Category: Optogenetics

How High Can You Go?

Validating optogenetic stimulation frequency

For my most recent optogenetics experiment, I did a full validation for the optimal optogenetics stimulation frequency. FYI, I would recommend doing this for any new paradigm.

I took a safe “positive control” measure that I knew would be influenced by my neurones of interest. I then applied a ramped increase in stimulation frequencies: 1, 2, 5, 10 and 20 Hz. This gave me what is essentially a dose response, with increasing food intake up to 5 Hz. But it then plateaued with no further increases at higher frequencies. I was then able to select the 5 Hz frequency as my optimal, as it gave me a maximal response while limiting the amount of light.

This is important, because the light is not only phototoxic at high levels, it can produce neuronal activation in the absence of ChR2. I say this, because I will always see some amount of c-fos at the fibre site, even in control mice. And this is why it is so important to include non-ChR2 mice in your study. You also find that at higher light power and frequencies, that your action potential fidelity drops.

What’s the optimal stimulation frequency?

I digress. And ramble. My point today is to talk about the optimal frequency with which to stimulate your opto mice. I have in the past used 10 Hz, or sometimes even 20 Hz, just as a kind of industry standard, without proper optimisation. This is easy to do, but as I’ve just shown, is often not the optimum for your experiment.

A common stimulation paradigm in the literature is to stimulate at 20 Hz in a pulsed manner – for example flashing for 1 second, then off for 3 seconds in a cycle. The popularity of this method likely stems from its use by Aponte, Atasoy and Betley in their early seminal works1-3.

And these come from the much earlier finding by Van Den Top that AgRP neurones fire in such bursting patterns following activation by ghrelin4. So, for experiments involving AgRP neurones, this stimulation paradigm does make sense, as it closely mimics normal physiological activity in the activated state.

A concerning pattern

However, I have noticed a collaborator who uses a similar stimulation pattern, but at even higher frequencies (30 Hz pulsed 1 second on, 3 seconds off). My problem with this begins with the fact that I have recorded from his neuronal population of interest, and they do not fire in such bursts (I have told him this).

Even more concerning, is the question as to whether those neurones are even capable of firing at 30 Hz. It might seem like I’m being overly dramatic, but this is a genuine concern; some neurones are capable of firing much faster, like 100 Hz, but many are not. And there is an even deeper concern, which is that if you overstimulate a neurone, you can drive it so depolarised that it is incapable of generating an action potential – in essence you silence the neurone.

Optogenetic frequency validation

The potential to optogenetically silence neurones was well shown by Lin et al.5, who compared various opsins including our perennial favourite, ChR2(h134r) (Figure 1). They found that at 25 Hz, ChR2(h134r) only has about 25-50 % fidelity, depending on the light irradiance.

But why is this? You need to take into account the time it takes for the opsin to close after light off, which is 18 ms for ChR2(h134r). And this leaves very little time for the neurone to recover at a high optogenetics stimulation frequency. It should be noted, as well, that Lin et al. used very short stimulation times of 0.5 ms, whereas most people use 10 ms in vivo. This means that if you were to stimulate at 30 Hz with 10 ms on time (as my colleague did), you have 23 ms of light off between each flash.

You then have to take into account an 18 ms delay for the ChR2 to close, and that gives 5 ms for neuronal recovery for the next action potential. My point here is not to bash on my colleague, but rather to stress the importance of optimising your stimulation protocol, and in particular not to overdo the irradiance and high frequency stimulation.

How to determine optimal stimulation protocol

For me, there are three factors to consider when planning your optimal stimulation protocol:

  • How do these neurones normally fire when activated? Trying to mimic as closely as possible the natural firing dynamics of your neurones of interest is, in my opinion, the best way to go. This is probably best done by current clamp patching of identified neurones and then applying something to activate them eg. applying ghrelin to AgRP neurones.
  • How fast can you drive electrical behaviour in these neurones? For this, I would patch clamp your opsin-expressing neurones, and then apply light pulses to the soma. This way you can determine likely irradiance power needed, as well as the electrical responsivity and action potential fidelity. This is particularly important if you intend to drive high frequency firing, as you need to know that your neurones are capable of keeping up.
  • Finally, test a range of firing frequencies (including pulse paradigms if relevant) in vivo against a known behavioural response. For my studies and for AgRP studies, it is simple to measure food intake; this lets you test how your predicted stimulation paradigm works in vivo, as well as confirm your current experiment is working eg. virus transfection and optic fibre placements are good.

