The catch-22 of recording from identified cell populations

Recording from individual cells in genetically identified populations is the hottest technique in systems neuroscience right now (I am, of course, totally biased since that's what I'm trying to do). To record from identified populations, you first choose a mouse line that expresses Cre driven by a cell-specific marker like D1R. Then you transduce those cells with floxed ChR2 or GCaMP so you can phototag or image them. Finally, you run the mice through behaviours while recording to see how the identified neurons respond. If everything works out, you can directly link identified neurons' activity with specific behaviours.

There is, though, a catch-22 in interpreting these results. If all the cells respond the same way, then you have confirmed that your population is unitary, but the single cell nature of your recording yields no new information. On the other hand, if the cells you record from respond to a bunch of different things, they're not really a unified population, but rather a conglomeration of different neuron types. To illustrate these issue, I'd like to briefly show figures from two papers.

All together now

If you stimulate AgRP neurons in the hypothalamus, you can get a mouse to shove chow in its face. What remained unclear until this year, however, was how these neurons fire naturally. Some evidence gave hints; cFos staining had shown that these neurons are more active in hungry mice; in vitro recordings have shown that in hungry mice, these neurons receive more excitatory input, a cool form of short-term plasticity (Yang, ..., Sternson, 2011); and imaging has shown that these neurons undergo rapid spinogenesis and pruning when mice are hungry and fed (Liu, ...,  Lowell, 2012). In general, the working hypothesis was that AgRP neurons fire at a high rate when a mouse is hungry, which causes a mouse to seek food, or eat; when a mouse is sated, AgRP neurons turn off.

Given that basic model, there were many unanswered questions. How fast do AgRP neurons turn on and off? Do they turn off when you start eating, or do they take time to integrate enteric (gut) signals? What rate do they fire at? To answer these types of questions, we needed the development of easier in-vivo recording techniques for deep brain areas.

Earlier this year, the Knight lab at UCSF answered many of these questions by doing fibre photometry of AgRP and POMC neurons expressing GCaMP6s. (Chen, ..., Knight, 2015). They found that the activity of AgRP neurons in hungry mice actually decreases before mice start eating, when the mice first sense food (see below). In addition to receiving gut information, AgRP neurons receive fast input from brain areas that can identify food, which was unexpected. These results also question whether AgRP neurons are "hunger" neurons, or something slightly different like food seeking neurons.

AgRP neurons decrease their activity when mice see food. B. When a hungry mouse sees food (red trace), AgRP neuron fluorescence decreases. When it see an object (black trace), the neuron does not change activity. C. Summary of AgRP neuron fluorescence changes in fasted and fed mice responding to objects and chow. Only fasted mice that see chow decrease fluorescence.
From Chen, et. al., 2015.

Even knowing how AgRP neurons respond on average, it is possible that individual AgRP neurons respond differentially due to differences in protein expression or projections. AgRP neurons express variable levels of metabolic receptors like insulin or ghrelin, which could influence firing. Subsets of AgRP neurons project to different brain regions without collaterals; stimulation of some of these projections induces feeding while others do not, implying functional differences. Given these differences, it's possible some AgRP neurons respond to food cues, while other respond to enteric signals. To answer these questions, you would need to record from single cells.

Fortuitously, the Sternson lab did just that, using an in-vivo endomicroscope to image individual AgRP neurons expressing GCaMP6 (Betley, ..., Sternson, 2015). They found that all AgRP neurons act pretty much the same. They quantified fluorescence changes from AgRP neurons when a mouse was well fed, or food deprived; 54/61 neurons had brighter fluorescence when the mouse was food deprived (panel e, below). Like Chen et. al., they found that AgRP neurons decreased their activity before the mice started to eat (panel f); 96% of them to be precise (panel i).

AgRP neurons have higher activity when a mouse is hungry. e. Magnitude of fluorescence events for ad-libitum (AL) and food restricted (FR) mice. f. Single trial fluorescence traces from neurons during consumption. Most neurons decrease their activity before food is consumed. i. Magnitude of fluorescence before eating, after eating for one trial, and during satiety. 96% of traces decrease between the first trial base and first trial food.
From Betley, et. al., 2015.

