Category: Photometry

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

Assessing Fidelity

I came across a paper recently that might cause us to rethink in vivo GCaMP recording, by validating fibre photometry. Or at least to rethink our interpretation of the data based on assumed fidelity of the GCaMP signal to neuronal spiking activity.

I’m talking about a recent submission to the BioArchive preprint server from the lab of Lex Kravitz1. Its title “Fiber photometry does not reflect spiking activity in the striatum” speaks for itself, and counters what one might suppose is the whole point of doing photometry recordings in the first place.

Before we get on to their paper validating fibre photometry, a quick bit of background about neuronal calcium signalling. Depolarisation of a neurone opens the voltage-dependent calcium channels, causing influx of Ca2+ and an increase in intracellular calcium – this is in fact what triggers vesicle fusion at the nerve terminal for release of neurotransmitters. Therefore, it makes sense to use [Ca2+] as a marker of neuronal activity.

However, it is easy to forget when using a surrogate marker in this way, such as using c-fos as a marker for neuronal activity following a stimulus, that there is no guarantee of a causal relationship. It is important, therefore, that these assumptions are put to the test, as Legaria et al. have done in this work. They only present 2 figures, so I will quickly go through the salient points.

To achieve this, Legaria et al. transfected mice with GCaMP6 and implanted a Mightex OASIS implant for calcium imaging of individual cells (Figure 1; the Mightex system looks interesting in that it can do optogenetics, multiunit photometry and miniscope imaging, I’ll try and do a blog post on it in the future).

Anyway, Legaria et al. analysed three signals from each recording: the GCaMP-transfected cell bodies, the surrounding background from non-transfected tissue (“neuropil”), and the overall signal (which would correlate to the signal seen from photometry which simply takes a global readout from the illuminated area; Figure 1C). Analysing these three signals alongside, they found that the “photometry” signal correlated extremely well with the “background” calcium signal, but not at all with the “cell body” signal (Figure 1D).

This is somewhat alarming for someone who performs photometry recordings, that your photometry signal should be detecting fluctuations in “background” rather than identifiable cells. To check whether this (mis)correlation also extended to the electrical activity of the cell, Legaria et al. next performed photometry combined with multi-electrode array recordings of the GCaMP-transfected cells. They saw essentially zero correlation between the two (Figure 2).

Now, before we go and chuck our £60k-worth of photometry recording equipment in the incinerator, I will say that this is a pre-print, so hasn’t been peer reviewed yet. The major questions I would pose as a referee to this work are:

  1. The electrophysiology array looks to be very widespread (and lopsided) compared to the photometry fibre (Figure 2A) – are we sure the two methods will be picking up signals from the same neurones?
  2. The electrophysiology records non-discriminantly from any nearby firing cell, so how well can we expect that to correlate with the GCaMP signal, which always label only a subset of those cells (even with non-cre-dependent expression and a global promoter, you will never transfect 100% of cells)

Having said that, the data looks solid to me, so I think it’s safe to assume the conclusions are sound. My further thoughts I had while looking at the data were also brought up in the text:

“There are also some limitations to our study. First, the results presented here are limited to recordings in the striatum. Striatal neurons have extensive dendritic arbors, which may accentuate the neuropil contribution to the fiber photometry signal. Fiber photometry signals from other brain structures may result in different conclusions concerning the relative contributions of somatic versus neuropil calcium. Second, our recordings were performed with GCaMP6s, a variant of GCaMP that has slow dynamics. Different relationships may be observed with GCaMP variants with faster kinetics, or those that target GCaMP to specific cellular compartments”1

This highlights exactly what also occurred to me, that these results may not extend to other brains areas, although I think it would be unsafe to assume they don’t. Also, if we want to mitigate the issues raised here, we should think about using miniscopes and/or use cell body-targeted GCaMP variants. In the meantime, however, I will continue doing photometry recordings as before, but I will keep this work in mind when analysing photometry data. I’m glad this work was done though, it’s good to see someone validating fibre photometry.

1. Legaria et al., BioArchive (2021) Fiber photometry does not reflect spiking activity in the striatum. doi: https://doi.org/10.1101/2021.01.20.427525

A Modern Classic, Part II

In 1900, Lord Kelvin addressed an assembly of physicists and claimed, “There is nothing new to be discovered in physics now. All that remains is more and more precise measurement.” Then just 5 years later, Einstein shook up the scientific world with his theory of relativity. Dare I compare this situation to the second paper in my Modern Classic series? No, that would be exaggeration on a disgusting scale. However, hyperbole aside, the findings from today’s paper were a revelation to me and, I’m sure, to many others, and has opened up a new way of thinking about the neuronal control of energy balance.

We saw last time that we can drive voracious feeding in mice by activating AgRP neurones, with DREADD’s or optogenetics. The thinking at the time was that, in a fasted mouse, Ghrelin is high (and satiety hormones such as PYY3-36 are low), so AgRP neurones are actively firing to drive food seeking and consumption. Then as the mouse eats and the balance of hormones switch, the AgRP neurone activity drops, and so does feeding drive. However, this thinking was soon to be upended, when Chen et al. used in vivo photometry recordings to measure the activity of AgRP and POMC neurones in awake behaving animals1.

Photometry is probably my favourite in vivo method, because it allows you to investigate the behaviour of an identified group of neurones in an awake behaving animal. Chen et al. transfected AgRP and POMC neurones with the calcium indicator GCaMP, which closely mimicked the electrical activity of the cell (Figure 1A-E). They then put it into live mice, which allowed them to record the activity of AgRP or POMC neurones in awake behaving animals (Figure 1F-H).

They next showed that Ghrelin injection in their AgRP or POMC photometry recordings caused a rapid increase or decrease, respectively, in activity in those neurone populations. No surprises so far. However, the revelation came when they fasted the animals (to drive up AgRP neurone activity), then presented food (Figure 2). The change in neuronal activity came rapidly after presentation of food. In fact they found, as stated in the name of the paper, that the AgRP and POMC neurone activity was modulated upon sensory detection of food – in fact they had already altered their behaviour before the mice had taken a bite of food (Figure 2H). This shows that the classic theories of AgRP and POMC neurone regulation by hormones and other homeostatic methods are too slow to drive actual feeding. The question then becomes, how do AgRP neurons drive food intake if they are switched off during the time that the animals are actually eating?

Chen et al. go on to show that the degree of response to food presentation is relative to the caloric content of the food, ie. they have a bigger response to peanut butter than to regular chow. There has since been a lot of work to investigate further the control of feeding behaviours by these neurone populations, but I won’t go into detail here. However, it is worthy to note two papers came out soon after the Chen paper, confirming the rapid modulation of AgRP neurones by sensory detection of food, using different methods (Betley et al. used GCaMP and head-mounted miniscopes; Mandelblat-Cerf et al. used optetrode ephys recordings to provide actual spiking data)2,3.

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. Mandelblat-Cerf et al., eLife 4, e07122 (2015) Arcuate hypothalamic AgRP and putative POMC neurons show opposite changes in spiking across multiple timescale.