How to Track a Mouse

Our old locomotor tracking

One of my projects is investigating a population of neurones that controls mouse locomotor activity and food intake. In the past I have used either implantable telemetry or IR beam break cages to quantify the mice’s movement. But the telemeters, even when they’re functioning well, don’t give particularly good quantification of mouse locomotor activity, which leaves the beam break cages.

For anyone that doesn’t know, these cages are set up to have a couple of IR beams that cross the cage. Whenever the beam is broken (ie. the mouse gets in the way), this is registered by the computer. It’s quite an effective (although crude) method to quantify mouse activity. And it does so completely non-invasively. However, our current IR beam break cages have a number of drawbacks that make them unattractive:

  • They only work with some of the older open cages, and don’t work at all if the mice have any bedding in the cage (it blocks the beams)
  • The beam break cages we have available in the facility, which actually belong to one of the other lecturers (although she is happy for us to use them), are a decade or two old and were built by a previous postdoc – as such they have suffered some degradation over the years and only have partial functionality left

Anyone who reads my blog will already know what I’m about to say – with these issues I’ve raised, I decided to try and build my own set of beam break cages.

Setting up beam breaks

Right, so first step was to find some IR LED’s and sensors that I could pair across 20-30 cm of a mouse’s cage. I’ve used things like this in the past, so I know you can detect an IR signal using an LED in the ~900 nm range and a phototransistor (Figure 1A).

Luckily, I had some sat around, so I hooked them up to an Arduino, but could only detect the IR signal up to around 5 cm distance. This is obviously not enough, so after some detective work, I found some “IR Beam Break Sensors” from PiHut (Figure 1B). If those didn’t work, it would require some more complex electrical engineering to make it work. Apparently you need to use modulated signals to be sensitive enough to work over multiple metres.

Fortunately, the IR sensors from PiHut worked a treat, up to about 40 cm, which is more than enough for my purposes. The next issue was how to fix the sensors in a way that they would remain aligned in a pairing across the cage.

Aligning the sensors

For this I turned to my trusted 3D printer. After borrowing an IVC from the animal facility, I figured I could make hanging holders that would hook onto the side ridges (Figure 2).

These worked great, with the only issue that the mice tended to move their bedding around and block the direct beams. A very simple solution to this problem was to use strong neodymium magnets to “pin” the tube/bedding at one end of the cage, out of the way of the sensor beams.

Right, so now I had 2 pairs of sensors successfully attached to each mouse cage, next I needed to actually track the data in some way.

Tracking data using Arduino

It turns out that tallying IR beam crosses is easy peasy using an Arduino. The only annoyance being having to duplicate the code 24 times (ie. 2 sensors each for 12 cages). But, I still need to get the data out of the Arduino. I figured I could either hook up an SD card reader and write the data to a removable card, or hook up to a PC and download the data directly.

As I was already connecting the Arduino to my laptop, I tried that first. A little Google sleuthing found me an open source (ie. free) “terminal” program, that will happily log data that comes in over a “COM” port, such as is used by the Arduino. It was actually really easy to set up, and will log the IR beam break data in a CSV (comma separated values) format, that can be directly opened by Excel.

For ease of later data analysis, I made the program log the data in 10 second intervals. However, it will be easy to change that depending on the experimental paradigm eg. 1 or even 10 minute intervals for longer term studies over days or weeks.

Just to prove how well the system works, you can see a massive increase in activity following injection of caffeine (Figure 3A). You also get fantastic circadian activity if you record for longer time periods (Figure 3B).

Where to get it from

As always, I am making this system available on my shop, far cheaper than any commercially available system. Obviously I’ll include a copy of the data logging software with instructions of how to use it. Anyone who wants to measure mouse locomotor activity easily and cheaply, check it out.

Edit 5/5/22: I have now uploaded details of how to make this kit to Hackaday, so head over there if you want to try and build it yourself.

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.

Animal Consumption

A shower thought

I was having an imaginary argument this morning – you know, the kind you have in the shower where all your points are zingers and your opponent can only be floored by your insightful oratory, whereas anything they come out with is antiquated and flawed. On this occasion, my imaginary antagonist was my father-in-law, who is great for such things because he is a classic dogmatic conservative who apparently changes his mind only when instructed to do so by the Daily Mail. He is also loud and steamrolls all other voices in his vicinity, such that my wife is the only person who successfully argues against him.

