Interesting papers Archives - Nic's Neuroscience Network

A Lamplight in dark places

Since its discovery over 15 years ago, optogenetics has exploded in popularity in research. Along with this increase in interest and use has been a coincident profusion of optogenetic tools. This includes excitatory and inhibitory opsins across a wide range of timescales and light sensitivities.

Optogenetics publications have been increasing for the past 15 years.

However, one type of opsin that has consistently failed to present itself is a long-term super-sensitive optogenetic silencer. All the *good* inhibitory opsins have very fast kinetics and low sensitivity in the 3-5 mW/mm² range.

A recent paper by Rodgers et al. changes all that¹. They have discovered a novel opsin from the lamprey, which they have named “Lamplight”. It’s a Gi-coupled receptor (unlike most opsins which are light-responsive channels), which means that it is slower to signal but orders of magnitude more sensitive. In fact, its EC50 of 2.4 µW/mm² is 1000-fold more sensitive than the classic inhibitory opsins like Arch and eNphR3.0.

However, as always, the sensitivity of an opsin is inversely correlated to its kinetics. Therefore, and as expected, Rodgers et al. show that Lamplight has a long and slow activation time (little to no diminishing of effect after 90 seconds). In addition to its extremely high sensitivity, Lamplight also has some other interesting qualities (Figure 1):

Scalable response – increasing light levels will produce a higher (stable) response from the opsin.
Switchable – the opsin is activated by 405 nm light and inhibited by 525 nm light. This has the added benefit that it won’t be accidentally activated by ambient light, which has much more green than UV

It should also be noted that Lamplight will limit neuronal damage, both by phototoxicity and electrophysiological. Normal opsins can stress (and potentially damage) neurones following chronic activation. This is not an issue with Gi signalling, you really can’t overactivate it.

Based on these unique characteristics, I can imagine Lamplight being a useful opsin for specific uses:

Extremely sensitive and longterm inhibition would be useful for use with lower power output wireless optogenetics, or for a single-stim inhibition that could work similarly to injecting CNO with inhibitory DREADDs.
Scalable inhibition for probing relative importance of a neurone population to mediate different behaviours/physiology. For example, we had an experiment where increasing the ChR2 stimulation frequency would shift the response from increasing glucose levels to aggressive/escape behaviour.
Using 2-colour opto stimulation to turn neurone populations on/off over medium-long term time scales.

Overall, I think this is an interesting opsin with potentially important applications for in vivo research. It is not yet available on Addgene, so anyone who is interested in this opsin should contact the lead author Rob Lucas.

1. Rodgers et al. EMBO Rep 22, e51866 (2021) Using a bistable animal opsin for switchable and scalable optogenetic inhibition of neurons

I Think, Therefore I Eat

Why do you eat? We’ve come to realise that it’s far more complex than simply saying you eat because you are hungry. Anyone who’s been reading my blog will understand the complexity of the neural networks involved in the control feeding behaviour.

A paper came out a couple of weeks ago that splits out a number of the drivers for food intake by differentiating a subpopulation within the lateral hypothalamus (LH)¹. This stems from an earlier paper that found that GABAergic (VGAT-expressing) neurones of the LH show heterogeneous feeding behaviours²:

Consummatory behaviour – the classic homeostatic drive to consume calories
Appetitive behaviour – the more complex drive for reward and rewarding foods

Anyway, back to the recent Siemian paper, where they investigate leptin receptor (LEPR)-expressing neurones of the LH as a subpopulation (~20 %) of the larger VGAT population. They start with my favourite method for inhibiting neurones, which is to ablate them using cre-dependent caspase (AAV-FlEX-tsCasp3-TEVp; Figure 1A/B). Ablating LH VGAT neurones with caspase causes a lean phenotype with reduced food intake (“consummatory behaviour”; Figure 1C/D), with delayed learning to cue-stimulated sucrose intake (“appetitive behaviour”; Figure 1E). However, LH Lepr-ablated mice had no change to gross food intake or body weight (Figure 1F/G), but did learn a cue-stimulated appetitive response (Figure 1H).

In order to directly control feeding behaviour, they next use my favourite method for driving neuronal activity, which is optogenetic stimulation of ChR2. Injecting cre-dependent ChR2 or the inhibitory NpHR into the LH of VGAT-cre or Lepr-cre mice (Figure 2A/B), they run two behaviour tests to mimic the data seen with caspase. In this case, because optogenetic stimulation is instantaneous (rather than the long-term chronic caspase), they use acute tests.