I can tell you from personal experience these experiments are not trivial. The pessimistic interpretation of these results is that the technical prowess did not yield any new information. In talking to colleagues, however, that is probably unfair. Since we know that stimulation of different subsets of AgRP neurons can elicit different behaviour, the uniformity of their responses is surprising. This creates the question of how subsets of neurons which express the same protein and respond to the environment in the same way can elicit different behaviour.

How am I different?

In contrast to all the neurons acting the same, there is the possibility that all the neurons act differently. To illustrate this, I've selected a recent paper from the Stuber lab at UNC which investigated GABA neurons in the lateral hypothalamus (LH; Jennings, ..., Stuber, 2015). They used an in-vivo endoscope to image Vgat-Cre neurons expressing GCaMP6m. They had previously shown that these neurons are involved in consummatory behaviour.

During imaging, they ran the mice through two sets of behaviours. First, they let the mice eat in a cage with food located in two corners. If neurons had increased activity in the food zone, they were categorized as food zone excited (FZe); if they decreased activity, they were food zone inhibited (FZi). Around 10-15% of neurons were FZi or FZe (panel F, below). In a second set of behaviours, the mice were taught a progressive ratio task (PR3) where they could nose poke for food. Here they found some neurons responded during the nose poke (23%), while others responded during the consumption of food (10%; panel H, below). Finally, one might imagine that FZe or FZi neurons were correlated with nose poke or consumption activity, so they investigated the overlap between these populations. 28/40 FZe neurons responded during the PR3 task, split between consumption and nose poke; 12/40 FZi neurons responded during the PR3 task, again split between consumption and nose poke (panel J).

LH GABA neurons do all sorts of stuff. F. Histogram of change in neuronal activity when a mouse enters a food zone. H. Venn diagram of cell responses during PR3 task. J. Overlap of FZe and FZi responses with PR3 responses. There does not appear to be a consistent pattern of activity either in the tasks individually, or across tasks.

The authors highlight the diversity of responses in their discussion:

Our data suggest that separate subsets of appetitive-coding and consumption-coding ensembles exist within the LH GABAergic network. Thus, the LH GABAergic network can be viewed as a mosaic of functionally and computationally distinct cell types, requiring further definition. Nevertheless, these important computational differences among individual LH GABAergic neurons would have gone unnoticed if only bulk neuromodulatory approaches were employed, further underscoring the necessity of identifying the natural activity dynamics within a network to better understand the precise neural underpinnings of complex behavioral states.

The pessimist in me is frustrated by these results. Now we have to track down the neuronal subtypes, and repeat the experiments for each subtype (and lord knows I hate repeating experiments)! On the positive side, this could be a building block for future experiments. There is now a lower bound for the number of cell types to look for (at least five). I think single cell sequencing is the only way to identify these cell types reliably, and without recording.

Anyway, that is a long way of getting at what I see as the catch-22 of single cell identified neuronal recording: if they're all the same, you didn't need single cell resolution; and if they're all different, you don't have an strongly identified cell type.

References

Betley, J., Xu, S., Cao, Z., Gong, R., Magnus, C., Yu, Y., & Sternson, S. (2015). Neurons for hunger and thirst transmit a negative-valence teaching signal Nature, 521 (7551), 180-185 DOI: 10.1038/nature14416

Chen Y, Lin YC, Kuo TW, & Knight ZA (2015). Sensory detection of food rapidly modulates arcuate feeding circuits. Cell, 160 (5), 829-41 PMID: 25703096

Jennings, J., Ung, R., Resendez, S., Stamatakis, A., Taylor, J., Huang, J., Veleta, K., Kantak, P., Aita, M., Shilling-Scrivo, K., Ramakrishnan, C., Deisseroth, K., Otte, S., & Stuber, G. (2015). Visualizing Hypothalamic Network Dynamics for Appetitive and Consummatory Behaviors Cell, 160 (3), 516-527 DOI: 10.1016/j.cell.2014.12.026

