A Hop, Skip, and a pre-synaptic Patch

 

This new technique is just too cool not to blog about. 

Novak et al. 2013 Figure 1A pre-synaptic patch clamp

The synapse is the connection between two neurons. The pre-synaptic part is from the neuron sending a signal and the post-synaptic part is from the neuron receiving the signal.

If you want to learn about the connection between the two neurons, you want to know what is happening on both sides of the synapse. It's relatively easy to record signals from the post-synaptic side using patch clamp or sharp electrode recording, but it is much much harder (basically impossible until now) to record from the pre-synaptic side.

Homeostatic platsicity in a thorny situation

Synapses, the connections between neurons can strengthen and weaken depending on the specific activity at that synapse. This is called synaptic plasticity, and we've talked about it a lot on this blog (here, here, here and here).

the strengthening and weakening of synaptic connections corresponds to the spine growing or shrinking (Matsuzaki 2007)

However, there is another kind of plasticity that can occur at synapses. This is called homeostatic plasticity. And instead of the synapse strengthening or weakening depending on the specific activity at that synapse, the synapses strengthen and weaken in homeostatic plasticity depending on the activity of the whole cell.

To drastically simplify, each cell 'wants' to fire about a certain amount, if it suddenly starts to fire a lot less, it will take steps to strengthen its connections or make itself more 'excitable' so it can get back to its preferred amount of firing. Similarly if the cell starts to fire a lot more than normal, it will take steps to make itself less excitable and to weaken its connections until it reaches the right amount of firing. 

Thorny Excrescences from Lee et al., (2013)

A recent paper from the Pak lab explains how in some specific neurons in the hippocampus (CA3 pyramidal cells), the activity of the whole cell is strongly controlled by a some very peculiar synapses. These synapses are close to the cell body, and are on these HUGE weirdly shaped spines (see above) called "Thorny Excrescences". For comparison 'normal' spines look more like this:

Spines from Lee et al. (2013)

The Thorny Excrescences (TEs) are massive spines that contain many separate synapses on them, but connect to the dendrite through 1 neck. 'Normal' spines, on the other hand, usually have 1 synapse at the spine head, and connect to the dendrite through 1 neck.

The size of the TEs, and their proximity to the soma makes them an extremely powerful way to control the signals that the soma receives. Lee et al (2013) shows that when you drastically reduce activity by blocking action potentials (using TTX), you get massive growth of these TEs, but the normal spines further away from the soma stay the same.

They test 3 things to determine whether the TEs have undergone homeostatic plasticity. They look at the morphology (they are bigger), the activity (the electrical signals from them are bigger) and the molecular signatures (the molecules indicative of new synapses are more plentiful). The paper is a really nice complete story showing that these TEs have a lot of control over the general activity of the cell.

It also solves an important problem with homeostatic plasticity. That is, how can the general activity of the cell be modulated without the specific differences between synapses being erased, and consequently the memories or pieces of information they encode? If homeostatic plasticity occurs at spines dedicated to it, then the other spines can still encode specific signals while the activity of the cell as a whole changes.

© TheCellularScale



Lee KJ, Queenan BN, Rozeboom AM, Bellmore R, Lim ST, Vicini S, & Pak DT (2013). Mossy fiber-CA3 synapses mediate homeostatic plasticity in mature hippocampal neurons. Neuron, 77 (1), 99-114 PMID: 23312519

a STORM inside a cell

We've been talking about some of the most cutting edge intracellular visualization techniques lately. Array tomography and Serial block-face electron microscopy have been featured. Today we'll talk about STORM imaging.

STORM imaging (Xu et al., 2013)

STORM stands for Stochastic Optical Reconstruction Microscopy. While Array tomography and Serial block-face EM are both revolutionary in that they can combine very high resolution imaging with relatively large volumes of tissue, STORM is an advancement that lets you see tiny tiny little molecules within the cell.

The problem with 'normal' imaging is that molecules are smaller than the diffraction of light.