First is free-access feeding to assess consummatory behaviour; second is real-time place preference to assess appetitive behaviour. I use both these tests frequently, and find them to be both robust and informative. The data show, as expected, that optogenetic stimulation of VGAT neurones drives consummatory behaviour (Figure 2D/E) and a strong preference (Figure 2H/I), whereas stimulation of Lepr neurones induced place preference (Figure 2J/K) without any impact on consummatory behaviour (Figure 2F/G).

Next comes the data that I think is the most interesting part of this paper, and also the most complex, where the authors use a fluorescent miniscope to investigate GCaMP activity in the two populations of interest (Figure 3A/B). What’s really interesting here is how they split out the neuronal populations based on the timing of their response to whether they were responsive during the cue or after it (they show “pre-responsive” neurones, but in a world without midi-chlorians, I don’t think we should put too much stock in neurones that predict when a cue is going to happen).

So anyway, what we’re interested in is the subpopulations of neurones that respond differently between the CS⁺and CS^– stimuli, which for LH^VGAT is the post-lick reward-responsive neurones (Figure 3G), but for LH^LEPR is both the cue-responsive and reward-responsive neurones (Figure 3J/K). This is quantified in Figure 3L-O, where they show that LH^LEPR neurones are strongly and significantly predictive of reward from cues. This means that in addition to the general LH drive for reward, the LH^LEPR neurones are the ones that can distinguish cues and therefore may be important for the behavioural discrimination of food cues.

There are a couple more figures that go into further detail to show the importance of the LH^LEPR projections to the VTA for mediating appetitive learning, and then show that the LH^LEPR neurones are not relevant for cocaine preference. But, I’ve shown here the results that I found most interesting, and how they inform us on the control of feeding behaviour. In particular, I want to highlight the use of miniscopes to pick out subpopulations of neurones based on behavioural responses.

1. Siemian et al. Cell Reports 36, 109615 (2021) Lateral hypothalamic LEPR neurons drive appetitive but no consummatory behaviors

2. Jennings et al. Cell 160, 516-527 (2015) Visualizing hypothalamic network dynamics for appetitive and consummatory behaviors.

A Ray of Insight

A paper came out recently that looks at optogenetics in a way I would have never thought of¹. It’s funny how much effort we put into lasers and optics and everything in order to deliver light into a mouse’s brain, because visible light just doesn’t pass through tissue well enough for us to try activating from outside the brain (Figure 1A). But it never occurred to me to use non-visible wavelengths of light for the purpose; in this case it’s x-ray optogenetics.

Obviously, this comes with its own challenges – if a wavelength of light passes straight through tissue, it can’t interact with the proteins, which includes any opsins. So, how do you do this? Well, today we’ll find out from Matsubara et al.¹

I actually remember an undergraduate practical that gave the answer to this, although my hazy memories suggest it was gamma waves from a radioactive isotope, rather than X-rays. Either way, the principle remains, which is the use of “scintillant”, which as far as I know is a fancy word for a chemical that is fluorescent under high energy light waves.

Anyway, Matsubara et al. show us the effects of UV and X-rays on their scintillant, which is Ce:GAGG, which emits yellow light upon stimulation (Figure 1B). They then test this scintillant for activating opsins in cultured cells, and find robust activation of the red-shifted opsins (Figure 1C/D), particularly of our favourite super-sensitive red-shifted opsin chRmine².

After some optimisation in brain slices, Matsubara et al. then take their system in vivo, and inject AAV-DIO-chRmine and their scintillant into the VTA of a DAT-cre mouse (Figure 2A). They get nice c-fos induction from X-ray stimulation of their model system (Figure 2B/C).

After next showing that their scintillant particles are not cytotoxic when injected into mouse brains, they test their opto system in a conditioned place preference experiment with stimulation of the VTA (Figure 3A). Due to the nature of X-rays, they set up the in vivo experiment in lead-lined 2-compartment preference cages (Figure 3B/C).

They then show that the mice show increased place preference with chRmine stimulation (Figure 3D), and decreased place preference with stGtACR1 (Figure 3E), as expected.