Liu, T., Kong, D., Shah, B., Ye, C., Koda, S., Saunders, A., Ding, J., Yang, Z., Sabatini, B., & Lowell, B. (2012). Fasting Activation of AgRP Neurons Requires NMDA Receptors and Involves Spinogenesis and Increased Excitatory Tone Neuron, 73 (3), 511-522 DOI: 10.1016/j.neuron.2011.11.027

Yang, Y., Atasoy, D., Su, H., & Sternson, S. (2011). Hunger States Switch a Flip-Flop Memory Circuit via a Synaptic AMPK-Dependent Positive Feedback Loop Cell, 146 (6), 992-1003 DOI: 10.1016/j.cell.2011.07.039

Playing with deconvolution and GCaMP6 imaging data

The Palmiter lab recently got an Inscopix microscope. We are still troubleshooting our surgeries and recordings right now, so we don't have any imaging data yet. Given that, I wanted to set up our analysis pipeline ahead of time. Specifically, I wanted to see how we can identify calcium events. In this post I will explore how well deconvolution works on calcium imaging data from the Svoboda lab.

Why deconvolution

Inscopix provides image analysis software, Mosaic, which is pretty good at motion correction, and identifying ROIs. For Mosaic's signal detection, it identifies events using the simple criterion:

ΔF / F > F₀ + 3SD

This works adequately for low frequency events on a low background, but completely ignores the magnitude of events, or what happens when events occur in quick succession. While I've done imaging before, I've never done deconvolution, so I thought it would be fun to try it out.

To get sample GCaMP6 imaging data, I turned to the CRCNS, which has a bunch of neuroscience datasets. The most relevant dataset was from the original GCaMP6 paper wherein they recorded calcium fluorescence in parallel with loose seal electrophysiology, to allow comparisons between calcium and spikes. For this example, I loaded the processed data from the GCaMP6s imaging dataset. If you don't care about loading data in python, you can skip the next section and head straight to Deconvolution time!

Loading Svoboda lab data into python

The data is stored in .mat files using the Svoboda lab data format. Since understanding someone else's data structure is as difficult as understanding their musical taste, I'll walk through how to load the data in python. To load the data, you can simply use the scipy.io.loadmat function, with the following arguments:

import scipy.io as scio

svoboda_data = scio.loadmat( 'data_20120521_cell5_003', squeeze_me = True, struct_as_record = False)

It is critical to use the squeeze_me and struct_as_record flags! If you don't, python will load a weird object that will crash your python instance. This took me an embarrassingly long time to figure out.

svoboda_data is a dictionary with four fields; the only one we care about is the 'obj' field, so we can extract it:

svoboda_obj = svoboda_data['obj']

svoboda_obj has a bunch of fields as well; the most important one is timeSeriesArrayHash.value, which contains the data. The value field is an array of five structs, number 0-4, which have:

0: average fluorescence of an ROI
1: average fluorescence of the surrounding neuropil
2: raw e-phys trace from the cell
3: high-pass filtered e-phys
4: detected spikes

The value struct has two fields we care about, .time (which has the... time) and .valueMatrix (which has the values of the fluorescence or voltage). Knowing this, we can load the fluorescence and spike data:

ca_data = svoboda_obj.timeSeriesArrayHash.value[0].valueMatrix

ca_time = svoboda_obj.timeSeriesArrayHash.value[0].time

spike_data = svoboda_obj.timeSeriesArrayHash.value[4].valueMatrix

spike_time = svoboda_obj.timeSeriesArrayHash.value[4].time

I wrote a small wrapper function in python to load full Svoboda lab experiments. You can then plot the calcium fluorescence and spike times on each other:

Fluorescence (blue trace, DF / F) and spike data (green) from the GCaMP6s data set, recorded May 15, 2012, cell 1.