Anyway, on this occasion, I was actually walking to the train station, which is another great time for introspection, when I started thinking about the recent news that South Korea’s president was considering banning the consumption of dog meat. Now, I could just imaging the FIL lauding this in his typical brash manner: finally some sense, how could this culture engage in such a disgusting practice for so long?

Animal lover

Now, for context, my FIL absolutely loves dogs, so this is a) a very reasonable position for me to give his fictional self, and b) not something I would ever argue with him in real life. But, as this is a fictional confrontation, there’s no problem. So my rebuttal would go along the lines that, yes I agree that eating dogs is distasteful, and not something that I would ever even consider doing, but how is it any different from our consumption of pigs, cows and sheep?

There is no argument you can make against the consumption of dog meat that doesn’t also preclude the eating of any animal without resorting to playing to our cultural history of keeping dogs as pets. And at that point, one can just point to the historical culture of eating dog meat in places such as Korea.

Pigs in particular are as sociable as dogs, and at least as clever. I’ve seen videos of cows bounding around like puppies and showing affection to their owners, and I know people who keep chickens (and other birds) as pets. The same could be true of rodents, not that we eat them, but we do exterminate them fairly indiscriminately, and I can testify that rats are both clever and sociable. Horses and dogs are often used as working animals (not that I enjoy eating horse meat, but there is a historical precedent of them ending up in food as a cheap substitute to beef).

Advocating veganism

This argument inexorably leads to advocating veganism, which my wife and I attempted a couple of years ago, but found it too challenging; if you ever check the ingredients of packaged food, 99% of the time it contains animal products (particularly dairy), even in food that you would never think it necessary. Instead, we went for drastically reducing our consumption of animal products and when we do, making ethical choices.

We have swapped out cow’s milk for oat milk (which is a bit more expensive but I actually prefer it), only buy free-range eggs (which we did anyway), and try to buy ethically produced meat on the occasions we do buy it (probably once a week). Unfortunately, I am a total cheese hound, which has been the hardest thing to cut.

Extending the argument at the other extreme, how can you argue against consuming any animal? I remember watching a program about people (poachers, I guess?) hunting and eating wild animals in the jungles of Borneo, which went by the deceptively innocuous term bush meat. As this is Borneo, every animal is likely to be in danger of going extinct, which makes it easy to vilify and argue for a total ban. But, as a middle class person who’s grown up in wealthy nations and never been hungry or homeless, how can I judge people for hunting for food?

As I said earlier, many of the ethical changes I’ve made to my diet also increased the cost, so how can I judge others who don’t have the means to do so? Well, when they show that sometimes “bush meat” includes orang-utans or chimpanzees, suddenly my sympathies evaporate. And finally, we come back to the arguments in the use of animals in research – balancing need against ethical usage and suffering.

Reducing suffering

A basic criterion is the ability of the animal to feel suffering, which increases with the innate intelligence of the animal, which is why we are so instantly disgusted by the suffering of primates. And also why a huge amount of animal research is performed on mice, who sit in a good balance between less capable of suffering but close enough to humans to enable important and relevant research.

At the end of the day, reducing the suffering of animals around the world comes down to two things: education (about the harm being done; eg. see the NC3R’s) and empowerment (particularly financial, to enable change). This is particularly true when it comes to eating animals, where we can obviate the need for animal consumption, but only with a huge and concerted effort.

The 3 R’s

Replacement. Reduction. Refinement. Also known as the 3 R’s.

On the face of it, the 3 R’s form a fairly straightforward guide to limit the amount of suffering endured by animals in your experiments. However, these are stepping stones to quite indepth process for advancing technologies and rigorous planning, as defined by the following on the NC3R’s website1:

  • Replacement – Accelerating the development and use of models and tools, based on the latest science and technologies, to address important scientific questions without the use of animals
  • Reduction – Appropriately designed and analysed animal experiments that are robust and reproducible, and truly add to the knowledge base
  • Refinement – Advancing animal welfare by exploiting the latest in vivo technologies and by improving understanding of the impact of welfare on scientific outcomes

These are major points that I think about frequently when planning and performing animal experiments.

Before you plan an experiment

Replacement would seem quite straightforward for someone who works on the mouse neural system, in that it’s not something in my control, so not to worry about it. And while it is true that I rarely have intentions to work in non-animals systems, that doesn’t mean it’s irrelevant.

Really, this needs addressing at the most fundamental level, before I even plan an experiment, ie. is the scientific question I want to answer relevant to a whole-animal neural system. Does it require the use of an animal to answer this question?