I’m also happy to report that the authors checked for long-term damage to the mice caused by exposure to X-rays. They found no change to locomotor activity or blood-brain barrier function.

However, after prolonged stimulation with the high-dose X-rays, mice did have reduced numbers of immature neurones in the detate gyrus. The low dose flashing of X-rays had no impact though, so I think this method would be fine to use so long as you were careful with your experimental planning to limit the X-ray exposure of the animals.

Having said that, x-ray optogenetics is not a technique that I ever envisage myself actually wanting to use. There is a high level of difficulty and complexity, which I don’t think outweigh the improvements to animal behaviour. I think other wireless opto methods have a much better balance of complexity to impact on the animal.

1. Matsubara et al. Nat Commun 22(1), 4478 (2021) Remote control of neural function by X-ray-induced scintillation.

2. https://nicneuro.net/2021/04/29/the-power-of-red-shift/

Mosquito vs Lamprey

A colleague sent me a paper about a novel opsin the other day, because he knows about my interest in optogenetics, and particularly in new tools that we can use to improve our experiments¹. And then a few days later I received an email alert of a second paper² that fulfils the same purpose as the first, namely producing new inhibitory opsins.

So, in this post I will investigate and compare these papers and what their results might mean for doing opto experiments. To begin, both papers aim to solve the same problem that has plagued optogenetics since its inception: the inability to optogenetically inhibit neurone terminals.

If this sounds untrue, let me quickly explain that while we have a number of inhibitory opsins available, none of them can produce reliable inhibition at the terminal. For example, ArchT is a proton pump, which causes hyperpolarisation, but in the tiny volume of the terminal also has a dramatic impact on the pH, which causes spontaneous neurotransmitter release.³

I’ll start with the common aspects of these new opsins: both are light responsive G_i/o-coupled GPCR’s, which means that they inhibit synaptic fusion by blocking production of cAMP and by suppression of Ca²⁺ release. However, the lamprey parapinopsin (PPO) is bistable, activated by UV and turned off by amber light (Figure 1A/B), whereas the mosquito panopsin homolog (OPN3; Mahn’s variant is called eOPN3) is activated by green light (Figure 1D/E).

Next, each paper goes on to demonstrate potent inhibition of neurone terminals in vitro. Both papers show extensive in vitro analysis, but for today I’m interested in the action at terminals, where they both show decreased amplitude of evoked post-synaptic currents (Figure 2A for Copits; Figure 2C for Mahn). They also both show they can decrease spontaneous post-synaptic current frequency without changing amplitude (Figure 2B for Copits; Figure 2D for Mahn).

Lastly, they both show they can impact animal behaviour in vivo by stimulating neurone terminals with their new opsins. For example, Copits et al. were able to block cocaine-induced conditioning in a VTA -> NAc projections (Figure 3A), whereas Mahn et al. managed to influence which direction mice were turning in an open field (Figure 3B).

All in all, I was very impressed by these new inhibitory opsins. If they ever become available, for example through Addgene, I would definitely look into them. It is important to be able to inhibit neurone projections like this.

However, from a purely practical point of view, I think I would lean towards the mosquito eOPN3 from Mahn et al, due to the stimulation wavelength of 500-550 nm as opposed to the UV stimulation of lamprey PPO from Copits et al.

1. Mahn et al. Neuron 109, 1621-1635 (2021) Efficient optogenetic silencing of neurotransmitter release with a mosquito rhodopsin.

2. Copits et al. Neuron 109, 1791-1809 (2021) A photoswitchable GPCR-based opsin for presynaptic inhibition.

3. Mahn et al. Nat Neurosci 19(4), 554-556 (2016) Biophysical constraints of optogenetic inhibition at presynaptic terminals.

Miniscopes et al.

I have written about the use of fibre photometry to record Ca2+ activity in vivo, and today I’ll be exploring a more advanced (and far more complex) version of that. Namely, the use of a head-mounted miniscope to record videos of individual neurones.

I first learned about head-mounted miniscopes at the same time as photometry – in 2015 when Chen and Betley showed how AgRP neurones really work^1,2. Nobody could read the Betley paper with their beautiful head-mounted miniscope data, and not be excited by that data and want to do it for themselves.

