Tag: Optogenetics

How Numerical is your Aperture?

Planning another optogenetics study, and I needed to cut the optic fibre cannulae ready for implantation. One of the other postdocs in the lab had been super organised and bought in a bunch of implants from Thorlabs at a variety of numerical apertures (thanks Amy). But, which is the best numerical aperture (NA) for my experiment?

I won’t go into details (because I’m not a physicist), but Wikipedia defines the NA of an optical system as “a dimensionless number that characterises the range of angles over which the system can accept or emit light”.

Essentially, as far as we are concerned for fibre optics, the NA is relevant for two things:

  1. The bigger the NA, the more light from the source will travel down the optic fibre – for a laser system, this doesn’t matter much because the coherent light can easily be focused down it, but for an LED, this can make a big difference for how much light is captured by the fibre (rather than scattering away)
  2. It determines how much the light spreads after exiting the fibre (for in vivo opto’s, this will be in the mouse’s brain) – the higher the NA, the greater the cone of light dispersion

So, back to cutting fibres, and I had to decide which ones to use – I normally use the 0.22 NA fibres out of habit, but I have read multiple recommendations to use as high an NA fibre as possible when using an LED system (which is what we have); the idea being to get as much light power as possible into the mouse’s brain, which is important considering LED systems can struggle to be bright enough for in vivo opto’s. Both Prizmatix and Doric suggest using 0.66 NA fibres for LED-connected systems, which is actually higher than the ones we have available from Thorlabs.

To test the light output, I hooked up fibres of different NA’s to our LED optogenetics system, and recorded the light power out the end of the fibre using a light meter, both under constant illumination and during 10 Hz flashing with 10 ms on times (Table 1).

True to form, the higher the NA of a fibre, the more light that is passed down it. Great, so at this point I’d pretty much settled on the 0.50 NA fibre, because it emitted approx. 50 % more power than the 0.22 NA fibre. However, for the sake of completeness, I decided to input the values into Karl Deisseroth’s irradiance predictor, to check how deep I would get good ChR2 activation. This is a useful step when planning placement of your optic fibres.

I plotted the values for all three NA fibres (Figure 1), and I’ve included the threshold level of 1 mW/mm2 that I’ve talked about previously (this is the measured EC50 of ChR2 H134R, which I use as a threshold irradiance to assume good activation).  

Now I’ll be honest, I was surprised by this outcome – despite having lower light output from the lower NA fibres, the irradiance was higher as soon as you go deeper than about 0.2 mm into the tissue. I can only assume this is because the lower NA results in less light spread coming out of the fibre – the 0.50 NA fibre remains above the critical 1 mW/mm2 down to about 1.0 mm, whereas the 0.22 NA fibre goes to about 1.4 mm.

The answer is simple – I’m going to use the 0.22 NA fibres, because they have the dual benefit of activating ChR2 to a greater depth, and also having lower brightness at the end of the fibre, which means less heating of the tissue and phototoxicity.

Doing Away With Fibre

My interest in wireless optogenetics has come up a couple of times. In fact, I’ll start with a quick correction: I prefer to call it fibre-free optogenetics, after multiple people mistook my wireless system I was designing as meaning controlled via Bluetooth or WiFi. Which it ain’t. And, for me at least, the whole point of going “wireless” is to do away with the optic fibres, which really embody all the issues and difficulties with in vivo optogenetics:

  • Impacts to the animal – the need to have the animals in an open cage, with an open lid and a sterile environment to prevent damage to the fibres. Also, they tend to be stiff, having severe behavioural impacts.
  • Loss of optical power – the optic fibres require additional optical connections, which inevitably leads to light loss, and therefore difficulties obtaining a high enough brightness.
  • Expensive and fragile – not much more to say, other than we have spent thousands of pounds maintaining the optic fibres for our optogenetics system. This may be more than is typical, but I think that’s because the Plexon fibres we use are very fine and lightweight – I have used more durable ones that were even worse for the mouse behaviour because of the added stiffness.

The most important reason to do away with the optic fibres, as far as I’m concerned, is the impact to the animal. Quite apart from minding the 3R’s with regards to animal welfare, tethering will inevitably cause stress, which is detrimental to the data you can acquire (Figure 1). In fact, it is to the NC3R’s that I am applying for funding to take my fibre-free opto system to the next level.