For this cell, most spikes generated calcium events, and multiple spikes in a short time period generated larger calcium events. However, some spikes did not yield much (if any) calcium response. Calcium events, in general, had mostly exponential decays.

Deconvolution time!

Now that the data is loaded, we can deconvolve it. Thankfully, Alistair Muldal has implemented the fast non-negative deconvolution described in Vogelstein, 2010 (PyFNND) . To be honest, I only partially understand the deconvolution algorithm. It starts by assuming that calcium can be modeled by spikes that return to baseline with exponential decay, but how they turn that generative model into a fit went beyond me.

In any case, we can run the PyFNND deconvolution on our calcium trace (I've calculated the ΔF / F separately), and get both a fit of the calcium trace, and an imputed spike train:

from pyfnnd import deconvolve, plotting 

n_best, c_best, LL, theta_best = deconvolve(
df_f.reshape, dt=0.0166, verbosity=1, learn_theta=(0, 1, 1, 1,0) )

plotting.plot_fit(df_f.reshape(-1, 1).T, n_best, c_best, theta_best, 0.0166)

Top: Original fluoresence (blue) and fit fluorescence (red), given the spike heuristics shown below.
Bottom: Imputed spike heuristics for the calcium trace shown above. Note that n_hat does not give a spike train, but rather a spike heuristic.

I played around with the theta parameters to see if I could get a better fit, but the best results I got were with the default ones above.

To get a spike train, you need to threshold the spike heuristic n_hat. Here I used a threshold of 0.1 (to avoid getting false positive spikes), and plotted the imputed spike train, real spike train, and calcium signal.

The imputed spike train (red) for a threshold of 0.1, and the real spikes (green).

Given how often the real spikes did not generate clean calcium events, I feel like the deconvolution did a pretty good job identifying spikes. The deconvolved spikes were often slightly delayed compared to the actual ones (although this could probably be fixed).

To get a sense for how accurately the deconvoluted spike train matched the real spike train, I calculated the Victor-Purpura distance (VP distance) between the two. The VP distance calculates how many times you would have to insert, delete, or move a spike to turn one spike train into another. VP was handily implemented by Nicolas Jimenez in the python module fit_neuron. (Note, if you want to use this module you should download the .tar.gz from Github, as the pip install has a bug at the moment.) I also wanted to use this metric to get a better sense of the optimal threshold for n_hat, so I ran the VP distance for thresholds of n_hat ranging from 0 to 1. I used a cost of q=5, so that spikes would be inserted or deleted only if there was not a nearby spike within 200 ms.

from fit_neuron.evaluate import spkd_lib as spkd

vp_cost = 5

spk_times = ca_data.spike_time[ca_data.spike_train>0.5]

vp_dist = spkd.victor_purpura_dist(ca_data.fluor_time[n_best >thresh], spk_times, vp_cost)

Similarity between the imputed spike train and real spike train. The normalized VP distance is the VP distance divided by the total number of real spikes. The threshold is the threshold for n_hat. 

A normalized VP distance of 0 means all the spikes were the same for each spike trains; and a distance of 2 means all the spikes from one train had to be deleted and re-inserted to recreate the other spike train. I think a distance of 0.8 means that just over half the real spikes were shared with the imputed spikes. I ran the deconvolution for all 9 neurons in the GCaMP6s dataset, and the minimum normalized distance ranged from 0.73-1. Some cells were undermined by bursts of firing, which caused a lot of false positives in the deconvolution. Others were difficult due to random non-spike related calcium fluctuations. I tried some filtering or baseline subtraction to make the results better but could not do significantly better than the default settings

A few concluding thoughts. Python is sweet, and people have implemented a lot of useful algorithms in it. This allowed me to try things out that would have been otherwise impossible. In the future I will try to clean up python posts by using iPython notebooks.

Deconvolution works pretty well at turning large calcium events into number of spikes. The timing won't be exact, but if you're doing calcium imaging, precise timing (<50ms) doesn't matter anyway.