Example of the STORM resolution (from Zhuang lab's webpage)

In the figure above, imaging some tiny molecules next to each other is impossible with traditional fluorescence microscopy, but with STORM, you can resolve 10s of nanometers (nm).

To do this, STORM uses photoswitchable dyes, which means that the dye can be turned on or off. This allows researchers to turn on tiny little areas and then turn them off. If all the dye is turned on all at once, the image will look like a big mess because the signals will all overlap each other. But turning on only a few at a time allows you to estimate where the actual protein or molecule is.

"The imaging process consists of many cycles during which fluorophores are activated, imaged, and deactivated. In each cycle only a subset of the fluorescent labels are switched on, such that each of the active fluorophores is optically resolvable from the rest. This allows the position of these fluorophores to be determined with nanometer precision." -Zhuang lab webpage

So what amazing things can they do with this STORM?
A recent paper by Xu et al. (2013) found that the actin which plays a huge role in the intracellular structure of a neuron, has a specific ring-like structure along the axons.

Xu et al., 2013 Figure 4F

This is the kind of research that will immediately go into neuroscience and cell biology textbooks. Xu et al. discovered how actin was structured along the axon simply by being able to 'see it'.

Not only did they discover the structure of actin and spectrin (magenta above) in the axon, but they also found some other interesting molecular patterns that appear to relate to the actin ring structure. The sodium channels, which control action potential propagation down the axon, are concentrated about half way between the ends of the spectrin tetramers. The potential for super-resolution microscopy like STORM is huge. The location of molecules with relation to one another probably plays a huge role in the function of cells and now we have the tools to map them.

© TheCellularScale



Xu K, Zhong G, & Zhuang X (2013). Actin, spectrin, and associated proteins form a periodic cytoskeletal structure in axons. Science (New York, N.Y.), 339 (6118), 452-6 PMID: 23239625

Connecting Form and Function: Serial Block-face EM

The retina is a beautiful and wondrous structure, and it has some really weird cells.

Retina by Cajal (source)

Retinal Ganglion Cells (RGC) have all sorts of differentiating characteristics. Some are directly sensitive to brightness (like rods and cones), while some are sensitive to the specific direction that a bar is traveling.

I am discussing really amazing new techniques to see inside cells this month, and have already posted about the magic that is Array Tomography. Today we'll look at another amazing new technique that (like array tomography) combines nano-scale detail with a scale large enough to see many neurons at once. This technique is called Serial Block-face Electron Microscopy (SBEM), and was recently used to investigate how starburst amacrine cells control the direction-sensitivity of  retinal ganglion cells.

Serial Block-face EM (source)

SBEM images are acquired by embedding a piece of tissue (like a retina) in some firm substance and slicing it superthin (like 10s of nanometers thick) with a diamond blade. The whole slicing apparatus is set up directly under a scanning electron microscope, so as soon as the blade cuts, an image is taken of the surface remaining. Then another thin slice is shaved off and the next image is taken, and so on.

Using this technique, Briggman et al. (2011) are able to trace individual neurons and their connections for a (relatively) large section of retina. What is so great about this paper is that before they sliced up the retina, they moved bars around in front of it and measured the directional selectivity of a bunch of neurons. Then, using blood vessels and landmarks to orient themselves, they were able to find the exact same cells in the SBEM data and trace them.

Briggman et al. (2011) Fig1C: Landmark blood vessels

The colored circles above represent the cell bodies and the black 'tree' shape are the blood vessel landmarks.

Once they found the cell bodies, the could trace the cells through the stacks of SBEM data. What is really neat is that you can try your hand at this yourself. This exact data set has been turned into a game called EYEWIRE by the Seung lab at MIT.

Reconstructing the cells, they could not only tell which cells connected to which other cells, but they could also see exactly where on the dendrites the cells connected. This is the really amazing part. They found that specific dendritic areas made synapses with specific cells.

Briggman et al. (2011) Fig4: dendrites as the computational unit

This starburst amacrine cell overlaps with many retinal ganglion cells (dotted lines represent the dendritic spread of individual RGCs)...BUT its specific dendrites (left, right, up down etc) synapse selectively onto RGCs sensitive to a particular direction. Each color represents synapses onto a specific direction-sensitivity. e.g. yellow dots are synapses from the amacrine cell onto RGCs which are sensitive to downward motion.