But, one must also recognise that it is clearly an exceptionally complex technique, and that you should only use it when you absolutely need to, ie. don’t do the super-difficult version when you can get just as good an answer with fibre photometry. And having said that, I don’t know if miniscopes were necessary for Betley’s paper – Chen found many similar results without them.

Anyway, my point here is to reiterate what I always say, which is to make your experiments as simple as possible, to give you the strongest and cleanest answer. So in that vein, I will investigate a paper that used miniscopes to find a response that wouldn’t have been possible using photometry, a 2018 paper by Chen et al.³

This paper combines head-mounted miniscope recordings of Galanin-expressing neurones (Gal-cre) of the dorsomedial hypothalamus (DMH) and telemetry-based EEG recordings of brain activity (Figure 1A). They combine the data to allow them to correlate the EEG activity showing different phases of sleep/wake and GCaMP signal from individual neurones (Figure 1C). What’s really interesting is that they show two distinct subpopulations of Galanin neurones, with opposite behaviour during REM and non-REM sleep.

So they performed a series of exhaustive tracing studies (which I won’t go into here), that showed strong and mutually exclusive projections from the DMH galanin neurones to the preoptic area (POA) and the raphe pallidus (RPa). To show these correlated with the REM and non-REM sleep patterns, they redid their miniscope experiments on the DMH, but this time they used a retro-transported AAV-GCaMP to label specifically the differently projecting subpopulations (Figure 2A/E). This elegant experiment showed that the POA-projecting subpopulation was active during non-REM sleep (Figure 2C/D), but the RPa-projecting population was active during REM sleep (Figure 2 G/H).

The authors then go on to perform another exhaustive series of experiments, this time using optogenetics to show that the different DMH projection sites don’t just correlate to REM or non-REM sleep, they can also drive changes between those sleep states.

Lastly, I’m just going to briefly go into my interest in doing these experiments myself. A year or so ago, I enquired with Inscopix (who make the benchmark miniscopes, and I think were spun out from the lab that originally developed them) about purchasing one from them⁴. The quote came to £60k, which was far too much for us, so I forgot about them for a while to focus on other things.

And then recently, while exploring options related to developing fibre photometry, I came across the open source head-mounted miniscope project from UCLA⁵. I had seen this before but the sheer complexity put me off. Essentially, they have developed their own miniscope, and have made the designs freely available online. The problem is that this is such a complex technique, I wouldn’t be happy having to build the microscope myself as well as learning and optimising the system; I could just see it being a massive waste of time to get it working well.

Anyway, when I revisited the UCLA miniscope site recently, I found that they have not only released a new lightweight and more advanced version of their miniscope, they also have started selling them fully assembled on the open ephys website⁶. And their price? £1,940 (including the acquisition box). So, needless to say, I will be requesting from my supervisor that we buy one. Or five. The price is reasonable enough that I think the only reason he’ll say no is if he considers it a waste of my time. Or more to the point, that playing around with one of these will distract me from my -real- work.

There is major challenge with getting a miniscope from anyone that isn’t Inscopix, and that comes down to the GRIN lenses that you need to do the imaging in the brain (for any that don’t know, the GRIN lens is like a fibre optic that has a precise structure that means you keep the image in focus). Anyway, it turns out that the only company in the world that makes GRIN lenses longer than about 4 mm of a type that you can use for in vivo imaging is called GrinTech, and they have an exclusivity deal with Inscopix. Which means that they won’t sell them to you, you need to go to Inscopix, which means spending £60k.

So, for any “real” neuroscientists that work on structures such as the hippocampus or cortex near the brain surface, you should be fine to get the cheap miniscope and get shorter GRIN lenses from places such as Edmund optics. I, on the other hand, and anyone else who works on more interesting and deeper brain regions, will have to keep searching.

1. Chen et al., Cell 160, 829-841 (2015) Sensory detection of food rapidly modulates arcuate feeding circuits.

2. Betley et al., Nature 521, 180-185 (2015) Neurons for hunger and thirst transmit a negative-valence teaching signal.

3. Chen et al., Neuron 97, 1168-1176 (2018) A hypothalamic switch for REM and non-REM sleep.

4. https://www.inscopix.com

5. www.miniscope.org

6. https://open-ephys.org/miniscope-v4

Assessing Fidelity

I came across a paper recently that might cause us to rethink in vivo GCaMP recording, by validating fibre photometry. Or at least to rethink our interpretation of the data based on assumed fidelity of the GCaMP signal to neuronal spiking activity.

