can be freely downloaded from the FlyCircuit website for offline analysis and evaluation of registration precision.” If you go here and click on the image, you can even watch a slide show of  all 16,226 individual neurons. So not only can you theoretically go and check that their assertions about their work are true, you can use the data they have generated in your own analysis. You can even add your own data to the database (You being drosophilists).

Chiang and friends’ work stands as an encouraging advance in the neuroscientific community. Not only do they emphasize that the continued mapping of the fly brain is set up to be a worldwide collaborative effort, but, judging by my meager travels in science, they also set the standard for transparency in an increasingly secretive scientific institution. Perhaps we should all take example.

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The Naive Observer Grand Opening

Welcome all to the Naive Observer. In the next couple weeks I’m going to squeeze a lot into a short period of time. Hopefully by doing that I’ll give you a relatively comprehensive impression of what I want to do here. Before it begins though, one quick note: this is just another blog about science, but I am experimenting with a new model that comes in two parts.

The first part is plain old neuroscience, under the heading Exciting Research. I will outline important and recent publications that tell new and exciting stories about connections in the brain. This is my area of expertise, and I’d like to share some knowledge. For those of you who are interested but unsure whether you’ll be able to understand, check out the Meat and Potatoes section of Exciting Research.  There I give basic overviews of the papers I review and put them into the greater neuroscientific context. For the aficionados and those of you who crave more than the gist, look at the In Depth section, where you’ll find a fine-toothed comb criticism and analysis of each paper. For every paper I review there is a Meat and Potatoes and an In Depth follow-up.

The second part of The Naive Observer is News and Opinion. Here I will cover current topics in the general world of science. Some topics I plan on hitting are Women in Science, Neuroscience of Gender and Sex, Ethics, Science and Art, Science and “The Public”, Scientific Scammery and Quack-/Hackery, SciPolicy, SciJournalism and accounts of whatever flight of fancy I am on or find interesting at the time.

Please feed back onto me. Let me know what you think, like, dislike and please educate me where you can (for more on this, see the Preambled disclaimer ).

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