[The following comments reflect my understanding after a few days of coding / playing. If I make any mistakes here, hopefully my commenters can correct me!] Finally, GCaMP6s was sold as having single spike resolution, but in my hands it does not, at least not for single trials. In Fig. 3F of the GCaMP6 paper they claim to detect 99% of single spikes in visual cortex with a 1% false positive rate. When I read that, I thought they had done something similar to what I just did: given a calcium fluorescence trace, and no prior knowledge, try to identify as many action potentials as you can, and compare to ground truth. What they actually did was answer the question: if you know what a single spike's fluorescent trace is, can you tell the difference between known spike events and random noise? In real imaging, of course, you don't know what spike calcium events look like (it's certainly not a simple exponential!). Also, that false positive rate, while sounding stringent, is quite permissive: if you image at 60Hz, with a 1% false positive rate, you will detect false spikes at a rate of 0.6Hz; if your cell fires at 0.5Hz, as their example cells do, that's a problem.

Having said that, using deconvolution with GCaMP6 works well depending on the type of data you're interested in. If you are recording from low frequency neurons where each action potential can cause spiking in downstream neurons, missing half the spikes is important. But if your neuron fires has a high baseline firing rate, and individual spikes aren't too important, the combination works well.

A cheap source for 230 μm ferrules

UPDATE: Somewhat embarrassingly, and fortuitously, I hadn't researched enough ferrule suppliers before making this post. After contacting a couple more, I found a supplier that sells 230 μm ID ferrules for $1.5 / pc. It is the Shenzhen Han Xin Hardware Mold Co. They do not have the 230 μm ID ferrules listed on their Alibaba page, but you can contact them for a custom order. Our lab ordered a set of 10 from them, and they worked well. If you use a lot of ferrules, I suggest ordering a test set yourself (shipping is $50).

If you want an American supplier, you can use Kientec. They sell 225 uμm ferrules (FZI-LC-225) for $3.25 / pc with an order of 100. These ferrules fit the fibre a little more snugly.

(The below is now outdated.)

Tl;dr: I am trying to get a bunch of labs to pool money to get create a new source for 230 μm ferrules.

The Palmiter lab makes its own fibre optic cannulae for implantation, using the protocol described in Sparta and Stuber. This has saved us a lot of money, making each cannula cost less than $10. As we have ramped up our optogentics, however, the monthly cost has risen enough that I wanted to find a way to save more money. The biggest cost in cannula construction is the zirconia ferrule (1.25 mm OD, 230 μm ID) we use, which cost $5 a piece. This is in contrast to ferrules with smaller 100 μm bores, which cost $1 a piece.

After hearing about Alibaba in the news, I looked there, and found lots of manufacturers for 100 μm ID ferrules, but none for 230 μm ID ferrules. Curious, I contacted Huangshi Sunshine Photoelectric, Co. to see if they could make 230 μm ID ferrules. They responded that they could, but that they would have to make a new "model." Making the new model would entail a one-time fixed cost of $7500, but once the model is made, the ferrules will cost $0.95 / piece for orders of 1000. (I sent them the specs of the Precision Fiber ferrules. They recommended a slight reduction of the diameter of the epoxy concavity, from 0.9 to 0.8mm; otherwise they should be the same. Once we have the money, they will make a confirmation diagram.)

I would like to get a group of labs together to cover the cost of the $7500 model (labs at UW should be able to cover $1500). Once the model is made, if your lab is big enough, you will be able to order directly for Sunshine Photoelectric in batches of 1000. If you can't use that many, the Palmiter lab should be able to pool together a bunch of smaller orders, and then distribute them.

If your lab uses these ferrules, and would be interested in contributing to getting the model made, please e-mail me at map222 at uw.edu.

UPDATE: The Zweifel lab here is on board, so we only need $6000 more!

Where's Bregma?

OR: A fun game for every surgery!

If you're interested in any of the important parts of the brain (i.e. not cortex), you're going to need do to stereotaxic surgery to target the area you're interested in. And to do stereotaxic surgery, you need fiducial coordinates on the skull, which canonically are bregma and lambda.