I’m talking about a recent submission to the BioArchive preprint server from the lab of Lex Kravitz¹. Its title “Fiber photometry does not reflect spiking activity in the striatum” speaks for itself, and counters what one might suppose is the whole point of doing photometry recordings in the first place.

Before we get on to their paper validating fibre photometry, a quick bit of background about neuronal calcium signalling. Depolarisation of a neurone opens the voltage-dependent calcium channels, causing influx of Ca2+ and an increase in intracellular calcium – this is in fact what triggers vesicle fusion at the nerve terminal for release of neurotransmitters. Therefore, it makes sense to use [Ca2+] as a marker of neuronal activity.

However, it is easy to forget when using a surrogate marker in this way, such as using c-fos as a marker for neuronal activity following a stimulus, that there is no guarantee of a causal relationship. It is important, therefore, that these assumptions are put to the test, as Legaria et al. have done in this work. They only present 2 figures, so I will quickly go through the salient points.

To achieve this, Legaria et al. transfected mice with GCaMP6 and implanted a Mightex OASIS implant for calcium imaging of individual cells (Figure 1; the Mightex system looks interesting in that it can do optogenetics, multiunit photometry and miniscope imaging, I’ll try and do a blog post on it in the future).

Anyway, Legaria et al. analysed three signals from each recording: the GCaMP-transfected cell bodies, the surrounding background from non-transfected tissue (“neuropil”), and the overall signal (which would correlate to the signal seen from photometry which simply takes a global readout from the illuminated area; Figure 1C). Analysing these three signals alongside, they found that the “photometry” signal correlated extremely well with the “background” calcium signal, but not at all with the “cell body” signal (Figure 1D).

This is somewhat alarming for someone who performs photometry recordings, that your photometry signal should be detecting fluctuations in “background” rather than identifiable cells. To check whether this (mis)correlation also extended to the electrical activity of the cell, Legaria et al. next performed photometry combined with multi-electrode array recordings of the GCaMP-transfected cells. They saw essentially zero correlation between the two (Figure 2).

Now, before we go and chuck our £60k-worth of photometry recording equipment in the incinerator, I will say that this is a pre-print, so hasn’t been peer reviewed yet. The major questions I would pose as a referee to this work are:

The electrophysiology array looks to be very widespread (and lopsided) compared to the photometry fibre (Figure 2A) – are we sure the two methods will be picking up signals from the same neurones?
The electrophysiology records non-discriminantly from any nearby firing cell, so how well can we expect that to correlate with the GCaMP signal, which always label only a subset of those cells (even with non-cre-dependent expression and a global promoter, you will never transfect 100% of cells)

Having said that, the data looks solid to me, so I think it’s safe to assume the conclusions are sound. My further thoughts I had while looking at the data were also brought up in the text:

“There are also some limitations to our study. First, the results presented here are limited to recordings in the striatum. Striatal neurons have extensive dendritic arbors, which may accentuate the neuropil contribution to the fiber photometry signal. Fiber photometry signals from other brain structures may result in different conclusions concerning the relative contributions of somatic versus neuropil calcium. Second, our recordings were performed with GCaMP6s, a variant of GCaMP that has slow dynamics. Different relationships may be observed with GCaMP variants with faster kinetics, or those that target GCaMP to specific cellular compartments”¹

This highlights exactly what also occurred to me, that these results may not extend to other brains areas, although I think it would be unsafe to assume they don’t. Also, if we want to mitigate the issues raised here, we should think about using miniscopes and/or use cell body-targeted GCaMP variants. In the meantime, however, I will continue doing photometry recordings as before, but I will keep this work in mind when analysing photometry data. I’m glad this work was done though, it’s good to see someone validating fibre photometry.

1. Legaria et al., BioArchive (2021) Fiber photometry does not reflect spiking activity in the striatum. doi: https://doi.org/10.1101/2021.01.20.427525

A Musing on Frequencies

This month a paper was published by one of my former colleagues, in Frontiers¹. Using acute brain slices on a multi-electrode array (MEA), they investigate neurone burst firing frequency.

The MEA is a piece of recording equipment that I have never used myself, but I have seen its use. And as someone used to doing single-cell patch clamping, I am interested by the type of data you can get from it, in particular the high quantities of recordings in a short space of time.

