Category: Methods

Optogenetic Stimulation Frequencies

Today I’ll be talking about the importance of optimising in vivo optogenetics frequency, having previously looked at the pulse on-times. All too often, I will see papers or talk to colleagues who use an unfeasible stimulation frequency for their in vivo optogenetics. For example, where I work in the hypothalamus, you often see stimulation at 20 Hz. And from my experience of patch clamping multiple neurone types in the hypothalamus, they just don’t fire that fast.

If you’re not an electrophysiologist, it might not be obvious, but action potentials are energetically expensive. So, neurones will only fire quickly if they need to. In fact, they will only be able to fire quickly if it is required for their function. Which it is for cognitive processing, but not for the much simpler processing required in many other brain regions.

Back to the beginning

As usual, first thing we do is go back to the early optogenetics publications from Karl Diesseroth. In their 2012 Nature Methods paper, Mattis et al. performed a thorough investigation of the opsins available at the time1. And, despite being a decade later, the data still stand, and are still very useful. I strongly recommend reading this paper for anyone who plans to perform optogenetic studies. It’s a huge paper with bags of useful info.

Mattis et al. measured spike fidelity, ie. the success rate of the cell to produce an action potential in response to a flash of light. They used a high light intensity, so there is no issue of there being not enough light to activate the opsin. Instead, the loss of fidelity comes from the neurone being unable to keep up. As I’ve mentioned before, the neurone needs to recover its membrane potential below a certain threshold or it won’t be able to trigger another action potential, so if you chronically overstimulate a neurone they become silenced.

I’ve shown here a comparison of ChR2h134r (also called ChR2R) and ChIEF (Figure 1A). The black lines show the spike fidelity to light pulses, and the grey lines show the fidelity to electrical pulses. Essentially, the grey lines show what the cell is intrinsically capable of, whereas the black lines show how it fares under optogenetic control. Notice how the ChR2h134r loses fidelity at 20 Hz, whereas ChIEF only loses it at 40 Hz. This is largely because of the “off kinetics”, which means that ChR2h134r takes a lot longer to close than ChIEF (Figure 1B). And it’s only after the opsin has closed that the cell can recover its membrane potential.

Optogenetic spike fidelity of ChR2 and ChIEF, from Mattis et al.

A self test

Luckily I have access to an electrophysiology rig, so I was able to test spiking fidelity my target neurones. Namely, AgRP neurones of the arcuate nucleus of the hypothalamus (Arc). I transfected AgRP neurones with ChR2h134r, cut ex vivo slices and patched using current clamp. I then flashed the neurone with increasing frequencies of 470 nm light at a high intensity (Figure 2).

Optogenetic spike fidelity in an AgRP neurone

As you can see, the cell responds nicely with big action potentials at low frequency stimulation. But the action potentials disappear even at 10 Hz. Remember that you really need the action potential to get the response you want, whether you are stimulating the soma or the terminal. Otherwise, you really don’t know what you’re doing to the neurone, although I strongly suspect you’ll be silencing the cells. Either way, I don’t recommend flashing faster than you are able to produce action potentials.

In fact, to demonstrate why you need to limit your flashing frequency, I’ve zoomed in on the 5 Hz flashing and aligned the electrical recording with a visual representation of the likely open/closed state of the ChR2 in those cells (Figure 3). I’ve drawn the light pulses and used the published τOFF to estimate the ChR2 channel close time1.

Now imagine that you have additional pulses in between the ones shown (1 extra for 10 Hz or 3 extra for 20 Hz). Between the slow closing of the ChR2 and the slow recovery of the neurone’s membrane potential, it’s easy to see why the neurone loses firing fidelity above 5 Hz.

An important message

One of the other tests done by Mattis et al. was to simply turn the blue light on continuously for 1 second in two different neurone types. A “regular-spiking” neurone fires one action potential before being silenced, whereas a “fast-spiking” neurone fires continuously through the illumination. The point here is that some neurones can fire at 200 Hz under optogenetic stimulation (mainly cortical neurones). And if your research involves them, you probably know that.

But every neurone I’ve ever investigated wasn’t capable of anything close to that rate. So please check the firing rate your neurone is actually capable of before deciding your in vivo optogenetics frequency. Or, if you are not able to do it or get a friend to, be very careful with your stimulation paradigm. And feel free to ask someone, it never hurts to ask for help.

1. Mattis et al. Nat Methods 9(2), 159-172 (2012) Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins

Effective Stimulation Depth

The effective stimulation depth is one of the critical factors in determining the success of an optogenetics study. But it is routinely ignored, particularly by the vendors of optogenetic LED systems. Be wary of any vendor of optogenetic LED’s that loudly proclaims the mW power they can achieve. Particularly if that fibre has a high NA or diameter.