Hopefully people will find this useful, if only as a reminder to test your optogenetics stimulation frequency, and not to just go for the brightest and fastest possible stim.

1. Aponte et al., Nature Neurosci 14(3) 351-355 (2011) AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training.

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

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

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

5. Lin et al., Biophysical J 96, 1803-1814 (2009) Characterization of engineered channelrhodopsin variants with improved properties and kinetics.

An Intense Calculation

Last week I was planning my next optogenetics experiment, and I thought I’d try to find the optimal fibre placement. Normally I just aim them to point close to the site of interest, but I’ve had some less-than-optimal experiments in the past so definitely time for some optogenetics irradiance optimisation.

First of all, we need to start with the intensity of light needed to activate the opsin, which in this experiment will be ChR2-H134R. Lin et al. investigated some of the early opsins back in 20091, and found that you get approximately half of the full activation of ChR2-H134R (EC50) at about 1 mW/mm2. Now, I know that I get, on average, 7.5 mW of blue light out the end of a 200 µm optic cannula using our opto setup. So, dividing through, that gives an irradiance (ie. power per surface area) of 7.5/(π*0.12), which comes out around 239 mW/mm2.

Obviously, this is vastly more than enough to activate the ChR2, but how does it spread through the brain? To answer this question, I headed over to Karl Deisseroth’s optogenetics website, where he has an “irradiance calculator”, that will estimate the dissipation of light through brain matter (Figure 1)2.

The light tails off dramatically in the brain; so much so that it is hard to see how deep you can retain ChR2 activation. I plotted the data on a logarithmic scale (which you can also do on the irradiance calculator), and included the EC50 of 1 mW/mm2 as well as an upper “phototoxicity” limit (Figure 2). There isn’t really a clearcut limit for causing neuronal damage, but an early paper by Cardin et al. found that 100 mw/mm2 was capable of causing phototoxicity, so I’m taking that as my upper limit. This produces a nice “Goldilocks zone” between 1 mW/mm2 and 100 mW/mm2, where we expect good neuronal activation with limited damage.

This produces a “Goldilocks zone” between about 0.2 mm and 1.3 mm from the tip of the optic cannula. Given experimental variance, I would put the ideal range to aim for at about 0.4 mm to 1.1 mm (Figure 2).

So, taking this all together, I can plot the fibre and light scatter onto the mouse brain atlas (Figure 3). My neurone population of interest lies in the mediobasal hypothalamic area surrounding the VMH, but particularly on the side near the fornix. Plotting the expected irradiance, we see that the entirety of the neuronal population lies within the “Goldilocks zone”. Great.

However, I have drawn an estimate of the spread of light from a .22 NA fibre, and you can see that it doesn’t successfully hit all the neurone population laterally. But, this is based on the spread through air, and doesn’t take into account the scatter of light by brain tissue, which will necessarily cause some amount of lateral spread. So, how to quantify this?

This takes us to the final stage of optogenetics irradiance optimisation, which uses a freely available light scatter tool called optogenSIM3. I won’t go into details, but essentially you input similar parameters as for the irradiance calculator, but also including the position of the fibre in the brain. The program then runs a simulation to predict light scatter based on the absorption and scatter coefficients of different brain areas, and outputs something like this (Figure 4).

The images aren’t great for visualising details, but note the extent of the green 1 mW/mm2 threshold. The light scatters far wider than I had expected, especially given that this is a low divergence .22 NA fibre. Either way, this shows that I will definitely hit the vast majority of my targeted neurone population with my planned fibre placement.

One final note from Figure 4: see how there is backscatter, so the light goes dorsal to the end of the fibre. Which means that even if your fibre ends up level, or even slightly below, your region of interest, you might well still activate the neurones. The issue then becomes, are you causing damage due to the high irradiance at that point? I have seen, in the brains of previous opto mice, plenty of c-fos at the end of the fibre, even in control mice that don’t express ChR2.