This suggests that each individual dendritic area of these starburst amacrine cells inhibits (probably) a specific type of RGC, and that these dendrites act relatively independently of one another.

"The specificity of each SAC dendritic branch for selecting a postsynaptic target goes well beyond the notion that neuron A selectively wires to neuron B, which is all that electrophysiological measurements can test. Instead the dendrite angle has an additional, perhaps dominant, role, which is consistent with SAC dendrites acting as independent computational units."  -Briggman et al (2011)(discussion)

These cells are weird for so many reasons, but the ability of the dendrites to act so independently of one another is a new and exciting development that I hope to see more research on soon.

© TheCellularScale



Briggman KL, Helmstaedter M, & Denk W (2011). Wiring specificity in the direction-selectivity circuit of the retina. Nature, 471 (7337), 183-8 PMID: 21390125

Seeing Inside Cells: Array Tomography

I wrote a lot about dopamine and its complicated nature last month after coming back from the IBAGs conference, so for a change of pace, I'll talk about some truly amazing new techniques that allow us to see inside cells with unprecedented resolution and at unprecedented volumes.

I've previously discussed some traditional techniques for visualizing specific details in neurons, and this month I'm going to talk about some of the newest fanciest ways to look at cellular scale information. 

First off, Array Tomography! 

Micheva et al. 2010 Figure 1 Array Tomography

Array Tomography combines the enhanced location information of the electron microscopy with the scale and context of immunohistochemistry or in situ hybridization. Not only that, but Array Tomography is done in such a way that the same preparation can be stained for 100s of different proteins.  This is a priceless gift to those who want to study protein co-localization.  Do certain receptors 'flock together', and if so does a mutation, or drug treatment alter their abundance or proximity to one another?

Micheva et al. 2010 Figure 4 spine head and neck locations of specific proteins

And just how do they accomplish this feat?

The trick is in the slicing. Using an ultramicrotome these guys can slice a section of brain 70 nm thin. That's 70 NANOmeters, which is really really thin. (Compare it to 'thick section staining' which works on sections 350,000 nanometers thin). The smallest cellular features, the necks of spines can be as thin as 50-100nm, so 70nm can really capture a lot of detail.

Here is a 'fly through' video of the cortical layers in a cortical column. The red dots are identified synapses, and around 2:11 of the video you get to the pyramidal cell bodies (green) which is pretty stunning.




While "Array Tomography" doesn't quite capture the public imagination like "neurons activated by light", it is huge leap forward in the domain of cellular neuroscience. With array tomography, it becomes possible to investigate co-localization of many proteins in a relatively large section of brain tissue. 

© TheCellularScale


Micheva KD, Busse B, Weiler NC, O'Rourke N, & Smith SJ (2010). Single-synapse analysis of a diverse synapse population: proteomic imaging methods and markers. Neuron, 68 (4), 639-53 PMID: 21092855

LMAYQ: Let me do your homework for you

Sometimes reading the textbook is just too hard. And sometimes it's much easier just to type your exact homework question into a search engine and find the answer. Before we get started you might want to take a look at Smith and Wren (2010) "What is Plagiarism and how can I avoid it?" 

This edition of Let Me Answer Your Questions will address 'homework questions.' As always, you can find previous LMAYQ questions here.

