Walk Along the Paper Trail: Drosophiliac Dopaminergic Sweet Tooth

Rather than repine about the worst Cell paper I've ever read*, let's cover a new Cell paper from David Anderson's lab. In my past posts on taste, I've covered a lot of the Zuker lab's work on mammalian taste receptors, and other labs' work on how neuromodulators can effect taste receptors. Parallel with the work on mammalian receptors, many taste receptors have been identified in the fly, including non-canonical receptors for water and carbonation. However, I am not aware of any papers that have investigated how neuromodulators effect feeding behaviour or taste perception in flies. In today's paper, Inagaki and colleagues used a new-ish fluorogenetic (sic?) probe, and typical fly genetic manipulations to show that dopamine can regulate sweet sensitivity in central circuits.


[Bad Tango Pun]

The Anderson lab's primary focus is neuromodulation, and not chemosensation. Indeed, the first line of the abstract emphasizes this, "Behavior cannot be predicted from a 'connectome' because the brain contains a chemical 'map' of neuromodulation superimposed upon its synaptic connectivity map." (Curiously, "connectome" does not appear again in the paper.) In this paper, they focused on the neuromodulator dopamine (DA), which they measured using a genetic reporter called Tango (see below). In the Tango system, you take a receptor of choice (here the dopamine receptor DopR1), and attach it to a bacterial transcription factor (lexA) by way of a cleavage site (TEV). When the receptor gets activated, it recruits arrestin fused to a protease (TEV protease), which releases the transcription factor, which in turn drives a reporter (GFP).

DopR-Tango reporter system. A. Diagram of construct used to express Tango system. The DopR1 protein is fused with a TEV cleavage site and lexA transcription factor. The vector uses a 2A site to also express TEV protease fused to arrestin. B. How dopamine binding drives expression. Dopamine binding recruits arrestin1, which brings with it TEV protease. The TEV protease cuts the cleavage site, releasing LexA, which presumably translocates to the nucleus, inducing reporter expression.

They first tested their DopR-Tango system in HEK cells, where it was able to report DA with an EC-50 of 1uM, and did not respond to 5-HT or octopamine. They then tested Tango in flies and found they could measure endogenous expression of the reporter. To verify that DopR-Tango could detect changes in DA levels in vivo, they fed the flies L-Dopa, a DA precursor, and found increased GFP expression in the subosephageal ganglion (SOG; associated with feeding), the antennal lobe, and the mushroom body (both associated with olfaction). The increase by L-Dopa could be prevented by feeding the flies DA synthesis inhibitors.

Having established that the DopR-Tango system works in vivo, and noticing that DA is expressed in feeding centers, they next investigated how satiety influences dopamine expression. They wet-starved (viz. water, no food) the flies for 6-24 hours, and measured the dopamine expression throughout the brain. They found that wet-starvation increased DA levels in the SOG, but not the olfactory centers (see below). Once again, this DA increase could be reversed by dopamine synthesis inhibitors.

Hunger increases dopamine levels in feeding areas of the brain. C1. Flies expressing DopR-Tango were wet-starved for 48h. Following wet-starvation, GFP intensity in the SOG increased, and this could be blocked by dopamine synthesis inhibitors (SCH...). D1. Dopamine levels in olfactory centers in the fly were not effected by wet-starvation.

Having established that DA and starvation are correlated, they next investigated how DA and starvation could modulate feeding behaviour. (I'm actually curious as to the chronological order of these experiments. It's not obvious to me that the next step here should be behaviour.) To assay behaviour, they measured flies' proboscis extension reflex (PER), a behaviour wherein flies extend their proboscis when presented with sugar. Sated flies only extend their proboscis when exposed to high concentration sucrose, while starved flies extend their proboscis at lower concentrations (below, left). When they fed L-Dopa to the flies, they found a similar phenotype: the flies extended their proboscis at lower concentrations (below, right).

A. Starved flies extend their proboscis at lower sugar concentrations. B. Similarly, flies fed L-dopa extend their proboscises at lower concentration.