Overall, I’m happy that this optogenetics irradiance optimisation has helped with my planned fibre placement, and hope for a good experiment.

1. Lin et al., Biophysical J 96, 1803-1814 (2009) Characterization of engineered channelrhodopsin variants with improved properties and kinetics.

2. https://web.stanford.edu/group/dlab/cgi-bin/graph/chart.php

3. Liu et al., Biomed Opt Express 6(12), 4859-4870 (2015) OptogenSIM: a 3D Monte Carlo simulation platform for light delivery design in optogenetics.

The Power of Red Shift

My recent attempts at making a red optogenetics stimulation system (in the 620 nm range) were partially successful, in that I was able to make something that would produce about 3 mW from the end of an implanted 200 µm fibre. Unfortunately, this is less power than I wanted (ideally more like 10 mW), but my research into which opsin to use led me to a recent paper from Karl Diesseroth’s group1, where they develop a new red-shifted super sensitive opsin.

In this work, Marshel et al. begin by data mining a marine microbes genetic database for potential channelrhodopsins (Figure 1A/B). This kind of largescale screening is not my forté, but it was very interesting the way they used previous channelrhodopsin crystal structure to inform their screen as to variants that had novel sequences specifically for the residues that form the ion pore, thereby increasing their chances of finding a variant with very different properties.

They then transfected these channelrhodopsin variants into cultured neurones and used patch clamping to determine the channel properties, which allowed them to narrow down to a the most promising super sensitive opsin, of a previously unknown class. They called it chRmine, supposedly to be pronounced carmine (like the colour), but my brain can’t call it anything other than chromine.

Anyway, naming aside, it seems to be an excellent opsin for use in optogenetics experiments. It shows huge excitatory photocurrents in the orange and red spectra, when compared to 2 other red-responsive opsins, Chrimson and bReaChES (Figure 1C). They then go through a series of opto-electrophysiology recordings to demonstrate the suitability for optogenetics experiments, beginning with the reversal potential, which demonstrates Na+/K+ permeability, which is great for driving neuronal spiking (Figure 1D).

ChRmine induces neuronal firing with very short pulses (Figure 1E) and very low irradiance (Figure 1F), both on an order of magnitude better than the compared opsins. This sensitivity is so important for optogenetics, as it makes it much easier to deliver the amount of light needed and you can limit the phototoxicity damage.

The other important quality of chRmine is that it has very quick rise and fall times –  essentially it opens and closes very quickly upon light activation, which allows very fast (40 Hz) spiking (see supplementary info from Marshel et al.).

The figure I’ve shown here is only half of the first of 6 figures (not including supplemental information) from this absolute behemoth of a paper. The complexity of the experiments is far beyond my experience and understanding in neuroscience, but they use their new red super sensitive opsin to control individual cortical neurones with millisecond precision for a series of learning and behaviours in mice. Please do check it out if that’s your bag, but it’s far too “neurosciencey” for me.

Anyway, back to my attempts at making a red-based opto system. I’ve managed to achieve 3 mW of 620 nm light from a 200µm fibre with an easy-to-use LED system, which is on the low end for classic red optogenetics, but should do great for the super-sensitive chRmine.

And to get an idea of how well it would work in a mouse’s brain, I went to Karl Diesseroth’s optogenetics website to use his irradiance prediction tool2. For those who’ve never used this, it’s quite handy – you input various parameters for your study, including the wavelength of light and details of your optic fibre cannula, and it shows you the light spread you can expect through the brain (Figure 2).

So, going back at the Marshel paper, they show 100% activation of chRmine at 0.08 mW/mm2. Looking at the predicted irradiance values, that comes out at a depth of 2.3 mm from the fibre tip, which is immense – in an experiment that would illuminate deeper than the entire hypothalamus. Clearly then, we could drop the intensity of the stimulation light, but by how much? Using Diesseroth’s irradiance calculator, I input a series of decreasing light intensities and noted how deep you can go and maintain 0.08 mW/mm2 (Figure 3).