There is of course the added bonus with wireless optogenetics that you can do optogenetic stimulation in otherwise impossible setups. For example, I am very keen to use my fibre-free opto’s in our calorimetry system to measure energy expenditure in response to opto stim. This is done in an air-tight sealed container, which to my knowledge this has never been done with optogenetic stimulation in the brain.

After a fair bit of research, I have found 4 commercially available wireless in vivo optogenetics systems (Figure 2).

Helios by Plexon and Teleopto by Amuza are both very similar, except that the Helios headstage attaches to “normal” implants, whereas Teleopto make their own custom implants. Both require you to point an IR remote at the headstage constantly (ie. the flashing stops if the signal stops). Fi-Wi from Doric connects over radio signal to drive opto flashing; similar to Teleopto they use custom implants. Neurolux is a very different system to the other three, and uses electromagnetic induction to remotely power the implants. Hence the Neurolux implants are tiny and custom (the LED is actually on the end of the fibre that gets implanted).

I have collated a summary table of the various systems, including a number of parameters (Table 1). Included is the cost to buy a complete setup to stimulate 1 mouse at a time, which usually comes with a few implants. However, I was unable to find out the irradiance available from the Plexon Helios system, despite asking the sales people for those details.

Overall, the Doric system seems the best of the bunch; despite being the heaviest it is very compact and produces by far the highest irradiance. In fact, it provides higher irradiance than the system I’ve been developing, which comes out around 150 mW/mm2. Stay tuned, and I’ll be talking more about my system in the coming months.

1. Won et al., Nat Biomed Eng (2021) Wireless and battery-free technologies for nanoengineering.

One Tiny Step for Man

I’ve been working on the next in my EasyTTL series. Whereas my previous iteration had additional functions and output, this time I had a single goal: make a portable optogenetics TTL driver. This means making it as small as possible and, most importantly, battery powered.

While it is possible to run an Arduino off a battery source, they are pretty big and relatively power hungry. So, I wanted to find a smaller microcontroller to use for this purpose. It is, of course, possible to design a circuit from scratch to use a microcontroller, but that is a huge amount of effort. I would only want to go to those lengths if I had a good reason, like I needed to fit it into a miniscule space, or I was intending to make hundreds.

Fortunately, others have thought the same, and helpfully produced microcontroller breakout boards. Essentially this puts the chip on a board with easily accessible pins and all the control circuitry you need for easy programming via USB, with power regulation etc. I won’t go into all the available microcontroller boards, there are loads out there.

I picked the Adafruit Trinket (Figure 1), because it is small and can be programmed using Arduino IDE, which means I don’t even need to learn any new programming languages. You can think of it as a tiny Arduino, perfect for making a simple and portable optogenetics TTL driver.

The biggest drawback of the Trinket, or any smaller and more basic microcontroller, is that I lose functions; in particular there are fewer I/O pins to connect my switches and dials to. Whereas the Arduino Uno has 14 digital I/O pins, the Trinket only has 4. Now, I obviously need the TTL output and a switch to turn the flashing on/off. I also like to have an LED indicator of the TTL being switched on, which leaves a single pin to control the flashing frequency, on times etc.

With the restriction of a single available pin to control the flashing, I can put in a toggle switch to allow the user to choose between two stimulation paradigms. I will therefore just program my two “favourites”, ie. those that I see most often in the literature or that I am most likely to use myself in the lab:

  • 10 ms flash on-time; 10 Hz frequency
  • 10 ms flash on-time; 20 Hz frequency for 1 second, then off for 3 seconds

My loyal readers will know about my dislike of the 20 Hz and higher frequencies, but as you see them so often in the literature it felt remiss not to include. So anyway, I programmed the Trinket, connected it to switches etc, and hooked the output up to an oscilloscope (Figure 2).

The timing is very good, although it runs about 100 µs fast for a 10 ms pulse, giving it a timing accuracy of 99 %. While this isn’t as good as the Arduino, it is still great, and to be honest is far better than you would ever need for an optogenetics study, either in vivo or in vitro.

Next, I printed a housing unit for the Trinket and a 9 V battery, and I also included a slide switch to cut the power and prevent battery drain when not in use. I think it looks quite smart (Figure 3).

I can’t wait to turn up somewhere with this little box, and hook it up to drive a laser or LED in an optogenetics study.

