Tag: Brain

Light Penetrance in Different Brain Regions

This post explains a further addition to my depth calculator. In this update, I’ve added options to predict optogenetics stimulation depth in different brain regions. I’ve mentioned before about the irrelevance of visible light wavelength compared to the density of brain matter, when calculating depth of light penetrance.

Light scattering measurements

Anyway, I went back to an early paper calculating light scattering in different types of brain tissue (Figure 1)1. As you can see, absorption of light is irrelevant compared to scattering (note the logarithmic scale). Also, the scattering doesn’t change much across visible wavelengths for grey matter, and not at all for white matter.

Visible light scattering in different types of brain matter.

So what I’ve done is to take the following estimates for scattering values for each type of brain region:

  • Grey matter (blue light): 11.2 (taken from Aravanis et al.2)
  • Grey matter (red light): 9
  • Thalamus (intermediate scattering): 20
  • White matter: 40

Predicting light penetrance

I’ve then plotted the light penetrance using the calculations from Aravanis et al. (also used by Karl Diesseroth) with these different scattering coefficients (Figure 2). Note the logarithmic scale. As I mentioned, shifting to red light makes very little difference to the light penetrance compared with changing the density of brain matter.

Light penetrance in different types of brain matter for optogenetics stimulation.

In order to make the light scattering relevant to optogenetics stimulation depth for in vivo experiments, I have updated my optogenetics depth calculator to include scattering in different types of brain tissue. Using the new calculator, I have predicted the following effective depths in different brain tissue using my standard parameters:

  • Grey matter: 1.57 mm
  • Intermediate: 1.24 mm
  • White matter: 0.92 mm

As you can see, changing the scattering level of the tissue has a dramatic effect on the effectiveness of your in vivo optogenetic stimulation depth. My suggestion for experiment planning is to use the “intermediate” value as default, and pick one of the others if you have a good idea of your target brain regions.

For example, if you’re working in the cortex, which is heavily “grey”, pick the low scattering value. On the other hand, if you are targeting the brain stem, which is densely “white”, pick the high scattering value. If you want a more accurate predictor of light spread, you need to do more complex modelling.

1. Yaroslavsky et al. Phys Med Biol 47, 2059 (2002) Optical properties of selected native and coagulated human brain tissues in vitro in the visible and near infrared spectral range

2. Aravanis et al. J Neural Eng 4, S143-S156 (2007) An optical neural interface: in vivo control of rodent motor cortex with integrated fiberoptic and optogenetic technology

Amy’s Microtome Counter

Yesterday something awesome happened: Amy (one of the other postdocs in the lab) came to me with an idea for a piece of kit that would help her in the lab. She found that when she was cutting brain sections on the microtome, she would sometimes zone out and forget which plate she was up to. Her request was for a microtome section counter.

The idea was to have something that would light up an indicator of which plate she needed to put the next section into. It would also require a sensor of some kind that would be activated each time she placed a section. We quickly came up with a workable idea that would use an IR sensor, and the user would sweep their hand close to the sensor after each brain section was added.

This project is just asking for an Arduino, so first thing I did was sketch out some code:

// Specify pin connections
int IR = 2;
int LED1 = 3;
int LED2 = 4;
int LED3 = 5;
int LED4 = 6;
int toggle = 7;

// Specify other variables
int count = 0;            // Counter
int maxcount = 4;         // Number of plates to count
long delayTime = 500;     // Delay time to prevent multiple activations
long lastTime = 0;        // Time stamp from last activation

void setup() {

// Specify pin setup
pinMode(IR, INPUT_PULLUP);
pinMode(LED1, OUTPUT);
pinMode(LED2, OUTPUT);
pinMode(LED3, OUTPUT);
pinMode(LED4, OUTPUT);
pinMode(toggle, INPUT_PULLUP);

}

void loop() {

// Toggle to select number of plates being used
if(digitalRead(toggle) == LOW){
  maxcount = 3;
}
else{
  maxcount = 4;
}

// Detection by IR sensor 
if (digitalRead(IR) == LOW){
  if(millis() - delayTime > lastTime){    // Have you exceeded time since last activation?
    count++;                              // Add to counter
    lastTime = millis();                  // Specify timestamp of IR activation
  }
}

// Overflow counter back to start
if (count >= maxcount){              
  count = 0;
}