Given experimental practicalities, I would be happy to have 1.2 mm of activation depth, which translates to about 0.3 mW of power, which is a 10-fold reduction even from my relatively low power LED system. This would obviously need validating in vivo, but it’s very promising, and should negate a lot of the classic difficulties with obtaining high light power for optogenetics. I’ve had a peek, and it seems that Addgene stock chRmine AAV’s, so I am excited to try this out.

1. Marshel et al., Science 365, eaaw5202 (2019) Cortical layer-specific critical dynamics triggering perception.

2. https://web.stanford.edu/group/dlab/cgi-bin/graph/chart.php

An Influential Choice

We live in a world of convenience and temptation, and it’s difficult to know how to influence food choice in a healthy way. I was particularly reminded of this since having a toddler, who knows what he wants and has very little impulse control.

We were recently on a walk round the park, him on his little balance bike and me walking the dog. He’s zooming around, so I’m happy to follow his lead, but it doesn’t take long to realise that his zoomies have a definite target, which is the café at the park. The café that just happens to sell ice cream. Soon enough we get there and, surprise surprise, he wants a tasty treat.

But then, we are biologically designed in a way to seek out the high reward palatable foods, and not at all for a modern world with multi-billion pound corporations whose business models depend on us gorging ourselves to morbid obesity. As another example, it only took one or two visits to a certain fast-food chain (parenting is hard, don’t judge me) before the little man recognised the signs for said shop and would request the rewarding food they sell.

It might be obvious, from an evolutionary perspective, why we seek high reward foods, but it’s not so obvious how this is coordinated by the brain. In this post I’ll explore some of what we know about the selection of highly palatable food, why this is important for the control of body weight, and some thoughts on how to influence food choice pharmacologically to improve health.

Starting from the beginning, we’ve known for a long time that giving animals access to palatable foods (high fat and/or sugar) causes an increase in body weight1. The story becomes more interesting when we look at intake related to food choice.

In an early study, human subjects were locked in a lab for a couple of weeks and either given unlimited access to monotonous food, or given normal food restricted to the same number of calories as the “monotonous” group. The first group voluntarily decreased their caloric intake (so the second group had theirs decreased), and both cohorts lost weight.

The interesting point of this study is that the monotonous group that voluntarily decreased their food intake didn’t notice their hunger to the same extent as the calorie restricted group. This clearly emphasises the importance of the food environment we live in when it comes to food choice.

My thoughts following on from such food-choice studies were about the possibility of how to influence food choice pharmacologically. As far as I can tell, all the pharmaceutical attempts at combating obesity aim to administer long-lasting modulators of hunger/satiety (increasing energy expenditure has proven problematic for reasons I may go into another time).

Unfortunately, the neuronal pathways that control food intake are so intertwined with other functions (such as mood and nausea), that you get off-target effects. Furthermore, the receptors and signalling pathways you target will naturally compensate to counter the effects, so any effects of food intake and body weight are short-lived.

What if we could administer short-acting compounds that, rather than hammering down our desire to eat with diminishing returns, merely changes the preference away from the unhealthy foods that cause the pathogenic weight gain? It doesn’t matter how hungry you are, if your appetite is limited to carrots and broccoli, it is impossible for you to become obese.

But, how would we go about doing this? I believe that some of our more recent knowledge about AgRP neurones hints at a solution. Back in 2015, Chen et al. showed that AgRP neurones become rapidly inhibited in response to sensory detection of food3, but more importantly that the degree of response was related to the palatability of the food (Figure 1).

We have since seen that this AgRP response is a teaching signal for caloric entrainment – the AgRP response to a particular food detection will change over time depending on the caloric value (Figure 2)4.

We have also seen that driving AgRP neurones activity (with opto stimulation), drives a marked decrease in preference (Figure 3)5.

So, it appears that AgRP neurones are a fundamental link between sensory detection of food, hunger, and the learned seeking of high caloric foods. More specifically, the drop in AgRP neurones activity upon sensory detection of a food seems to be the determinant of how much of that food the animal wants to eat.