Further Musings on Food Choice

This is a follow-on from my previous post, where I laid out the beginnings of my ideas for influencing food choice. To summarise, I’ll highlight a few sentences from that post: “…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 […] 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.”

This is still conjecture on my part, so if you have any reason to suggest my conclusions may be faulty, please do leave a message to explain where my reasoning falls short. Anyway, in order to further my hypothesis, I had suggested an experiment where we provide a mouse with continuous access to two foods of differing reward value (eg. normal chow and high fat/high sugar diet, what we usually term high energy diet or HED). The mouse would have its AgRP neurones optogenetically activated every time it eats some of the high reward HED, thus limiting the drop in AgRP neurone activity that is the teaching signal for how rewarding that food is.

However, upon further musing, I don’t think we should do this with our usual ChR2 stimulation paradigm, as this would drive action potentials upon stimulation – we are interested in limiting the drop rather than driving further hunger. What we want instead is mild depolarisation of the membrane. In that case, it might be best to use a stabilised step-function opsin (SSFO), because they have a longer milder activation; however, their typical on-time is about 30 mins, which might be too long for our purposes.

It is important to consider the time-course of any neuronal control. Ideally, we should make sure that our manipulations match the normal time-course for a response. We could either do this crudely, where we allow hungry mice access to just HED for 30 mins, and trigger a single SSFO activation when they start eating, or we could do a more advanced (and challenging, and hopefully more informative) paradigm, where we allow access to chow and HED and only trigger depolarisation for the expected time of response for each bite of HED. I will keep SSFO activation during a purely HED meal as a plan B, but I would prefer do allow a fair choice between foods, as I think this allows us to capitalise on the instantaneous nature of optogenetics, and should allow the mice to redirect their appetite in real time.

To have any hope of influencing food choice, we need to drive an appropriate AgRP neurone response every time they consume HED. And to do this, there are two parameters we need to determine experimentally:

  1. The expected AgRP neurone response to each consumption of HED, which we can check using GCaMP photometry in vivo. The important aspect to quantify is the timecourse of the response to a predetermined meal. Probably the easiest way is to use “bitesize” pellets (such as these sucrose pellets that I’ve used in the past).
  2. An appropriate optogenetic stimulation paradigm to elicit a mild depolarisation without triggering action potentials. I could do this quite easily using patch clamping of the opsin-transfected AgRP neurones and test a range of stimulation paradigms. I think likely a long (4-8 seconds) stim at a lower than usual brightness would be likely to produce the outcome I want.

Once I have determined these two parameters, it will be time to perform the experiment, which should be relatively straightforward as opto studies go:

  • Implant optic fibre into mice expressing ChR2 in AgRP neurones
  • Allow continuous access to chow and sucrose pellets throughout a long (6 hours?) stimulation session, with no food available at other times
  • Track consumption of each food, and trigger continuous “light-on” for the experimentally derived response time after consumption of each sucrose pellet
  • Perform repeated stimulation sessions and see if preference shifts away from sucrose

Assuming this shows the outcome I predict, the next step would be to investigate this effect using pharmacology. I would give mice set “meals” with normal chow or HED, and try to shift their perception of the reward using a low dose of AgRP neurone modulators eg. PYY antagonist. Here’s a possible plan:

  • Mice given calorie restricted meals for chow and HED separated by a few hours, then a calorically unlimited meal of both for an hour in the evening.
  • Mice given injection of low dose PYY at onset of chow meal, and PYY antagonist at onset of HED meal
  • Track food intake and body weights

The question here would be, does the treatment shift preference away from the unhealthy HED? I’ll be honest and say that I really have no idea whether these experiments would pan out the way I envisage. But if they do succeed in influencing food choice, I think it could pave the way for some very interesting (and completely novel) therapeutics to combat obesity.

How Bright is Bright?

I have previously written about the importance of brightness for in vivo optogenetics experiments. It’s just as important for in vitro optogenetics, which is what I’ll be looking at today. This came about because we’re planning a publication, and we need quantification of the light irradiance that we get on the brain slices.

When I started in vitro optogenetics, I tested the brightness of the LED system I had bought for the purpose, but not with a light meter – instead I tested directly on ChR2-expressing neurones, and found that 2 % brightness of the 470 nm LED was sufficient to elicit action potentials.