Tough Homework Questions are for the Internet (source)

1. "cells that fire together a) wire together. b) definitely don’t wire together. c) become overheated and die. d) wire with inactive cells." Ok, student. If you have to turn to the Internet for this multiple choice question, you need a serious lesson in test-taking skills. Here's a tip. If you don't know the answer, eliminate some because they are obviously not the right answer. A good rule to follow is if it says definitely or all or always in the answer it is unlikely that that is the right one. So we can eliminate B. If you know anything about neurons from your class, you should at least know that neurons fire. If they didn't fire they wouldn't 'work'. So you can eliminate C. If all the cells that fired together overheated and died, you would basically not have any neurons left in your brain after reading this sentence. Now you have a 50-50 chance of guessing it correctly. Not too bad. But seriously, if one answer creates a cute little rhyme with the question... it's probably going to be that one. So yes, neurons that fire together wire together. 2. "explain simply where the hippocampus is" The hippocampus is one of the most famous brain structures because it has to do with encoding new memories. It's name comes from the Greek hippo (horse) and campus (sea), because it looks like a seahorse: Hippocampus = Seahorse (source) (By the way, potamus=river, so a hippopotamus is a 'river-horse'). Back to the hippocampus: It is a structure in the brain and it is located subcortically meaning under the cortex. Specifically it's located under the temporal lobe of the cortex. There are two of them, one on each side of brain. 3. "Why are neurons and blood cells structured and shaped differently from each other?" Neuron and Blood cell Knitting (by Estonia76) This is a great question, but it has the ring of 'I need help with my homework' about it. I talk a lot about the shapes of neurons in this blog, usually speculating on why different neurons would be shaped differently from each other. But this is a good question, why do neurons have dendrites and axons in the first place? Well basically neurons need to receive and transfer information, while blood cells need to physically move to transfer oxygen. Neurons stay in place, while blood cells travel all over the body. Blood cells need to be small and hydrodynamic to float through your blood vessels. Neurons need to 'cover space' to contact many other neurons, so they have branching dendrites and axons. © TheCellularScale Smith N Jr, & Wren KR (2010). Ethical and legal aspects part 2: plagiarism--"what is it and how do I avoid it?". Journal of perianesthesia nursing : official journal of the American Society of PeriAnesthesia Nurses / American Society of PeriAnesthesia Nurses, 25 (5), 327-30 PMID: 20875892

Cortical spine growth and learning how to eat pasta

There are two aspects to neuron shape. One is the pattern of dendritic or axonal branching, and the other is the pattern of spines. Spines are the little protrusions that come off of the dendrite often receiving synaptic inputs.

spines on a pyramidal neuron (source)

Because these spines are associated with excitatory synapses, and because synapse development is thought to be the cellular basis of learning, it makes sense that spines would grow when we learn.

But how would they grow exactly?

Using transcranial two-photon microscopy (a window into the brain of a living mouse), Fu et al. (2012) have caught images of neural learning in action.

A window into the mouse brain (source)

 The authors used two learning tasks to investigate how spines grow during learning. In the "reaching" task, mice had to reach their paw into a slit and grab a seed. In the "capellini handling task" the mouse is given a 2.5 cm length of (I am not making this up) angel hair pasta and learns how to handle it for eating. learning is measured by how fast the mouse eats the pasta. 

learning how to eat pasta makes mouse cortical spines grow (source)

They found that spines grow during learning (not too surprising). But spines also grow when the mouse is exposed to a motor-enriched environment (like a mouse-sized playground).

Fu et al. 2012 (Figure 2C+D)

The interesting difference between learning a specific task rather than just playing is that the spines grow in distinct clusters when the mice are taught a learning task. C shows the total spine growth, while D shows the proportion of clustered spines to total spines. Reach only means the mice were only taught the reaching task, and cross-training means they were taught both the reaching task and the pasta handling task. 

The authors explain two possible functions for these spine clusters:

"Positioning multiple synapses between a pair of neurons in close proximity allows nonlinear summation of synaptic strength, and potentially increases the dynamic range of synaptic transmission well beyond what can be achieved by random positioning of the same number of synapses."

Meaning spines that are clustered and receive inputs from the same neuron have more power to influence the cell than spines further apart.

"Alternatively, clustered new spines may synapse with distinct (but presumably functionally related) presynaptic partners. In this case, they could potentially integrate inputs from different neurons nonlinearly and increase the circuit’s computational power. "

Meaning that maybe the spines don't receive input from the same neuron, but are clustered so they can integrate signals across neurons more powerfully.

And of course...