By performing a lot of qPCR (and other molecular techniques that all got shoved in a supplemental figure), they found that the SOG expresses three types of dopamine receptor: DopR1, D2, and DopEcR. Then by utilizing various knockout and RNAi techniques, they found that flies with DopEcR missing did not extend their proboscis at lower concentrations, showing it was the receptor responsible for the signaling (below, right). Furthermore, by specifically knocking it out in Gr5a-expressing neurons (Gr5a is the fly sugar receptor), they identified the gustatory receptor neurons as the site of modulation.

DopEcR is the DA receptor responsible for the increase in PER. A2. To knock out DopEcR function, they used flies expressing a hypomorphic mutation, DopEcRc02142 throughout the fly. These mutant flies, when fed L-dopa, did not show PER at low sucrose concentrations.

Having established the site of modulation, one might think that DA simply increases the firing of the receptor neurons, increasing sugar sensitivity. However, when they recorded extracellularly from these neurons, they found that the firing rate was uneffected starvation (again shoved into the supplemental figures). The next possibility they looked at was synaptic release, which they measured via confocal calcium imaging on the terminals of Gr5a neurons. Here they found that both wet-starvation and L-dopa treatment increasing the calcium in the terminals (to vary degrees of statistical significance; for some reason they used the Bonferroni correction).

Dopamine and wet-starvation increase calcium release from Gr5a terminals. C. They performed calcium imaging on Gr5a terminals while applying increasing concentrations of sucrose. At high sucrose concentrations, wet-starved or L-dopa fed neurons had higher calcium. D. Quantification of C.

Discussion


In summary, they 1. refined a fluorogenetic probe for neuromodulator activity in flies; 2. found that dopamine expression increases following starvation; 3. that the dopamine increases sugar sensitivity; and 4. that its site of action is Gr5a nerve terminals.

This is the first paper I'm aware of that has investigated how neuromodulators effect taste in flies. In mammals, numerous neuromodulators - including leptin, endocannabinoids, and oxytocin - act in taste buds; and centrally, leptin and cannabinoids have multiple sites of action in the lateral hypothalamus and other nuclei. None of this mammalian research, however, was mentioned in Inagaki's discussion. I'm not sure whether this is due to the divide between mammalian and fly research, that the Anderson lab is not familiar with taste literature, or they simply thought it was not relevant.

In any case, the discussion focused on neuromodulation in the fly, which I'm not informed enough to comment on. It will be interesting to see if this reporter can be adapted to other receptors or animal models. On the whole, though, this paper is reporting something completely new, so there is little room to compare to other findings.

* Ok, I will complain. Without naming names, this paper had behavioural experiments without any statistics (literally none in the entire paper); and used ChR2 in a conditioning paradigm, which has been done repeatedly, without really caring about the neurons infected. Let's just say that lab is no longer on a roll.

Reference


Inagaki HK, Ben-Tabou de-Leon S, Wong AM, Jagadish S, Ishimoto H, Barnea G, Kitamoto T, Axel R, & Anderson DJ (2012). Visualizing Neuromodulation In Vivo: TANGO-Mapping of Dopamine Signaling Reveals Appetite Control of Sugar Sensing. Cell, 148 (3), 583-95 PMID: 22304923

Persistent odor information in the absence of odorant

A couple posts (months) ago, I posted some data that indicate that when a mouse sniffs, the odor code evolves over repeated sniffs. While inspecting that data I also observed that some cells seemed to respond AFTER the odor stopped, which I call a post-odor response. Today I am going to show examples of cells that have post-odor responses, and that the population contains enough information to identify odors.