This was enough for me at the time, and I never bothered doing the metred quantification because the light meter didn’t fit under the objective (even after removing the tissue perfusion bath). However, for publishing I wanted a proper irradiance value, which meant me and our ephys technician spent an afternoon trying to dismantle the condenser under the stage. I say trying to, because microscope has been in pretty heavy use for at least a decade without any kind of service, and we found a lot of salt residue from past aCSF leakages.

Hopefully y’all cringed at the thought of that, because salt build-up inevitably means corrosion of expensive microscope parts. And, surprise surprise, we found the screws and bolts holding the condenser together and onto the microscope are all rusted in place. In the end, we managed to unscrew the top part of the condenser’s lens and wiggle the light meter in place under the objective. Phew! So now we went through a range of LED brightness and measured the brightness coming out the bottom (Figure 1).

LED brightness on my electrophysiology rig.

As ever, the brightness is not the important parameter here. What matters for activating opsins is the irradiance hitting the slice (irradiance being intensity of light per unit area). So now comes the difficult bit – how do I know the area that the light is hitting on the slice? It is possible to get a microscope ruler, put that under the objective and measure the diameter of your field of view. However, how do I know the camera or eyepiece are visualising the entirety of the illuminated area?

The answer is to go back to physics, and field of view of the objective in use. I found a useful guide from the makers of my LED’s1:

Diagram for calculating irradiance for in vitro optogenetics.

A quick investigation shows me that the Field Number for my objective is 22. Dividing that by the magnification of 40 gives a diameter of 0.55 mm. I know the area of a circle is πr2, which gives me a surface area of 0.238 mm2. So, adjusting the brightness values obtained earlier gives the irradiance output from the LED’s (Figure 2).

LED irradiance on my electrophysiology rig.

I have also plotted a simple linear regression line on the irradiance graph to give an easy formula to give a rough estimate of the irradiance at any given LED brightness. However, I will still make sure to use actual measured values in any publication, rather than the estimates obtained from the regression. Anyway, this does match up nicely with my earlier test data, as the EC50 for ChR2h134r is 1 mW/mm2, and 2 % brightness on my blue LED gives an irradiance of just over 2 mW/mm2.

For any future in vitro optogenetics studies on this rig, I will aim to use 10 % LED intensity, as this will give a solid irradiance of 10-15 mW/mm2, without going into higher saturating irradiance levels.

1. www.coolled.com

A Mega Piece of Kit

I have exciting news for my loyal readers: I have finally completed the prototype for my EasyTTL Mega controller! This is a 4-channel optogenetics TTL driver, which I plan to use for in vivo optogenetics experiments.

I’ve mentioned parts of the development of this project before. But, for those who haven’t read the previous blog posts, the idea behind it was to make an optogenetics TTL driver that is simple and easy to use. I get so annoyed by the unnecessary complexity (and associated costs and difficulties) inherent in most neuroscience research equipment. As such, I have produced a massively simplified device that is controlled by the user with knobs (teehee) and switches.

The EasyTTL Mega (Figure 1) is a 4-channel optogenetics TTL driver built on an Arduino Mega core1. The Arduino Mega is a robust open-source microcontroller board with a massive array of I/O pins. I have given it four TTL outputs, each individually switchable by tactile toggle switches. The stimulation settings are determined by three dials: one for the flash duration, one for the frequency of stimulation and one for the brightness. The flash settings have been taken from a decade of optogenetics literature and experiments by yours truly, and covers 99% of flashing paradigms that I have seen.

My prototype 4-channel optogenetics TTL driver.

Being a good scientist, I needed to test the system, so I connected a couple of the outputs to an oscilloscope and turned on the pulsing. First, I checked the pulse durations (Figure 2), which are consistently accurate across the range.

Confirming accurate timing from my optogenetics TTL driver.

Next, I tested the range of flashing frequencies (Figure 3), which are also bang tidy.

Confirming accurate frequency timing from my optogenetics TTL driver.

Finally, I needed to check the brightness control (Figure 4). A quick note: the brightness control is based on pulse width modulation, which means that the laser or LED controller can be set to a constant current. However, the frequency of the PWM is 980 Hz for TTL outputs 1 and 2, but only 490 Hz for TTL outputs 3 and 4. What this means practically is that there will be a threshold flash duration below which the PWM makes the brightness unstable from one flash to the next, and this will be worse for the pins 3 and 4 which run a slower PWM.

Investigating the PWM dimming function on my 4-channel optogenetics TTL driver.