In case anyone doesn’t know, the MEA uses an array of recording electrodes in 2D matrix, and the brain slice is set on top. This lets you record action potentials from a number of points across a region of interest, which in this paper was the NTS, PVN and SON.

The benefits of the MEA is that lets you record natural firing dynamics from many neurones simultaneously, and this enable Chrobok et al. to identify some neurones in the NTS that exhibit phasic firing behaviours (Figure 1). This was very interesting behaviour, with 5-10 Hz neurone burst firing frequency interspersed with long periods of complete silence.

My particular interest in these results is how it pertains to in vivo optogenetics, in particular trying to mimic natural neuronal firing behaviours with the stimulation pattern. I usually try to keep the experimental paradigm as straightforward as possible, so I go with 5, 10 or 20 Hz continuous stimulation.

However, it is worth noticing that Betley et al. used a phasic stimulation pattern when investigating AgRP neurone-driven behaviours (they did 20 Hz stim for 1 second, then off for 3 seconds)². The reason they picked this stimulation paradigm is to mimic AgRP firing behaviours as seen by Van den Top et al.³ I have found this to be an interesting approach, to hopefully improve your in vivo behavioural data by trying to closely mimic the natural firing dynamics.

The importance of matching firing dynamics in an experimental setting extends beyond simply trying to mimic any information that might be encoded in such pattern. In fact, it has been known for over 40 years that phasic firing can enhance the release of neuropeptides from the nerve terminal⁴. This is an important aspect of optogenetics experiments that is often ignored – when stimulating neurones you are likely to be getting fast neurotransmitter release (ie. glutamate and GABA), but depending on your stimulation paradigm you may not be getting commensurate release of neuropeptide.

We have seen in our lab how this can change the animal behaviour, beyond simply increasing the degree of any behavioural response, eg. going from changes to stress hormone levels at low frequency stimulation to freezing and escape behaviour at high frequency stimulation.

So, back to Chrobok et al., who saw much slower phasic behaviour: approx. 2-4 Hz firing for 4-8 seconds, repeated every 10-100 seconds. Next time I (or someone in the lab) want to optogenetically stimulate NTS circuits in vivo, I will point them towards this paper and suggest they try phasic stimulation to see if that produces better behaviour resoponses.

There is one final point I want to take from the Chrobok paper, which is that the phasic behaviour they see in the NTS is not in sync. This is in contrast to nuclei such as the SCN which has strong synchronicity. Anyway, this got me thinking, if we intend to mimic natural firing dynamics as closely as possible, shouldn’t we try to perform unsynchronised phasic stimulation of the NTS neurones?

I envisage an AAV that has multiple opsins, responsive to different colours of light, whose activity is randomly chosen by mixed LoxP sites, similar to the Brainbow construct. For example, if we had an AAV with ChR2(h134r), C1V1_TT, and Chrimson (Figure 2), after a stop codon so you get no expression, but under cross-reactive LoxP sites such that cre will randomly switch on one of the variants only, you could produce a selected population of neurones (eg. TH neurones in the NTS) expressing a variety of opsins.

Then you would need to set up an in vivo optogenetics stimulation system that could switch between 450 nm, 530 nm, and 620 nm, allowing you to produce desynchronised phasic behaviour among that single population. You could easily set it up to cycle through flashing blue light for 5 seconds, then green light for 5 seconds, then red light for 5 seconds to produce desynchronised phasic behaviour.

Furthermore, given that you can easily get multiple insertions in a single neurone, you might well have some members of the population expressing more than one opsin variant, which would give you further variety in phasic behaviour, and even some that simply fire continuously. This is unfortunately A LOT of work simply to see what happens to the behaviour when you try to closely mimic natural phasic firing dynamics, so, while someone out there might be brave enough to do something like this, I don’t think I or anyone in my lab is likely to.

1. Chrobok et al., Front Phys 12, 638695 (2021) Phasic neuronal firing in the rodent nucleus of the solitary tract ex vivo.

2. Betley et al., Cell 155, 1337-1350 (2013) Parallel, redundant circuit organization for homeostatic control of feeding behavior.

3. Van den Top et al., Nat Neurosci 7, 493-494 (2004) Orexigen-sensitive NPY/AgRP pacemaker neurons in the hypothalamic arcuate nucleus.