Individual cells' responses

(Brief methods: I recorded from the olfactory bulb of awake mice using tetrodes. Odors were presented for 2-2.5s for 10-12 trials. During analysis, to look at whether a given breath is responsive, I segmented the recordings into breaths, and fit each breath to a standard breath length.  To quantify whether breaths were "responsive," I compared a breath's tonic firing rate to the control, pre-odor breaths (using ANOVA with p<0.05, and Tukey's post-hoc testing); and I tested whether the "phase" or timing of the breath differed from the pre-firing rate (using a Kolmogorov Smirnov test; here I used p<0.02 as the threshold for significance as using p<0.05 yielded many false positives when comparing different control breaths))


First, I will show how a few cells that continue to respond after the odor has stopped. Individual cell-odor pairs can respond during the post-odor phase in a variety of ways. Some cells that fire phasically during the odor maintain that firing during post-odor breaths (below, top), while other cells change the phase of their firing (below, bottom). In general, it seems that following the odor, cells shift the phase of their firing to earlier in the breathing cycle (figure not shown).

This cell maintained its phasic response after the odor was no longer present. During the odor (0-2.5 seconds), the cell fired early in the breathing cycle, and maintained this phase during the post-odor period. Top. Histogram of response binned at ~40ms. Inspiration shown as dashed grey line. There is a break between the odor and post-odor responses because there is a variable number of breaths during the odor. Bottom. Cumulative spike count within breaths before, during, and after the odor. Traces are:
black - ctl breath
blue - odor response breath
red/yellow/grey - post-odor breaths

This cell had a phasic response during the post-odor period, but had a markedly different phase. During the odor, the cell was phasically inhibited between 150-250ms; during the post-odor, the inhibition (and excitation) shifted to earlier in the breathing cycle.

In addition to cells that had phasic firing during the post-odor period, many cells had clear, prolonged excitations and inhibitions (see below). Of >300 cell-odor pairs recorded, ~20% had a phasic response during the post-odor period, 10% had a tonic response (excitation or inhibition), and 5% had both a tonic and phase response. Approximately 10% of cell-odor pairs had a post-odor response without having an odor response.

Example responses from two different cell-odor pairs show prolonged inhibition and excitation

The above responses were all performed at a single, high concentration (5% dilution of pure odorant). This led me to worry that these post-odor responses were artifacts of the high concentration used, perhaps due to lingering odorant in the olfactory epithelium. To test this idea, I repeated the experiment using lower concentrations of odor (2%, 0.4%, and 0.1%). When I did so, I found that individual cells continued to have information at the lowest concentration, and that the post-odor responses contain concentration-specific information as well.

This cell's post-odor response is different for three concentrations. From top to bottom, concentrations are 2%, 0.4%, and 0.1%.

Population level

To verify that the above responses contain odor- and concentration specific information, I built a population vector, and used a prediction algorithm to see whether the vector can predict the odor and concentration in single trials. The predictor was quite good during the odor, predicting both odor and concentration at >60% (figure below). During the post-odor breaths, it continued to predict above chance for ~15 post-odor breaths, showing that the information is long-lasting. To see whether the predictor's post-odor success is due to odor- rather than concentration discrimination, I also looked at the predictor's ability to choose an odor within each concentration, or to choose the concentration within each odor. In all cases, the results were similar.

The post-odor response contains odor- and concentration-specific information. Prediction algorithm was built as described previously, and forced to identify both the odor and concentration. Pre-odor breaths (blue bars) are at chance. During the odor (black), the predictor is quite accurate given the difficult task. During the post-odor, the predictor continues to be above chance (white bars) Chance level is 1-in-9 (3 odors at 3 concentrations). Odors are Amyl acetate, 3-hexanone, and butanal at concentrations of 2%, 0.4%, and 0.1%. N = 110 cells from 2 mice.

Ideas

The above figures show that individual cells can continue responding in the absence of odor, and that these responses are odor- and concentration specific. The Laurent lab have reported similar findings in insects, although they did not focus on the post-odor responses (Stopfer et al, 2003, Mazor and Laurent, 2005). It should be noted that the sampling dynamics are quite different between insects and mammals. I am also reticent to draw parallels with vision's OFF responses given the different timescales.


In thinking about these post-odor responses, I think the two key questions are how and why?



Regarding the how (mechanism), I can think of a few sources. First, there could be a lingering odor presence, but I think this is unlikely. Physically, odors diffuse quickly in air, and our experimental setup includes aspiration, which should make the lifetime of the odorant short. In contrast, the cells and the population contain information for >3 seconds.