A dose of light

Quoting the power at the fibre tip is all well and good, but it doesn’t take into account the spread and scatter of light in the brain. So, unless you intend to have your optic fibre literally touching your neurone population of interest, you must consider how the irradiance (light power over area, mW/mm2) drops relative to distance from the fibre tip.

Irradiance loss in the mouse brain

Now that we have plotted the irradiance loss as we move further from the fibre tip, what next? We need to know at what point the light ceases to be effective in activating our optogenetics. If you are used to pharmacology, you can think of it as a drug dilution, but one that occurs spatially through the tissue. So in that case, we need to find the EC50 of the opsin. Then, we can determine an irradiance “dose” to aim for, below which we lose efficacy.

Opsin characterisation

Fortunately, the early pioneers of optogenetics went through a lot of effort to validate and characterise everything. A 2012 paper from Karl Diesseroth’s lab characterised a range of opsins in exhaustive detail1.

The important info for this post is the determination of irradiance needed for activation. Mattis et al. determined photocurrent at a range of irradiances, firstly for a selection of stimulatory opsins (Figure 1A). From this they were able to calculate the irradiance needed for half activation, or the effective power density for 50% activation (EPD50; Figure 1B). This is analogous to the EC50 for pharmacology.

The EPD50 is helpful in that it provides a measure of the sensitivity of the opsins regardless of the expression level in the cell. Having said that, I don’t think we should disregard the magnitude of the photocurrent. Particularly when measured in a directly comparable system as we see here. My takeaway here is that these ChR2-based opsins have an EPD50 around 0.8 – 1.5 mw/mm2; the exception is the CatCh and C1V1 variants which all have very slow (>50 ms) off kinetics.

Inhibitory opsins

Mattis et al. also investigated a number of inhibitory opsins in the same way (Figure 2). These universally have a much higher EPD50 than the excitatory ChR2-based opsin. My takeaway from this figure is that the eArchT3 seems to be the best of these. It has a comparable EPD50 to eNpHR3.0, but a much higher photocurrent. Also, Arch opsins have peak excitation at 520 nm, which is technically easier to obtain than the ~590 nm peak of eNpHR3.0.

Right, so now we have a good idea of the threshold irradiance needed to activate our opsin of choice. Ideally, you would back this up by validating in vitro, using patch-clamp electrophysiology of your neurone system.

Predicting effective stimulation depth

So now I can replot the predicted irradiance loss from the tip of the fibre. Only this time, if I add the threshold irradiance of 1 mW/mm2 (as tested for ChR2h134r in vitro) it will highlight how deep I can expect to activate my opsin. This gives us a predicted effective stimulation depth.

Based on this graph, it appears that I will produce effective stimulation of my neurones for just over 1.2 mm. This is good, as it will allow me to aim the fibre 0.5 mm away from my population of interest, while still having plenty of leeway for experimental variability and still expect to activate the entire population.

In order to simplify this process, I have put this effective stimulation depth calculation into a handy (and free) online tool. Please do try it out, as it should inform you about your experimental design.

1. Mattis et al., Nat Methods 9(2), 159-172 (2012) Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins

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

A Powerful Issue

This will be a short post building off a previous blog post about my depth calculator tool. The principle is also based on Karl Deisseroth’s irradiance calculator, only this time the calculation is reversed. The goal is to predict the in vivo optogenetics power required for your experiment.

So, instead of predicting effective opsin stimulation depth, you input the effective depth you want for you study, and the calculator with predict an estimate of the light power you need out the end of your optic cannula:

In vivo optogenetics power calculator input values.

As with my depth calculator, I have got some recommended starting values for fibre core, NA and irradiance threshold. Then press “Calculate” and it will estimate the power you need to achieve effective stimulation at your desired depth:

In vivo optogenetics power calculator output.

That’s it for today, and I hope some people use my free tool to gain insight into their in vivo optogenetics power requirements.

Who’s Behind the Curtain?

Calculating irradiance depth

My previous blog post was about my newly developed optogenetics irradiance depth calculator. The goal was to produce a simplified version of Karl Diesseroth’s more famous irradiance calculator.

Irradiance decreases with depth from fibre tip.
Typical irradiance dropoff values

After some sleuthing, I found that Diesseroth based his calculator off a paper from 2007 by Aravanis1. It predicts the spread of light through tissue based on two major factors:

  1. Geometric spread – how much the light spreads out the end of the fibre, which for the multimode fibres used for in vivo opto’s will be mainly determined by the NA
  2. Tissue scatter – light absorption and scatter by the (brain) tissue the light is penetrating

Here is the relevant section from the paper:

Optogenetics irradiance depth calculations from Aravanis et al.

The important equation is the bottom one, which calculates the irradiance (I) at distance (z), relative to the starting irradiance. The user can therefore input optical power (I at z=0), threshold irradiance (I at z), fibre radius (r) and numerical aperture (NA).