4. Dutton and Dyball, J Physiol 290, 433-440 (1979) Phasic firing enhances vasopressin release from the rat neurohypophysis.

A Modern Classic, Part II

In 1900, Lord Kelvin addressed an assembly of physicists and claimed, “There is nothing new to be discovered in physics now. All that remains is more and more precise measurement.” Then just 5 years later, Einstein shook up the scientific world with his theory of relativity. Dare I compare this situation to the second paper in my Modern Classic series? No, that would be exaggeration on a disgusting scale. However, hyperbole aside, the findings from today’s paper were a revelation to me and, I’m sure, to many others, and has opened up a new way of thinking about the neuronal control of energy balance.

We saw last time that we can drive voracious feeding in mice by activating AgRP neurones, with DREADD’s or optogenetics. The thinking at the time was that, in a fasted mouse, Ghrelin is high (and satiety hormones such as PYY_3-36 are low), so AgRP neurones are actively firing to drive food seeking and consumption. Then as the mouse eats and the balance of hormones switch, the AgRP neurone activity drops, and so does feeding drive. However, this thinking was soon to be upended, when Chen et al. used in vivo photometry recordings to measure the activity of AgRP and POMC neurones in awake behaving animals¹.

Photometry is probably my favourite in vivo method, because it allows you to investigate the behaviour of an identified group of neurones in an awake behaving animal. Chen et al. transfected AgRP and POMC neurones with the calcium indicator GCaMP, which closely mimicked the electrical activity of the cell (Figure 1A-E). They then put it into live mice, which allowed them to record the activity of AgRP or POMC neurones in awake behaving animals (Figure 1F-H).

They next showed that Ghrelin injection in their AgRP or POMC photometry recordings caused a rapid increase or decrease, respectively, in activity in those neurone populations. No surprises so far. However, the revelation came when they fasted the animals (to drive up AgRP neurone activity), then presented food (Figure 2). The change in neuronal activity came rapidly after presentation of food. In fact they found, as stated in the name of the paper, that the AgRP and POMC neurone activity was modulated upon sensory detection of food – in fact they had already altered their behaviour before the mice had taken a bite of food (Figure 2H). This shows that the classic theories of AgRP and POMC neurone regulation by hormones and other homeostatic methods are too slow to drive actual feeding. The question then becomes, how do AgRP neurons drive food intake if they are switched off during the time that the animals are actually eating?

Chen et al. go on to show that the degree of response to food presentation is relative to the caloric content of the food, ie. they have a bigger response to peanut butter than to regular chow. There has since been a lot of work to investigate further the control of feeding behaviours by these neurone populations, but I won’t go into detail here. However, it is worthy to note two papers came out soon after the Chen paper, confirming the rapid modulation of AgRP neurones by sensory detection of food, using different methods (Betley et al. used GCaMP and head-mounted miniscopes; Mandelblat-Cerf et al. used optetrode ephys recordings to provide actual spiking data)^2,3.

1. Chen et al., Cell 160, 829-841 (2015) Sensory detection of food rapidly modulates arcuate feeding circuits.

2. Betley et al., Nature 521, 180-185 (2015) Neurons for hunger and thirst transmit a negative-valence teaching signal.

3. Mandelblat-Cerf et al., eLife 4, e07122 (2015) Arcuate hypothalamic AgRP and putative POMC neurons show opposite changes in spiking across multiple timescale.

A Modern Classic, Part I

Today I’ll be revisiting a paper that had a massive impact both on what we know about control of energy balance, but also how I think about and approach my experiments. I’m talking about Atasoy’s 2012 Nature paper with the pretentious title¹, in the study that first introduced me to optogenetics. Or at least to the possibility of controlling awake mouse behaviour using optogenetics.

This paper came out not long after the seminal work by Krashes et al., where he used DREADDs to drive to activity of AgRP neurones in vivo, and show the direct effect on feeding when these neurones are activated². These papers were published towards the end of my PhD, and I was very keen to use these exciting new tools in my own research.

In fact, one of the first things I did in my postdoc was to help set up the use of targeted nanoinjections of AAV DREADD’s in our transgenic mice. It was only after a couple of successful experiments with DREADD’s that I even began to think about using optogenetics – I really wanted to develop the easier stages before jumping straight into the more advanced stuff.

