Category: Behaviour

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.

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.

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.