"Distinguishing between these two possibilities would probably require circuit reconstruction by electron microscopy following in vivo imaging to reveal the identities of presynaptic partners of newly formed spines."

 More work is needed to figure out what is really going on.

 © TheCellularScale


Fu M, Yu X, Lu J, & Zuo Y (2012). Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo. Nature, 483 (7387), 92-5 PMID: 22343892

Growing 3D Cells

Neurons don't grow in a vacuum. They have white fibers, other neurons, blood vessels and all sorts of other obstacles to grow around.

Some NeuroArt (source)

A recent paper from France details the making of a 3D environment that can facilitate 'realistic' neural growth. Labour et al. (2012) created a collagen biomimetic matrix which contains neural growth factor (NGF). 

Labour et al., (2012) Figure 3

These scanning electron microscope images show the porous fibril texture of the collagen matrix. Most of the paper is spent explaining the methods for making this biomimetic matrix, but they also actually grow some pseudo-neurons (PC-12 cells) on the matrix.

They show that when cultured on top of this collagen surface, the cells extend neurons in three dimensions into the matrices and are affected by the NGF. (when there is no NGF, the neurites don't grow and the cells die.)

This paper is mostly about the methods, but I like the new possibilities that growing 3D cells opens up. With these biomimetic collagen matrices, the factors that cause specific dendritic arborizations in three dimensions can be analyzed. The environment can be completely controlled and the neurons easily visualized during growth. The authors suggest using these matrices to study neurodegeneration as well.

Another interesting thing this paper introduced me to is the 'graphical abstract.' I didn't know that that was a thing, but it seems like a good idea. However, trying to summarize an entire paper in one figure seems pretty difficult. Here is their attempt:

Labour et al. (2012) graphical abstract

I think it does actually get the feel of the paper across pretty well, though it's not really informative without the actual abstract next to it.


© TheCellularScale


Labour MN, Banc A, Tourrette A, Cunin F, Verdier JM, Devoisselle JM, Marcilhac A, & Belamie E (2012). Thick collagen-based 3D matrices including growth factors to induce neurite outgrowth. Acta biomaterialia, 8 (9), 3302-12 PMID: 22617741

How to Build a Neuron: Shortcuts

So you want to build a neuron, but don't have the time to fill and stain it, digitally reconstruct it, or even to knit one.

Knitting Neuroscience from Knit a Neuron

Well you are in luck because a lot of scientists have collected a lot of data already and some of them are even willing to openly share their work. 

While it is great that people are willing to share their data, that willingness alone is not enough to actually make the data widely accessible (or searchable for that matter). To bridge the chasm, other scientists have developed databases and repositories.  These databases and repositories store large datasets and organize them in a searchable way. 

The first shortcut to building a neuron I will discuss is the Cell Centered Database (CCDB).

Sounds a little like "self-centered" but represents just the opposite: scientists willing to share their data with everyone

In 2003, Martone and colleagues created the CCDB as a repository for 2D, 3D, and 4D images of cells that could be downloaded and used by researchers around the globe. There is a ton of data here, protein stains, electron microscopy, and fluorescent confocal images just to name a few.  While you could do a lot with this kind of information, I am just going to give you one example of how it can be used as a major short cut in the process of building a neuron.

So say you want to make a model of a cerebellum purkinje cell, but you don't have the time or lab facilities to fill and stain your own neuron.  You could go to CCDB, type in 'purkinje neuron' in the search box and download whichever 3D image stack suits your fancy. 

example Purkinje neuron that I just got from CCDB

With this data you could go straight to step 2: reconstructing the neuron. 

But what if you don't have the time to digitally reconstruct the neuron?  We have already discussed how much time reconstructing a neuron can take, so it's pretty easy to see why you would want to bypass that step too. And in fact, there is a database for that!

Halavi et al (2008) developed Neuromorpho.org as a repository for neural reconstructions. Neuromorpho.org has almost 8,000 downloadable digital reconstructions of neurons, which as they say on the website represents over 200,000 hours of manual reconstruction time. 

NeuroMorpho.org, for all your neural needs.