Anyway, back to Atasoy’s paper. After some initial testing to make sure they can express ChR2 in AgRP neurones, and to demonstrate inhibitory input onto POMC neurones with electrophysiology, they take the optogenetic stimulation in vivo. They show, firstly, that you get increased food intake with coincident stimulation of AgRP and POMC neurones, demonstrating for the first time that the feeding drive for AgRP neurones is outside the Arcuate nucleus, ie. that the acute feeding action of AgRP neurones was not mediated by the suppression of POMC neurone activity (Figure 1).

But, if the acute feeding effects of AgRP neurones are not mediated by action on POMC neurones in the Arcuate, where are they mediated? Atasoy demonstrates the power of optogenetics for investigating neuronal circuits, by activating AgRP neurone terminals in awake behaving animals (Figure 2). Picking 2 areas with dense AgRP neurone terminals and known for controlling food intake, they target the PVH and the PBN. The results speak for themselves, with such a drastic response from the PVH, and nothing at all from the PBN.

There are more figures in this paper, but for me these were the most important findings. I think the power of optogenetics comes down to several factors, allowing us to overcome a number of the most challenging aspects of studying the brain:

High temporal precision – can get physiological responses instantly, and influence behaviours that rely on millisecond response times
Circuits – optogenetics allows us to investigate the neuronal circuitry involved in complex behaviours by stimulating neurotransmitter release in target areas
Stimulation patterns – the flashing light can be patterned to mimic neuronal firing patterns, which can produce differing behaviours even from an otherwise identical experiment

For those interested to read more, there is another early paper that really influenced my thoughts on optogenetics, which was a 2013 paper from Betley et al.³ For Part II, I’ll be investigating the next big advance in in vivo technology which has changed my approach to understanding energy balance.

1. Atasoy et al., Nature 488, 172-177 (2012) Deconstruction of a neural circuit for hunger.

2. Krashes et al., J Clin Invest 121(4), 1424-1428 (2011) Rapid, reversible activation of AgRP neurones drives feeding behaviour in mice.

3. Betley et al., Cell 155, 1337-1350 (2013) Parallel, redundant circuit organisation for homeostatic control of feeding behaviour.

Forsaking cre

I read an interesting paper that came out recently, by Garau et al. about an inhibitory AgRP to Orexin connection that mediates exploratory behaviours¹. They use optogenetics and photometry to investigate the role of Orexin neurones in some exploratory behaviours, with particular relevance to valence and anxiety.

I’m particularly interested in their use of AAV’s to investigate the Orexin neurones in this paper. I have had serious issue trying to investigate Orexin neurones in my own work, because of the lack of suitable cre-driver lines (we tried an Orexin-cre in the past, but were unable to get any detectable cre recombination using AAV’s or crossing with reporter lines). However, Garau et al. forsake the usual cre-dependent AAV’s when it comes to investigating the Orexin neurones, and instead use Orexin promoter-driven expression of GCaMP6 or inhibitory ArchT.

Trying to copy the promoter region for a gene can be troublesome, particularly if you want to then package it inside an AAV (which can only take around 5-6kb, whereas promoter regions can extend for 10’s of kb). However, it seems to work well for the Orexin gene, and has been done previously by Saito et al. to drive tdTomato in a reportor AAV². Here, Garau et al. report that their Orexin-ArchT AAV tags around 60% of Orexin neurones, with an extremely high fidelity of over 99% (Figure 1). This is important, because it means that, while they might miss some number of the Orexin neurones, they don’t have off-target expression.

They also demonstrate using current-clamp electrophysiology that the ArchT functions as expected in the Orexin neurones (Figure 1C; always an important control to check in any new optogenetics paradigm you use). They then proceed to show a definitive effect of silencing the Orexin neurones in vivo on real-time place preference (Figure 1E), which is a test I have used as well, and I find very useful in its simplicity.

I won’t go into more detail here, but it is an interesting paper, and I would recommend anyone reading this to go and check it out.

1 Garau et al. (2020) J Physiol 598.19 pp 4371-4383 Orexin neurons and inhibitory AgRP -> orexin circuits guide spatial exploration in mice.

2 Saito et al. (2013) Front Neural Circ 7: 192 GABAergic neurons in the preoptic area send direct inhibitory projections to orexin neurons.