Apart from that, there are two more variables that are determined by experiment: the index of refraction of the tissue (n) and the scatter coefficient (S). I have used the same values as Aravanis, which are based on mouse brain grey matter.

And then we solve for distance (z). The tricky bit here is that solving for z produces a cubic equation, but fortunately cleverer people than me have written scripts for solving cubics. After that, it was fairly straightforward to write the script in Javascript and attach to the webpage.

Scattering of different wavelengths

A quick note on wavelengths of light. The astute among you will likely have noticed that my depth calculator does not allow you to pick the wavelength of light to use in the calculator, whereas Diesseroth’s does. The reason for this is that wavelength has little impact on the scattering of light in the visible range (Figure 1).

Scattering and absorption coefficients in brain grey matter.

In fact, by far the biggest impact on predicted scattering comes from the difference between white and grey matter, or even who did the measurements! Which bring me to the caveats for using my depth calculator:

  1. It is an estimate predictor of optogenetics irradiance depth, so take the output values as a guide rather than absolute truth
  2. The calculator assumes you are targeting grey matter, so if you place your fibres in denser white matter regions (such as the brainstem), the predictions will no longer be accurate.

Despite the caveats, I do believe my depth calculator tool is useful, and hopefully people will find it easier to decipher than Karl Diesseroth’s.

1. 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

2. 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

A Deep Issue

I have mentioned Karl Diesseroth’s irradiance calculator in my blog. It’s a tool I’ve found useful in the past for predicting the depth of opsin activation for in vivo optogenetics experiments.

However, gleaning the pertinent information from Diesseroth’s irradiance calculator can itself be challenging. I almost find that it provides too much information. In fact, you can see from my blog post the only information I actually take from it is the depth at which the irradiance remains above the activation threshold for the opsin I’m using.

The standard irradiance threshold I use is 1 mW/mm^2 for two reasons:

  1. It is the published EC50 for my opsin of choice ChR2H134R1
  2. I have tested the response using electrophysiology and found good consistent activation of ChR2-expressing neurones using 1mW/mm^2
Using the calculator


For the purposes of my depth calculator, there are 4 values for you to input:

Depth of opsin activation calculator input values.

For light power, my recommendation is to use 10 mW, or as high as your system goes if it can’t reach 10 mW. For the fibre core and NA, I recommend using 200 µm and 0.22 NA, respectively (I use these from Thorlabs; check out my blog post for further info). Finally, the threshold irradiance you expect from the opsin you have used. For the fibre details and irradiance, I have put in recommended preset values, but you can of course change them to suit your specific experimental setup.

Once you have input your values, simply press “Calculate”, and the predicted stimulation depth of opsin activation will be calculated. A typical value to aim for is about 1.0-1.5 mm, as this gives plenty of leeway for experimental variability.

Depth of opsin activation calculator output.

I hope people find my depth calculator to be useful and easy to use. I have also developed a tool to do the opposite calculation, ie. to predict the required light power needed based on your experimental needs.

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

An Acute Issue

A couple of years ago, I ran an optogenetics experiment with bilateral light stimulation in the hypothalamus. Or rather, that was how I planned it. However, when it came to tethering and stimulating the mice, the fibres were too close to each other. I ended up doing unilateral stimulations, and realised that I would need to do angled opto fibres for any future bilateral studies.

Luckily, a stereotaxic frame comes with a pivot to allow exactly this kind of thing. There’s usually a pin holding it vertically; take that out and you can tilt the top half as needed to your desired angle. There’s an “angle scale” to do this accurately, and then you can clamp the whole thing in place.

How to angle a stereotaxic frame.

So now comes the sticky question: how do you work out a new set of coordinates based on an angled implant? Turns out, all you need is a bit of trigonometry (Mr Turner did tell me it would be useful later in life, I just never believed him).

I drew a diagram to calculate the new coordinates based on my chosen angle of 10⁰. I used sine and cosine to derive the unknown lengths:

Calculating angled opto fibres.

Now, I can tell you, these calculations were a faff, so I will avoid them in the future if possible. And, I will also say that I am the only person in the lab who has attempted angled fibres. Everyone else just does unilateral, and I thing a large part of their reticence is caused by ignorance of how to calculate the angled opto fibres.

With that in mind, I have developed a handy tool to do these calculations for you. Just input your starting lateral and dorsal coordinates, and the angle to tilt your fibres. I recommend 10⁰. Then press “Calculate” and the script will output your new lateral and dorsal lengths.

I hope people find my tool useful, and encourage them to use angled opto fibres in their studies.

How Long is Bright Enough?

Last blog post I had a revelation about the best numerical aperture to use for in vivo implanted optical fibres. Today, as part of my indepth study planning, I’ll be investigating the best opto flash time. My default has always been 10 ms, because a) it seems to be what most others use and b) it’s always worked well for me.