Now, what if we could (briefly) activate AgRP neurones during consumption of an unhealthy meal? I say activate, but it could equally mean limit the inhibition upon detection and consumption of the meal. Classic wisdom would suggest that when you activate AgRP neurones you increase hunger and food intake. And that may happen initially.

However, given the results from Betley et al.5, I would argue that over time, with repeated exposures to the same high calorie meal and activation of the AgRP neurones, you would drive a preference away from that unhealthy meal in the future.

How I envisage this working in practice: we would have short-acting (half-life of 20 minutes or so) modulators of AgRP neurone activity, ideally in an easily administered form, such as in an asthma-type inhaler. An overweight individual who wants to eat better to lose weight and become healthier would then take a hit from an AgRP activator at the start of an unhealthy meal, which will decrease their preference for that food. Conversely, they could take a hit from an AgRP inhibitor at the start of a healthy meal, which will increase their preference for that food.

The goal of this pharmacology is not to alter a person’s hunger in any way, but rather to break the evolutionary drive to overconsume high caloric foods, and in that way to give their willpower a boost to selecting healthy food choices. The idea is that you turn any unhealthy foods into the “monotonous” type that we saw earlier, so the person will voluntarily decrease intake of that food. And the best thing about this is that it hijacks the obscenely effective marketing that companies use to push unhealthy food, and instead links that advertising with unrewarding food.

Great, so I like this idea, but how would we go about showing this experimentally? Well, I would start by continuing on from Betley’s work5, but see if I could use optogenetic stimulation of AgRP neurones to shift preference between foods of unequal palatability.

Ideally, we would provide opto-connected AgRP-ChR2 mice long-term access to chow and high energy diet (HED), and set up the optogenetic system to stimulate the AgRP neurones whenever the mice go to eat the HED, but not the chow. Hopefully, the mice would shift their natural preference for HED away to chow.

If this is successful, the next step would be to mimic the same response using pharmacology – my thought would be to test out a number of known compounds that affect AgRP neurone activity (eg. PYY and CCK, or their antagonists), possibly using combinations to yield a bigger effect.

Well, that’s about as far as I’ve come with this idea of how to influence food choice. I did pitch the concept at lab meeting a few months ago, and it went down about as well as season 8 of Game of Thrones. Oh well, hopefully my loyal readers will find it more interesting than my colleagues.

1. Sclafani and Springer, Physiol Behav 17(3), 461-471 (1976) Dietary obesity in adult rats: similarities to hypothalamic and human obesity syndromes.

2. Cabanac and Rabe, Physiol Behav 17(4), 675-8 (1976) Influence of a monotonous food on body weight regulation in humans.

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

4. Su et al., Cell Reports 21, 2724-2736 (2017) Nutritive, post-ingestive signals are the primary regulators of AgRP neuron activity.

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

An Illuminating Journey 2

Following the wildly successful first instalment of “An Illuminating Journey”, a sequel was all but inevitable. We pick up the story during the giddy highs induced by the first successful optogenetic test experiment, by our protagonist Nic and his sidekick Ed. This orgasmic euphoria was unfortunately shortlived, when their ignorance was bound to collide with the harsh reality of optimising challenging new techniques.

I think, in hindsight, that using AgRP neurones for our initial optogenetics test may have been reaching for fruit that was hanging too low, so to speak. Essentially, we had outrageously good behavioural responses from that experiment, even with very low (<1 mW) light stimulation, almost no acclimatisation for the animals, and no testing/optimisation of stimulating protocols. So when it came time for the next opto experiment, this time using a different cre line to target a neurone population I’d been investigating for a few years, we didn’t have such good fortune.

We kept the experimental protocol very similar (measuring food intake responses to opto stimulation), but initially saw no effect whatsoever. But then, we had used a low light intensity for the AgRP experiment, so we tried ramping the light output up as high as it would go, and this gave us an inkling of a (non-significant) trend towards a feeding response. Ok, maybe if the mice were less stressed? Unfortunately, they had to be in an open cage to allow for the optic fibre tethers, but we could implement a rigorous acclimatisation protocol, to get the animals used to being both in that stressful open environment and to having their head tethered.