The next source could be persistent input from ORNs. Some ORNs in flies have “super-sustained," "ultra-prolonged" responses, and continue firing after the odor is gone (Montague et al., 2011; Turner et al., 2011). Calcium imaging in dendritic tufts show that the calcium activity continues at a low level after the odor presentation has finished (Charpak et al., 2001). However, this persistent ORN input would have to be at a lower magnitude, given that calcium imaging does not show whole-glomerulus activity. This lower activity would be in stark contrast to the strong responses during the post-odor.


The final possibility is that recurrent connections in the olfactory bulb, or higher areas, maintains a dynamic representation of the previous odor. Recordings from mitral cells in slices have shown that olfactory nerve stimulation can cause sustained activity for seconds (Chen and Shepherd, 1997; Aroniadou-Anderjaska and Ennis, 1999; Carlson et al., 2000; Lagier et al., 2004), due to dendrodendritic interactions between mitral cells (I think). While olfactory nerve shock in slices is a simpler stimulation than odors presented in vivo, similar mechanisms could result in continued activity in mitral cells absent odor.


Regarding the why (purpose), the long duration, and odor specificity of the post-odor responses make me think that the post-odor responses are a form of short-term or working memory. What is intriguing about this sensory memory is that there has been no report of a perceptual analog: that is, odor perception in the absence of odor. In contrast, stimulus aftereffects in vision and audition are typically associated with perceptual analogs. This olfactory memory could be useful in complex contexts where animals encounter multiple odorants in a time-varying fashion. Being able to store information about previously encountered odors could make it easier to identify new odors.

Breathing, Fast and Slow

Mice breathe at three speeds. At rest, they breathe at ~3Hz. When they smell something interesting, they start rapid-sniffing at ~8Hz. And when mice encounter something malodorous, they hold their breath, and but still breathe every second. Given the differences between these breathing regimes, how can the olfactory system encode odor information across all cases?

Breathing Fast

The best attempt to address this question was by Cury and Uchida (although credit should be given to Verhagen and Wachowiak for the first crack). Cury recorded from mitral cells in freely moving rats while the rats performed odor tasks. During the tasks, the rats switched between normal and fast-breathing, which allowed Cury to compare the neurons' firing during both conditions. They found that the spike timing (or odor code) does not depend on the duration of the breath length (see below; similar to what I blogged about, arguing against a phase-mapping of the sniff cycle). They also noted that during fast breathing, there could be hysteresis, where activity during one breath bleeds into the next.

Response of a mitral cell during breaths of different duration. Top. Raster plot of a neuron's spikes, aligned to breath onset. Inhalation is show as dark grey area, and full breath in light grey. Bottom. PETH of firing during breath during rapid and slow breathing. Note, there is no odor present, but the result holds for odor responses as well.
From Cury and Uchida, 2010.

Seeing this, they turned to the population, and asked how the odor information evolved over the course of one breath. They calculated the population spike distance between different odors, and found that for both fast and slow breathing, the odor representations started to diverge 40ms following inspiration, and were maximally separate around 50-80ms. Following the peak, the representations "converged" (is there a word for "move closer together, but not bunched"?) for the rest of the breathing cycle. Notably, there is only a small difference between fast and slow breathing in the amount or speed of information .

Population spike distance ("inter-odor distance") for fast ("discrimination") and slow ("stay") breathing regimes. The distance peaks between 50-80ms before plateauing for the rest of the breath. This timecourse of the distance is reminiscent of structural plasticity following uncaging.
From Cury and Uchida, 2010.

Given these results, what is the role of fast sniffing? Rodents fast-sniff when they encounter a novel odor, or when they are searching for an odor source. People have hypothesized that fast-sniffing might give more information about the odor. However, the data above argue that there is no more information during fast-sniffing than slow. Rather than more information during a breath, fast-sniffing may provide a more frequent sampling of the environment.