Similar to CCDB, Neuromorpho offers much more than just a shortcut for lazy computational modelers. It has such detailed information about each neuron that a whole project could be done simply by comparing neural characteristics of different cell classes or different species. 

But my job here is to tell you how you can use it as a shortcut to building a neuron.

Say you want to build a computational model of a CA1 Hippocampal Pyramidal Cell, but you don't want to stain it and you don't want to reconstruct it.  Well, just go to Neuromorpho.org and click 'browse by brain region' and then on 'hippocampus'. Then look through the 1,000 hippocampal cells (organized by class) that have already been reconstructed for you...

Pyramidal cell in the Hippocampus from Neuromorpho.org
 

...and pick your favorite. 

Then you can jump right on through to step 3. (coming soon)


Halavi M, Polavaram S, Donohue DE, Hamilton G, Hoyt J, Smith KP, & Ascoli GA (2008). NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF. Neuroinformatics, 6 (3), 241-52 PMID: 18949582

Martone ME, Tran J, Wong WW, Sargis J, Fong L, Larson S, Lamont SP, Gupta A, & Ellisman MH (2008). The cell centered database project: an update on building community resources for managing and sharing 3D imaging data. Journal of structural biology, 161 (3), 220-31 PMID: 18054501

Taste cells in weird parts of your body

Everyone knows that taste and smell are intimately related, but what you might not know is that you have actual 'taste' cells in your nose (the nasal epithelium to be exact). 

Don't drink this way (source).

But before you go try to drink through your nose, read on, the story gets weirder.  These 'taste' cells express the T2R receptor which senses 'bitterness'. However, if you sniff some 'bitter' molecules into your nose, you won't feel like you are tasting bitterness because these cells don't go to the official 'taste' part of the brain.  In fact, they do something even cooler.  I'll let a previously-blogged-about author, Dr. Finger, explain:

"Since the SCCs synapse onto polymodal pain fibers in the trigeminal nerve, activation of the SCCs by bitter ligands evokes trigeminally mediated reflex changes in respiration." (Finger and Kinnamon 2011)

The SCCs are the 'solitary chemosensory cells' which are the 'taste' cells in the nose that I was talking about. And basically what Dr. Finger is saying is that when stimulated, these cells cause you pain and change the rate at which you breath. This is probably because it is not evolutionarily healthy to have something bitter up your nose and you might not want to breath it in deeply. Might be poison. 

If taste cells in the nose isn't weird enough, here is a diagram of all the other strange places in your body where 'taste' cells have been found:


Taste cells in the body Figure 2 (Finger and Kinnamon 2011)

So why do you need taste cells in your stomach? Well these cells don't send signals to the taste center of the brain either, but they do release ghrelin, which is an appetite-inducting peptide.  Since the taste receptors in the stomach have T1R receptors which respond to sweetness and amino acids (glutamate), this could be a signal saying 'yum, this is good stuff, keep eating'.

But why would there be taste cells in the bile duct? 
The authors of this review paper don't have that answer either:

"The composition of fluid in the bile ducts is dictated by secretions of the liver, pancreas, and gall bladder, so why is it necessary to diligently monitor the composition of biliary fluids and they move from gall bladder to intestines?" (Finger and Kinnamon 2011)

The moral of the story: Even though cells in weird parts of the body are shaped like taste cells and have taste receptors on them, they don't necessarily make you feel the feeling of taste, but they might serve other important survival functions.

© TheCellularScale


Finger TE, & Kinnamon SC (2011). Taste isn't just for taste buds anymore. F1000 biology reports, 3 PMID: 21941599

How to Build a Neuron: Step 2

Recently we've discussed the first step in how to build a neuron. Today we will discuss step 2: reconstructing that stained cell.

Hippocampus CA1 Pyramidal neuron (from Neuromorpho.org)

There are a couple of ways that you turn an image (or image stack) of a neuron into a digital neuron file like the one pictured above.  Basically there is an easy way and a hard way.  The hard way is to reconstruct the neuron manually, where you literally trace the neuron by hand.  The easy way is to auto-trace the neuron.