You would think that the definitions of bregma and lambda are well known, but I found out last year that I had a completely wrong definition of lambda. So in the interest of clarity, here's a diagram of a mouse skull with four possible locations for bregma and lambda (nose to the bottom). Without looking at the caption, can you, dear reader, identify the correct locations?

Diagram of mouse skull (not to scale). The bregma, for mice, is where the coronal suture intersects the midline, point B. Lambda is defined as the intersection of the midpoint and a curve fitting the "lamboid structure" (whatever that is). Basically, it's where the rear sinus would intersect the midline if the rear sinus didn't curve, point D. It is NOT point C, as any reasonable person would guess.

Now that you know the actual definitions of bregma and lambda, you can impress your professors at happy hour, and do accurate surgeries! Except bregma, is almost NEVER that simple in practice. The coronal suture curves this way and that; the left and right sides almost never meet at the same place on the midline. Sometimes the midline curves too!

Just for fun, I've diagrammed some common bregmas you might encounter during surgery. Which of the points listed below do you think are bregma? My opinion is in the caption.

Realistic bregmas (not to scale).
Top left: bregma where the two sides do not meet at the midline. The bregma here is at point B.
Top right: bregma where the mouse's right coronal suture veers. Here I'd say Bregmas is at C.
Bottom left: bregma where the midline curves. This is a bit trickier, but I'd say point B.
Bottom right: bregma where the midline shifts. Here there is actually not enough information! I would follow the midline all the way to lambda to see if it shifts back.

Disagree? Tell me in the comments!

The (Near) Future of Cell Type Specificity

I have been growing more interested in genomics, so this quarter I took a class on new techniques in genomics.* What I learned is that the most important aspect of modern genomics is cost. Sequencing gets exponentially cheaper every year, which makes answering old questions less expensive, and opens up new experimental possibilities. For example for cancer, in the past doctors might have genotyped a tumor once, while doctors now can sequence tumors in real time to identify drug resistance.

* If you miss the days of scientific tables, I suggest you check out the supplement of any genomics paper.

So how can genomics be useful for neuroscience? Sequencing is most famously used on DNA, but can be used on RNA too (RNA-seq). This makes it possible to characterize transcriptomes using sequencing rather than use microarrays. Recently, with cost reductions and new techniques, this has enabled single neuron transcriptomics. If you believe than a neuron’s mRNA and proteins define it, this means we can characterize single neurons with RNA-seq, then look at populations of neurons to figure out how neurons can be grouped into cell types.

So today I'd like to write about what cell specificity is, review two papers on single neuron transcriptomics, and the implications that this has on how systems neuroscience looks at cell-specific circuits.

What is a cell type?

The brain is composed of billions of neurons, each of which do something different, but many of which do something similar. Given that protein expression determines what a cell does, I would define a theoretical "cell type" as a group of cells that express a similar set of proteins and encodes a similar type of information. (I say "similar type of information" since neurons of the same type could encode different versions of the same information. For example, I would say all direction selective retinal ganglion cells are the same cell type whether they encode up or down movement. If different directions express different proteins, then that would change things (which I believe may actually be true)).

While that is a simple theoretical definition of a cell type, there is also the issue of how we deal with cell types in practice. Right now, the most popular way to approach cell-type specificity is transgenic mice (while I poked fun at Nature titles last month, a majority of them were about specific circuits controlling behaviour). To understand how cell-type specificity is done, it would be useful to have an example, so let's look at how Haubensak et. al. in David Anderson's lab did it.

Haubensak et al were interested in the central amygdala, which is involved in conditioned fear. To find genes specific to the central amygdala, they performed a microarray analysis on the transcripts from the brain region (today, people would probably scour the Allen Brain Atlas). This analysis yielded a few candidate genes like PKC-delta, CRH, and enkaphalin. Once they had their candidate genes, they performed immunohistochemistry and patching to characterize the cells' expression patterns and electrical properties. When they were satisfied that PKC-delta neurons were interesting, they injected viruses containing ChR2 into the central amygdala of PKC-delta-Cre mice, and performed their main fear conditioning experiments.

