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.




A Study in Green

I have recently been working towards an in vitro GCaMP imaging system. This came about when I inherited a patch/imaging rig from a colleague who moved on to pastures new. The rig was set up for calcium imaging using calcium-sensitive dyes, such as Fura, and included a nice Zeiss inverted microscope, perfusion stage and Hamamatsu Orca 2 camera.

However, rather than chemical indicators of calcium activity, I wanted to use our genetically encoded calcium indicators. Luckily, the excitation/emission spectra for GCaMP align very closely with GFP, which meant that I could use the GFP filters for imaging. I was starting tests with a technician in the lab, and then one day the camera just wouldn’t switch on (seems the control box died – to be fair it’s quite old, but still costs several thousand pounds to replace).

Well, that was about a year and a half ago, and for obvious reasons put a complete halt to further imaging studies. Which is a shame, because the GCaMP imaging provides a number of features that can make it desirable over electrophysiology (Table 1).

Luckily, my supervisor had a sizeable pot of grant cash that had been earmarked for electrophysiology equipment, but could instead be rerouted towards a new imaging camera. So after some research, I found a couple of very good (but also very expensive, >£15k) cameras to test out, the BSI Express from Teledyne Photometrics and the Fusion BT from Hamamatsu. These are both equivalent high-end CMOS cameras, which means they have outstanding sensitivity, imaging speed and resolution.

My plan was to only buy what was absolutely needed, and use what was in place until we want to/can afford to upgrade the system (Figure 1A). I arranged loans of the two cameras I was interested in; the BSI express came first so that’s the one I’ll show today.

The light source we have is a Prior white light with excitation filter changer. I purchased 2 excitation filters for this experiment at 470 nm and 410 nm (Figure 1B), which represent Ca2+-dependent and Ca2+-independent excitation wavelengths of GCaMP, respectively.

Having set up the system, it was time to test it out. Reaching for the low-hanging fruit, I found that one of our mouse lines that we had been gifted by a collaborator included a GCaMP3 reporter (we were actively trying to breed the reporter out, but in the mean time we had a bunch of cre and GCaMP3 double-positive mice that would otherwise not be used). The mouse line in question is a GLP1R-cre mouse, which means that all the GCaMP-expressing cells should have GLP1R. Therefore, the obvious experiment to validate the system was to apply the GLP1R agonist Exendin-4.

Anyway, I took a video, drew regions of interest round identifiable neurones and plotted the change in fluorescence in response to 1 µM Exendin-4 (Figure 2).

All in all, the in vitro GCaMP imaging system worked well, despite a number of problems along the way that I haven’t gone into here. I am very much a convert to this kind of experiment; having spent many years patching individual neurones, it’s lovely how visually obvious these data are. I would highly recommend anyone reading this to look into GCaMP imaging as a quick and easy alternative to patch clamping.

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