For example, I have often used relatively unknown neuropeptide agonists in my work; if I wanted to know more about the intracellular signalling mechanisms these agonists use, it would be both unethical and a waste of time, money and animals to test this on live brain slices (which I use for patch clamping). Instead, one would use a cultured cell line, such as HeLa cells.

Robust and reproducible

Reduction is an interesting one. It’s easy to think, well I’ll just use fewer animals in my experiment. However, this misses the key point of “robust and reproducible” experiments. What if you used fewer animals and didn’t see an effect? Is that because there is no biological effect of your treatment, or is it because you didn’t have enough animals in your study to show a statistical effect? This is where power analyses come in to play: they help you plan a robust study without using an unnecessary number of animals.

It is also important to think of optimising your study design to produce the most statistical power (eg. using crossover studies and repeated measures ANOVA) and to negate the need of repeating studies in the future. Even during an experiment, I am conscious of this metric, because I am always trying to reduce the variability in a study (for example, by reducing animal stress) in order to improve the power and numbers needed for future studies.

Improve data quality and impact

Refinement is really where it’s at for me. I spend a lot of time optimising techniques and, more recently, developing technology, to improve experimental conditions. The thing is, when your mice are unhappy and stressed, they will not behave naturally and your data will be more variable. So it makes sense, from both a pragmatic point of view and for animal welfare, to refine your experiments as best you can.

Refinement can include anything from your study design, acclimatisation of the animals and their housing conditions to advances in technology allowing better data to be collected.

Technology and the 3 R’s

Ever since my PhD, I have been interested in the use of technology to produce better data, and improve animal welfare along the way. I was particularly keen on the use of telemetry to obtain high quality physiology data while minimising the stressful environment. Since then, I’ve been interested in using AAV’s to target neurone populations of interest, and then more advanced technologies including optogenetics and fibre photometry.

In addition to improving the animal welfare in a single experiment, these more advanced technologies can provide more impactful data with deeper insights, which means fewer studies need to be performed to provide a clear picture of the biology in question.

I have also become very interested in developing in vivo technologies myself to improve on aspects that I know impact negatively on animal welfare, for example trying to perform fibre-free optogenetics to limit a lot of the negative aspects of those experiments (such as the need to have head-tethered animals in open cages during experimentation).

A 3 R’s framework

The 3 R’s provide an excellent framework with which to approach animal research in a way that aims to be as ethical as possible. And in fact, I would argue that we are morally bound to consider such questions whenever we intend to perform experiments on animals.


Animals in Research

“What do you think about the use of animals in research?”

This is the one question you can always guarantee will be asked in a job interview that has anything to do with the use of animals in research. And it’s actually quite a difficult question to answer well. The answer clearly lies somewhere between, “Yeah, I’m fine with it, I don’t care” and “We have no right to use animals like that for our own benefit.” But, how do you justify the experimentation of animals without coming across as glib or self-serving?

Quite apart from job interviews, I’ve always found this to be a difficult topic. I guess because it is both emotive and there is so much misinformation surrounding it. Generally, the only time you hear about animal research in the news is when there’s been a particularly horrific protest. And those who are most able to talk about the reality of animals in research, ie. the researchers, are scared and drilled into not speaking about it. To the point that only my family and a couple of close friends know what I actually do for a living.

Protesters against animal research.
Anti-vivisection protest in the US

Almost 75% of animal research is performed on mice

I will occasionally, once every year or two, come across an anti-vivisection “information” stand at a market or something, where they are handing out leaflets to try and persuade the public as to the evils of animal research, and the pleasure the scientists take from doing horrific things to the animals. This is obviously not the case, and it’s somewhat indicative of the weakness of their position that they have to cherrypick and inflate the stats, focussing on the most photogenic species, like dogs, cats and primates.

They completely misrepresent the fact that the vast majority of animal research is performed on mice (Figure 1), and more than 90% in mice, rats or fish.

In fact, there are only a couple of facilities in the country that perform research on monkeys, and in fact all research with chimpanzees and other great apes was stopped in 1998. Speaking of which, the reason all the pictures they use always look so terrible, is that they are all ancient (mostly from the 80’s or earlier, before the regulations were brought in).