This illustrates the functional definition of neuron types today: Cre-driver lines with viruses injected in a specific location.

This functional definition of a cell type is incomplete a number of ways. It ignores that multiple types of cells in a given region could express the same gene; that is, there could be two types of cells that express PKC-delta. It also ignores that gene expression changes during development (this is partially overcome by the temporal precision of viruses). And it ignores the importance of that gene for the function of the cell.

As a general rule, I would guess that the farther an "identifying gene" gets from the synapse, the less specific it is as a cell marker. The closest genes to the synapse would be neurotransmitters and receptors. For example, in the arcuate hypothalamus, there are cell types identified for the neurotransmitters AgRP, POMC, and KNDy, each of which segregate. Acknowledging that there might be subsets of these cells (e.g. those that project to different places), these cell types are probably well defined. The major exceptions to this neurotransmitter heuristic are probably the neurotransmitters glutamate and GABA, which are expressed so ubiquitously that they are non-specific. (This has not, of course, stopped people from publishing well with VGat or Vglut2 cell lines.)

Examples of cell types in arcuate nucelus as determined by neurotransmitter. The set of neurons expressing GABA includes three different subsets of neurons, an example of how GABA and glutamate are poor cell markers. 

Moving farther from the synapse, many interneurons can be identified by the types of calcium binding proteins they express (e.g. calretinin vs calcitonin). Given how important calcium regulation is, these cells might have different firing properties, and be functionally different. Even farther from the synapse, you can identify cells by secondary messengers like PKC-delta. To be honest, though, without a clear link between secondary messengers and actual function, I am skeptical whether they can truly be cell specific markers.

Besides identifying cell types based on their transcripts, you can subdivide cells based on the projection patterns as well. For example, the AgRP set pictured above is actually composed of multiple groups of cells the project independently to different brain areas.

At the moment, our ability to identify and manipulate cell types is lacking. We use brain atlases and microarrays to identify populations of cells, but don't really know the boundaries of each cell type. So how can we define them better?

Single neuron transcriptomics

My favourite answer is single neuron transcriptomics. The basic idea is that you can isolate the mRNA from a single neuron, amplify it via PCR, and then sequence it; this would give you a quantitative assessment of all the RNA that a single cell expresses. You then repeat this process on dozens or hundreds of cells chosen at random, and use clustering algorithms to see which cells have similar expression profiles, which would constitute a defined cell type.

A paper came out in Nature Biotechnology this year which illustrates the process in developing human cortex. They took tissue, dissociated it, and isolated individual cells using microfluidics. Once single cells were isolated in their little compartments, they were able to perform all the steps outlined above: lysation, amplification, and sequencing.

Clustering of cells based on single-cell transcripts. On the x-axis are individual genes. On the y-axis are individual neurons. The color of each rectangle is the gene expression in the cell. Neurons could be clustered into 4 groups. Some groups, like group 1, could be further subdivided.

From Fig. 3 in Pollen, et. al., 2014

Once they quantified expression for all the mRNA, they performed PCA to identify the most informative genes; then hierarchical clustering of cells using the 500 most PCA-loaded genes. Following clustering, they identified four major groups of neurons that corresponded to cells at different stages of cell division (see above). They were furthermore able to subdivide these clusters, like group 1, into smaller subsets at different stages of differentiation. Using this technique they found that two transcription factors, Egr1, and Fos, were more important in development and differentiation than previously realized.

While cell sorting is a great technique for developing cells, it is less useful for mature neurons which send neurites everywhere; if you were to try to shove a neuron in a cell sorting tube, you would rip off the axons and dendrites, and leak mRNA everywhere. To do single cell transcriptomics in adult neurons, James Eberwine's lab at UPenn developed a method they call TIVA (see figure below).

How TIVA works. "The TIVA tag is composed of several functional groups: biotin, Cy3, poly(A) tail binding 2′-F RNA poly(U) oligo (orange), photocleavable linker (PL), 2′-OMe RNA poly(A) oligo (yellow), Cy5, disulfide bond (S-S) and CPP."