Based on my recording of the PWM outputs, the dimming control is unusable for flash durations of 1 ms on all outputs, and 2 ms or less on outputs 3 and 4. Despite my earlier misgivings, I think the 2 ms flash on outputs 1 and 2 looks fine. I’ve put the EasyTTL Mega in the shop, in case anyone wants one for their own research.

1. https://store.arduino.cc/products/Arduino-mega-2560-rev3

A Terminal Question

My interest today is in optimising optogenetic terminal stimulation. I have previously talked about optimising your opto stim frequency, and I had a little dig at a collaborator who stimulates at 30 Hz.

Optogenetic stimulation fidelity

To summarise my previous post regarding optogenetic stimulation fidelity: if you optogenetically stimulate a neurone too fast (and the definition of “too fast” depends on a number of factors including the next type, opsin, duration and intensity of light flash etc.), they cannot keep up, and instead of firing action potentials they become chronically depolarised without firing.

Essentially, the neurones need time to come recover their membrane potential back below a certain threshold (typically around -50 mV) before they can produce another action potential, thus they effectively become silenced when you want to be activating them.

However, it has since occurred to me that my collaborator only ever stimulates the projection sites of his ChR2-expressing neurones. The neurones he is interested in are found in the hindbrain, and as such he can’t reliably stimulate the soma.

So I had a thought: does stimulating neurone terminals work differently from soma because you don’t need to send the signal down an axon, and as such allow his high frequency stim to work? In particular, my collaborator maintains that high frequency stim results in more release of neuropeptides (eg. AgRP/NPY), rather than fast amino acid transmitters (eg. GABA). This has, in principle, been known for a long time1, but it doesn’t mean a) it’s true for all neurones everywhere b) it’s possible to stimulate neurones that fast in vivo using optogenetics.

In this post, I’ll be exploring the second point in more detail, by looking at the fundamental biology of a neuronal synapse, what causes release of neurotransmitter and how we can successfully control that with optogenetics.

Biology of a synapse

A quick biology lesson: we all know that neurones have action potentials, which is a transient spike in electrical activity across the membrane, and this is how they send information down an axon. However, it isn’t the voltage change that causes neurotransmitter release at the nerve terminal. At least, not directly. Instead, the increase in membrane potential causes an influx of calcium, and it’s the increase in calcium that causes vesicle fusion and release of the neurotransmitter (Figure 1)2.

So, my question regarding optogenetic stimulation of nerve terminals is this: if you overstimulate a nerve terminal into a chronically depolarised (silent) state, do you still drive release of neurotransmitter? This might sound paradoxical, but it could theoretically happen if the calcium release occurs at a slightly depolarised membrane potential (maybe -30 mV, which is easily obtainable by opto-overstimulation). In that case, action potentials would not be necessary, as we are already at the terminal end of the axon and don’t care about sending electrical signals, only about triggering vesicular fusion by increasing calcium.

To answer my question, we need to look at the channels that cause the increase in calcium upon membrane depolarisation, and in particular at what membrane potential the calcium release is triggered. If calcium release occurs at a low (-30 mV or below) membrane potential, then we could happily see neurotransmitter release from chronic depolarisation. However, if it occurs at a more depolarised level, it is extremely unlikely that the neurone terminal would reach that membrane potential from chronic overstimulation.

The channels we’re interested in are called, surprise surprise, voltage-activated calcium channels. This actually comprises a large family of channels, with multiple groups. I won’t go in to depth here, because there is so much literature concerning these channels. However, of the ten mammalian variants, there are three that are important in neurones for synaptic release (Table 1)3.

To quote the review by Dolphin3:

“For most synapses, CaV2.1 (P/Q)- and CaV2.2 (N)-type channels are involved in varying proportions in synaptic transmission, depending on the synapse in question and the developmental stage… At some synapses, CaV2.3 channels, activated by smaller depolarizations, play an important role, rarely as the main channel involved in vesicular release”

So there you have it, straight from the … Dolphin’s mouth. The two channels that are most important for vesicular fusion and the release of neurotransmitters are activated at very high membrane potentials (-5.7 mV and -13 mV), which are far too high to be activated by non-firing chronic depolarisation of the membrane.