Experimenting on animals is difficult and expensive

I’m not sure how many of the public actually know that you need a license to perform scientific research on animals. I say a license, when in fact you need multiple: a personal license for the researcher actually performing the experiments, a project license for the person (usually professor) in charge of the work, and a site license for the location the experiments will actually take place. And everything needs to be justified and planned beforehand, with expected outcomes and experimental group sizes. Then you need named training and competency officers, a vet, animal welfare officer and the technicians who will actually be caring for the animals.

And we mustn’t forget the home office inspectors who can, and will, drop by to check on the welfare of the animals, and make sure all paperwork and training is up to date. All of which means that experimenting on animals is difficult and expensive, and requires huge amounts of training and expertise. So anyone who thinks that overworked and underfunded scientists, quite apart from the moral and legal implications, will be frivolous with their use of animals, is deluded.

How to combat misinformation

There are, of course, institutions trying to combat the spread of misinformation, such as the National Centre for 3 R’s research ( It is, however, very difficult to get the public interested in science and statistics when compared to emotive pictures and moral outrage. Which is why it is so important for those that know better to spread good information about this topic.

But where do you begin, when the scientists have been conditioned to be silent about anything to do with vivisection, and the public are so conditioned to fear the evil scientist? It really has to come down to education, both about the realities of animals research – they are treated far better than farm animals, but the moral outrage clearly lands heavier on experimentation – as well as the benefits to medicine and society that come out of this research.

I’m not going to lecture my readers about all the great advances coming out of animal research; suffice it to say that any medical advance you have ever heard of was borne on the back of a huge amount of scientific research, much of it requiring the use of animals. And by the way, this includes many benefits to modern medicine that people may not think of. So even if there are people out there who take a moral stand and refuse any kind of medication because it required vivisection, you live in a world without smallpox and polio (and, might I add, with a Covid19 vaccine) thanks to the use of animals in research.

One of my goals with this website and blog that I have started is to help spread actual and interesting information about research involving the use of animals. Now it is up to us to be thoughtful and diligent with our use of animals, and make sure their sacrifice is not wasted.

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.


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 Gi/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.

The CrumbleHopper™

A dietary issue

A colleague approached me a few weeks ago about a laboratory mouse feeder. She was having an issue with one of her experiments; she knew I had done some 3D printing and thought I might be able to provide a solution. Her problem was that she was feeding her mice a special, but very crumbly, diet.

The crumbly diet was making it impossible for her to monitor caloric intake using our currently available food hoppers. Depending on the type of hopper, either the food would crumble all over the cage bottom, or the mice would kick bedding up in with the food and make any accurate food measurement impossible. What she needed was a laboratory mouse feeder that was up to the task.

Designing a mouse feeder

So, my colleague asked me to design and produce a small food hopper that could hold crumbly food without losing it, while still allowing the mice easy access to eat without being able to kick bedding in. It was also important that the hopper be easily added or removed from the cage to allow daily weighing of the food. And as the planned experiment involved some 40-odd animals, they really needed to be low maintenance, and quick and easy to use.

Based on the cages that we have in our animal facility (pretty standard IVC’s from Tecniplast1), I figured the most obvious solution would be to have a hopper that you could hang off the metal grill hopper already present in the cage. Then it would be easily removable as well as being elevated off the floor, and it would be a simple matter to design it to be an easily munchable height from the cage floor.

I “borrowed” a cage from the facility to measure up, and designed what is essentially a hanging basket with hooks. Then after a suggestion from my colleague I made a few variants with different size/shape holes for the mice to access the food:

Initial designs for a laboratory mouse feeder.

Improving the design

I printed these 3 prototypes and gave them to my colleague to test out on the mice. She obviously monitored them very closely, and found that the mice would happily eat from any of them. She did, however, find that the supporting struts were a bit flimsy, so I strengthened those.

Of the three designs, she liked the vertical rectangular slits the best, but wanted the holes slightly wider to allow better access for mouse nom-noms. So, along with a slight adjustment to the height of the hooks to give better attachment, I present the final version of my laboratory mouse feeder, the CrumbleHopper™:

The Crumblehopper: a handy laboratory mouse feeder.

I actually quite enjoyed trying to make these for my colleague, and it does seem to fulfil an otherwise unmet need. So to that end, I am setting myself a couple of goals:

  • To keep an eye open for other experimental situations that could do with having an improved piece of kit. I already have a couple in mind that I am working to produce a solution for.
  • I hope these pieces of lab kit I am developing will be useful to people outside my lab, so to that end I am working towards setting up a small shop on this website to sell the things at a reasonable cost. Where I can, I will also make the designs available online so people with access to 3D printers and such can make them themselves.