In the discussion, Cury and Uchida also note the robustness of the odor coding across different breathing regimes. This means the odor code is invariant with respect to inhalation amplitude or duration. In another part of their paper they look at how behaviour relates to odor coding, and found that the responses are relatively insensitive to top-down processing like attention as well.

Breathing Slow

While Cury and Uchida showed that the first 150ms of odor coding during fast and slow breath are the same, it still leaves the question of why so many cells respond after 150ms. For example, the cell below responds to an odor by firing a burst of action potentials at XXXms (top panel below). However, when the mouse fast-breathes during the early trials, this odor response does not have time to evolve. This can be clearly seen if you truncate and aligned all responses to a 150ms window (lower panel).

Raster plot of a cell's response to 3-Hexanone. Spikes shown in black, and inspiration in blue. Odor presented from 6-8s. A. With only the first respiration aligned, you can see a clear response to the odor approximately 200-300ms following inspiration. After the burst of activity, the cell is inhibited again, then returns to basal firing before the next breath. During the first two trials, the mouse breaths faster, and there is no odor response. B. Same response, with each breath truncated to 150ms, as if the mouse were fast-sniffing. The response is no longer evident.

The cell above also shows what happens to the odor code when the animal stops breathing. The cell above is inhibited after its burst of spikes, but the inhibition only last ~500ms. Afterwards, it just returns to its basal firing rate. In other words, the odor code only seems to exist for 500ms. It is easy to sense this perceptually: if you sniff something and then hold your breath, the sensation dissipates rapidly.

So, why do many mitral cells only respond to odors after 150ms, which is after the subject has identified the odor? I have two hypotheses.

Hypothesis 1 (why?): The slow breaths do contain more odor information overall, which is contained in the spikes later in the breath. In another figure of the paper, Cury and Uchida show that their odor predictor monotonically increases its accuracy over the entire breathing cycle. However, behavioural data argues against this: when rodents are performing a freely moving odor discrimination task, they fast-sniff.

To test this hypothesis, you could measure a subject's odor discrimination or detection thresholds while it employs a fast- or slow-breathing strategy. If the slow-breathing threshold is lower, it would imply that the late spikes add information. However, if the thresholds are the same (or fast-sniffing lower), then it would argue against the information hypothesis. In the end, this may be difficult to test in rodents, as you need to force them to employ a specific breathing strategy.

Hypothesis 2 (how?): The responses after 150ms are vestigial, due to continued ORN input, or reverberations in the olfactory bulb. ORNs can have complex temporal responses that last for seconds. It is possible that they continue feeding odor-specific information to the olfactoroy bulb after the olfactory bulb no longer needs it. This input would manifest itself as late-arriving responses.

Another possibility is that the olfactory bulb contains recurrent connections, like dendro-dendritic inhibition, that allow the structure to reverberate. For example, stimulating the olfactory nerve can cause mitral cell activity that lasts for seconds. The mitral cells may already have all the information they need after 150ms, but continue firing due to these reverberations.

This hypothesis is more easily testable. You can record from mitral cells in OMP-Halorhodopsin mice. Then during each sniff, you can turn on the light after 150ms, shutting down ORN input to the mitral cells. If the mitral cell activity is elided, then they require ORN input to continuously fire; if, however, the mitral cells continue to fire, then the ORN input is not needed.

Delineating cognitive and neuro sciences

I was discussing possible PhD labs with a student, and she said, "I was thinking about some fMRI labs, but..."

"Yeah, that's not neuroscience," I said.

I've long held this belief, and when I mention it to other neuroscientists, they generally agree. But I've not heard it vocalized (birdsong term!) publicly, out of politeness or politics (fMRI gets a lot of funding and publicity, so badmouthing it looks envious). Today I'm going to lay out what I think the difference is between cognitive science and neuroscience, and why the distinction has blurred.

The dif

What irks me most is the application of the term "cognitive neuroscience" to research that uses techniques like fMRI or EEG.  In my mind, there is a clear delineation between cognitive science and neuroscience, due to differences of technique and perspective. If I could summarize the difference in one sentence I'd say, "if it ain't describing neurons, it ain't neuroscience."