It was during this acclimatisation period that the implanted optic fibre cannulae starting coming out; essentially the small, smooth steel fibre cannulae weren’t adhering to the dental cement well enough to resist the tight optical connections. And I won’t even start on the ceramic cannulae we tried at the same time – they seized up with the connectors and got ripped out on the first connection every time. Any remaining ceramic fibres were rapidly disposed of.

We have since improved our opto techniques, especially with regards to the surgery, to almost entirely negate the chance of lost fibres (the most important step was improving our dental cement; we now use modern light-curable stuff which is super sticky, and turns rock solid in a matter of seconds so works much better than the old mix-and-cure stuff we had). However, at the time Ed and I were getting more and more frustrated with these failing and partially successful opto experiments.

It was then that an event happened that would alter the course of my life, career and professional interests. I remember distinctly when and where it happened. We were just clearing away after another partially successful/partially disastrous opto experiment; I had been thinking how much of a problem it was to have to tether the mice in open cages, and how likely that was to be the root cause of our experimental issues, and my various Google searches had shown me zero viable fibre-free alternatives on the market. I turned to Ed, and voiced my hitherto private thoughts that it would be so great to do away with the optic fibres for these experiments, but there were no wireless opto systems available, and in my frustration said that I could bloody well make a system myself. Ed looked at me and said, “Yeah, let’s do it. I’ll front the cash and you develop it.”

I laughed it off at the time, but his serious suggestion that we do this stuck with me, and a few days later we sat down with a coffee and came up with a serious plan of how we might go about doing this. I brought along a new Lego Batman notebook dedicated to this project, and we sketched out some ideas for how our fibre-free optogenetics system might work. We also came up with a realistic timeline to develop a prototype (alongside doing our actual lab projects, obviously), and we conservatively came up with a target of 6 months till were able to test in vivo, and if all went well with the tests we could start production and begin sales of our ground-breaking product to other like-minded neuroscientists a few months later. Spoiler alert: 3 years later, we still haven’t made it to the in vivo testing stage.

Now, it is important to note at this point that I was our resident electrical engineer, having obtained a C-grade in GCSE electronics a mere 15 years previously. So, after a swift Google to catch me up on the advances in micro-electronics since then, I came up with a plan for how to achieve fibre-free opto’s. We ordered some hobbyist electronics sets off eBay, and I built a circuit on a breadboard that would flash a blue LED in specified patterns under infra-red control. Great. Next step, I downloaded a free electronics circuit mapping software (“Fritzing”), designed a printed circuit board (PCB), and we got it printed from a company in China for a few quid. Ed bought me a soldering iron, and then when my PCB’s arrived, I was able to solder in the components to recreate the breadboard circuit. And that worked! But, this was about the size of a credit card, so next step was to shrink the circuit down to something we could attach to a mouse’s head.

So I redesigned and shrunk the PCB’s, and we got them printed from the same company in China, but this time we paid them to solder the micro-components to the boards (my newfound soldering skills weren’t up to that particular task). It was a very exciting day when our new circuits arrived. I managed to solder in some contacts to connect batteries, and we had yet more success – these mini circuits also worked as I had planned. So, we were now almost ready to take our prototypes in vivo, we just needed to find a way to connect the LED on the circuit to an optic fibre implanted in a mouse.

Tune in next time to find out just how terribly it all went wrong.

An Illuminating Journey

Back in 2016, I decided to make the leap towards my first optogenetics study. A couple of years previously, I had helped set up targeted intracranial nanoinjections for the lab, which meant we were routinely doing experiments with AAV’s (mostly DREADD’s) and retrotracers. And it was only a few years before that that our lab had acquired our first cre line.

So, while the use of transgenic mice in this way was relatively new to us, we were learning quickly and were keen to advance our in vivo capabilities. More and more it was becoming difficult to publish in good journals without showing manipulation of complex behaviours by identified neuronal populations (either with DREADD’s or optotenetics) and demonstrating the circuits involved.