I now feel confident in saying that high frequency optogenetic stimulation (eg. 30 Hz) of a nerve terminal, which is likely to induce chronic depolarisation rather than action potentials, is not likely to cause the release of neurotransmitter from the presynaptic terminal. I would therefore urge my fellow researchers to refrain from such high frequency optogenetic stimulation.

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

2. Südhof Cold Spring Harb Perspect Biol 4(1), a011353 (2012) Calcium control of neurotransmitter release

3. Dolphin Function 2(1), zqaa027 (2021) Functions of presynaptic voltage-gated calcium channels

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 LHVGAT is the post-lick reward-responsive neurones (Figure 3G), but for LHLEPR is both the cue-responsive and reward-responsive neurones (Figure 3J/K). This is quantified in Figure 3L-O, where they show that LHLEPR neurones are strongly and significantly predictive of reward from cues. This means that in addition to the general LH drive for reward, the LHLEPR 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 LHLEPR projections to the VTA for mediating appetitive learning, and then show that the LHLEPR 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.

Fine Control

I have recently been working on a more advanced version of my EasyTTL stimulator, this time including a dimming control for the TTL output. This is crucial if we wish to control our LED brightness for optogenetics.

Quick lesson on electronics: how do we adjust the brightness of an LED? The typical answer is to say “Adjust the voltage across the LED, or adjust the resistance of the circuit”, and in the simplest terms this will change the power the LED can use. But, it’s not a reliable way to do this, because of the exponential increase in light output across a narrow voltage range (Figure 1); with the LED I’ve shown here, a voltage change of 0.5 V will take you from very low current and negligible light output to maximal output. Furthermore, LED’s get hot during use, which changes their properties and makes them produce more light at the same voltage – this is not only terrible for ensuring precise brightness output, it can also lead to runaway heating and the LED burning out.

So, how do we fix this? This is really the whole point of using a TTL control device. The LED (or laser) is set up to drive a constant current (and it’s the current which determines the actual amount of light being produced), and then you pulse a controlling input to switch the light source on and off as you please. What this means is that a TTL is a very simple digital signal, on or off, with no information about the intensity of the signal.

So, what is an easy way to control an LED brightness for optogenetics? One answer is to use Pulse Width Modulation (PWM), which produces small pulses within your signal, but faster than your eye can detect. Essentially, you’re turning the LED on and off very fast (hundreds or thousands of times a second), and you control the brightness by adjusting the relative levels of on and off. More “on” and it’s brighter, more “off” and it’s dimmer. Simples.

Here’s how I’ve put PWM control into an Arduino Uno (Figure 2). It uses a potentiometer (also known as a variable resistor), but rather than connect directly to the LED output, we connect this to an analog input on the Arduino. The Arduino can then read the value of the potentiometer and using software we can drive an appropriate PWM output on our TTL signal.

See below for the code I wrote to control the circuit. We very simply set the LED as an output and the potentiometer as an analog input (read as “sensorValue”) – the reading needs to be adjusted by a factor of 4 to take into account the change in “bit” rate; essentially it reads the analog input on a different scale than it drives PWM output. Helpfully, Arduino has a built-in PWM function called “analogWrite”, which will drive a 490 Hz PWM based on your value.

Setting up a PWM manually is of course possible, but more of a pain. However, it is something I am likely to do in the future because, as the sharp-eyed among you may have noticed, 490 Hz produces PWM widths of around 1 ms, so it only works for pulses that are longer than that. In fact, I wouldn’t want to use it for pulses much shorter than 10 ms, because it will make the brightness output unstable from one pulse to the next. However, for the immediate future, I’m not overly bothered because I never use a pulse shorter than 10 ms anyway and the PWM produces fantastic brightness control at those speeds.

I hope you’ve found this post interesting, and I would absolutely recommend any readers to buy themselves an Arduino and some components and try this out, see how easy it is to control your LED brightness for optogenetics. However, if you do want an easy-to-use TTL driver for your optogenetics system but don’t want the hassle of building one yourself, I am planning to build some myself and make them available on this website at a reasonable cost.

EDIT 3/9/21: Doing some further investigations today, I found that the Arduino Mega has 980 Hz PWM control from pins 4 and 13, so I will connect 2 of the TTL outputs from my EasyTTL Mega device to those pins instead. This means that you will have access to higher resolution PWM control from TTL outputs 1 and 2, and should work well with on-times of 5 ms, and would probably be acceptable (but not perfect) for 2 ms on-times.

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/