The difference between neuroscience and cognitive science is most clear on the technical side. Neuroscience techniques focus, naturally, on individual neurons (patch, imaging, extracellular recording) or groups of neurons (voltage sensitive dyes, wide field imaging, immunoblotting). Cognitive techniques, on the other hand, look at areas of the brain as a whole, like EEG, fMRI, or DTI.

The techniques one uses in turn determine the types of questions one can answer.* In broad terms, neuroscience techniques have specific measures, and so the questions neuroscientists ask are concerned with concrete, well defined inputs and outputs. How does this neuron represent that stimulus? How do two signaling molecules interact? We want to break the brain down like a car, so we can better understand it.

* Intuitively, one might think that the questions drive the technical split, and thus the difference between neuroscience and cognitive science. Certainly, individuals (and labs) are interested in questions, and acquire the techniques to answer those questions. Yet, questions can be answered on multiple levels. For example, if you're interested in sensory perception, your techniques can range from biophysics to neuroscience to psychophysics. You can explore the question via neuroscience or cognitive science.
In the end, I think identity is driving this distinction. I feel kinship with people who perform similar techniques, even in different systems. I feel like I could walk into any neuroscience lab, and start producing data within a few months, while it would take longer to become competent in a psych lab. So when I think of cognitive neuroscientists, I think of not-me, and would like clear labels to distinguish us.

Cognitive science's techniques, on the other hand, are more imprecise, but have much more interesting model organisms: humans and primates. Thus, they leverage their animal model by asking more slippery questions** regarding topics like consciousness, decision making, or emotion, which neuroscience is unable to answer at the moment. They move beyond treating the brain like a black box as in psychology, and do the best they can with the tools available.

** Dale Purves, the former chair of Duke Neurobiology, switched from cellular neuroscience to cognitive science late in his career. Whenever he went to talks, he would quasi-troll people by asking simple questions like, "What is a decision?", and arguing with their answer).

Then there is, of course, the overlapping field of cognitive neuroscience (I love Wikipedia's attribution of the term to two guys in a cab). While others may think fMRI is cognitive neuroscience, in my mind, the only cognitive neuroscientists are those who attempt to address those fuzzy cognitive questions by sticking electrodes in primates and humans

Why the confusion

So why have cognitive scientists coopted the prefix "neuro?" It's a pure status play.

Cognitive science is a small step removed from psychology, and psychology, despite decades of normalization, is still a dirty word to the public. When people think psychology, they think clinical psychology, people laying on couches, and the bizarre, foundational theories of Freud. They don't think rigorous science, they think feelings.

What the public doesn't realize is that most psychology, and most research psychology, is non-clinical, and encompasses cognitive, developmental, and other psychologies. And that non-clinical psychology has undergone tremendous improvement over the last few decades (say, post-Skinner), and employs all the standard tools of science like control groups, replication, etc. I enjoy me a good pop-psychology book.

So, given the low status of psychology, and neuroscience's higher relative status (neuro is an obscure Greek prefix, always a good sign), cognitive scientists doing fMRI rebranded the field "cognitive neuroscience." And lo, the NIH money flowed.

You can see other fields doing this as well, like neuroeconomics. There are some true neuroeconomists doing risk/reward research in primates, but most of it is just rebranded experimental economics.

Reconciliation

To recapitulate: cognitive scientists and neuroscientists use different techniques to answer different questions. But cognitive scientists are wary of being mistaken for psychologists, and so coopted the term "neuro." It's mostly just a matter of semantics, but I thought fMRI people should know, when they call themselves neuroscientists, we ain't buying it.

(And I should reiterate here that I like psychology and cognitive science. They study the brain through a different lens, which is important. I just take issue with the misapplication of "neuroscience.")

Neuroscience graffiti

You know you've been in the lab too much when you start to see LFPs everywhere:

(Pardon the crappy quality, but the Swiss being Swiss, this was covered up a few days later when I came back for a good picture.)