1.) The CPP tag on TIVA allows it to penetrate cells; once TIVA is in the cytosol, CPP is cleaved. You can see which cells have taken up TIVA by monitoring Cy5 fluorescence while exciting Cy3 (Cy3/Cy5 is a FRET pair).

2.) To release the RNA binding sequence, shine light on a single cell, cleaving the PL linker. You can monitor this via the Cy3/Cy5 fluorescence ratio.

3) Once released, TIVA will bind to the polyA tails of mRNA.

4) You lyse the cell, then extract TIVA and associated mRNA via the biotin tag.

6) ???

7) Amplify and sequence

8) Profit!

Fig. 1 from Lovatt et al (2014)

They first verified that TIVA worked in cultures before turning to hippocampal slices. In slices they bath applied TIVA, and monitored its uptake in cells. (panel b, below). They then photocleaved the construct in a single cell (cell ii), and verified the cleavage using fluorescence (panel c, right). Finally, they sucked up the tissue in a pipette, extracted the cell, and sequenced. Using sequencing they were able to verify that they were sequencing neurons, as opposed to the surrounding tissue (panel d).

Single neuron sequencing in hippocampal slices.

TIVA, as presented in the paper, has a couple major downsides. First, it cannot be targeted to specific cell populations. This means that you would have to profile all the cells in a given region, rather than specific subsets your are interested in. If there are many small populations in a given area, identifying them would require large sample sizes. Second, the throughput for the technique appears to be one neuron per hemisphere; in the paper they sequenced on the order of tens of neurons. If you wanted to phenotype one hundred cells in a brain region, you would have to sacrifice fifty mice, which is a hefty burden. On the positive side, once you characterize a brain, you won't have to profile the region again.

Whatever the current caveats, I think single cell transcriptomics is the future. It will allow us to accurately profile cells entire transcriptome, and cluster then to truly identify cells that have similar identities.

The future

So what does this mean for you (or me), dear neuroscientist?

First, I would bet that whatever cell type you (or I) are working on is not actually a single homogenous cell type, but probably composed of multiple cell types. This should not stop you from doing experiments, since the experiments of today are always superseded by tomorrow; but if you encounter ambiguous results, this is likely one source.

Two, intersectional methods are going to be essential going forward. For example, the POMC neurons shown in the first figure can release glutamate, GABA, or neither. If you truly want to understand the subsets of POMC neurons, you would need to use cell lines specific for POMC and glutamate, and viruses that could recognize the combination. Experiments like this will certainly require more breeding, but will give a more precise answer. There is probably a nice faculty position for whoever can come up with an easy intersectional transgenic model.

Third, the future is nigh.There are grants on RePORTER using single-cell transcriptomics to characterize prefrontal cortex, retina and zebrafish, and the dorsomedial hypothalamus. Just in the couple of weeks I was mulling this blog post, a paper in Nature Neuroscience came out characterizing dorsal root ganglion cells. If you want to Better Know a Cell Type, now is probably the time to act.

Tl;dr: If you're a systems neuroscientist who hasn't kept abreast of genomics, just remember: single-cell transcriptomics is getting cheaper by the month, and is going to redefine our understanding of cell types in the next few years.

(If you would like more informed opinion, James Eberwine, who's lab developed TIVA, wrote a commentary in Nature Neuroscience earlier this year.)

References

Haubensak, W., Kunwar, P., ..., Anderson, D. (2010). Genetic dissection of an amygdala microcircuit that gates conditioned fear Nature, 468 (7321), 270-276 DOI: 10.1038/nature09553

Lovatt, D., Ruble, B., ..., Eberwine, J. (2014). Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue Nature Methods, 11 (2), 190-196 DOI: 10.1038/nmeth.2804

Pollen, A., Nowakowski, T., ..., West, J. (2014). Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex Nature Biotechnology, 32 (10), 1053-1058 DOI: 10.1038/nbt.2967