In a recent Frontier's in Neuroinformatics article, Myatt et al. (2012) explain the hard to easy gradient in reconstruction methods.

• "Manual (Camera lucida). Prisms are employed to visually overlay the microscope image onto a piece of paper, and the neuron is then traced by hand. Although primarily used for 2D tracings, 3D reconstructions can be derived from these with time consuming post-processing (Ropireddy et al., 2011).
• Semi-manual (e.g., Neuron_Morpho, Neurolucida). Digital segments are added by hand through a software interface, typically sequentially, beginning at the soma, and working down the dendritic tree.
• Semi-automatic [e.g., NeuronJ (Meijering et al., 2004; 2D reconstruction only) and Imaris (3D reconstruction)]. User interaction defines the basic morphology, such as identifying the tree root and terminations, but branch paths are traced by the computer
• Fully automatic (e.g., Imaris, NeuronStudio; Rodriguez et al., 2003, AutoNeuron add-on for Neurolucida). The entire morphology is extracted with minimal user-input. " (Myatt et al., 2012)

You may ask: "Why not just do it the easy way?" Good question.  It is actually surprisingly difficult to make a versatile program that can accurately reconstruct neurons.  So difficult in fact that in 2010 an open challenge was issued with a monetary prize for the best automatic reconstruction algorithm. Five teams competed in this DIADEM challenge and the results and process are explained in detail in a special issue of Neuroinformatics. (And in less detail in this HHMI press release)

automatic reconstructions of neurons (source)

Advances in automatic reconstruction are being made at an astounding pace, but most neural reconstructions are still being done in a semi-manual or semi-automatic way. 

If you are interested in reconstructing some neurons, you can download Neuromantic for free or Neurolucida for money. There is other reconstruction software available, summarized nicely in Myatt et al. 2012, but these are the two I am most familiar with. 

In the next edition of "How to Build a Neuron" I will tell you how you can completely skip step 1 (the staining of the neuron) and step 2 (the reconstruction of the neuron). 

For ease of access, the whole "How to Build a Neuron" series is archived.


© TheCellularScale


Myatt DR, Hadlington T, Ascoli GA, & Nasuto SJ (2012). Neuromantic - from semi-manual to semi-automatic reconstruction of neuron morphology. Frontiers in neuroinformatics, 6 PMID: 22438842

Twists and turns on smell's evolutionary road

Smell is a complicated sense and its evolutionary path is a convoluted one. Olfactory receptor cells developed different shapes and different chemical receptors and were sometimes divided into separate organs and sometimes not.

Rainbow Goldfish: experimental animal (source)

A research group from the Rocky Mountain Taste and Smell Center (not affiliated with Coors) decided to research the olfactory cells of the noble goldfish. Goldfish are an interesting vertebrate because they, like humans, do not have the pheromone-sensing vomeronasal organ (though rats, a much closer evolutionary relative, do have it).

This group published a paper analyzing the morphology and chemical signature of the different types of smell cells in the goldfish olfactory epithelium (basically the back of the nose). Since Form and Function is one of my favorite topics, this paper sparked my interest. 

Hansen et al. (2004) show that there are three main shapes for the goldfish smell cells.

Hansen et al., 2004 Figure 3 (3 types of cells in the goldfish olfactory epithelium)

There are the Ciliated, the microvillous, and the crypt cells. 

"Ciliated ORNs are tall cells, with their nuclei usually located in the lower half of the OE. The cells possess a narrow dendrite and long apical processes radiating from an olfactory knob at the distal end (Fig. 3). Crypt receptor cells are obvious because of their typical ovoid shape and location in the upper half of the OE (Fig. 3). These ORNs possess microvilli that border the apical rim of the cell. At the same time, they possess cilia that are located in a “crypt”-like invagination." Hansen et al., 2004

Hansen et al. wanted to see whether these morphological characteristics correlated with the chemical signature of the cell. More specifically, they wanted to see which type of receptors these cells had and which g protein they expressed. 

They found that there was a direct correlation between the shape of the neuron and the type of smells it was sensitive to (as indicated by the receptors and g proteins it expresses). 