Finally, if any of my loyal readers have a niggling problem that could be solved with a relatively straightforward (but otherwise non-existent) solution, please contact me and maybe I can design a solution, as I did for my colleague.


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 work1,2. 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.




Depths of Detection

A fortuitous chat

The other week I had a chance conversation with a colleague about one of her experiments she was struggling with. It involved recording AgRP neurone activity with in vivo fibre photometry. She was particularly having problems with her fibre placements. Her AAV injections were fine, as she was getting great GCaMP expression in the Arcuate nucleus. But, she was struggling to get good fibre photometry signal. It seemed that she was either overshooting with her fibre and causing damage to the base of the mouse’s brain, or she was not going deep enough to get close enough to the AgRP neurones to pick up the signal.

This led me to wonder about photometry fibre placement. How close do you actually need to get to the fluorescent cells to pick up a good fibre photometry signal? However, it’s difficult to find information about this related to in vivo fibre photometry. The couple of studies I found both used 2-photon excitation for the photometry, but that has a very different excitation profile than “normal” epifluorescent photometry1,2.

Photometry signal detection

After some sleuthing, I found a paper by Simone et al. developing an open-source photometry system. As part of the validation process, they tested the detection power of their system using an artificial setup (small pieces of fluorescent tape submerged in 2% intralipid; Figure 1). They found that detection tailed off dramatically even before 100 µm displacement.

However, the Simone data uses a system that is very different from our in vivo setup. In particular, they used low diameter fibres with intralipid as the confounding medium.

After some further scouring of the internet, I found a thesis from the University of Florida, where the author had set our specifically to investigate and optimise fibre photometry recording4. A quick caveat: as a thesis this work has not been published through peer review. But, the work does look very thorough and will have passed a viva board so I think can be trusted.

Anyway, as part of the thesis, Mansy set up an in vitro system using fluorescent beads obscured by acute brain slices to investigate detection profiles with different fibre optics (Figure 2). Using 400 µm fibres, they found that fluorescent detection dropped off rapidly upon distance from the fibre tip. Interestingly, this was far more pronounced in the .50 NA fibre than the .22 NA fibre (Figure 2A). This surprised me, as we are always told to use the highest NA fibre possible for photometry. The reasoning being to increase the amount of light collection.

However, upon reflection, it makes sense to use lower NA fibres if you think of the detection based not just on the fluorescent collection distance, but also the depth of excitation light penetration (for more info, check out my Depth calculator and blog posts). In that case, it would absolutely make sense for the high NA fibre to have a much decreased detection profile. The difference was even more pronounced when looking at the 3D detection volume (Figure 2B).

How to relate this to our work? I know that my colleague who was having photometry troubles was using a 400 µm .48 NA fibre. These should give an almost identical detection profile to the .50 NA fibre investigated by Mansy (Figure 1A, left). I have since suggested to her that she use lower NA fibres. Switching to the .22 NA fibre should extend her 50% detection depth from about 150 µm to about 300 µm, based on this work (Figure 1A, right).

A note on tapered fibres

Finally, I found a paper which improves the depth of fibre photometry signal detection even further, by moving away from flat-ended fibres2. The problem with imaging from a flat-ended fibre is that the light emission tails of exponentially, and the detection along with that. Furthermore, the detection will also be heavily biased towards the neurones nearer to the fibre. This is dramatically improved by using a tapered-ended fibre to provide more uniform light emission and signal detection (Figure 3).

I had a quick search online, and found that Doric sell tapered photometry fibres (we have a Doric photometry system, and we purchase our photometry fibres from them). My recommendation to my colleague, and anyone else doing photometry, is to try out the tapered fibres provided they will work in your experimental system, and failing that to use lower NA flat-ended fibres.

1. Pisanello et al., Front Neurosci 13(82) 1-16 (2019) The three-dimensional signal collection field for fiber photometry in brain tissue

2. Pisano et al., Nature Methods 16, 1185-1192 (2019) Depth-resolved fiber photometry with a single tapered optical fiber implant

3. Simone et al., Neurophotonics 5(2), 1-10 (2018) Open-source, cost-effective system for low-light in vivo fibre photometry

4. Mansy, PhD Thesis for the University of Florida (2019) A systematic characterization of fiber photometry for optical interrogation of neural circuit dynamics