// Switch on each LED in turn depending on counter
if(count == 0){
  digitalWrite(LED1,HIGH);
  digitalWrite(LED2,LOW);
  digitalWrite(LED3,LOW);
  digitalWrite(LED4,LOW);
}

if(count == 1){
  digitalWrite(LED1,LOW);
  digitalWrite(LED2,HIGH);
  digitalWrite(LED3,LOW);
  digitalWrite(LED4,LOW);
}

if(count == 2){
  digitalWrite(LED1,LOW);
  digitalWrite(LED2,LOW);
  digitalWrite(LED3,HIGH);
  digitalWrite(LED4,LOW);
}

if(count == 3){
  digitalWrite(LED1,LOW);
  digitalWrite(LED2,LOW);
  digitalWrite(LED3,LOW);
  digitalWrite(LED4,HIGH);
}

// Reset timer overflow
if(millis() - lastTime < 0){
  lastTime = millis() - lastTime;
}

}

The electronics is fairly straightforward:

Amy's microtome section counter schematic.

A small amount of assembly later, and we had a working prototype:

Microtome sectioning counter
Amy’s microtome counter

Amy has promised to test it. I’ll let you guys know how it goes.

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.

Stick or Float?

I recently ended my optogenetics experiment (that I showed the planning for in a previous post), and the next step was to cut the brains. As part of the study, I stimulated the mice 90 mins before perfusion, so that I could immuno stain for c-fos as a marker of neuronal activation. This is important for showing that I can successfully activate my ChR2-expressing neurones, but it also allows me to investigate potential downstream sites of activation throughout the brain. All of which is to say that I had 10 brains to cut, and had to cut and save every section throughout their entirety. Which is a lot of brain sections to cut.

A quick mention of my methods, I am cutting the brains to 30 µm sections on a freezing sledge microtome, and placing the sections in phosphate buffer for free-floating immuno. This is not a technique that I had used, or even seen, before coming to my current lab, so my goal for today is to highlight some of the differences between this method and the one I used previously, namely using a cryostat and doing on-slide immuno.

The microtome is a lovely old-school piece of kit, made of massive cast-iron segments, with a cooling stage to freeze your tissue on, and smooth steel rails to run the stage back and forth on the blade. Speaking of the blade, we use modern microtome blades that fit into a holder, but we still have the original chunk of steel that needs sharpening by hand. For those who’ve never used one, it is slightly odd the first time you use it, because the tissue is frozen on the stage, and when you cut a section it melts at the same time and ends up in a little pile of mush on the blade. You then use a paintbrush to sweep it off and into a well of buffer.

Doing the immuno on floating sections takes some getting used to as well, because you need to suction out the buffer for each step of the immuno, but it takes practice to avoid sucking up the sections (water tension is a bitch). But the most challenging aspect of this method is mounting the sections at the end of the protocol, because you now have to half-float half-push your section onto the slide. It takes a lot of practice to do this in an orderly row, without folding or damaging the sections, and without taking endless hours.

By contrast, mounting sections using a cryostat is easy. Because you do all the cutting and mounting in a frozen chamber, each section is frozen and, for want of a better word, stiff. There are two schools of thought for the mounting procedure: I was originally taught do it onto frozen slides, in which case you manoeuvre the section to where you want it on the slide using a paintbrush then using the warmth of your finger on the underside of the slide you melt the section into place. This works well but it does tend to take a toll on the warmth in your fingers (I manage fine because my circulation is great and my hands are always really warm; the PhD student that taught me had to stop every couple of hours after her fingers became too cold to melt the section into place). The alternative is to leave the slice out on the bench, and then you touch them to the section and they melt on in place.

When you mount sections onto the slides as you cut them, you then have to do the immuno on the slide, which makes washes really easy because you can just dunk them into a well of buffer – no suctioning of tissue to worry about. However, it becomes slightly more challenging to do the antibody steps, because you need to do it on the slide, which means placing the slides flat on the bench and pipetting a few hundred microlitres or so of solution to sit on the slide – in this case water tension is your friend. I found that the bubble of solution on the slide is not level all the way across, which means that you end up with a gradient of antibody and therefore the staining is not uniform across your sections. This doesn’t have to matter much, unless what you need is a quantitative staining.

When it comes to planning which method to use for doing immunohistochemistry, I use the following criteria:

  • Use the microtome and free-floating immuno if you want a quantitative readout, such as c-fos, or if you have a lot of brains to cut (I find it’s quicker overall)
  • Use the cryostat and on-slide immuno if you are using a method with very low volumes, such as in situ hybridisation, or you want precise placement of sections