However, I like to be sure, and it never hurts to optimise your methodology. But where to start? I’ve mentioned in the past about the EC50 of ChR2 being 1 mW/mm2. However, this is actually misleading, as it doesn’t take into account the duration of illumination.

Power, energy and time

The important point here is that mW is a unit of Power (Watts), which is energy (Joules) over time (seconds). And the thing that actually determines activation of the opsin is the energy that it is exposed to. What this means is that, in principle, you could have wildly differing power output activating ChR2 to the same extent, so long as you adjusted the length of time of illumination accordingly.

If we think of a typical in vivo light flash of 10 mW for 10 ms from a 200 µm fibre, we can calculate the energy emitted in this flash with the equation Power (W) = Energy (J)/Time (s):

Energy (J)             = 0.01 W x 0.01 s

                                = 0.0001 J

So we can say that 0.0001 J (or 100 µJ) of 470 nm light is enough energy from a 200 µm diameter fibre to robustly activate ChR2 in the brain in an experimental setting.

Low opto power

Now let’s say we could only produce 100 µW from our fibre (100-fold less than in the previous example). We could theoretically activate ChR2 by adjusting the illumination time accordingly:

Time (s)                = 0.0001 J / 0.0001 W

                                = 1 s

What this means is that if we had a pitifully weak light source, we could still activate ChR2. Although, I’m not sure how useful 1 Hz neuronal stimulation would be biologically. However, there is a way to make this dim level of illumination biologically relevant, as Anpilov et al. did in their recent wireless opto study1. They did this by using a stabilised step-function opsin (SSFO), which acts more like a toggle switch – a single activation turns it on for 30 mins or so.

Fast opto flashing

We can also look in the other direction, power wise. Let’s say you were interested in making neurones fire at 100 Hz. To maintain a 10 % duty cycle (to allow the neurones to recover electrically and to limit tissue warming), we might want a 1 ms light flash, and we could calculate the required optical power like this:

Power (W)           = 0.0001 J / 0.001 s

                                = 0.1 W

So, to drive a fast-frequency neurone like this with an equivalently robust activation of ChR2, we would need to be able to produce 100 mW out the end of a 200 µm fibre, which would be possible with a laser system. A quick note: 100 mW is actually a lot of light power to pump into a mouse’s brain. So, I would not advise aiming that high. I would worry about heating or damaging the tissue, so better to limit yourself to 15 mW or so, and validate your experiment accordingly.

Measured opto flash times

Anyway, back to my planned experiment. The question was: do I need my full 10 ms flash time to produce the firing I want? A recent paper by Herman et al. investigated the silencing of ChR2-expressing neurones at higher light exposures2. It includes a nice overall picture of light pulse duration-dependent spike probabilities in a variety of neurones (Figure 1).

Firing dynamics in response to varying light pulse duration.

What they find, flashing various neurone types at 20 Hz, is that with light pulses of 5 or 10 ms they have increasing spike probabilities up to 95 – 100 % depending on the neurone type. Then at on-times of 25 ms or longer, the spiking fidelity drops in all neurone types except for fast-spiking neurones in the cortex. Based on this work, I would suggest 5-10 ms appear to be optimal across various neurone types. At any pulse length above or below that, the spiking falls away.

Right, while 5-10 ms looks like a good time duration, that study was performed at a single light intensity, so only provides a partial answer. However, I found an early paper that investigated the threshold light power needed to stimulate an action potential at various distances from the end of the fibre, across a range of pulse widths (Figure 2)3.

Strength-duration relationships for optogenetic stimulation.

A couple of things are clear from Figure 2:

  1. Longer pulse widths drop the power threshold needed to trigger an action potential.
  2. The threshold power needed to trigger an action potential increases with distance from the fibre tip.

It’s difficult to tell from the tiny scale on this graph, but it looks like 5 ms might just be enough to trigger an action potential at 1 mm from fibre tip at the ~9 mW power we get from our system. However, this is dependent on other factors, such as the NA of the implanted fibre.

The best opto flash time

My verdict form this investigation is that 5 ms would likely be fine to trigger a response. However, increasing the flash duration to 10 ms would increase your likelihood of triggering action potentials without any noticeable drawbacks. So after all that, we come back to 10 ms as the best opto flash time (in my opinion).

1. Anplilov et al. Neuron 107(4), 644-655 (2020) Wireless Optogenetic Stimulation of Oxytocin Neurons in a Semi-natural Setup Dynamically Elevates Both Pro-social and Agonistic Behaviors

2. Herman et al. eLife 3, e01481 (2014) Cell type-specific and time-dependent light exposure contribute to silencing in neurons expressing Channelrhodopsin-2

3. Foutz et al. J Neurophysiol 107, 3235-3245 (2012) Theoretical principles underlying optical stimulation of a channelrhodopsin-2positive pyramidal neuron

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.

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