However, optogenetics was still quite new, and totally novel to me, and as I’ve said before one of my failings is my reticence to seek help, so I was figuring this out myself. Not that I was completely alone, I did have a great PhD student to help me, particularly with the in vivo aspects. So anyway, I started by looking at what others had done, focussing on some of the early, high impact work; I was particularly drawn to work from Scott Sternson and Denis Burdakov, as well as the original pioneers of optogenetics including Karl Diesseroth. Picking out the common factors in their methodologies, I wrote up the following list of requirements for my first optogenetics study:

  • Use lasers to produce blue light (~470 nm)
  • Light is pulsed at a maximum 20% duty cycles to limit heat damage and phototoxicity; typically 10 ms ON at 10-20 Hz
  • Light is delivered via fibre optics with a rotary joint to a 200 µm fibre into the mouse’s brain
  • Typical light power from the end of the fibre optic cannula (ie. what is actually entering the mouse’s brain) is around 10-15 mW

If I’m honest, setting up one of the laser systems for my first optogenetics study scared me a little. They’re big, expensive, dangerous and difficult to use. Or at least, so it appeared to someone who’s never used them, and I would be facing a mountain of paperwork if I wanted to get a laser system approved for use at the University.

It was around this time that we started seeing LED-based optogenetics systems coming on the market, which definitely appealed to me. The problem with LED’s is that the light scatters (Figure 1), making it challenging to get sufficient light through an optic fibre.

Laser vs LED light into optic fibre.

If you want to use LED’s to provide sufficient light output for in vivo optogenetics, you need to have an extremely high power light source with very good lensing and/or reduce the number of optical connections to reduce the light lost along the delivery path (Figure 2).

Looking at the possible applications of LED’s, I could safely discount implanting micro-LED’s into the brain (Figure 2D) due to the highly advanced nature of that method and the fact that nobody sells them, as well as having head-attached LED’s (Figure 2C) because there don’t seem to be any trustworthy versions for sale, although the latter does lend to doing wireless optogenetics which does appeal to me but not for my first optogenetics study.

So, between the “normal” desktop-mounted LED’s (Figure 2A) and the intermediate rotating LED’s (Figure 2B), there were 2 options on the market that seemed likely to work. I say this, because it was very rare for any of these manufacturers to actually state what the light output from the fibre cannula would be in an experiment; hats off to Plexon and Prizmatix as the only ones that seemed to do this.

Number of optical connections in different in vivo optogenetics setups.

So, I had narrowed down my options to the Prixmatix desktop LED1 and the Plexon rotary Plexbright system2. However, my distrust of having optical connections, for fear of excessive loss of light, led me to pick the latter. I had already tested an AAV ChR2 construct in vitro, so, together with my experience doing targeted AAV-DREADD injections and cementing ICV cannulae into mouse brains, I was ready for my first optogenetics study.

As Ed (my PhD student) was already working on NPY/AgRP neurones and feeding behaviour, we had the AgRP-cre mouse and we both thought that stimulating AgRP neurones would be the best initial experiment. I maintain you always want to go for the low-hanging fruit when starting anything new.

Ed and I assembled a half dozen AgRP-cre mice, injected them with AAV-DIO-ChR2-mCherry into the arcuate and cemented an optic fibre pointing at the same place. Then came the waiting game – 2 weeks while the transfected neurones ramped up expression of ChR2. We rigged up the Plexon rotary LED’s (we stuck them to a shelf above a bench using electrical tape), wrestled with the Plexon Radiant software to produce a nice stimulation pattern, and finally connected some mice to the ends of the optic fibres.

I can still remember the day we first switched on the LED’s – without a doubt the best moment I’ve ever had in science, and to be honest one of my best in general. How can a simple wedding, or the birth of a child, compare to watching a mouse gorge itself because you flicked a switch on an LED. Absolutely magnificent.

1. https://www.prizmatix.com/Optogenetics/optogenetics-led-Blue.htm

2. https://plexon.com/products/plexbright-optogenetic-stimulation-system/

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.

Going Fibre-Free

Optogenetics is a fantastic technique, enabling the control of mouse behaviour with a high degree of temporal and neuron-specific precision. However, due to the high levels of light power needed to activate channelrhodopsin and its variants, a typical system will use a high intensity laser or LED connected via fibre-optics.