The most interesting finding was that the microvillous and crypt cells in the goldfish have very similar characteristics to the cells in the rat vomeronasal organ, and probably also serve the function of sensing pheromones. The paper inspires questions about why rats might have evolved a separate organ to house their pheromone receptors, while goldfish have all their receptors packed into one organ. Why would a separate organ be necessary if a range of informative odors can be sensed using one organ?

Eisthen (2004)

In her commentary on the paper, Eisthen presents an evolutionary tree showing the animals that have the vomeronasal organ and those that do not.  (I've blogged about her work on the olfactory sense of the axolotl here)


Even though goldfish have all these cells in one organ, the cell types aren't evenly intermixed.  The microvillous and crypt cells are concentrated closer to one end. The authors speculate that the differential location of these cells within the goldfish olfactory epithelium might be an intermediate evolutionary step towards an actual separate organ.


© TheCellularScale


Hansen A, Anderson KT, & Finger TE (2004). Differential distribution of olfactory receptor neurons in goldfish: structural and molecular correlates. The Journal of comparative neurology, 477 (4), 347-59 PMID: 15329885


Eisthen HL (2004). The goldfish knows: olfactory receptor cell morphology predicts receptor gene expression. The Journal of comparative neurology, 477 (4), 341-6 PMID: 15329884

The Zebra Neuron

A Brown Baby Zebra (source)

If I told you there was a special neuron that only Zebras had in their brains, what function would you predict this neuron to have? 

I can think of a few:

1. Eating Grass
2. .....
3. ...

Ok, so I can only think of one.

It seem reasonable to assume that maybe this neuron has something to do with eating grass, and it seems reasonable to conduct experiments testing whether zebras who are bad at eating grass have fewer of these neurons and the like.

Now, what if I told you that new research has found that manatees also have these neurons!

baby animals are just so much cuter (source)
also, your animal cells are firing right now.

You might think 'ah, the noble manatee, cow of the sea, well that confirms it. These neurons must be for eating grass, because both zebras and manatees spend all their time eating some kind of grass.'

But wait, not so fast. Next, you found out that actually several animals have these neurons: elephants, whales, apes, the common human, and so forth. 

Now what do you make of these neurons?

Humans don't spend much time eating grass...

Well this is basically the situation the Von Economo Neurons (VENs) are in, except reversed.  They were first found in humans and great apes, leading to much speculation that these neurons were responsible for consciousness, self-awareness, and empathy. 

But now it has become clear that manatees, elephants, whales, hippos, and 'the common zebra' also have these neurons. 

I have been skeptical about VENs in the past, speculating that their unique shape might just be related to brain size, but a recent paper reveals a new development in VEN study that could prove me completely mostly wrong. 

This new Neuron paper has found that VENs are also present in the brain of the macaque monkey.

Unless you are familiar with neuroscience research, this may not sound that exciting to you.  It may sound like just another animal to add to the list reinforcing how 'unspecial' the VENs are. 

However, if you are familiar with neuroscience research, you will realize the one thing that is different about macaque monkeys from every other animal currently on the VEN list. 

Basically ,the difference is that you can implant electrodes in the macaque brain.  Humans, hippos, gorillas, whales, elephants, zebras, and manatees are all (for either technical or ethical reasons) off-limits for this kind of neuroscience research.  And as of now, electrode implantation is pretty much the only way to test whether specific neurons are active during a specific task. Other methods, such as fMRI can tell which area of the brain is active during a task, but cannot resolve which neuron or even which class of neuron is active at the time. 

I look forward to studies investigating the physiological (rather than anatomical) properties of the VENs. Specifically I am eager to see studies directly investigating VEN activity during self-recognition or cognitive tasks.

If you want to read more about VENs, they have been covered quite nicely by The NeuroCritic.

© TheCellularScale


Evrard HC, Forro T, & Logothetis NK (2012). Von economo neurons in the anterior insula of the macaque monkey. Neuron, 74 (3), 482-9 PMID: 22578500