In my experience of doing opto’s, the fibres have proved to be the biggest technical issue, due to the high level of stress they incur in the mice. This comes as a result of a number of factors, including the requirement for housing the mice in an open cage, with a tether on the head that places some amount of torque on them at all times, and requires them to have their head upright. Also, because of the fragility of the optic fibres, the mice will often be housed alone and in a sterile environment. All this amounts to both unnecessary suffering for the animals, which as researchers we a morally bound to reduce whenever possible, but also to stress that will inevitably impact on any behavioural measure you are investigating.

This brings me to a paper that was published recently by Anpilov et al. in Neuron1, where they developed a wireless optogenetic stimulator to overcome these issues with fibre-connected opto’s and investigate social behaviours in a “semi-natural” setup.

From a technological standpoint, I am very interested in the device they developed, which is my primary interest in this paper. Their device is almost ludicrously simple – it’s just an LED connected to 2 button batteries via a magnetic switch, which results in a total weight of about 1g (Figure 1A). They connect this to the implanted optic fibre and cement the whole lot onto the mouse’s head. Then the LED can be switched on externally by proximity of a magnet (Figure 1B). This means that the mice can be kept in a complex group-housed environment and the opto’s switched on at will remotely (Figure 1C).

In fact, the researchers placed the magnet above the feeder, so they don’t ever need to disturb the animals. This enabled Anpilov et al. to influence aggression, grooming and other social behaviours in response to oxytocin activation, which would otherwise be extremely challenging to investigate using classical fibre-connected opto’s.

Figure 1. A Wireless Device for Prolonged Optogenetic Manipulation in a Semi-natural
Setup
(A) Schematic illustration of the wireless device. Two batteries are connected in series to an LED through a magnetic-field dependent reed-switch. The LED is attached on top of an optic cannula positioned above the dorsal part of the PVN.
(B) A device mounted on a freely behaving mouse activated by a magnet.
(C) Schematic illustration of the semi-ethological arena and software-controlled electromagnet installed on the feeder. The arena consists of an open 70 x 50 cm box containing a nest, feeders, water, elevated areas, and barriers.
(D) Light power emitted at the tip of the optic fiber as a function of the number of 2 s light pulses. Battery capacity is sufficient for over 215 pulses.
(E) Section through a 3D map of blue light intensity along the axis of an illuminating fiber in graymatter. The slice was imaged from below as the fiber was lowered through. The section is superimposed with a contour map of iso-intensity lines corresponding to light intensity levels. Light intensity >= 8microwatt/mm2 is sufficient for effective SSFO photoactivation. Taken from Anpilov et al. 2020 Neuron.

However, I do have some concerns to voice about the device they used, related to the actual light output they can achieve. Essentially, you can’t expect high light power through an optic fibre from a battery-connected LED – the light scatter doesn’t allow it. The upshot being that Anpilov only got 3.2mW/mm2, which is much dimmer than a typical opto system which will deliver 100-300mW/mm2 (our current system delivers around 175mW/mm2). Furthermore, their system has a maximum on-time of around 400s, which is why they cleverly used the SSFO to provide long-term activation after a very short stimulation pulse.

So, while the system developed in this paper clearly works well for their application of oxytocin-mediated social behaviours, I can imagine the further applications being relatively limited due to the following reasons:

  • Need to use SSFO or equivalent long-term responsive rhodopsin loses the high temporal precision you need for many optogenetic applications
  • Very low light output will likely mean further applications will be limited to those with very strong/sensitive behavioural responses, and probably with very dense neuron populations
  • The LED is only ON in close proximity to a magnet, which limits the design of environments that allow for activation without disturbing the animals.

Overall, I was impressed by the paper and am very interested in their approach to negate the drawbacks of fibre-connected optogenetics. But, I think their device has its own drawbacks, which if overcome would produce some very powerful tools with wide-ranging applications for in vivo optogenetics.

1 Anpilov et al. 2020 Neuron Wireless Optogenetic stimulation of Oxytocin neurons in a semi-natural setup dynamically elevates both pro-social and agonistic behaviours.